Editor-In-Chief Chair Dr. Shiv Kumar

Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India.

Associated Editor-In-Chief Chair Prof. MPS Chawla

Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &

Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India

Dr. Vinod Kumar Singh

Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India

Dr. Rachana Dubey

Ph.D.(CSE), MTech(CSE), B.E(CSE)

Professor & Head, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE),

Bhopal (M.P.), India

Associated Editor-In-Chief Members

Dr. Hai Shanker Hota

Ph.D. (CSE), MCA, MSc (Mathematics)

Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India

Dr. Gamal Abd El-Nasser Ahmed Mohamed Said

Ph.D(CSE), MS(CSE), BSc(EE)

Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science ,Technology and Maritime

Transport, Egypt

Dr. Mayank Singh

PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT

Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-

Natal, Durban, South Africa.

Scientific Editors

Prof. (Dr.) Hamid Saremi

Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran

Dr. Moinuddin Sarker

Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)

Stamford, USA.

Prof. (Dr.) Nishakant Ojha

Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India

Dr. Shanmugha Priya. Pon

Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East Africa, Tanzania

Dr. Veronica Mc Gowan

Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,

China.

Dr. Fadiya Samson Oluwaseun

Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern

Cyprus, Turkey.

Dr. Robert Brian Smith

International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie

Centre, North Ryde, New South Wales, Australia

Dr. Durgesh Mishra

Professor & Dean (R&D), Acropolis Institute of Technology, Indore (M.P.), India

Special Issue Section Editor

Mr. Siddth Kumar

Founder and Managing Director, IFERP, Technoarete Groups, India

Mr. Rudra Bhanu Satpathy

Founder and Managing Director, IFERP, Technoarete Groups, India

Dr. Mahdi Esmaeilzadeh

Founder & Chairman, of Scientific Research Publishing House (SRPH), Mashhad, Iran

Executive Editor Chair

Dr. Deepak Garg

Professor & Head, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India

Executive Editor Members

Dr. Vahid Nourani

Professor, Faculty of Civil Engineering, University of Tabriz, Iran.

Dr. Saber Mohamed Abd-Allah

Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.

Dr. Xiaoguang Yue

Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.

Dr. Labib Francis Gergis Rofaiel

Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,

Mansoura, Egypt.

Dr. Hugo A.F.A. Santos

ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.

Dr. Sunandan Bhunia

Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia

(Bengal), India.

Dr. Awatif Mohammed Ali Elsiddieg

Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,

Saudi Arabia.

Technical Program Committee Chair

Dr. Mohd. Nazri Ismail

Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.

Technical Program Committee Members

Dr. Haw Su Cheng

Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.

Dr. Hasan. A. M Al Dabbas

Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.

Dr. Gabil Adilov

Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.

Dr.Ch.V. Raghavendran

Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.

Dr. Thanhtrung Dang

Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education,

Hochiminh, Vietnam.

Dr. Wilson Udo Udofia

Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.

Manager Chair

Mr. Jitendra Kumar Sen

Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India

Editorial Chair

Dr. Arun Murlidhar Ingle

Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar

(Maharashtra) India.

Editorial Members

Dr. Wameedh Riyadh Abdul-Adheem

Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq

Dr. T. Sheela

Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar

Engineering College, Periyaseeragapadi (Tamil Nadu), India

Dr. Manavalan Ilakkuvan

Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India

Dr. Shivanna S.

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. H. Ravi Kumar

Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India

Dr. Pratik Gite

Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),

India

Dr. S. Murugan

Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India

Dr. S. Brilly Sangeetha

Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),

India

Dr. P. Malyadri

Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India

Dr. K. Prabha

Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India

Dr. Liladhar R. Rewatkar

Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India

Dr. Raja Praveen.N

Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India

Dr. Issa Atoum

Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education

University, Amman- Jordan

Dr. Balachander K

Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi

(Coimbatore), India

Dr. Sudhan M.B

Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,

Anna University, (Tamilnadu), India

Dr. T. Velumani

Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India

Dr. Subramanya.G.Bhagwath

Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management

Bhatkal (Karnataka), India

Dr. Mohan P. Thakre

Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai

Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India

Dr. Umar Lawal Aliyu

Lecturer, Department of Management, Texila American University Guyana USA.

Dr. K. Kannan

Professor & Head, Department of IT, Adhiparasakthi College of Engineering, Kalavai, Vellore, (Tamilnadu), India

S. No

Volume-9 Issue-1S3, December 2019, ISSN: 2278-3075 (Online)

Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors: Mahesh R. Patil, L. Agilandeeswari

Paper Title: Rate Based Congestion Control for Wireless Links in Information Centric Network

Abstract: Information-centric networking (ICN) is the prominent network architecture with the features of name

based forwarding and in-network caching. These features enables ICN to provide solutions to all demands of the emerging

networks. Among the proposed hop by hop congestion control schemes in ICN, almost all schemes assume the available link

capacity as known and fixed which is not true for wireless links. Here we propose dynamic link capacity estimation for

wireless links using kalman filter and each node maintains the data rate value estimated. Consumer forwards interest with

initial data rate, each hop updates estimated data rate in outgoing data packets proactively, then consumer adjusts the data

rate according to new value received.

Keyword: Congestion Control, ICN, NDN, ndnSIM

References: 1. Mahesh R Patil & Agilandeeswari L. (2019, July) A Role of Routing, Transport and Security Mechanisms in Information Centric

Network. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, No. 2S4, pp. 196-203)

2. Ahlgren, B., Hurtig, P., Abrahamsson, H., Grinnemo, K. J., & Brunstrom, A. (2018, April). ICN congestion control for wireless links.

In Wireless Communications and Networking Conference (WCNC), 2018 IEEE (pp. 1-6). IEEE.

3. Mahdian, M., Arianfar, S., Gibson, J., & Oran, D. (2016, September). MIRCC: Multipath-aware ICN rate-based congestion control.

In Proceedings of the 3rd ACM Conference on Information-Centric Networking (pp. 1-10). ACM.

4. Ekelin, S., Nilsson, M., Hartikainen, E., Johnsson, A., Mangs, J. E., Melander, B., & Bjorkman, M. (2006, April). Real-time

measurement of end-to-end available bandwidth using kalman filtering. In 2006 IEEE/IFIP Network Operations and Management

Symposium NOMS 2006 (pp. 73-84). IEEE.

5. Carofiglio, G., Gallo, M., & Muscariello, L. (2012). Joint hop-by-hop and receiver-driven interest control protocol for content-centric

networks. ACM SIGCOMM Computer Communication Review, 42(4), 491-496.

6. Wang, Y., Rozhnova, N., Narayanan, A., Oran, D., & Rhee, I. (2013, August). An improved hop-by-hop interest shaper for

congestion control in named data networking. In ACM SIGCOMM Computer Communication Review (Vol. 43, No. 4, pp. 55-60).

ACM.

7. Rozhnova, N., & Fdida, S. (2014, December). An extended Hop-by-hop Interest shaping mechanism for Content-Centric Networking.

In IEEE GLOBECOM 2014.

8. Ren, Y., Li, J., Shi, S., Li, L., & Wang, G. (2016, April). An explicit congestion control algorithm for named data networking.

In Computer Communications Workshops (INFOCOM WKSHPS), 2016 IEEE Conference on (pp. 294-299). IEEE.

9. Amadeo, M., Molinaro, A., Campolo, C., Sifalakis, M., & Tschudin, C. (2014, April). Transport layer design for named data wireless

networking. In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on(pp. 464-469). IEEE.

10. Zhang, F., Zhang, Y., Reznik, A., Liu, H., Qian, C., & Xu, C. (2014, August). A transport protocol for content-centric networking

with explicit congestion control. In Computer Communication and Networks (ICCCN), 2014 23rd International Conference on (pp. 1-

8). IEEE.

11. Ndikumana, A., Ullah, S., Kamal, R., Thar, K., Kang, H. S., Moon, S. I., & Hong, C. S. (2015, August). Network-assisted congestion

control for information centric networking. In Network Operations and Management Symposium (APNOMS), 2015 17th Asia-

Pacific (pp. 464-467). IEEE.

12. Mahesh Patil, Sumathy S., R. Hegadi, IPv6 Enabled Smart Home Using Arduino, In 2016 International Conference on

Communications, Information Management and Network Security, Atlantis press 2352-538X.

13. R. Desai, Harish H.S., Raghu H. Mahesh Patil, Novel and Energy Efficient Routing in Wireless Sensor Networks. In National

Conference on Knowledge, Innovation in Technology and Engineering, International Journal of Computer Applications (0975 –

8887).

14. Jain, S., Zhang, Y., & Loguinov, D. (2008, June). Towards experimental evaluation of explicit congestion control. In Quality of

Service, 2008. IWQoS 2008. 16th International Workshop on (pp. 121-130). IEEE.

15. Hegadi, R., Kammar, A., & Budihal, S. (2019, March). Performance Evaluation of In-network Caching: A Core Functionality of

Information Centric Networking. In 2019 International Conference on Data Science and Communication (IconDSC) (pp. 1-8). IEEE.

16. Mejri, S., Touati, H., Malouch, N., & Kamoun, F. (2017, October). Hop-by-Hop Congestion Control for Named Data Networks.

In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) (pp. 114-119). IEEE.

17. Hayamizu, Y., & Yamamoto, M. (2015, May). Receiver-driven congestion control for content oriented application with multiple

sources. In Communications Quality and Reliability (CQR), 2015 IEEE International Workshop Technical Committee on (pp. 1-6).

IEEE.

18. Xia, C., & Xu, M. (2012, October). RRCP: A Receiver-Driven and Router-Feedback Congestion Control Protocol for ICN.

In Networking and Distributed Computing (ICNDC), 2012 Third International Conference on (pp. 77-81). IEEE.

19. Wang, Z., Luo, H., Zhou, H., & Li, J. (2018). R 2 T: A Rapid and Reliable Hop-by-Hop Transport Mechanism for Information-

Centric Networking. IEEE Access, 6, 15311-15325.

20. Zhou, J., Wu, Q., Li, Z., Kaafar, M. A., & Xie, G. (2015, June). A proactive transport mechanism with explicit congestion notification

for NDN. In Communications (ICC), 2015 IEEE International Conference on (pp. 5242-5247). IEEE.

21. Carofiglio, G., Gallo, M., & Muscariello, L. (2016). Optimal multipath congestion control and request forwarding in information-

centric networks: Protocol design and experimentation. Computer Networks, 110, 104-117.

22. Li, C., Xie, R., Huang, T., & Liu, Y. (2017). Jointly optimal congestion control, forwarding strategy and power control for named-

data multihop wireless network. IEEE Access, 5, 1013-1026.

23. Arianfar, S., Nikander, P., Eggert, L., & Ott, J. (2010). Contug: A receiver-driven transport protocol for content-centric

networks. Under submission.

24. Jacobson, V., Smetters, D. K., Thornton, J. D., Plass, M. F., Briggs, N. H., & Braynard, R. L. (2009, December). Networking named

content. In Proceedings of the 5th international conference on Emerging networking experiments and technologies (pp. 1-12). ACM.

25. Yeh, E., Ho, T., Cui, Y., Liu, R., Burd, M., & Leong, D. (2013). Forwarding, caching and congestion control in named data

networks. arXiv preprint arXiv:1310.5569.

26. Dukkipati, N., & McKeown, N. (2006). Why flow-completion time is the right metric for congestion control. ACM SIGCOMM

Computer Communication Review, 36(1), 59-62.

27. Jose, L., Yan, L., Alizadeh, M., Varghese, G., McKeown, N., & Katti, S. (2015, November). High speed networks need proactive

congestion control. In Proceedings of the 14th ACM Workshop on Hot Topics in Networks (p. 14). ACM.

1-5

28. Dukkipati, N. (2008). Rate Control Protocol (RCP): Congestion control to make flows complete quickly. Stanford University.

29. Ahlgren, B., Dannewitz, C., Imbrenda, C., Kutscher, D., & Ohlman, B. (2012). A survey of information-centric networking. IEEE

Communications Magazine, 50(7).

30. Tanaka, D., & Kawarasaki, M. (2016, June). Congestion control in named data networking. In Local and Metropolitan Area Networks

(LANMAN), 2016 IEEE International Symposium on (pp. 1-6). IEEE.

31. Saino, L., Cocora, C., & Pavlou, G. (2013, June). Cctcp: A scalable receiver-driven congestion control protocol for content centric

networking. In Communications (ICC), 2013 IEEE International Conference on (pp. 3775-3780). IEEE.

2.

Authors: Sakthivel R., Savika Singha, Alaida H. M., Akhila S.

Paper Title: Efficient VLSI Architecture for Odor Recognition with a Spiking Neural Network

Abstract: In this paper spiking neural network (SNN) is presented which can discriminate odor data. Spike

timing dependent synaptic plasticity (STDP) means a plasticity which is controlled by the presynaptic and

postsynaptic spikes time difference. Using this STDP rule the synaptic weights are modified after the mitral and

before the cortical cells. In order to determine whether the circuit has correctly identified the odor the SNN has

either a high or a low response at the output for any odor given as the input. Keyword: Olfactory system, Spike timing, Spiking neural network, STDP References:

1. [Online].Available: https://universe-review.ca/I10-85-smell.jpg (Date last accessed 20- March-2019)

2. Hung-Yi Hsieh and Kea-Tiong Tang, Member, “VLSI Implementation of a Bio-Inspired Olfactory Spiking Neural Network”,

IEEE transactions on neural networks and learning systems, vol. 23, no. 7, July 2012 3. T. J. Koickal, A. Hamilton, S. L. Tan, J. A. Covington, J. W. Gardner, and T. C. Pearce, “Analog VLSI circuit implementation of

an adaptive neuromorphic olfaction chip,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 54, no. 1, pp. 60–73, Jan. 2007 4. G. Lozowski, M. Lysetskil, and J. M. Zurada, “Signal processing with temporal sequences in olfactory systems,” IEEE

Trans.Neur. Networks, vol. 15, no. 5, pp. 1268–1275, Sep. 2004

5. N.Caticha, J.E.P. Tejada, D. Lancet, and E. Domany, “Computational capacity of an odorant discriminator: The linear separability of curves,” Neur. Computation, vol. 14, pp. 2201–2220, 2002

6. T. Schaefer and T. W. Margrie, “Spatiotemporal representations in the olfactory system,” Trends Neurosci., vol. 30, no. 3, pp.

92–100, 2007 7. Sushma Srivastava, Shridhar Sahu, S. S. Rathod, “Computation and Analysis of Excitatory Synapse and Integrate & Fire Neuron:

180nm MOSFET and CNFET Technology” IOSR Journal of VLSI and Signal Processing (IOSR-JVSP), 2018, PP 60-72.

8. G. Indiveri, “Circuits for bistable spike-timing-dependent plasticity neuromorphic VLSI synapses,” Advances in Neural Information Processing System, 2002.

6-9

3.

Authors: Pranav S. Vaidya, Avinash Yadav, Linknath Surya, John J. Lee

Paper Title: An Energy Efficient Register File Architecture for VLIW Streaming Processors on FPGAs

Abstract: The design of a register file with large scalability, high bandwidth, and energy efficiency is the

major issue in the execution of streaming Very Long Instruction Word (VLIW) processors on Field

Programmable Gate Arrays (FPGA's). This problem arises due to the fact that accessing multi-ported register

files that can use optimized on-chip memory resources as well as enabling the maximum sharing of register

operands are difficult provided that FPGA's on-chip memory resources only support up to two ports. To handle

this issue, an Inverted Distributed Register File (IDRF) architecture is proposed in this article. This new IDRF is

compared with the existing Central Register File (CRF) and the Distributed Register File (DRF) architectures on

parameters such as kernel performance, circuit area, access delay, dynamic power, and energy. Experimental

results show that IDRF matches the kernel performance with the CRF architecture but 10.4% improvement in

kernel performance as compared to DRF architecture. Similar experimental results related to the circuit area,

dynamic power, and energy are discussed in this article.

Keyword: Inverted distributed register file architecture, VLIW streaming multiprocessor, FPGA, multi-ported

memory. References:

1. J. H. Ahn, W. J. Dally, B. Khailany, U. J. Kapasi, and A. Das, Evaluating the Imagine stream architecture, Proc. of ISCA (2004) 14-25.

2. P. Vaidya, J. Lee, F. Bowen, Y. Du, C. Nadungodage, and Y. Xia, Symbiote: A Reconfigurable Logic Assisted Data Stream

Management System (RLADSMS), Proc. of SIGMOD, (2010) 1147-1150. 3. Virtex-5 Multi-Platform FPGA, http://www.xilinx.com/products/siliconsolutions/fpgas/virtex/virtex5/

index.htm.

4. Virtex-5 Multi-Platform FPGA, http://www.xilinx.com/products/siliconsolutions/fpgas/virtex/virtex5/ index.htm.

5. LaForest and S. Gregory, Efficient multi-ported memories for FPGAs, in Proc. of IEEE FPGA (2010) 41-50. 6. J. Zalamea, J. Llosa, E. Ayguad, and M. Valero, Software and hardware techniques to optimize register file utilization in VLIW

architectures, International Journal on Parallel Programming (2004) 447-474.

7. J. Owens et al., Media processing applications on the Imagine stream processor, Proc. of IEEE ICCD (2002) 295-302. 8. F. Gerneth, FIR Filter Algorithm Implementation Using Intel SSE Instructions, Intel Whitepaper,

http://download.intel.com/design/intarch/papers/323411.pdf.

10-14

4.

Authors: Tareq S. Alqaisi, Linknath Surya Balasubramanian, Avinash Yadav, John J. Lee

Paper Title: Microblaze-Based Coprocessor for Data Stream Management System

Abstract: Data generation speed and volume have increased exponentially with the boom in Internet usage

and with the advent of Internet of Things (IoT). Consequently, the need for processing these data faster and with 15-18

higher efficiency has also grown in-over time. Many previous works tried to address this need and among them

is Symbiote Coprocessor Unit (SCU), an accelerator capable of providing speedup of up to 150x compared with

traditional data stream processors. The proposed architecture aims to reduce the complexity of SCU, making it

flexible and still retaining its performance. The new design is more software driven and thus is very easy to be

altered in the future if needed. We have also changed the older interface to industrial standard PCIe interface and

AMBA AXI4 bus interconnect in order to make the design simple and open for future expansions.

Keyword: Symbiote Coprocessor Unit, PCIe, AMBA AXI4. References:

1. Abadi et al., “Aurora: A Data Stream Management System,” in Proc. of ACM SIGMOD, p.666, 2003.

2. Cranor, T. Johnson, O. Spatascheck, “Gigascope: A stream database for network applications,” in proc. of ACM SIGMOD, pp. 647-651, 2003.

3. M. A. Hammad et al., “Nile: a query processing engine for data streams,” in proc. of ICDE, p. 851, IEEE Computer Society,

2004. 4. P. S. Vaidya, Hardware-software co-designed data stream management systems. PhD thesis, Purdue University, West Lafayette,

IN, USA, Dec. 2015.

5. Holden, J. Trodden, D. Anderson, “HyperTransport 3.1 Interconnect Technology.” http://www.mindshare.com/files/ebooks/HyperTransport%203.1%20Interconnect%20Technology.pdf, September 2008.

Accessed: Dec. 2017.

6. Xilinx Inc, “MicroBlaze Processor Reference Guide.” http://www.xilinx.com/support/documentation/sw_manuals/xilinx2016_1/ug984-vivado-microblaze-ref.pdf, April 2016.

Accessed: Nov. 2017.

7. Arm Limited, “AMBA AXI and ACE Protocol Specification.” http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.ihi0022d/index.html, October 2011. Accessed: Oct. 2017.

8. M. Jackson and R. Budruk, PCI Express Technology. MindShare, Inc., 2012.

5.

Authors: J. V. Alamelu, A. Mythili

Paper Title: Examination of Control Parameters for Medical Grade Insulin Pump

Abstract: In this work, an attempt has been made to identify the appropriate parameters of Permanent

Magnet Direct Current (PMDC) motor for infusion pump. PMDC motor plays important role in medical devices.

In this, selection of parameters such as rotor inertia, armature resistance, armature inductance and back electro

motive force constant is crucial that help to achieve the required speed. The proposed work uses PID controller

(Proportional Integral Derivative) and LQG (Linear-Quadratic Gaussian) control algorithm to evaluate the

parameters for transient response of the PMDC motor. It is demonstrated that the chosen parameters are able to

reach the required speed with quick rise time by 0.691 seconds by employing LQG.

Keyword: Permanent Magnet Direct Current; infusion pump; control algorithm; Linear-Quadratic Gaussian;

Proportional Integral Derivative.

References: 1. O. Vahidi, K. E. Kwok, R. B. Gopaluni, and F. K. Knop, “A comprehensive compartmental model of blood glucose regulation for

healthy and type 2 diabetic subjects,” Med. Biol. Eng. Comput., vol. 54, no. 9, pp. 1383–1398, 2016.

2. P. Masci, R. Ruksėnas, P. Oladimeji, A. Cauchi, A. Gimblett, Y. Li, P. Curzon, and H. Thimbleby, “The benefits of formalising design guidelines: a case study on the predictability of drug infusion pumps,” Innov. Syst. Softw. Eng., vol. 11, no. 2, pp. 73–93,

2015.

3. M. Bozic, D. Todorovic, M. Petkovic, V. Zerbe, and G. S. Dordevic, “Advanced DC motor drive for haptic devices,” Proc. Small Syst. Simul. Symp., vol. 16, no. 1, pp. 97–100, 2012.

4. V. Iacovacci, L. Ricotti, P. Dario, and A. Menciassi, “Design and development of a mechatronic system for noninvasive refilling

of implantable artificial pancreas,” IEEE/ASME Trans. Mechatronics, vol. 20, no. 3, pp. 1160–1169, 2015. 5. M. F. Moussa, M. Saad, and Y. G. Dessouky, “Adaptive control and one-line identification of sensorless Permanent Magnet DC

motor,” Proc. - 2010 IEEE Reg. 8 Int. Conf. Comput. Technol. Electr. Electron. Eng. Sib., pp. 852–857, 2010.

6. M. Cescon and R. Johansson, “Linear Modeling and Prediction in Diabetes Physiology,” pp. 187–222, 2014. 7. S. Sankaranarayanan and G. Fainekos, “Simulating insulin infusion pump risks by in-silico modeling of the insulin-glucose

regulatory system,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7605

LNBI, pp. 322–341, 2012. 8. C. Pratthanaruk and J. Srisertpol, “A High Accuracy Speed Control of DC Motor Using Adaptive Torque Compensation on

Lyapunov Stability with Kalman Filter,” vol. 835, pp. 673–680, 2016.

9. Y. L. Karnavas and I. D. Chasiotis, “PMDC coreless micro-motor parameters estimation through Grey Wolf Optimizer,” Proc. - 2016 22nd Int. Conf. Electr. Mach. ICEM 2016, pp. 865–870, 2016.

10. P. Zhao, Y. Chong, A. Zhao, and L. Lang, “A rapid infusion pump driven by micro electromagnetic linear actuation for pre-

hospital intravenous fluid administration,” no. January 2015, 2016. 11. C. Paper and R. Kumar, “Kalman Filter for Speed Control of Dc Motor for Robotic Safety Critical Kalman Filter for Speed

Control of Dc Motor for Robotic Safety Critical Applications,” no. May 2015, 2016.

12. S. M. Khot and Y. Khan, “Simulation of Active Vibration Control of a Cantilever Beam using LQR , LQG and H- ∞ Optimal Controllers,” no. December, 2015.

13. Faulhaber, “DC-Gearmotors Precious Metal Commutation with integrated Encoder 100 mNm Series 2619 ... SR ... IE2-16,” Data

Sheet, pp. 1–2, 2018. 14. R. Akbari-Hasanjani, S. Javadi, and R. Sabbaghi-Nadooshan, “DC motor speed control by self-tuning fuzzy PID algorithm,”

Trans. Inst. Meas. Control, vol. 37, no. 2, pp. 164–176, 2015.

15. P. L. Technology, “Study on the Speed Control Algorithm of DC Motor Based on the Software Phase-locked Loop Technology,” Third Int. Conf. Intell. Networks Intell. Syst. Study, pp. 1–4, 2010.

16. M. A. Aravind, N. Saikumar, and N. S. Dinesh, “Optimal Position Control of a DC Motor Using LQG with EKF,” 2017 Int.

Conf. Mech. Syst. Control Eng., no. 2, pp. 149–154, 2017.

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6. Authors: P. Venkateswara Babu, Syed Ismail, Satish Ben Beera

Paper Title: Modification of Surface Topography and Analysis of Its Impact on Friction and Wear Reduction of

Sliding Contact

Abstract: Proper lubrication and surface modification are key factors to improve the tribological behavior

of interacting sliding surfaces under lubricated conditions. Surface texturing of interacting surfaces has found to

be an emerging technique that modifies the surfaces deterministically by producing surface features in the form

of surface asperities or grooves with specific shape, size and distribution. The present paper address the impact

of positive surface textures (protrusions) and number of positive textures in the sliding direction on friction and

wear behavior of parallel sliding contacts. The square shaped positive surface textures are created on the

specimen by ink-jet followed by chemical etching process. The sliding experiments are conducted on pin on disc

friction and wear test rig by providing different sliding conditions such as plain dry, plain with lubricant and

textures with lubricant between the interacting surfaces. The results indicated that the textures with lubricated

condition exhibit lower friction and wear compared to other two conditions. Furthermore, it is reported that

among the tested samples, the textured sample with number of textures three in sliding direction has shown a

prominent effect in reducing friction and wear of parallel sliding contact.

Keyword: Friction coefficient; Parallel sliding contact; Surface modification; Surface texture; Wear rate References:

1. Fowell, M.T., Medina, S., Olver, A.V., Spikes, H.A. and Pegg, I.G., 2012. Parametric study of texturing in convergent

bearings. Tribology International, 52, pp.7-16. 2. Ma, C., Duan, Y., Yu, B., Sun, J. and Tu, Q., 2017. The comprehensive effect of surface texture and roughness under

hydrodynamic and mixed lubrication conditions. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of

Engineering Tribology, 231(10), pp.1307-1319. 3. Sudeep, U., Tandon, N. and Pandey, R.K., 2015. Performance of lubricated rolling/sliding concentrated contacts with surface

textures: a review. Journal of Tribology, 137(3), p.031501.

4. ZumGahr, K.H., Wahl, R. and Wauthier, K., 2009. Experimental study of the effect of micro-texturing on oil lubricated ceramic/steel friction pairs. Wear, 267(5-8), pp.1241-1251.

5. Wahl, R., Schneider, J. and Gumbsch, P., 2012. In situ observation of cavitation in crossed microchannels. Tribology

International, 55, pp.81-86. 6. Wahl, R., Schneider, J. and Gumbsch, P., 2012. Influence of the real geometry of the protrusions in micro textured surfaces on

frictional behaviour. Tribology letters, 47(3), pp.447-453.

7. P., V., Syed, I. and Beera, S., 2019. Influence of positive texturing on friction and wear properties of piston ring-cylinder liner tribo pair under lubricated conditions. Industrial Lubrication and Tribology, 71 (4), pp. 515-524.

8. Wakuda, M., Yamauchi, Y., Kanzaki, S. and Yasuda, Y., 2003. Effect of surface texturing on friction reduction between ceramic

and steel materials under lubricated sliding contact. Wear, 254(3-4), pp.356-363.

9. Borghi, A., Gualtieri, E., Marchetto, D., Moretti, L. and Valeri, S., 2008. Tribological effects of surface texturing on nitriding

steel for high-performance engine applications. Wear, 265(7-8), pp.1046-1051.

10. Gachot, C., Rosenkranz, A., Hsu, S.M. and Costa, H.L., 2017. A critical assessment of surface texturing for friction and wear improvement. Wear, 372, pp.21-41.

11. Costa, H.L. and Hutchings, I.M., 2009. Effects of die surface patterning on lubrication in strip drawing. Journal of Materials

Processing Technology, 209(3), pp.1175-1180. 12. Blatter, A., Maillat, M., Pimenov, S.M., Shafeev, G.A., Simakin, A.V. and Loubnin, E.N., 1999. Lubricated sliding performance

of laser-patterned sapphire. Wear, 232(2), pp.226-230.

13. Křupka, I., Vrbka, M. and Hartl, M., 2008. Effect of surface texturing on mixed lubricated non-conformal contacts. Tribology International, 41(11), pp.1063-1073.

14. Pettersson, U. and Jacobson, S., 2003. Influence of surface texture on boundary lubricated sliding contacts. Tribology

International, 36(11), pp.857-864. Pettersson, U. and Jacobson, S., 2003. Influence of surface texture on boundary lubricated sliding contacts. Tribology International, 36(11), pp.857-864.

15. Pettersson, U. and Jacobson, S., 2004. Friction and wear properties of micro textured DLC coated surfaces in boundary lubricated

sliding. Tribology letters, 17(3), pp.553-559. 16. ZumGahr, K.H., Mathieu, M. and Brylka, B., 2007. Friction control by surface engineering of ceramic sliding pairs in

water. Wear, 263(7-12), pp.920-929.

17. Vlădescu, S.C., Medina, S., Olver, A.V., Pegg, I.G. and Reddyhoff, T., 2016. Lubricant film thickness and friction force measurements in a laser surface textured reciprocating line contact simulating the piston ring–liner pairing. Tribology

International, 98, pp.317-329.

18. Zhang, H., Zhang, D.Y., Hua, M., Dong, G.N. and Chin, K.S., 2014. A study on the tribological behavior of surface texturing on babbitt alloy under mixed or starved lubrication. Tribology Letters, 56(2), pp.305-315.

19. Etsion, I. and Sher, E., 2009. Improving fuel efficiency with laser surface textured piston rings. Tribology International, 42(4),

pp.542-547. 20. Wan, Y. and Xiong, D.S., 2008. The effect of laser surface texturing on frictional performance of face seal. Journal of Materials

Processing Technology, 197(1-3), pp.96-100.

21. Wang, X., Kato, K., Adachi, K. and Aizawa, K., 2003. Loads carrying capacity map for the surface texture design of SiC thrust bearing sliding in water. Tribology International, 36(3), pp.189-197.

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7.

Authors: Prakash Kodali

Paper Title: Use of Flat Ribbon like Electrode Geometry to Pole PVDF Piezoelectrics in Solution Processing

Abstract: We study how ribbons of fluids subjected to electric fields can serve applications in energy

harvesting. In particular the emphasis is on how the geometry (i.e. 2-D ribbons) can influence functionality. For

applications related to energy harvesting, we consider the use of polymer Piezo-electric PolyvinylideneFluoride

(PVDF). Corona poling, photo-induced, photo-thermal and electron beam poling are the different conventional

techniques used for PVDF poling. The parallel plate capacitor structure made for poling the PVDF material

while the PVDF is being cured. One key advantage of preparing PVDF is the ability of solution processing.

Normally, the liquid is then spin coated on a substrate and left to dry. Either during the process of spin coating,

or after drying - the film of PVDF is poled so as to align the dipoles and make a piezoelectric. We propose the

use of a metal-insulator ribbon like electrode geometry to combine the process of fabrication and poling thereby

making the process more efficient. On the application of a voltage across the electrodes, the voltage of Vs is

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developed across the fluid. This result in a field of Vs/d across the PVDF fills aiding the process of poling while

the film is in liquid phase. Therefore the ribbon like geometry aids the use of PVDF piezo-electrics in two ways.

Firstly, it makes the fabrication process efficient by combining the poling with the structure development.

Secondly, the control of width (w) and length (l) aids the setup of the PVDF piezoelectric resonant frequency for

a given thickness (d). This helps match the resonant frequency of the ribbon with the incoming low frequency

vibration to improve the energy harvesting levels. Piezo-electrics can be used in submerged applications, large

area PVDF energy scavengers, mechanical filters and sensors, rural electrification, and charging circuits for

hand-held devices.

Keyword: Piezo-electrets; Large Electronics; Energy Harvesting; Impedance Matching, Sensing and

Resonance. References:

1. H. Kawai, "The piezoelectricity of poly (vinylidene fluoride)," Japanese Journal of Applied Physics, vol. 8, 1969, pp. 975.

2. J. Xu, M. Dapino, D. Gallego-Perez, and D. Hansford, "Microphone based on polyvinylidene fluoride (PVDF) micro-pillars and

patterned electrodes," Sensors and Actuators A: Physical, vol. 153, 2009, pp. 24-32. 3. B.-T. Wang, "The PVDF-based wave number domain sensing techniques for active sound radiation control from a simply

supported beam," The Journal of the Acoustical Society of America, vol. 103, 1998, pp. 1904-1915.

4. Q. Zhang, V. Bharti, and G. Kavarnos, "Poly (vinylidene fluoride) (PVDF) and its copolymers," Encyclopedia of Smart

Materials, 2002.

5. D. Reneker, J. Gorse, D. Lolla, C. Kisielowski, J. Miao, P. Taylor, et al., "Polyvinylidene fluoride molecules in nanofibers,

imaged at atomic scale by aberration corrected electron microscopy," Bulletin of the American Physical Society, 2016. 6. K. Omote, H. Ohigashi, and K. Koga, "Temperature dependence of elastic, dielectric, and piezoelectric properties of “single

crystalline’’films of vinylidene fluoride trifluoroethylene copolymer," Journal of applied physics, vol. 81,1997, pp. 2760-2769.

7. K. Prevedouros, I. T. Cousins, R. C. Buck, and S. H. Korzeniowski, "Sources, fate and transport of perfluorocarboxylates," Environmental science & technology, vol. 40, 2006, pp. 32-44.

8. C.-H. Hung, Y.-L. Lin, and T.-H. Young, "The effect of chitosan and PVDF substrates on the behavior of embryonic rat cerebral

cortical stem cells," Biomaterials, vol. 27, 2006, pp. 4461-4469. 9. D. Gallego-Perez, N. J. Ferrell, N. Higuita-Castro, and D. J. Hansford, "Versatile methods for the fabrication of polyvinylidene

fluoride microstructures," Biomedical microdevices, vol. 12, 2010, pp. 1009-1017.

10. M. W. Shafer, M. Bryant, and E. Garcia, "Designing maximum power output into piezoelectric energy harvesters," Smart materials and structures, vol. 21, 2012, pp. 085008.

11. F. Wang, M. Tanaka, and S. Chonan, "Development of a PVDF piezopolymer sensor for unconstrained in-sleep cardiorespiratory

monitoring," Journal of intelligent material systems and structures, vol. 14, 2003, pp. 185-190. 12. Lee and H. Sung, "Development of an array of pressure sensors with PVDF film," Experiments in Fluids, vol. 26, 1999, pp. 27-

35.

13. B. P. Mahale, D. Bodas, and S. Gangal, "Development of PVdF based pressure sensor for low pressure application," in Nano/Micro Engineered and Molecular Systems (NEMS), 2011 IEEE International Conference on, 2011, pp. 658-661.

14. T. Sharma, S.-S. Je, B. Gill, and J. X. Zhang, "Patterning piezoelectric thin film PVDF–TrFE based pressure sensor for catheter

application," Sensors and Actuators A: Physical, vol. 177, 2012, pp. 87-92. 15. Y.-J. Hsu, Z. Jia, and I. Kymissis, "A locally amplified strain sensor based on a piezoelectric polymer and organic field-effect

transistors," Electron Devices, IEEE Transactions on, vol. 58, 2011, pp. 910-917.

16. J. Yi and H. Liang, "A PVDF-based deformation and motion sensor: Modeling and experiments," Sensors Journal, IEEE, vol. 8, 2008, pp. 384-391.

17. B. Choi, H. R. Choi, and S. Kang, "Development of tactile sensor for detecting contact force and slip," in Intelligent Robots and

Systems, 2005.(IROS 2005). 2005 IEEE/RSJ International Conference on, 2005, pp. 2638-2643. 18. Y. Yamada, H. Morita, and Y. Umetani, "Vibrotactile sensor generating impulsive signals for distinguishing only slipping states,"

in Intelligent Robots and Systems, 1999. IROS'99. Proceedings. 1999 IEEE/RSJ International Conference on, 1999, pp. 844-850.

19. S. Sokhanvar, M. Packirisamy, and J. Dargahi, "A multifunctional PVDF-based tactile sensor for minimally invasive surgery," Smart materials and structures, vol. 16, 2007, pp. 989.

20. C.-K. Lee and F. C. Moon, "Modal sensors/actuators," Journal of applied mechanics, vol. 57, 1990, pp. 434-441.

21. H. Sumali, K. Meissner, and H. H. Cudney, "A piezoelectric array for sensing vibration modal coordinates," Sensors and Actuators A: Physical, vol. 93, 2001, pp. 123-131.

22. T. Bailey and J. Ubbard, "Distributed piezoelectric-polymer active vibration control of a cantilever beam," Journal of Guidance,

Control, and Dynamics, vol. 8, 1985, pp. 605-611. 23. P. Ueberschlag, "PVDF piezoelectric polymer," Sensor Review, vol. 21, 2001, pp. 118-126.

24. M. Tamura, T. Yamaguchi, T. Oyaba, and T. Yoshimi, "Electroacoustic transducers with piezoelectric high polymer films,"

Journal of the Audio Engineering Society, vol. 23, 1975, pp. 21-26. 25. V. Sencadas, M. V. Moreira, S. Lanceros-Méndez, A. S. Pouzada, and R. GregórioFilho, "α-to β Transformation on PVDF films

obtained by uniaxial stretch," in Materials science forum, 2006, pp. 872-876.

26. X. J. Zhao, J. Cheng, S. J. Chen, J. Zhang, and X. L. Wang, Controlled Crystallization of Poly (vinylidene fluoride) Chains from Mixed Solvents Composed of Its Good Solvent and Nonsolvent. J. Polym. Sci. B. 48, 2010, pp. 575–581.

27. W. T. Mead, A. E. Zacharidas, T. Shimada, and R. S. Porter, Solid State Extrusion of Poly (viny1idene fluoride). 1. Ram and

Hydrostatic Extrusion. Macromolecules. 12, 1979, pp. 473–478.

8.

Authors: D. Srinivasa Rao, V. Berlin Hency

Paper Title: QoS-Based Two-Level user Scheduling Scheme for MU-MIMO Wireless LANs

Abstract: The advances in physical layer technology has led to the performance upgradation of wireless local area

networks (WLANs). More recently, multi-input multi-output (MIMO) is considered to be a key technology to enable high

data rate transmission in WLANs. However, the actual benefit of this approach can be utilized, if there is an appropriate

mechanism to select and schedule the users. Also, providing Quality of Service (QoS) support to user demands has become a

major task in WLANs. In this paper, a two-level user scheduling approach for WLANs is discussed and its performance is

evaluated using high-transmission rates with the assumption of frequency selective fading. For the purpose of comparison,

some well-known medium access control (MAC) scheduling schemes are considered. It is shown that, the proposed scheme

enhances throughput and achieves fairness among the users. Further, this scheme can be used to reduce contention during the

acquisition of channel feedback. Keyword: quality of service, channel feedback, scheduler, throughput, fairness

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References: 1. Jim Geier, Designing and deploying 802.11 Wireless Networks, Cisco press, 2010. 2. A.J.Paulraj, D.A.Gore, R.U.Nabar,H.Bolcskei, “An overview of MIMO communications - a key to gigabit wireless,” Proceedings

of the IEEE, vol.92, no.2, 2004.

3. Edward Au, “Exciting Projects for PHY and MAC Layers of IEEE 802.11,” IEEE Vehicular technology magazine, vol.11, no.2,

2016.

4. D.Nojima, L.Lanante, Y.Nagao, M.Kurosaki, H.Ochi, “Performance evaluation for multi-user MIMO IEEE 802.11ac wireless

LAN system,” 14th International Conference on Advanced Communication Technology (ICACT), Pyeong Chang, South Korea, 2012.

5. R.Pierre F.Hoefel, “Multi-user OFDM MIMO in IEEE 802.11ac WLAN: A simulation framework to analysis and synthesis”,

IEEE Latin-America Conference on Communications, Santiago, Chile, 2013. 6. G.Z.Khan, R.Gonzalez, E.C.Park, “A performance analysis of MAC and PHY layers in IEEE 802.11ac wireless network,” 18th

International Conference on Advanced Communication Technology (ICACT), Pyeongchang, South Korea, 2016. 7. B.Bellalta, J.Barcelo, D.Staehle, A.Vinel, M.Oliver, “On the performance of packet aggregation in IEEE 802.11 ac MU-MIMO

WLANs”, IEEE communications letters, vol. 16, no. 10, october 2012.

8. J.Cha, H.Jin, B.Chul Jung, D.K.Sung, “Performance comparison of downlink user multiplexing schemes in IEEE 802.11 ac: Multi-user MIMO vs. frame aggregation”, IEEE Wireless Communications and Networking Conference (WCNC), Shanghai,

China, 2012.

9. Y.Nomura, K.Mori, H.Kobayashi, “Efficient frame aggregation with frame size adaptation for next generation MU-MIMO WLANs”, 9th International Conference on Next Generation Mobile Applications, Services and Technologies, Cambridge, UK,

2015.

10. M.X.Gong, E.Perahia, R.Want, S.Mao, “Training protocols for multi-user MIMO wireless LANs”, 21st Annual IEEE

International Symposium on Personal, Indoor and Mobile Radio Communications, Instanbul, Turkey, 2010.

11. X.Xie, X.Zhang, K.Sundaresan, “Adaptive feedback compression for MIMO networks”, Proceedings of the 19th annual

international conference on Mobile computing & networking, Miami, Florida, USA, 2013. 12. H. Lou, M. Ghosh, P. Xia, and R. Olesen, “A comparison of implicit and explicit channel feedback methods for MU-MIMO

WLAN systems”, 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) , pp.

419-424, 2013. 13. C.Zhu, A.Bhatt, Y.Kim, O.A.Magd, C.Ngo,“MAC enhancements for downlink multi-user MIMO transmission in next

generation WLAN”, IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, USA, 2012.

14. M.Yazid, A.Ksentini, L.B.Medjkoune, D.Aïssani,“Performance analysis of the TXOP sharing mechanism in the VHT IEEE 802.11 ac WLANs”, IEEE Communications Letters, vol.18, no.9, 2014.

15. M.Aajami, J.B.Suk, “Optimal TXOP Sharing in IEEE 802.11ac”, IEEE Communications Letters, vol.19, no.7, 2015.

16. S. Mangold, S. Choi, G. R. Hiertz, O. Klein, and B. Walke, “Analysis of IEEE 802.11e for QoS support in wireless LANs,” Wireless

Communications, IEEE, vol. 10, no. 6, pp. 40–50, 2003.

17. G. Redieteab, L. Cariou, P. Christin, and J. F. Helard, “PHY+MAC channel sounding interval analysis for IEEE 802.11ac MUMIMO”, in Proc. 9th IEEE Int. Symp. on Wireless Commun. Syst.

ISWCS 2012, Paris, France, 2012.

18. O. Bejarano, E. Magistretti, O. Gurewitz, and E. W. Knightly, “MUTE: sounding inhibition for MU-MIMO WLANs”, in Proc. 11th

Ann. IEEE Int. Conf. on Sensing, Commun., and Network. SECON,

Singapore, Singapore, 2014, pp. 135-143. 19. IEEE, IEEE Std P802.11ac: Part 11: Wireless LAN Medium Access

Control (MAC) and Physical Layer (PHY) specifications: enhancements for very high throughput for operation in bands below

6GHz, 2013. 20. S. Huang, H. Yin, J. Wu, V. Leung, “User selection for multiuser MIMO downlink with zero-forcing beamforming,” IEEE

Transactions on Vehicular Technology, vol. 62, no. 7, pp. 3084-3097, 2013.

21. X. Xie and X. Zhang, “Scalable user selection for MU-MIMO networks,” in Proc. of IEEE International Conference on Computer Communications (INFOCOM), pp. 808-816, 2014.

22. K. Lee and C. Kim, “User scheduling for MUMIMO transmission

with active CSI feedback”, EURASIP journal on Wireless Communication and Networking, vol. 112, 2015. 23. Zhou, T. Wei, X. Zhang and et al., “Signpost: Scalable MU-MIMO Signaling with Zero CSI Feedback,” in Proc. of ACM

International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 327-336, 2015.

24. Y. Zhou, A. Zhou, and M. Liu, “OUS: Optimal user selection in MU-MIMO WLANs”, in Proc. Int. Conf. on Computer Network. and

Comm. ICNC 2016, Kauai, HI, USA, 2016.

25. N.Anand, J.Lee, S.J.Lee, E.W.Knightly, “Mode and User Selection for Multi-User MIMO WLANs without CSI”, IEEE Conference on Computer Communications (INFOCOM), 2015.

26. M.Esslaoui, F.Riera-Palou and G.Femenias, “A fair MU-MIMO scheme for IEEE 802.11ac”, International Symposium on

Wireless Communication Systems (ISWCS), 2012. 27. Kyeongjun Ko and Jungwoo Lee, “Multiuser MIMO User Selection Based on Chordal Distance”, IEEE transactions on

communications, Vol. 60, No. 3, March 2012. 28. Shanshan Wu, Wenguang Mao, and Xudong Wang, “Performance Study on a CSMA/CA-Based MAC Protocol for Multi-User

MIMO Wireless LANs”, IEEE transactions on wireless communications, vol. 13, no. 6, June 2014.

29. Suhua Tang, “Distributed Multiuser Scheduling for Improving Throughput of Wireless LAN”, IEEE transactions on wireless communications, vol. 13, no. 5, May 2014.

30. D.Srinivasa Rao, V.Berlin Hency, “QoS-based joint user selection and scheduling for MU-MIMO WLANs”, Journal of

telecommunication and information technology, vol.4, 2017.

9.

Authors: Swapna P. S., Sakuntala S. Pillai, Syama Sasikumar, Sreeni K. G.

Paper Title: A Novel Genetic Resource Allocation Algorithm for Symmetrical Services in OFDMA Systems

Abstract: Performance enhancement of symmetrical services has been very essential today owing to the

widespread acceptance and demand of these services in the present generation communication systems. An

algorithm with reduced complexity for subcarrier allocation in OFDMA/SC-FDMA system for specific

applications that demand similar bidirectional quality is proposed in this paper. The resource allocation problem

devised is a multiobjective optimization problem with objectives to maximize bidirectional data rates and

minimize the difference in bidirectional data rates, with fairness as a significant constraint. The original problem

is mathematically intractable due to non-convexity and therefore linear programming techniques fail to find an

optimal solution. The subcarrier allocation problem has been undertaken using an innovative multiobjective

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optimization technique that employs the concept of non-dominance in evolutionary algorithms. The results are

extremely encouraging, while significantly reducing the complexity involved in the processing of algorithm.

Keyword: Evolutionary algorithm, Multiobjective Optimization, Non-dominance, OFDMA/SC-FDMA References:

1. Ahmad M. El-Hajj, Zaher Dawy, “On Probabilistic Queue Length based Joint Uplink/Downlink resource allocation in OFDMA networks”, 19th International Conference on Telecommunications 2012.

2. Ahmad M El-Hajj, Zaher Dawy and Walid Saad, “A stable matching game for joint Uplink/Downlink resource allocation in

OFDMA wireless networks”, IEEE ICC 2012-Wireless network Symposium. 3. Ahmad M El-Hajji, Zaher Dawy, “On optimized joint uplink/downlink resource allocation in OFDMA networks”, 2011 IEEE

Symposium on Computers and Communications.

4. Ahmad M El-Hajji, Mariette Awad, Zaher Dawy, “SIRA:A socially inspired game theoretic uplink/downlink resource aware allocation in OFDMA systems”, 2011 IEEE International Conference on Systems, Man and Cybernetics.

5. Lukai Xu, Guanding Yu and Yuhuan Jiang,”Energy Efficient Resource Allocation in Single Cell OFDMA Systems: Multi-

Objective Approach”, IEEE Transactions on Wireless Communications,2015. 6. Lukai Xu, Guanding Yu, Yuhuan Jiang and Qimei Chen, “Multi-Objective Bandwidth and Power Allocation for Energy-Efficient

Uplink Communications”, 2015 IEEE 26th Annual International Symposium on Personal ,Indoor and Mobile Radio

Communications. 7. Zhengyu Song, Qiang Ni, Keivan Navaie, Shujuan Hou and Siliang Wu, “Energy and Spectral Efficiency Tradeoff with α-

Fairness in Downlink OFDMA Systems”,IEEE Communications Letters, 2015.

8. U. Akgul, B. Canberk, ‘Self-Organized Things (SoT): An Energy Efficient Next Generation Network Management’, Computer Communications (Elsevier), vol. 74, pp. 52-62, January 2016.

9. Guopeng Zhang, Kun Yang, and Hsiao-Hwa Chen, “Resource Allocation for Wireless Cooperative Networks - A Unified

Cooperative Bargaining Game Theoretic Framework”, IEEE Wireless Communications, Volume: 19, Issue: 2, Page(s): 38-43, April 2012. (SCI).

10. Elias Yaacoub and Zaher Dawy. “A survey on uplink resource allocation in OFDMA wireless networks”. IEEE Communications

Surveys & Tutorials,14(2):322–337, 2012.

10.

Authors: B. Nagajayanthi

Paper Title: Energy Efficient Light Weight Security Algorithm for Low Power IOT Devices

Abstract: Internet of Things (IoT) is the state of art which connects, communicates, intelligently resolves

and processes data between physical devices and smart phone or to a centralized server. Billions of users are

centrally coordinated via the internet. The number of ubiquitous IoT devices will surpass the number of humans.

For secured data transfer, IoT requires strenuous focus on security. Inspite of the secured IoT layered approach

integrated in its architecture, yet they are susceptible to thwarting attacks. With proliferating applications and

innovations, there is a stringent need to preserve user privacy and anonymize interactions using a lightweight

cryptographic algorithm. Existing cryptographic algorithms have constraints on power, limited battery, real time

execution, latency, code length and memory. In this research, initially comparison of the existing algorithms is

made. Subsequently, Augmented Security and Optimized memory space is achieved for the data channelized via

IoT by using the combination of the Light weight masked AES (Advanced Encryption Standard) and MD5

(Message Digest) hash algorithm. This chaining technique is implemented using VHDL Coding, Xilinx ISE and

ModelSim 6.5 software tool. In the proposed algorithm, area, power and timing factors are reduced using

optimization techniques, which drastically reduces the power consumed, and chip area. Chip area is calculated in

terms of gate equivalents and power consumption is reduced through clock gating and operand isolation

techniques. Keyword: Chip Area, Gate Equivalents, Light Weight, S-Box. References:

1. Safi ,A .(2017). ‘Improving the Security of Internet of Things using Encryption Algorithms ‘, International Journal of Computer

and Information Engineering, vol. 11(5), pp .546-549.

2. Al-Anazi, H., Zaidan, B. B., Zaidan, A. A., Jalab, H. A., Shabbir, M., & Al-Nabhani, Y.(March 2010).‘ New Comparative Study between DES, 3DES and AES within nine factors’, Journal of Computing , vol. 2 (3), ISSN 2151-9617 , pp.152-157.

3. Bansod, G., Raval, N., & Pisharoty, N.(January 2015).’ Implementation of a new lightweight encryption design for embedded security’, IEEE Transactions on information forensics and security,vol.10 (1), pp.142-151.

4. Canright, D. (2007).’Masking a compact AES S-box’,Calhoun :Institutional Archive of the Naval Postgraduate School, Monterey

CA ,pp.1-19. 5. Devadas, S., Malik, S. (1995).’ A survey of optimization techniques targeting low power VLSI circuits’, Proceedings of the 32nd

annual ACM/IEEE Design Automation Conference .

6. Eisenbarth, T., & Kumar, S. (2007). ’A survey of lightweight-cryptography implementations’, IEEE Design & Test of Computers, vol.24(6),pp.522-533.

7. Erguler, I., & Anarim, E.(March 2012). ’Security flaws in a recent RFID delegation protocol’ , Personal and Ubiquitous

Computing , vol.16(3),pp.337-349. 8. Hamalainen, P., Alho, T., Hannikainen, M., & Hamalainen, T. D. (2006).’Design and implementation of low-area and low-power

AES encryption hardware core’ , 9th EUROMICRO Conference on Digital System Design: Architectures, Methods and Tools,

DSD 2006,pp.577-583. 9. Kaps, J.P., & Sunar, B. (2006). ‘Energy comparison of AES and SHA-1 for ubiquitous computing’ , International Conference on

Embedded and Ubiquitous Computing, EUC 2006,pp.372-381.

10. Montoyo .B.A.,Munoz G .M., & Kofuji, S. T. (2013).’ Performance analysis of encryption algorithms on mobile devices ‘, 47th International Carnahan Conference on Security Technology (ICCST), 2013.

11. Misra, S., Maheswaran, M., & Hashmi, S. (2017).’ Security challenges and approaches in internet of things’, Springer Briefs in

Electrical and Computer Engineering. 12. Perera, C., Liu, C. H., Jayawardena, S., & Chen, M. (2014). ‘A survey on internet of things from industrial market perspective’,

IEEE Access,vol.2,pp.1660-1669.

13. Poschmann, A., Leander, G., Schramm, K., & Paar, C. (2007). ‘ New light-weight crypto algorithms for RFID’, IEEE International Symposium on Circuits and Systems, ISCAS 2007.

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14. Seth, S. M., & Mishra, R. (June 2011). ‘Comparative analysis of encryption algorithms for data communication’ ,International Journal of Computer Science and Technology,Vol.2 ( 2),pp.292-294.

15. Stallings, W. (2003). ‘Cryptography and network security: principles and practice’, Pearson Education India.

16. Stankovic, J. A. (2014). ‘Research directions for the internet of things’, IEEE Internet of Things Journal. 17. Suo, H., Wan, J., Zou, C., & Liu, J. (2012).’ Security in the internet of things’, International conference on Computer Science

and Electronics Engineering (ICCSEE),pp.648-651.

18. The Statistics Portal. (2017). Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions). [Online]. Available: https://www.statista.com/statistics/471264/iot-number-of-connected-dev ices-worldwide/

19. Zodpe, H. D., Wani, P. W., & Mehta, R. R. (2012). ‘Design and implementation of algorithm for DES cryptanalysis’, 12th

International Conference on Hybrid Intelligent Systems (HIS),pp.278-282.

11.

Authors: M. Sam Navin, L. Agilandeeswari

Paper Title: Land use Land Cover Change Detection using K-means Clustering and Maximum Likelihood

Classification Method in the Javadi Hills, Tamil Nadu, India

Abstract: Land use/Land cover (LU/LC) change analysis is the present-day challenging task for the

researchers in defining the environmental change across the world in the field of remote sensing and GIS

(Geographic Information System). This paper analyzes the LU/LC changes between the years 2009 and 2019 in

the region of Javadi Hills located in Tamil Nadu, India. Images from the Indian remote sensing satellite

Resourcesat-1 LISS III and American earth observation satellite Landsat-8 were used for analyzing the LU/LC

change for the study area. In this work, the classification was performed by using the hybrid approach of

unsupervised and supervised classifiers. The classified LU/LC map for the study area defines forest and non-

forest covered region. The key objective of this work was to identify the percentage of LU/LC change occurred

in our study area for the years 2009 to 2014 and 2014 to 2019. Observing and examining the changes occurred in

the study area provides a clear view to the land resources management to take effective measures in protecting

the environment.

Keyword: Land Use/ Land Cover, Remote Sensing, GIS (Geographic Information System), Supervised and

Unsupervised Classifiers, Accuracy Assessment, Change Analysis and Land Resource Management References:

1. G Mishra, Prabuddh Kumar, Aman Rai, and Suresh Chand Rai. "Land use and land cover change detection using geospatial

techniques in the Sikkim Himalaya, India." The Egyptian Journal of Remote Sensing and Space Science (2019). 2. Pande, Chaitanya B., et al. "Study of land use classification in an arid region using multispectral satellite images." Applied Water

Science 8.5 (2018): 123.

3. Manandhar, Ramita, Inakwu Odeh, and Tiho Ancev. "Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement." Remote Sensing 1.3 (2009): 330-344.

4. Nagne, Ajay D., et al. "Land use land cover change detection by different supervised classifiers on LISS-III temporal

datasets." 2017 1st International Conference on Intelligent Systems and Information Management (ICISIM). IEEE, 2017. 5. Heidarlou, Hadi Beygi, et al. "Effects of preservation policy on land use changes in Iranian Northern Zagros forests." Land Use

Policy 81 (2019): 76-90.

6. Mabwoga, Samson Okongo, and Ashwani Kumar Thukral. "Characterization of change in the Harike wetland, a Ramsar site in India, using landsat satellite data." SpringerPlus 3.1 (2014): 576.

7. Karimi, Hazhir, et al. "Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of

Ravansar County in Iran." Arabian Journal of Geosciences11.19 (2018): 592. 8. Firoozynejad, M., and A. A. Torahi. "Evaluation of IRS1D-LISS-III and Landsat 8-OLI Images for Mapping in Maroon Riparian

Forest." Iran. J Geogr Nat Disast 7.198 (2017): 2167-0587.

9. Mohajane, Meriame, et al. "Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco." Environments 5.12 (2018): 131.

10. Alkaradaghi, Karwan, et al. "Evaluation of Land Use & Land Cover Change Using Multi-Temporal Landsat Imagery: A Case

Study Sulaimaniyah Governorate, Iraq." Journal of Geographic Information System 10.6 (2018): 247-260. 11. Shah, Shipra, and D. P. Sharma. "Land use change detection in Solan forest division, Himachal Pradesh, India." Forest

Ecosystems 2.1 (2015): 26.

12. Sisodia, Pushpendra Singh, Vivekanand Tiwari, and Anil Kumar. "Analysis of supervised maximum likelihood classification for remote sensing image." International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014). IEEE,

2014.

13. Nithya, K., R. Shanmugasundaram, and N. Santhiyakumari. "Study of salem city resource management using k-means clustering." 2017 Conference on Emerging Devices and Smart Systems (ICEDSS). IEEE, 2017.

14. Venkateswaran, K., et al. "Performance Analysis of K-Means Clustering For Remotely Sensed Images." International Journal of

Computer Applications 84.12 (2013). 15. Gashaw, Temesgen, et al. "Evaluation and prediction of land use/land cover changes in the Andassa watershed, Blue Nile Basin,

Ethiopia." Environmental Systems Research 6.1 (2017): 17.

16. Cheruto, Mercy C., et al. "Assessment of land use and land cover change using GIS and remote sensing techniques: a case study of Makueni County, Kenya." (2016).

17. Hassan, Zahra, et al. "Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of

Islamabad Pakistan." SpringerPlus 5.1 (2016): 812. 18. Rwanga, Sophia S., and J. M. Ndambuki. "Accuracy assessment of land use/land cover classification using remote sensing and

GIS." International Journal of Geosciences 8.04 (2017): 611.

19. Restrepo, Angela M. Cadavid, et al. "Land cover change during a period of extensive landscape restoration in Ningxia Hui

Autonomous Region, China." Science of the total environment 598 (2017): 669-679.

20. Miheretu, Birhan Asmame, and Assefa Abegaz Yimer. "Land use/land cover changes and their environmental implications in the

Gelana sub-watershed of northern highlands of Ethiopia." Environmental Systems Research 6.1 (2018): 7.

51-56

12.

Authors: Nirmal Varghese Babu, Fabeela Ali Rawther

Paper Title: Multiclass Sentiment Analysis of Social Media Data using Neural Networks

Abstract: Sentiment analysis, also known as Opinion Mining is one of the hottest topic Nowadays. in

various social networking sites is one of the hottest topic and field nowadays. Here, we are using Twitter, the

biggest web destinations for people to communicate with each other to perform the sentiment analysis and 57-62

opinion mining by extracting the tweets by various users. The users can post brief text updates in twitter as it

only allows 140 characters in one text message. Hashtags helps to search for tweets dealing with the specified

subject. In previous researches, binary classification usually relies on the sentiment polarity(Positive , Negative

and Neutral). The advantage is that multiple meaning of the same world might have different polarity, so it can

be easily identified. In Multiclass classification, many tweets of one class are classified as if they belong to the

others. The Neutral class presented the lowest precision in all the researches happened in this particular area.

The set of tweets containing text and emoticon data will be classified into 13 classes. From each tweet, we

extract different set of features using one hot encoding algorithm and use machine learning algorithms to

perform classification. The entire tweets will be divided into training data sets and testing data sets. Training

dataset will be pre-processed and classified using various Artificial Neural Network algorithms such as

Reccurent Neural Network, Convolutional Neural Network etc. Moreover, the same procedure will be followed

for the Text and Emoticon data. The developed model or system will be tested using the testing dataset. More

precise and correct accuracy can be obtained or experienced using this multiclass classification of text and

emoticons. 4 Key performance indicators will be used to evaluate the effectiveness of the corresponding

approach.

Keyword: Multiclass Sentiment Analysis, Data Pre-processing, Natural Language Processing, Feature

Extraction, Classification, Emoticons, Neural Networks References:

1. Chhaya Chauhan, Smriti Sehgal “Sentiment Analysis on Product Reviews,” International Conference on Computing,

Communication and Automation, 2017..

2. ZHU Nanli, ZOU Ping, LI Weiguo, CHENG Meng “Sentiment Analysis: A Literature Review,” IEEE ISMOT, 2012. 3. Balakrishnan Gokulakrishnan, Pavalanathan Priyanthan, Thiruchittampalam Ragavan, Nadarajah Prasath, AShehan Perera

“Opinion Mining and Sentiment Analysis on a Twitter Data Stream ,” The International Conference on Advances in ICT for

Emerging Regions , 2012. 4. Huma Parveen, Prof. Shikha Pandey “Sentiment Analysis on Twitter Data-set using Naive Bayes Algorithm,” IEEE, 2016

5. . M. Tirupati, Suresh Pabboku, G. Narasimha, “Sentiment Analysis on Twitter using Streaming API,” The International Advance

Computing Conference , 2017. 6. Kiichi Tago, Qun Jin “Analyzing Influence of Emotional Tweets on User Relationships by Naive Bayes Classification and

Statistical Tests,” 10th International Conference on Service-Oriented Computing and Applications, IEEE, 2017.

7. Nasser Alsaedi, Pete Burnap “Feature Extraction and Analysis for Identifying Disruptive Events from Social Media,” International Conference on Advances in Social Networks Analysis and Mining IEEE/ACM, 2015

8. Mondher Bouazizi, Tomoaki Ohtsuki “Sarcasm Detection in Twitter,” IEEE, 2015

9. Hajime Watanabe, Mondher Bouazizi, Tomoaki Ohtsuki “Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection,” IEEE Access, 2018

10. Mondher Bouazizi, Tomoaki Ohtsuki “Sentiment Analysis in Twitter: From Classification to Quantification of Sentiments within Tweets,” IEEE, 2016

11. Mondher Bouazizi, Tomoaki Ohtsuki “A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter,” IEEE Access,

2017 12. Alexandros Baltas, Andreas Kanavos, Athanasios K. Tsakalidis “An Apache Spark Implementation for Sentiment Analysis on

Twitter Data,” 2017

13. Mondher Bouazizi, Tomoaki Ohtsuki “A Large-Scale Sentiment Data Classification for Online Reviews under Apache Spark,” 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, 2018

14. Hao Wang, Jorge A. Castanon “Sentiment Expression via Emoticons on Social Media ,” International Conference on Big

Data,IEEE 2017 15. Wies law Wolny, “Sentiment Analysis of Twitter data using Emoticons and Emoji ideograms ,” 2016

16. Pranali Borele, Dilipkumar A. Borikar, “An Approach to Sentiment Analysis using Artificial Neural Network with Comparative

Analysis of Different Techniques,” 2016 17. Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Giannis Tzimas “Large Scale Sentiment Analysis on Twitter with

Spark,” EDBT/ICDT Joint Conference, 2016

18. Andreas Kanavos, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Dimitrios Tsolis, Giannis Tzimas “Large Scale Implementations for Twitter Sentiment Classification,” MPDI Algorithms, 2018

19. R. Ragupathy, Lakshmana Phaneendra Maguluri “Comparative analysis of machine learning algorithms on social media test,”

International Journal of Engineering Technology, 2018 20. Ahan M R, Honnesh Rohmetra, Ayush Mungad “Social Network Analysis using Data Segmentation and Neural Networks,”

International Research Journal of Engineering and Technology (IRJET), 2018

21. Georgios S. Solakidis, Konstantinos N. Vavliakis, Pericles A. Mitkas “Multilingual Sentiment Analysis using Emoticons and Keywords,” IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies

(IAT), 2014

22. N. AZMINA M. ZAMANI, SITI Z. Z. ABIDIN, NASIROH OMAR1, M. Z. Z.ABIDEN “Sentiment Analysis: Determining People’s Emotions in Facebook ,” Applied Computational Science

23. Wies law Wolny “Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms,” 25TH INTERNATIONAL

CONFERENCE ON INFORMATION SYSTEMS DEVELOPMENT, 2016 24. Ga¨el Guibon, Magalie Ochs, Patrice Ballot “From Emojis to Sentiment Analysis,” https://hal-amu.archives-ouvertes.fr/hal-

01529708, 2017

25. Xi Ouyang, Pan Zhou, Cheng Hua Li, Lijun Liu “Sentiment Analysis Using Convolutional Neural Network,” IEEE International Conference on Computer and Information Technology, 2015

26. Aliaksei Severyn, Alessandro Moschitti “Twitter Sentiment Analysis with Deep Convolutional Neural Networks,” ACM, 2015

27. Isabel Segura-Bedmar1, Antonio Quiros, Paloma Martınez1 “Exploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets”, ACL,15th Conference of the European Chapter of the Association for Computational Linguistics, 2017

28. Dinh Nguyen, Khoung An Vo, Dang Pham, Mao Nguyen “A Deep Architecture for Sentiment Analysis of News Articles”,

Advances in Intelligent Systems and Computing, 2017 29. Qurat Tul Ain, Mubashir Ali, Amna Riaz, Amna Noureen, Muhammad Kamran, Babar Haya, A. Rehman “Sentiment Analysis

Using Deep Learning Techniques: A Review”, (IJACSA) International Journal of Advanced Computer Science and Applications,

2017

30. iwei Lai, Liheng Xu, Kang Liu, Jun Zhao “Recurrent Convolutional Neural Networks for Text Classification”, Twenty-Ninth

AAAI Conference on Artificial Intelligence, 2015

31. Pranali Borele, Dilipkumar A. Borikar “An Approach to Sentiment Analysis using Artificial Neural Network with Comparative Analysis of Different Techniques”, IOSR Journal of Computer Engineering (IOSR-JCE) , 2016

32. Shiyang Liao, Junbo Wang, Ruiyun Yu, Koichi Sato, Zixue Cheng “CNN for situations understanding based on sentiment analysis of twitter data”, 2017

33. https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-touse-it-e3c6186d008f

34. https://kite.com/python/docs/nltk.tokenize.regexp.WordPunctTokenizer 35. https://tartarus.org/martin/PorterStemmer/

36. https://towardsdatascience.com/recurrent-neural-networks-and-lstm4b601dd822a5

37. https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neuralnetwork-cnn-deep-learning-99760835f148

13.

Authors: Pawar V. N., Mane K. A., Ghodke S. V.

Paper Title: Development of Regulatory Mechanism of Food Product Accessible Antioxidants: Lycopene and

Curcumin

Abstract: The lycopene and curcumin are plant base notified nutraceuticals specifically commercialized

for pharmaceutical and health benefits base food product development scenario. Antioxidant efficacy driven

potential is rightly entrapped by food corporate domain to boost up innovation in favor of consumer satisfaction

through health benefits. The consolidated review base information of both the above potent antioxidants is

rightly awaiting for transformation as a technology base on their molecular reorientation and active site location.

The fractionation leading to all trans and cis forms of lycopene and demethoxy and bisdemethoxy forms of

curcumin recorded benchmark study to modulate molecular regulatory mechanism of antioxidant efficacy. The

molecular fractionation generative quotients (All trans to cis quotient- 24 to 34and DMC/BDMC quotient-1.94)

are emerged out as cutting edge indices of desired antioxidant efficacy. Advance technique specified

confirmation (HPLC) recorded quantification of respective components admissible for regulatory mechanism

governed by notified quotients which are first time reported in this investigation. The present investigation is

coiling around determination of potent antioxidant fractionate quotient of both bioactive compounds as a

competent index to formulate food ingredient as an admissible access to justify suitability in food product

innovation.

Keyword: Antioxidants, lycopene, trans and cis isomers, curcumin, DMC/BDMC quotient, demethoxylation References:

1. F. Rincon-Leon, Encyclopedia of food sciences and nutrition. 2ndEdn, 2003 Available: https://www.sciencedirect.com. 2. M. Nicoletti, (2012). Nutraceutical and botanical overview and perspectives. Int. J Food Sci. Nutr. 1, pp. 2-6.

3. A. Vicentini, L. Liberatore, and D. Mastrocola (2016). Functional foods: trends and development of the global market. Ital.

J. Food Sci.28, pp. 338-351. 4. O. D. Omodamiro and U. Amechi (2013). The phytochemical content, antioxidant, antimicrobial and anti-inflammatory

activities of Lycopersicon esculentum (Tomato). Asian J Plant Sci. Res. 3(5), pp. 70-81.

5. S. S. Moselhy, S. Razvi, N. Hasan, K. S Balamash, K. O.Abulnaja, S. S. Yaghmoor, M. A. Youssri, T. A. Kumosani and A. L. Al-Malki (2018). Multifaceted role of a marvel golden molecule, curcumin: a review. Indian J Pharm Sci. 80(3),

pp. 400-411.

6. A. R. Davis, W. W. Fish, and V. P. Perkins (2003). “A rapid spectrophotometric method for analyzing lycopene content in tomato and tomato”. Postharvest Bio. Technol. 28, pp. 425-430.

7. A. V. Rao and L. G. Rao (2007). Review on carotenoids and human health. Pharmacol. Res., 55 (3), pp. 207–216.

8. R. P. Sing and D. A. Jain (2012). Evaluation of antimicrobial activity of curcuminoids isolated from turmeric, Int. J. Pharma. Life Sci., 3(1), pp. 1368-1376.

9. K. A. Mane, and V. N. Pawar, (2018). “Study on effect of processing parameters on lycopene content of tomato puree”.

Int. J Res. Anal. Rev. 5 (3), pp. 512-517. 10. S. K. Thimmaiah. Standard methods of biochemical analysis, Kalyani Publishers, 2016, pp. 53-62.

11. M. K. Roh, M. H. Jeon, J. N. Moon , W. S. Moon, S. M. Park, and J. S. Choi (2013). A simple method for the isolation of

lycopene from Lycopersicon esculentum. Bot Sci. 91 (2), pp. 187-192. 12. S.J. Kulkarni, K.N. Maske, M.P. Budre, and R.P. Mahajan (2012). “Extraction and purification of curcuminoids from Turmeric

(curcuma longa L.)”. Int. J. Pharmacol. Pharma. Tech. 1(2), pp. 81-84.

13. S. V. Ghodke and V. N. Pawar (2018). “Studies on food value base curcumin extraction for commercial exploration”. J. pharma.

phyto. 7(6), pp. 1173-1176.

14. H. Soni, S. Patel, K. Mishra, G. Mayak and A. K. Singhai (2011). Qualitative and quantitative profile of curcumin from ethanolic

extract of Curcuma Longa, International Res. J. Pharma. 2(4), pp. 180-184. 15. J. Shi and M. L. Maguer (2010). “Lycopene in tomatoes: Chemical and physical properties affected by food processing,” pp.1-

42. Available: http://www.tandfonline.com/

16. G. B. Martinez-Hernandez, M. Boluda-Aguilar, A. Taboada-Rodriguez, S. Soto-Jover, F. Marin-Iniesta, and A. Lopez-Gomez, (2015). “Processing, packaging, and storage of tomato products: influence on the lycopene content”. Food Engg. Rev. doi

10.1007/s12393-015-9113-3

17. S. Revathy, S. Elumalai, Merina Benny and Benny Antony (2011). Isolation, purification and identification of curcuminoids from turmeric (Curcuma longa L.) by column chromatography. J Expt Sci., 2 (7), pp. 21-25.

18. B. T. Kurien, H. Matsumoto and R. H. Scofield, (2017). “Nutraceutical value of pure curcumin”. Pharmacognasy Magazine. pp.

161-163. 19. P. Basnet, and N. S. Basnet (2011). “Curcumin: anti- inflammatory molecule from a curry spice on the path to cancer treatment”.

Molecules.16, pp. 4567-4598.

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14.

Authors: Nithin M, Harish M. Kittur

Paper Title: Design of Beyond Millimeter Wave Oscillator in 22nm Bulk CMOS technology

Abstract: This paper presents the design of an LC oscillator using inductive feedback technique in bulk

CMOS 22 nm technology using predictive technology models. The core oscillator is based on the popular

Cherry Hooper amplifier topology. The development of the oscillator circuit from the standard Cherry Hooper

topology is discussed in this paper with detailed analysis. The inductors are modelled by considering the various

effects in this frequency range. The broadband technique used in the CH topology enables the circuit to oscillate

at frequencies more than millimeter wave regime. By employing MOS varactors the designed oscillator was

converted into a voltage-controlled oscillator also. The fundamental mode of the oscillator is around 372 GHz

68-72

with a phase noise measured around -70 dBc/ Hz at 10 MHz offset. The VCO has a tuning range of about 490

MHz when the voltage is varied from 0.3 to 0.8 V. The oscillator circuit consumes power of 3.75 mW from a

power supply of 0.8 V.

Keyword: Cherry Hooper, Millimeter Wave Regime, Phase Noise. References:

1. Behzad Razavi, “ A Millimeter-Wave Circuit Technique”, IEEE journal of solid-state circuits, vol. 43, no. 9, september 2008 2. E. M. Cherry and D. E. Hooper, “The design of wideband transistor feedback amplifiers,” Proc. IEE, vol. 110, pp. 375–389, Feb.

1963

3. Behzad Razavi, “Design of Integrated Circuits for Optical Communications”, 2nd Edition Wiley, 2015 4. I. Nasr, B. Laemmle, K. Aufinger, G. Fischer, R. Weigel, D. Kissinger, A 70–90-GHz highlinearity multi-band quadrature

receiver in 0.35μ m SiGe technology. IEEE Trans. Microwave Theory Tech. 61(12), 4600–4612 2013 5. E. Laskin et al., Nanoscale CMOS transceiver design in the 90170-GHz range. IEEE Trans. Microw. Theory Tech. 57(12), 3477–

3490,2009

6. Marco Vigilante • Patrick Reynaert, 5G and E-Band Communication Circuits in Deep-Scaled CMOS, Analog Circuits & Signal

Processing, Springer 2018.

15.

Authors: Arockia Selvakumar A., Aniket Babar

Paper Title: Design and Development of IoT Integrated PLC Based Material Handling System

Abstract: The industry faces with constant demand of tremendous Production criteria to be met with human

workers this production rate is highly likely to fall behind as the time progresses, to solve the problem to obtain

continuous production without any interruption, this paper helps understand how a manipulator will work for a

very basic operations and how will the industry executive will be able to manage the shop floor. This project

aims to show how a PLC is used in a automation and how a PLC based system is integrated with a IoT platform

Keyword: Failure mode, Force, IoT, Manipulator, PLC, Torque. References:

1. R D Schraft and M C Wanner (1985) "Determination of Important Design Parameters for Industrial Robots from the Application Point of View Survey Paper ", RoManSy and contributors

2. M. C., Sweet, L. M., & Strobe). K. L. (1985). Dynamic Models for Control System Design of Integrated Robot and Drive

Systems. Journal of Dynamic Systems, Measurement, an< Control, 107(1), 53.

3. Jan Swevers, Walter Verdonck, and Joris De Schutte Dynamic Model Identification for Industrial Robots (2007), IEEE, 17(5),

58-71

4. Mikell P. Groover, Automations Production systems and computer aided manufacturing 5. Jingfu Jin, Nicholas Gans Parameter identification for industrial robots with a fast and robust trajectory design approach Robotics

and Computer-Integrated Manufacturing 31(2015)21–29

6. K. Chincholkar and O. V. Krishnaiah Chetty Simultaneous optimisation of control factors in automated storage and retrieval systems and FMS using stochastic coloured Petri nets and the Taguchi method. Int J Adv Manuf Technol (1996) 12:137-144

7. J. Ashayeri, L. Gelders, L. Van Wassenhove :A microcomputer-based optimization model for the Storage system International

Journal of Production Research, 23:4, 825-839. 8. E. A. ELSAYED (1981): Algorithms for optimal material handling in automatic warehousing systems, International Journal of

Production Research, 19:5, 525-535

9. Henri Tokola, Esko Niemi (2015) : Avoiding Fragmentation in Miniload Automated Storage and Retrieval Systems IFAC-PapersOnLine 48-3 (2015) 1973–1977

10. Ramchandran Jaikumar & Marius M. Solomon (1990) Dynamic Operational Policies in an Automated Warehouse, IIE Transactions, 22:4, 370-376

11. Pius J. Egbelu (1991) Framework for dynamic positioning of storage/retrieval machines in an automated storage/retrieval system,

International Journal of Production Research, 29:1, 17-37

12. F. Eldemir , R. J. Graves & C. J. Malmborg (2004) New cycle time and space estimation models for automated storage and

retrieval system conceptualization, International Journal of Production Research, 42:22, 4767-4783

13. Bidyut Mukherjee, Songjie Wang, Wenyi Lu, Roshan Lal Neupane, Daniel Dunn, Yijie Ren, Qi Su, Prasad Calyam (2018). Flexible IoT security middleware for end-to-end cloud–fog communication, Future Generation Computer Systems Volume

87, October 2018, Pages 688-703

14. A.E. Khaled, S. Helal, Interoperable communication framework for bridging RESTful and topic-based communication in IoT, Future Generation Computer Systems (2018).

15. Matteo Cristani, Florenc Demrozi, Claudio Tomazzoli (2018), An Ontology - Driven methodology for converting PLC

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16.

Authors: Mohamed Fazil M. S., Arockia Selvakumar A., Daniel Schilberg

Paper Title: Stereo Vision Based Robot for Remote Monitoring with VR Support

Abstract: The machine vision systems have been playing a significant role in visual monitoring systems.

With the help of stereovision and machine learning, it will be able to mimic human-like visual system and

behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be

used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit

human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time.

This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones

giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also

in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural

networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects

to monitor in a space. The stereovision enables us to track the depth information of different objects detected and

will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is

developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and

79-84

machine learning.

Keyword: Cloud Server, Deep neural network, Internet of things, Machine Vision, Stereovision, robotics,

Virtual Reality. References:

1. Lei Chen1, Zhen Dong, Sheng Gao1, Baofeng Yuan1, and Mingtao Pei, “Stereovision-only based Interactive Mobile Robot for Human-Robot Face-to-face Interaction,” 2014 22nd International Conference on Pattern Recognition

2. Jong-Kyu Oh and Chan-Ho Lee, “Development of a Stereo Vision System for Industrial Robots,” International Conference on

Control, Automation and Systems 2007 Oct. 17-20,2007 in COEX, Seoul, Korea 3. Mehdi Ahmadi, Warren J. Gross, and Samuel Kadoury, “A Real-Time Remote Video Streaming Platform for Ultrasound

Imaging,” at IEEE

4. Ajmal Mian, “Realtime Face Detection and Tracking Using a Single Pan, Tilt, Zoom Camer,” The University of Western Australia

5. Xiai Chen, Hong Xu , Ling Wang, Bingrui Wang, and Chenna Yang, “Humanoid Robot Head Interaction Based on Face

Recognition,” 2009 Asia-Pacific Conference on Information Processing 6. Masaaki Shibatal and Nobuaki Kobayashi. “Image-based visual tracking for moving targets with active stereo vision robot,”

SICE-ICASE International Joint Conference 2006 Oct. 18-21, 2006 in Bexco, Busan, Korea

7. Byeong-Hyeon Moon, Jae-Won Choi, Kun-Tak Jung, Dong-Hyun Kim, Hyun-Jeong Song, Ki-Jong Gi, and Jong-Wook Kim, “Connecting Motion Control Mobile Robot and VR Content,” 2017 14th International Conference on Ubiquitous Robots and

Ambient Intelligence (URAI)

8. Yoshinobu Hagiwara, “Cloud Based VR System with Immersive Interfaces to Collect Multimodal Data in Human-Robot Interaction,” 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)

9. Yeong-Hwa Chang, Ping-Lun Chung, and Hung-Wei Lin, “ Deep Learning for Object Identification in ROS-based Mobile

Robots,” Proceedings of IEEE International Conference on Applied System Innovation 2018 10. Florian Schroff, Dmitry Kalenichenko and James Philbin, “ FaceNet: A Unified Embedding for Face Recognition and

Clustering,” 2015 IEEE

11. Yundong Zhang, Haomin Peng and Pan Hu “CS341 Final Report: Towards Real-time Detection and Camera Triggering” 12. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, and A. C. Berg. “SSD: Single shot multibox detector.” In

European Conference on Computer Vision, pages 21–37. Springer, 2016

13. A.G.Howard,M.Zhu,B.Chen,D.Kalenichenko,W.Wang, T. Weyand, M. Andreetto, and H. Adam. “Mobilenets: Efficient convolutional neural networks for mobile vision applications” arXiv preprint arXiv:1704.04861, 2017.

14. Jonathan Huang, Vivek Rathod, Chen Sun, Menglong Zhu, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna,

Yang Song, Sergio Guadarrama, Kevin Murphy. “ Speed/accuracy trade-offs for modern convolutional object detectors ”

arXiv:1611.10012 , 2017

17.

Authors: Vikas Balikai, Harish Kittur

Paper Title: Time Amplifier Based Bang-Bang Phase Frequency Detector in 0.18μm CMOS Technology

Abstract: A CMOS Implementation of Time amplifier (TA) based Bang-Bang Phase Frequency Detector

(BBPFD) using Sense amplifier based flip flop (SAFF) is presented in this paper using 0.18μm CMOS

technology. A time amplifier based on feedback output generator concept is utilized in minimizing the

metastability and increasing the gain of TA which in turn boosts the gain of Phase Frequency Detector (PFD).

Also, a modified SAFF was built in CMOS 0.18μm technology at 1.8V which further reduces the hysteresis and

metastability aspect related to PFD. The proposed PFD works at a maximum frequency of 4GHz consuming

0.46mW of power with no dead zone.

Keyword: Time amplifier, BBPFD, SAFF. References:

1. Majeed, K.K.A., Kailath, B.J., "Low power, high frequency, free dead zone PFD for a PLL design," (FTFC), vol., no., pp.1,4, 20-

21, 2013. 2. Lechang Liu; Binhong Li, "Reduced pull-in time of PLL with a novel non-linear phase-frequency detector", Proc APMC, vol.5,

pp. 4-7, 2005.

3. Jinbao Lan et al, "A nonlinear PFD for fast-lock phase-locked loops", Proc. IEEE ASIC, pp.1117-1120, 2009. 4. P. Effendrik, W. Jiang, M. van de Gevel, F. Verwaal, and R. B. Staszewski, “Time-to-digital converter (TDC) for WiMAX

ADPLL in 40-nm CMOS,” in Proc. IEEE Circuit Theory Design Conf., pp. 365–368.

5. M. Ramezani and C. Salama, “An improved bang-bang phase detector clock and data recovery applications”, in Proc. Int’l Symp. Circuits Syst Sydney., Vol. 1, pp. 715-718, June 2001.

6. K. Lee, S. Kim, G. Ahn, and D. Jeong, “A CMOS serial link for fully duplexed data communication”, IEEE J. Solid-State

Circuits, Vol. 30, No. 4, pp.353-364. 1995. 7. Samira Bashiri, Sadok Aouini, Naim Ben-Hamida, and Calvin Plett “Analysis and Modeling of the Phase Detector Hysteresis in

Bang-Bang.PLLs”, in IEEE Transactions on Circuits and Systems : regular papers, vol. 62, no. 2, 2015.

8. D. Liu, P. Basedau,M. Helfenstein, J. Wei, T. Burger, and Y.Chen, “A frequency-based model for limit cycle and spur prediction in bang-bang all digital PLL”, IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 59, no. 6, pp. 1205–1214., 2012.

9. M. Zanuso, D. Tasca, S. Levantino, A. Donadel, C. Samori, and A. Lacaita, “Noise analysis and minimization in bang-bang

digital PLLs”, IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 56, no. 11, pp. 835–839, 2009. 10. M. Ferriss and M.P. Flynn. “A 14mW Fractional-N PLL Modulator with an Enhanced Digital Phase Detector and Frequency

Switching Scheme”. In Proc. Digest of Technical Papers. IEEE International Solid-State Circuits Conference ISSCC , pages 352–

608, 2007. 11. D. Li, P. Chuang, and M. Sachdev, “Comparative analysis and study of metastability on high-performance flip-flops,” in

Proc.11th Int. Symp. Quality Electron. Design (ISQED), pp. 853–860, 2010.

12. Qiwei Huang, Chenchang Zhan and Jinwook Burm “A low-complexity locking-accelerated digital PLL with multi-output bang bang phase detector, Microelectronics Journal 67 pages 19–24, 2017.

13. D.S. Kim, H. Song, T. Kim, S. Kim, D.K. Jeong, “A 0.3–1.4 GHz all-digital fractional-N PLL With adaptive loop gain controller”, IEEE J. Solid-State Circuits 45 (11), 2300–2311, 2010.

14. T. Hu, S. Hao, and Q. J. Gu, “A 10.01 - 10.1 GHz bang-bang PD based phase noise filter with 12.6 dB noise suppression,” in

Proc. IEEE Int. Symp. Radio-Freq. Integr. Technol. (RFIT), pp. 1–3., 2016 15. Abas, A. M., Bystrov, A., Kinniment, D. J., Maevsky, O. V., Russell, G.,& Yakovlev, A. V. “Time difference amplifier”.

85-89

Electronics Letters, 38(23), 1437-1438, 2002 16. Lee, M., & Abidi, A. A. “A 9 b, 1.25 ps resolution coarsefine time-to-digital converter in 90 nm CMOS that amplifies a time

residue”. IEEE Journal of Solid-State Circuits, 43(4), 769–777. 2008

17. Babazadeh H, Esmaili A, Hadidi K. A high-speed and wide detectable frequency range phase detector for DLLs. NORCHIP,1-3, 2009.

18.

Authors: Praiselin W. J., J. Belwin Edward

Paper Title: Droop Control Based Grid-Connected Solar Photovoltaic Inverters for Distributed Generation

Abstract: In day to day the demand of electrical energy has been increasing in worldwide, as well the share

of solar photovoltaic power generation has increased extremely because of population growth, urbanization, etc.

Although the power generated from solar photovoltaic is erratically, and it makes the stability and reliability

problems in a utility grid. This paper projects a P/Q droop control strategy for a grid-tied PWM inverter. This

paper introduces an entire model of grid-connected solar photovoltaic array; inverter with droop control, and

loads are developed for this operation. The locus points of the both power sharing of the DG system is developed

by the proposed control operation. PI controllers were used in this droop control was espoused to adjust the

constraints of PI controller. The results of the proposed droop control inject positive and reactive power into a

variation of loads and improving the quality of power as compared to the conventional PID controllers.

Keyword: distributed generation, droop control, power quality, solar photovoltaic. References:

1. S. M. Moosavian, N. A. Rahim, and J. Selvaraj, “ Energy policy to promote photovoltaic generation,” Renewable and Sustainable

Energy Reviews, vol. 25, Sep. 2013, pp.44-58. 2. N. Prabaharan, and K. Palanisamy, “ A single phase grid connected hybrid multilevel inverter for interfacing photo-voltaic

system,” Energy Procedia, vol. 103, Dec. 2016, pp. 250-255.

3. E. Bullich Massagua, M. A. P. Nalpa, A. Sumper, and O. Boix Aragones, “ Active power control in a hybrid PV-storage power plant for frequency support,” Solar Energy, vol. 144, Mar. 2017, pp. 49-52.

4. P. Monica, and M. Kowsalya, “ Control strategies of parallel operated inverters in renewable energy applications: A review,”

Renewable and Sustainable Energy, vol. 65, Nov. 2016, pp. 885-901. 5. W. J. Praiselin, and J. Belwin Edward, “ A review on impacts of power quality, control and optimization strategies of integration

of renewable energy based microgrid operation,” International Journal of Intelligent Systems and Applications, vol. 3, Mar.

2018, pp. 67-81. 6. S. Messalti, A. Harrag, and A. Loukriz, “ A new variable step size neural networks MPPT controller: review, simulation and

hardware implementation,” Renewable and Sustainable Energy Reviews, vol. 68, Feb. 2017, pp.221-233.

7. Yancheng Liu, Qinjin Zhang, and Chuan Wang, “ A control strategy for microgrid inverters based on a adaptive droop controls,” Electric Power Systems Research, vol. 117, Dec. 2014, pp. 192-201.

8. Xisheng Tang, Xiao Hu, Ningning Li, Wei Deng, and Guowei Zhang, “ A novel frequency and voltage control method for

islanded microgrid based on multi energy storages,” IEEE Trans. On Smart Grid, vol. 7, no.1, Jan. 2016, pp. 410-419. 9. J. He, Y. W. Li, and M. S. Munir, “ A flexible harmonic control approach through voltage-controlled DC-grid interfacing

converters,” IEEE Trans. On Industrial Electronics, vol. 59, no.1, Feb. 2012, pp. 444-455.

10. Mohammad A. Abusara, Suleiman M. Sharkh, and Josep M. Guerrero, “ Improved droop control strategy for grid-connected inverters,” Sustainable Energy, Grid and Networks, vol. 1, Mar. 2015, pp. 10-19.

11. B. G. Sujatha, and G. S. Anitha, “ Enhancement of PQ in grid connected PV system using hybrid technique,” Ain Shams

Engineering Journal, vol. 9, no. 4, Dec 2018, pp. 869-881. 12. Yang Yongheng, and Blaabjerg Frede, “ Low-voltage-ride-through capability of a single stage single-phase photovoltaic system

connected to the low-voltage grid,” International Journal of Photoenergy, vol. 2013, no. 1, Feb. 2013, pp. 1-9.

13. Nejib Hamrouni, Moncef Jraidi, Ahmed Dhouib, and Adnen Cherif, “ Design of a command scheme for grid connected PV systems using classical controllers,” Electric Power Systems Research, vol. 143, Feb 2017, pp. 503-512.

14. Rameen, and Abdel hady, “ Modeling and simulation of a microgrid-connected solar PV system,” Water Science, vol. 31, no. 1,

April. 2017, pp. 1-10. 15. Wenlei Bai, Kwang Lee, “ Distributed generation system control strategies in microgrid operation,” IFAC Proceedings Volumes,

vol. 47, no. 3, 2014, pp. 11938-11943 [19th World Congress, IFAC].

16. Soha Mansour, Mostafa I. Marei, and Ahmed A. Sattar, “ Droop based control strategy for a microgrid,” Global Journals Inc.,

USA, vol.16, July. 2016, pp. 1-9.

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19.

Authors: N. Kamala, J. Venkatesh Balaji

Paper Title: Gain Scheduled Multivariable Control of a Continuous Stirred Tank Reactor

Abstract: Most chemical reactors, representing Multi-Input Multi-Output (MIMO) systems, are highly

nonlinear and require complex control than single-input single-output (SISO) systems. In the present work, the

system is linearized around different operating points. As linear design strategies are intended to work in linear

regions, they are employed to evaluate local controller parameters. Decentralized controllers using constant

detuning factor and decoupling controllers using static decouplers are designed. A gain scheduler is developed to

deal with changes in operating conditions. The performances are evaluated by simulating the nonlinear equations

of the system. Decentralized controllers cannot mitigate the interactions due to its structure possessing single

loop. It is exhibited that the decoupling controllers offer better control for tracking of desired set point and

rejection of load disturbances than those of decentralized controllers. The superiority of the suggested controller

has been portrayed by performing simulation study on a Continuous Stirred Tank Reactor (CSTR).

Keyword: CSTR, decoupling control, decentralized control, Integral Absolute Error (IAE), Integral Square

Error (ISE), gain schedule. References:

1. Astrom K.J, Hagglund T, “PID controllers: Theory, designand tuning”, Instrument society of America,2nd edition (1995)

2. Xiong Q., W.J.Cai, M.J.He 2007, “Equivalent transfer function method for PI/PID controller design of MIMO

95-100

processes”,Int.journal of Process control, 17, pp665-673 3. M.W.Iruthayarajan,S.Bhaskar, “Evolutionary algorithms based design of multivariable PID controller”,Expert systems with

Applications,36, 2009, pp 9159-9167

4. Koivo, H.N. and J.T.Tanttu, “Tuning of PID controllers: survey of SISO and MIMO techniques, preprint of IFAC International symposium on intelligent tuning and adaptive Control session, Singapore, 1991

5. Qiang Xiong, Wen-Jian Cai, Ming He, “A practical

6. Decentralized PID Auto tuning Method for TITO systems under closed loop control”, International Journal of Innovative computing, Information and Control.vol 2, number 2, April 2006

7. Lee,J.W.Cho and T.F.Edgar, “Multiloop PI controller tuning for interacting multivariable processes”, Computers and Chemical

Engineering,vol.22,pp.1711-1723,1998 8. J.Lee, T.Edgar,” Interaction measure for decentralized control of multivariable process”, Proc of American Control Conference,

Anchorage, AK, 2002, pp 454-458

9. Bristol,E.h,”On a new measure of interaction for multivariable process control”,IEEE Trans on Automatic control,1966,Vol.AC-11,pp 133-134

10. Glad,T.&Ljung,L.ControlTheory-Multivariable and Nonlinear methods, Taylor and Francis, 2000, 467p, ISBN 0-7484-0878-9

11. Milanovic,J.V, & Duque,A.C. “Identification of Electromechanical Modes and placement of PSSs Using Relative Gain array”, IEEE Trans. On Power Systems,2004,Vol.19,pp 410-417

12. K.Astrom, Johansson, Wang, “Design of decoupled PID controllers for MIMO systems”, Proc of American Control Conference,

Arlington, VA 2001,pp 2015-2020 13. Tanttu, J.T., and Lieslehto,J, “ A Comparative Study of Some Multivariable PI Controller Tuning Methods”, IFAC Intelligent

Tuning and Adaptive Control, Singapore, 1991, pp357-362

14. Skogestad, Postlethwaite.I: Multivariable Feedback Control, John Wiley& Sons, New York, 1996

15. Ogunnaike, B.A, and Ray, W.H.: Process Dynamics, Modeling and Control, Oxford University Press, New York,1994 16. Astrom,K.J and Witten mark, B 2001.Adaptive control, Pearson Education Press, Boston 17. Rahul Upadhyay, Rajesh Singla, “Application of adaptive control in aprocess control”, 2nd international conference on education

technologyand computer (ICETE), 2010. (IEEE).

20.

Authors: Farhan Rahman, Siddharth Singh

Paper Title: Enhancement of A5/1 Stream Cipher with Non-Linear Function using MOSFET

Abstract: Living in this modern era – the epitome of communication GSM networks is one of the mainly

used architectures. But GSM architecture has its own shortcomings; the GSM network is vulnerable to various

security threats. For any network to provide security to the user, the algorithms should be planned and designed

in such a way that it provides cellular secrecy, data and signaling confidentiality to the concerned user. Keeping

in mind the above features, the A5/1 algorithm provides network security. Initially, the A5/1 algorithm dealt

with a pre-defined secret key but they still possess the threat of being decrypted by cryptanalytic attacks.

Although decrypting this algorithm is not easy and requires high computational power. Such attacks lead to the

necessity to modify the A5/1 algorithm; in our paper, we have proposed a better method to enhance the already

existing algorithm.

Keyword: A5/1 algorithm, GSM Networks, non-linear, session key, stream cipher.

References: 1. JOURNAL OF NETWORKS, VOL. 1, NO. 6, NOVEMBER/DECEMBER (2006) Threats and Countermeasures in GSM

Networks Valer BOCAN Department of Computer Science and Engineering, Politehnica University of Timişoara, Romania Alcatel Romania

2. https://asecuritysite.com/encryption/a5

3. IOP Conf. Series: Materials Science and Engineering 263 (2017) 042084 doi:10.1088/1757-899X/263/4/042084 Enhancement of A5/1 encryption algorithm Ria Elin Thomas, Chandhiny G, Katyayani Sharma, H Santhi and P Gayathri School of Computer

Science and Engineering, VIT University, Vellore- 632014, India

4. Marappan D 2017 Securing Mobile Technology of GSM using A5 / 1 Algorithm, 111-113 5. https://www.electronics-notes.com/articles/connectivity/2g-gsm/network-architecture.php

6. A Modified Stream Generator For The Gsm Encryption Algorithms A5/1 And A5/2 Imran Erguler1,2, And Emin Anarim2

7. Enhancement of A5/1 Stream Cipher Overcoming its Weakneses, Mahdi Madani, Salim Chitroub Signal and Image Processing Laboratory Electronics and Computer Science Faculty, USTHB Algiers, Algeria

8. Randomness analysis of A5/1 Stream Cipher for secure mobile communication, Prof. Darshana Upadhyay1, Dr. Priyanka Sharma2, Prof.Sharada Valiveti3 Department of Computer Science and Engineering Institute Of Technology, Nirma University

Ahmedabad, Gujarat, India

9. Randomness analysis of A5/1 Stream Cipher for secure mobile communication, Prof. Darshana Upadhyay1, Dr. Priyanka Sharma2, Prof.Sharada Valiveti3 Department of Computer Science and Engineering Institute Of Technology,Nirma University

Ahmedabad, Gujarat, India

10. INFORMATICA, 2013, Vol. 24, No. 3, 339–356 339 2013 Vilnius University, A New Randomness Test for Bit Sequences.

Pedro María ALCOVER1∗, Antonio GUILLAMÓN2, María del Carmen RUIZ3.

11. TRINITY COLLEGE DUBLIN Management Science and Information Systems Studies Project Report. THE DISTRIBUTED SYSTEMS GROUP, Computer Science Department, TCD. Random Number Generators: An Evaluation and Comparison of

Random number sequence.

12. Christof Paar and Jan Pelzl, Understanding Cryptography, A Textbook for Students and Practitioners, Foreword by Bart Preneel. 13. Mavoungou S, Kaddoum G, Taha M and Matar G (2016) Survey on threats and attacks on mobile networks IEEE Access 4 4543–

4572.

14. Naveen C (2016) Image Encryption Technique Using Improved A5 / 1 Cipher on Image Bitplanes for Wireless Data Security. 15. Bird R, Canada B C and Layers A G S M (2015) Investigating Vulnerabilities in GSM Security.

101-105

21.

Authors: Abhinav Kumar, J. V. Muruga Lal Jeyan

Paper Title: Feasibility Analysis on Cryogenic Properties of Supercritical Nitrogen to be used in the Cooling of

Hg-Based High Temperature Superconductors for Electric Aircraft Propulsion

Abstract: Electrified Aircraft Propulsion (EAP) and Advanced Hybrid Electric Aircrafts (AHEA) like

NASA NX-3, SUGAR, NASA X-57 and STARC ABL are going to be the future of avionics as they have

potential to improve fuel economy, emissions and noise levels. The agencies have suggested using 106-110

superconducting cables for the electric transmission to reduce heat losses. The limit of critical current has

reached 134 K where Hg- based ceramic materials are available that can superconduct at this temperature range.

In order to retain the superconductivity, the cables have to be cooled below its critical temperature. Liquid

nitrogen (LN2) boils of at 77 K which further leads to multiphase heat transfer challenges. An attempt has been

made in the present work to overcome such challenges and a novel concept of using Supercritical Nitrogen

(SCN), having critical temperature 126.19K and pressure as 3.3958MPa (consist single phase), as a cryogen for

the cooling of Hg-Based Superconductors, has been introduced. Drastic variations have been found for

thermophysical properties of SCN near the critical point. It has been concluded that few temperature and

pressure ranges are suitable if one wants to incorporate SCN as cryogen for Hg-based superconductors.

Keyword: Electrified Aircraft Propulsion, Supercritical Nitrogen, Hg-Based Superconductors,

Superconducting cables, Supercritical Fluids, Hybrid Electric Aircrafts. References:

1. S. Samoilenkov et al., “Effective Management of MVA-range electric Power in Aircraft enabled by high Tc superconducting

systems,” presented at More Electric Aircraft, Toulouse, France, Feb. 2015.

2. “Strategic Research and Innovation Agenda (SRIA).” Advisory Council for Aeronautics Research in Europe (ACARE) (2012) [Online]. Available: http://www.acare4europe.org/sria, Accessed on 12 May 2019.

3. “HTS Triax™ energy cable systems,” NKT Cables (2008) [Online]. Available:

http://www.nktcables.com/~/media/Files/NktCables/download%20files/com/HTS-Triax_engl_061108.pdf, Accessed on 12 May 2019.

4. Kario, “Superconducting properties of Roebel coated conductor cable from Superpower and SuperOx tapes with different

transposition length,” presented at CCA-2014, Jeju, Korea, Nov. 2014. 5. S. S. Fetisov et al., "Development and Characterization of a 2G HTS Roebel Cable for Aircraft Power Systems," in IEEE

Transactions on Applied Superconductivity, vol. 26, no. 3, pp. 1-4, April 2016, Art no. 4803204.

doi: 10.1109/TASC.2016.2549036 6. Kumar and R. Kaur, “Electromagnetic analysis of 1MJ class of high temperature superconducting magnetic energy storage

(SMES) coil to be used in power applications,” vol. 050003, p. 050003, 2018.

7. Morandi et al., “Design and Comparison of a 1-MW / 5-s HTS SMES With Toroidal and Solenoidal Geometry,” vol. 26, no. 4, pp. 1–6, 2016.

8. Abhinav Kumar, J V Muruga Lal Jeyan, Ashish Agarwal, Numerical analysis on 10 MJ solenoidal high temperature

superconducting magnetic energy storage system to evaluate magnetic flux and Lorentz force distribution, Physica C: Superconductivity and its Applications, Volume 558, 2019, Pages 17-24. https://doi.org/10.1016/j.physc.2019.01.001.

9. M. Ohya et al., “In-grid Demonstration of High-temperature Superconducting Cable,” Phys. Procedia, vol. 45, pp. 273–276,

2013. 10. H. Kim, S.-K. Kim, L. Graber, and S. V Pamidi, “Cryogenic Thermal Studies on Terminations for Helium Gas Cooled

Superconducting Cables,” Phys. Procedia, vol. 67, pp. 201–207, 2015. 11. N. G. Suttell, J. V. C. Vargas, J. C. Ordonez, S. V Pamidi, and C. H. Kim, “Modeling and optimization of gaseous helium (GHe)

cooled high temperature superconducting (HTS) DC cables for high power density transmission,” Appl. Therm. Eng., vol. 143,

pp. 922–934, 2018. 12. H. Ohsaki and Y. Tsuboi, “Study on electric motors with bulk superconductors in the rotor,” J. Mater. Process. Technol., vol.

108, no. 2, pp. 148–151, 2001.

13. P. Tixador, “Superconducting electrical motors,” Int. J. Refrig., vol. 22, no. 2, pp. 150–157, 1999. 14. Driscoll, V. Dombrovski, and B. Zhang, “Development status of superconducting motors,” IEEE Power Eng. Rev., vol. 20, no. 5,

pp. 12–15, 2000.

15. D. Manolopoulos, M. F. Iacchetti, A. C. Smith, K. Berger, M. Husband, and P. Miller, “Stator Design and Performance of Superconducting Motors for Aerospace Electric Propulsion Systems,” IEEE Trans. Appl. Supercond., vol. 28, no. 4, pp. 1–5,

2018.

16. M. Furuse, S. Fuchino, M. Okano, N. Natori, and H. Yamasaki, “Development of a cooling system for superconducting wind turbine generator,” Cryogenics (Guildf)., vol. 80, pp. 199–203, 2016.

17. B. Abrahamsen et al., “Feasibility study of 5MW superconducting wind turbine generator,” Phys. C Supercond. its Appl., vol.

471, no. 21, pp. 1464–1469, 2011. 18. V. M. R. Zermeno, A. B. Abrahamsen, N. Mijatovic, M. P. Sorensen, B. B. Jensen, and N. F. Pedersen, “Simulation of an HTS

Synchronous Superconducting Generator,” Phys. Procedia, vol. 36, pp. 786–790, 2012.

19. J. Xie, P. Zhao, Z. Jing, C. Zhang, N. Xia, and J. Fu, “Research on the sensitivity of magnetic levitation (MagLev) devices,” J. Magn. Magn. Mater., vol. 468, pp. 100–104, 2018.

20. L. Schultz and M. M. B. T.-R. M. in M. S. and M. E. Arafat, “Superconducting YBCO Magnetic Levitation Train☆,” Elsevier,

2018. 21. J. A. Demko et al., “Practical AC loss and thermal considerations for HTS power transmission cable systems,” IEEE Trans. Appl.

Supercond., vol. 11, no. 1, pp. 1789–1792, 2001.

22. H. Noji, K. Haji, and T. Hamada, “AC loss analysis of 114 MVA high-Tc superconducting model cable,” Phys. C Supercond., vol. 392–396, pp. 1134–1139, 2003.

23. H. Noji, K. Ikeda, K. Uto, and T. Hamada, “Numerical analysis of the AC loss in a high-TC superconducting cable measured by

calorimetric method,” Phys. C Supercond., vol. 425, no. 3, pp. 97–100, 2005. 24. J. A. Demko and R. C. Duckworth, “Cooling Configuration Design Considerations for Long-Length HTS Cables,” IEEE Trans.

Appl. Supercond., vol. 19, no. 3, pp. 1752–1755, 2009.

25. Kumar, P. R. Usurumarti, and R. S. Dondapati, “Comparison between the thermophysical properties of compressed LOX and supercritical oxygen to be used as cryogen in HTS power applications,” in IET Conference Publications, 2016, vol. 2016, no.

CP739.

26. H.-M. Chang, K. N. Ryu, and H. S. Yang, “Cryogenic design of liquid-nitrogen circulation system for long-length HTS cables

with altitude variation,” Cryogenics (Guildf)., vol. 83, pp. 50–56, 2017.

27. L. Jin, C. Lee, S. Baek, and S. Jeong, “Design of high-efficiency Joule-Thomson cycles for high-temperature superconductor

power cable cooling,” Cryogenics (Guildf)., vol. 93, pp. 17–25, 2018. 28. H.-M. Chang, K. N. Ryu, and H. S. Yang, “Integrated design of cryogenic refrigerator and liquid-nitrogen circulation loop for

HTS cable,” Cryogenics (Guildf)., vol. 80, pp. 183–192, 2016.

29. M. Furuse, S. Fuchino, and N. Higuchi, “Counter flow cooling characteristics with liquid nitrogen for superconducting power cables,” Cryogenics (Guildf)., vol. 42, no. 6, pp. 405–409, 2002.

30. W. Lemmon, M. O. Mclinden, and M. L. Huber, “NIST standard reference database, physical and chemical properties division,”

version7. 0, beta, vol. 7, no. 30, p. 2. 31. W. Lemmon, M. L. Huber, and M. O. McLinden, “NIST reference fluid thermodynamic and transport properties—REFPROP,”

NIST Stand. Ref. database, vol. 23, p. v7, 2002.

22.

Authors: Kartheek Vankadara, I. Jacob Raglend

Paper Title: Electrical Load Prediction for Short Term using Support Vector Machine Techniques

Abstract: The electrical load prediction during an interval of a week or a day plays an important role for

scheduling and controlling operations of any power system. The techniques which are presently being used and

are used for Short Term Load Forecasting (STLF) by utilizing various prediction models try for the performance

improvement. The prediction models and their performance mainly depend upon the training data and its quality.

The different forecasting approaches using Support Vector Machine (SVM) depending on several performance

indices has been discussed. The accuracy of the forecasting approaches is measured by Mean Absolute Error

(MAE), Root Mean Square Error (RMSE), prediction speed and training time. The approach with least RMSE

reveals as the best among the SVM methods for short term load forecasting.

Keyword: Load forecasting, machine learning, RMSE, support vector machine. References:

1. K. Metaxiotis, A. Kagiannas, D. Askounis, and J. Psarras, “Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher,” Energy Convers. Manag., vol. 44, no. 9, pp. 1525–1534, 2003.

2. B. J. Chen, M. W. Chang, and C. J. Lin, “Load forecasting using support vector machines: A study on EUNITE Competition

2001,” IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1821–1830, 2004. 3. O. A. S. Carpinteiro, R. C. Leme, A. C. Z. de Souza, C. A. M. Pinheiro, and E. M. Moreira, “Long-term load forecasting via a

hierarchical neural model with time integrators,” Electr. Power Syst. Res., vol. 77, no. 3–4, pp. 371–378, 2007.

4. T. Yalcinoz and U. Eminoglu, “Short term and medium term power distribution load forecasting by neural networks,” Energy Convers. Manag., vol. 46, no. 9–10, pp. 1393–1405, 2005.

5. Z. L. Shahidehpour, Mohammad, Hatim Yamin, Market operations in electric power systems: forecasting, scheduling, and risk

management. John Wiley & Sons, 2002. 6. S. S. K. Liu R.R. Shoults M.T. Manry C. Kwan F.L. Lewis J. Naccarino, “Comparison of Very Short-Term Load Forecasting

Techniques,” IEEE Trans. Power Syst., vol. 11, no. 2, pp. 877–882, 1996.

7. J. R. Reis and A. P. A. Silva, “Feature Extraction via Multiresolution Analysis for Short-Term Load Forecasting,” IEEE Trans. Power Syst., vol. 20, no. 1, pp. 189–198, 2005.

8. Shyh-Jier Huang and Kuang-Rong Shih, “Short-term load forecasting via ARMA model identification including non-gaussian

process considerations,” IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673–679, 2003. 9. V. Vapnik, The nature of statistical learning theory. Springer-Verlag, 1995.

10. L. S. Flake, Gary William, “Efficient SVM Regression Training with SMO,” Mach. Learn., vol. 46, pp. 271–290, 2002.

11. C.-J. L. Chen, Pai-Hsuen, Rong-En Fan, “A study on SMO-type decomposition methods for support vector machines,” IEEE

Trans. Neural Networks, vol. 17, no. 4, pp. 893–908, 2006.

111-116

23.

Authors: Sreenath Thangarajan, Reena Monica P.

Paper Title: Technique to Dynamically Reconfigure FPGAs using Control Registers

Abstract: Due to increased use of FPGAs computation intensive applications, the need for embedded

processing system integrated with programmable logic device has also increased. Configuration of

programmable logic device by the processing system through its interface improves the efficiency of the device.

In order to operate as a stand-alone device and to have a better efficiency, the programmable logic device must

be capable of dynamically programming its own configuration memory. In this paper, we propose a configurable

logic block with a control register to improve performance of the programmable logic device. The control

register acts like a decentralized configuration memory array which can be programmed by other such

configurable logic blocks. The FPGAs are fault tolerant devices with repetitive structures requiring high

packaging density. This property of FPGA enables the use of CNTFETs for design of FPGAs. CNTFETs offer

high trans-conductance and 1-D ballistic transport of electrons and holes which minimizes the power consumed

by the FPGA. The proposed control register based architecture was implemented using Cadence Virtuoso using

virtual source CNTFET model from Stanford University. A power reduction of 17.62% is achieved using

CNTFETs when compared with FINFET at same technology node and the architecture was verified for various

configurations of the control register.

Keyword: FPGA, ARM, Control Register, Dynamic configurability, CNTFET, CLB References:

1. A. Chaudhry and M. J. Kumar, "Controlling short-channel effects in deep submicron SOI MOSFETs for improved reliability: A

review," arXiv preprint arXiv:1008.2427, 2010. 2. D. Hisamoto, W.-C. Lee, J. Kedzierski, H. Takeuchi, K. Asano, C. Kuo, E. Anderson, T.-J. King, J. Bokor and C. Hu, "FinFET-a

self-aligned double-gate MOSFET scalable to 20 nm," IEEE Transactions on Electron Devices, vol. 47, no. 12, pp. 2320-2325,

2000. 3. Y.-C. Huang, M.-H. Chiang, S.-J. Wang and J. G. Fossum, "GAAFET Versus Pragmatic FinFET at the 5nm Si-Based CMOS

Technology Node," IEEE Journal of the Electron Devices Society, vol. 5, no. 3, pp. 164-169, 2017.

4. M. M. Shulaker, G. Hills, N. Patil, H. Wei, H.-Y. Chen, H.-S. P. Wong and S. Mitra, "Carbon nanotube computer," Nature, vol. 501, no. 7468, p. 526, 2013.

5. Z. Kordrostami and M. H. Sheikhi, "Fundamental physical aspects of carbon nanotube transistors," Carbon Nanotubes Intech,

2010. 6. Z. Hajduk, "An FPGA embedded microcontroller," Elseiver Microprocessors and Microsystems, vol. 38, no. 1, 2014.

7. U. Meyer-Baese and U. Meyer-Baese, Digital signal processing with field programmable gate arrays, vol. 2, 2004.

8. Xilinx, "Zynq-7000 SoC Technical Reference Manual". 9. J. Luo, L. Wei, C.-S. Lee, A. D. Franklin, X. Guan, E. Pop, D. A. Antoniadis and H.-S. P. Wong, "Compact model for carbon

nanotube field-effect transistors including nonidealities and calibrated with experimental data down to 9-nm gate length," IEEE

transactions on electron devices, vol. 60, no. 6, pp. 1834-1843, 2013.

117-121

10. R. Hajare, C. Lakshminarayana, S. C. Sumanth and A. Anish, "Design and evaluation of FinFET based digital circuits for high speed ICs," International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), pp.

162-167, 2015.

24.

Authors: N. Saravanan, R. Sivaramakrishnan

Paper Title: Design and Control of Dual-Arm Cooperative Manipulator using Speech Commands

Abstract: Cooperative manipulators are among the subject of interest in the scientific community for the

last few years. Here an overview of the design and control of such cooperative manipulators using Speech

Commands in English, Hindi, and Tamil is discussed. Here we choose two identical Robot arms from

lynxmotion, and both manipulators move in conjunction with one another to achieve more payload while

grasping or handling the object by the end effector. The simultaneous control of identical robot manipulators

could be performed by pronouncing simple speech commands by the end user using a smartphone, which then is

converted into text format using a speech recognition engine and this text fed to servo controller helps in

actuating the joints of identical robot arms. Cooperative manipulators are used for handling radioactive elements

and also in the field of medicine as rehabilitation aid and also in surgeries. An Android app specifically built for

this purpose communicates through Bluetooth technology makes the interface for end-user simple to control

both identical robot arms simultaneously.

Keyword: Cooperative manipulators; Speech Recognition and Control; Android Application; Assistive

manipulators; References:

1. Caccavale, F., Cooperative Manipulators, in Encyclopedia of Systems and Control, J. Baillieul and T. Samad, Editors. 2015,

Springer London: London. p. 230-235.

2. White, K.S., Speech recognition implementation in radiology. Pediatric radiology, 2005. 35(9): p. 841-846. 3. Hu, B., B. Li, and H. Cui, Design and kinematics analysis of a novel serial–parallel kinematic machine. Proceedings of the

Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016. 230(18): p. 3331-3346. 4. Hyassat, H. and R. Abu Zitar, Arabic speech recognition using SPHINX engine. International Journal of Speech Technology,

2006. 9(3): p. 133-150.

5. Saraswathi, S. and T.V. Geetha. Improvement in Performance of Tamil Phoneme Recognition using Variable Length and Hybrid Language Models. in 2007 International Conference on Signal Processing, Communications and Networking. 2007.

6. Lei, Z., et al. Artificial robot navigation based on gesture and speech recognition. in Proceedings 2014 IEEE International

Conference on Security, Pattern Analysis, and Cybernetics (SPAC). 2014. IEEE.

7. Gandhewar, N., R.J.I.J.o.C.S. Sheikh, and Engineering, Google Android: An emerging software platform for mobile devices.

2010. 1(1): p. 12-17.

8. Wen, J.T. and K. Kreutz. Motion and force control for multiple cooperative manipulators. in Proceedings, 1989 International Conference on Robotics and Automation. 1989. IEEE.

9. Shashidhar, G., K. Koolagudi, and R. Sreenivasa, Emotion recognition from speech: a review. Springer Science+ Business Media,

2012. 15: p. 99-117.

10. Caccavale, F., et al., Six-dof impedance control of dual-arm cooperative manipulators. IEEE/ASME Transactions On

Mechatronics, 2008. 13(5): p. 576-586.

122-129

25.

Authors: Eadala Sarath Yadav, I. Thirunavukkarasu, Ashutha K., S. Shanmugapriya

Paper Title: Experimental Validation of Amigo Based PID for a Binary Distillation Column

Abstract: Distillation is highly energy consuming process in industrial application concern. This paper

focuses on energy consumption of an actuator through appropriate control design for a binary distillation

column. Temperature control of binary distillation column is challenging because of existence of interaction

between the variables. Independent variables of the process are fast acting (Reflux flow) and slow acting

(Reboiler) with respect to process variables (Tray temperatures). Energy consumption of manipulated variable

depends on efficient tuning of controller. AMIGO based PID controller design is implemented in this paper to

show the optimal energy utilization of actuator. Performance analysis has been carried out to validate the control

structure. It is been analyzed that based on the speed and quality requirement of the process, set of controller

values can be obtained and best set of PID can be selected for real time implementation. Results depicts the

efficiency of control scheme with performance index.

Keyword: Binary distillation column, AMIGO, PID, Decoupler, Interaction, FOPDT. References:

1. Levine, William S. The Control Handbook (three volume set). CRC press, 2018.

2. Vrančić, Damir, et al. "Improving disturbance rejection of PID controllers by means of the magnitude optimum method." ISA

transactions 49.1 2010, pp. 47-56. 3. Tore Hägglund, and Karl J. Astrom. Advanced PID control. Vol. 461. Research Triangle Park, NC: ISA-The Instrumentation,

Systems, and Automation Society, 2006.

4. Chen, Dan, and Dale E. Seborg. "PI/PID controller design based on direct synthesis and disturbance rejection." Industrial & engineering chemistry research 41.19 2002, pp.4807-4822.

5. Skogestad, Sigurd. "Simple analytic rules for model reduction and PID controller tuning." Journal of process control 13.4 2003,

pp.291-309.

6. Shamsuzzoha, M., and Moonyong Lee. "Design of advanced PID controller for enhanced disturbance rejection of second‐order

processes with time delay." AIChE Journal 54.6 2008, pp.1526-1536.

7. Åström, Karl Johan, and Tore Hägglund. "Revisiting the Ziegler–Nichols step response method for PID control." Journal of

process control 14.6 2004, pp.635-650. 8. Yadav, Eadala Sarath, et al. "Parameter Estimation and an Extended Predictive-Based Tuning Method for a Lab-Scale Distillation

Column." ACS Omega 2019, DOI: 10.1021/acsomega.9b02713

130-134

9. Yadav, Eadala Sarath, and Thirunavukkarasu Indiran. "PRBS based model identification and GPC PID control design for MIMO Process." Materials Today: Proceedings 17 2019, pp.16-25.

10. Ravi, V. R., and T. Thyagarajan. "A decentralized PID controller for interacting nonlinear systems." 2011 International

Conference on Emerging Trends in Electrical and Computer Technology. IEEE, 2011. 11. Martin, Paulo A., Darci Odloak, and Fuad Kassab. "Robust model predictive control of a pilot plant distillation column." Control

Engineering Practice 21.3 2013, pp.231-241.

12. Panda, Rames C., et al. "Parameter estimation of integrating and time delay processes using single relay feedback test." ISA transactions 50.4 2011, pp.529-537.

13. Yadav, Eadala Sarath, et al. "Parameter estimation and control of a pilot plant binary distillation." Journal of Advanced Research

in Dynamical and Control Systems 9.Special Issue 15 2017, pp.877-886.

26.

Authors: S. Pavani, Augusta Sophy Beulet P.

Paper Title: Heuristic Prediction of Crop Yield using Machine Learning Technique

Abstract: Vast research has been done and several attempts are made for application of Machine learning in

agricultural field. Major challenge in agriculture is to increase the production in the farm and deliver it to the end

customers with best possible price and good quality. It is found that at least 50 percent of the farm produce never

reach the end consumer due to wastage and high-end prices. Machine learning based solutions developed to

solve the difficulties faced by the farmers are being discussed in this work. The real time environmental

parameters of Telangana District like soil moisture, temperature, rainfall, humidity are collected and crop yield

is being predicted using KNN Algorithm.

Keyword: Agriculture, Crop Yield Prediction, KNN, Machine learning.

References: 1. Statistical yearbook 2017, Directorate of Economics and Statistics, Government of Telangana. 2. Nivetha, R. Yamini, and C.Dhaya, Developing a Prediction Model for Stock Analysis, Technical Advancements in Computers

and Communications (ICTACC), 2017 International Conference on IEEE, 2017.

3. Hemantkumar Wani, Nilima Ashtankar, An Appropriate Model Predicting Pest/Diseases of Crops UsingMachine Learning Algorithms, 2017 International Conference on Advanced Computing and Communication Systems (ICACCS -2017), Jan. 06 –

07, 2017, Coimbatore, INDIA.

4. Haiguang Wang, Guanlin Li, ZhanhongMa, Xiaolong Li, “Image Recognition of Plant Diseases Based on Backpropagation Networks”, 5th International Congress on Image and Signal Processing (CISP 2012)201.

5. Introduction to Data Mining, P.N. Tan, M. Steinbach, V. Kumar, Addison Wesley.

6. Shambhavi Dighe , Anuja Jagdale , Amar Chadchankar, Crop Production Prediction System, International Journal of Research in

Engineering, Science and Management Volume-1, Issue-12, December-2018 www.ijresm.com | ISSN (Online): 2581-5792.

7. An efficient k-means clustering algorithm: Analysis and implementation, T. Kanungo, D. M. Mount, N. Netanyahu, C.Piatko, R.

Silverman, and A. Y. Wu, IEEE Trans. PatternAnalysis and Machine Intelligence, 24 (2002), 881-892. 8. [8] Hema Geetha N, A survey on application of data mining techniques to analyze the soil for agricultural purpose, Computing for

Sustainable Global Development (INDIAcom), 3rd International Conference on IEEE, 2016.

9. Chandrasegar Thirumalai, IEEE Member, M Lakshmi Deepak, Heuristic Prediction of Rainfall Using Machine Learning Techniques, International Conference on Trends in Electronics and Informatics ICEI 2017.

10. S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh, Machine learning approach for forecasting crop yield based on climatic

parameters,2014 International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 –05, 2014, Coimbatore, INDIA.

11. Ramesh, V. and Ramar, K., 2011. Classification of agricultural land soils: a data mining approach. Agricultural Journal, 6(3),

pp.82-86. 12. Kentaro Kuwata and Ryosuke Shibasaki, Estimating crop yields with deep learning and remotely sensing data, IIS, The

University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, JAPAN.

13. Rani Pagariya and MahipBartere, “Review paper on identification of plant diseases using image processing technique”, 2014. 14. Gautam Kaushal1, Rajni Bala2, GLCM and KNN based Algorithm for Plant Disease Detection, International Journal of

Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 6, Issue 7,July 2017.

15. Mr A Suresh, Dr. P. Ganesh Kumar, Dr.M.Ramalatha, Prediction of major crop yields of Tamilnadu using K-means and Modified

KNN, Proceedings of the International Conference on Communication and Electronics Systems (ICCES 2018) IEEE Xplore Part

Number:CFP18AWO-ART; ISBN:978-1-5386-47653. 16. Yan, Xiaozhen, Hong Xie, and Wang Tong, “A multiple linear regression data predicting method using correlation analysis for

wireless sensor networks,” Cross strait quad-regional radio science and wireless technology conference, 2011. Vol. 2. IEEE,

2011. 17. Sk Al Zaminur Rahman, Kaushik Chandra Mitra, Soil Classification using Machine Learning Methods and Crop Suggestion

Based on Soil Series,2018 21st International Conference of Computer and Information Technology (ICCIT), 21-23 December

2018. 18. HAI-YANG JIA1, JUAN CHEN1, HE-LONG YU1,2, DA-YOU LIU1, Soil fertility grading with Bayesian network transfer

learning, Proceedings of the Ninth International Conference on Machine Learning and Cybernetics, Qingdao, 11-14 July 2010.

19. K.R.SriPreethaa,,S.Nishanthini,D.SanthiyaK.VaniShree ,“CropYield Prediction”,International Journal On Engineering Technology and Sciences– IJETS™ISSN(P): 2349-3968, ISSN (O):2349-3976 Volume III,Issue III, March- 2016.

20. Ana Laura Diedrichs , Facundo Bromberg, Diego Dujovne, Keoma Brun-Laguna,and Thomas Watteyne, Senior Member, IEEE,

Prediction of Frost Events Using MachineLearning and IoT Sensing Devices, IEEE INTERNETOF THINGS JOURNAL, VOL. 5, NO. 6, DECEMBER 2018.

135-138

27.

Authors: Ashvini Kulkarni, Augusta Sophy Beulet P.

Paper Title: Statistical Analysis of Accelerometer,Gyroscope with State Estimation

Abstract: This paper describes the tracking of the object with the utility of a three-axis accelerometer and

gyroscope for navigation. The sensor fusion is receiving enormous research interest which is used in monitoring

and tracking position. For dynamic modeling, for real-time data, the perceptual mixing of the signals is required.

For proper mixing of the signals, sensor fusion techniques are used. This paper presents the comparative review

analysis for the three-axis accelerometer and gyroscope for linear acceleration and angular rotation.

139-143

Keyword: IMU, Kalman Filter, Sensor fusion References:

1. Michael Wright, Dion Stallings, and Dr. Derrek PU, "The Effectiveness of Global Positioning System Electronic Navigation "

IEEE Southeast Con, 2003. Proceedings. ISBN: 0-7803-7856-3 , 03 March 2004

2. Kia Fallahi, Chi-Tsun Cheng, Michel Fattouche, "Robust Positioning Systems in the Presence of Outliers Under Weak GPS Signal Conditions", IEEE Systems Journal ( Volume: 6, Issue: 3, Sept. 2012 ), Page(s): 401 - 413 ISSN: 1937-9234

3. Gianfranco Fornaro,Nicola D’Agostino,Roberta Giuliani,Carlo Noviello,Diego Reale,Simona Verde,” Assimilation of GPS-

Derived Atmospheric Propagation Delay in DInSAR Data Processing”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,Volume: 8, Issue: 2, Feb. 2015, Page(s): 784 – 799,ISSN: 1939-1404

4. Susan Skone and Sudhir M. Shrestha, "Limitations in DGPS positioning accuracies at low latitudes during solar maximum"

Geophysical Research Letter, published 28 May 2002 5. Kar-Ming Cheung, Charles Lee," A Trilateration Scheme for Relative Positioning”, IEEE Aerospace Conference ISBN: 978-1-

5090-1613-6, March 2017

6. Peter A Crossley, Hao Guo, Zhao Ma, “Time Synchronization for Transmission Substations Using GPS and IEEE 1588”, Journal of Power and Energy Systems, VOL. 2, NO. 3, Sepetember2016, Page(s): 91 – 99,ISSN: 2096-0042

7. Xiaojing Du, Li Liu, Huaijian Li, "Experimental Study on GPS Non-linear Least Squares Positioning Algorithm", International

Conference on Intelligent Computation Technology and Automation, 11-12 May 2010, Page(s): 262 – 265, ISBN:978-1-4244-7280-2

8. Maiying Zhong, Jia Guo, Zhaohua Yang, "On the real-time performance evaluation of the inertial sensors for INS/GPS integrated

systems", IEEE Sensors Journal ( Volume: 16, Issue: 17, Sept.1, 2016), Page(s): 6652 – 6661, ISSN: 1558-1748 9. Least Squares Positioning Algorithm", International Conference on Intelligent Computation Technology and Automation, 11-12

May 2010, Page(s): 262 – 265, ISBN:978-1-4244-7280-2

10. Jos´e Gaspar, Jos´e Santos-Victor, "Vision-based Navigation and Environmental Representations with an Omni-directional Camera”, 2015 IEEE International Conference on Big Data 978-1-4673-7278-7/15, DOI 10.1109/BigDataCongress.2015.103

11. Zhou Haoyin, Zhana Tao, "A Vision-Based Navigation Approach with Multiple Radial Shape Marks for Indoor Aircraft

Locating", Chinese Journal of Aeronautics, doi.org/10.1016/j.cja.2013.12.005,Volume 27, Issue 1, February 2014, Pages 76-84 12. Zheming Wu, Zhenguo Sun, Wenzeng Zhang, Qiang Chen, "A Novel Approach for Attitude Estimation Based on MEMS Inertial

Sensors using Nonlinear Complementary Filters", IEEE Sensors Journal ( Volume: 16, Issue: 10, May 15, 2016 ), Page(s): 3856 –

3864, ISSN: 1558-1748 13. D. Chattaraj*, K.B.M. Swamy and S. Sen, "Design and Analysis of Dual Axis MEMS Accelerometer", International Conference

on Electronics and Communication Systems (ICECS), DOI: 0.1109/ECS.2015.7125014, 26-27 Feb. 2015

14. Zdzisław Kowalczuk, Tomasz Merta, "Evaluation of position estimation based on accelerometer data", International Workshop on Robot Motion and Control, Poznan University of Technology, Poznan, Poland, ISBN: 978-1-4799-7043-8, July 6-2015

15. O. J. Woodman, "An introduction to inertial navigation," University of Cambridge, Computer Laboratory, Cambridge, UK, Tech.

Rep. 696,2007 16. J.Z.Sasiadek, P. Haryana, "GPS/INS sensor fusion for accurate positioning & navigation based on Kalman filter", IFAC

Proceedings Volumes, Volume 37, Issue 5, June 2004, Pages 115-120

17. Mohinder S.Grewal, Angus P.Andrew, "Kalman Filtering: Theory and Practice Using MATLAB", Second Edition, Published by

John Wiley & sons,2001

18. Sen Qiu, Zhelong Wang, Hongyu Zhao, Kairong Qin, Zhenglin Li, Huosheng Hu, "Inertial/Magnetic Sensor Based Pedestrian Dead Reckoning by Means of Multi-Sensor Fusion" Information Fusion, Volume 39, Page No.108-119, Science direct, April

2017

19. Hang Geng, Yan Liang, Yurong Liu, Fuad E. Alsaadi, "Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs" Information Fusion, Volume 39, Page No.139-153, Science direct, April 2017

20. Kajiro Watanabe, Kazuyuki Kobayashi, and Fumio Munekata, "Multiple Sensor Fusion for Navigation Systems”, Vehicle

Navigation information System IEEE Conference proceedings, 06 August 2002, DOI:10.1109/ VNIS.1994.396787 21. InvenSense, “MPU-6000and MPU-6050 Product Specification,” InvenSense Inc., Document Number: PS-MPU-6000A-

00Revision: 3.4Release Date: 08/19/2013

28.

Authors: Pydikalva Padmavathi, Sudhakar Natarajan

Paper Title: Performance Analysis of Modified High Voltage Gain Boost Converter for PV-fed LED Lighting

Applications

Abstract: Nowadays, the development of PV (Photo Voltaic)-fed LED (Light Emitting Diode) lighting technology is

requires high gain ratios with efficient performance of the converter. The presented converter topology is non-isolated

possess high gain voltage with low stress voltage. The design of the modified high voltage gain boost configuration is

projected with continuous current at input, which is flexible to control. The conduction, switching loss at the switch, reverse

recovery problem and electromagnetic interference are mitigated due to low duty cycle. But to explore the differentiation of

their characteristics, advantages and several reasonable evaluations are carried out. The operating principle, theoretical

analysis and experimental results of modified high gain step-up converter are provided for PV-fed LED lighting applications

to verify the efficient performance in all aspects.

Keyword: Solar PV, Boost Converter, High Gain, PWM Controller, LED Lighting. References:

1. F.Blaabjerg, F.Iov, T.Kerekes, R.Teodorescu, “Trends in power electronics and control of renewable energy systems”, in 14th Int.

Power Electron. & Motion Control Conf., EPE-PEMC’10, K-1–K-19, 2010. 2. J.M. Ho, & C.C. Lou, “The Design and Implementation of Stand-Alone Solar Power LED Lighting Systems,” Recent Researches

in Circuits, Systems, Electronics, Control & Signal Processing, Athens, Greece, pp. 66-69, 2011.

3. E. F. Schubert, Light-Emitting Diodes, 2nd ed. CambridgeU.K.Cambridge Univ. Press, 2006.

4. C.DiLouie, Advanced Lighting Controls: Energy Savings, Productivity, Technology and Applications. Lilburn, GA: Fairmont

Press, 2005.

5. E.Koutroulis, K.Kalaitzakis, N.C.Voulgaris, “Development of a microcontroller based photovoltaic maximum power point tracking system”, in IEEE Trans. on Power Electron., vol. 16, no. 1, pp. 46–54, 2001.

6. C. Kobougias and E. C. Tatakis, “Optimal design of a half-wave Cockcroft-Walton voltage multiplier with minimum total

capacitance,” IEEE Trans. Power Electron., vol. 25, no. 9, pp. 2460–2468, Sep. 2010. 7. J. A. Starzyk, J. Ying-Wei, and F. Qiu, “A DC-DC charge pump design based on voltage doublers,” IEEE Trans. Circuits Syst. I,

Fundam. Theory Appl., vol. 48, no. 3, pp. 350–359, Mar. 2001.

8. E. H. Ismail, M. A. Al-Saffar, A. J. Sabzali, andA.A. Fardoun, “A family of single-switch PWM converters with high step-up conversion ratio,” IEEE Trans. Circuits Syst. I, Reg. Papers, vol. 55, no. 4, pp. 1159–1171,May 2008.

9. M. S. Makowski, “Realizability conditions and bounds on synthesis of switched-capacitor DC-DC voltage multiplier circuits,”

144-149

IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol. 44, no. 8, pp. 684–691, Aug. 1997. 10. Y. P. Hsieh, J. F. Chen, T. J. Liang, and L. S. Yang, “Novel high step-up DC-DC converter with coupled-inductor and switched-

capacitor techniques for a sustainable energy system,” IEEE Trans. Power Electron., vol. 26, no. 12, pp. 3481–3490, Dec. 2011.

11. F. L. Luo, “Six self-lift DC-DC converters, voltage lift technique,” IEEE Trans. Ind. Electron., vol. 48, no. 6, pp. 1268–1272, Dec. 2001.

12. F. L. Luo, “Analysis of super-lift Luo-converters with capacitor voltage drop,” in Proc. 3rd IEEE Conf. Ind. Electron. Appl.,

2008, pp. 417–422. 13. Y. Jiao, F. L. Luo, andM. Zhu, “Voltage-lift-type switched-inductor cells for enhancing DC-DC boost ability: principles and

integrations in Luo converter,” IET Power Electron., vol. 4, no. 1, pp. 131–142, 2011.

14. Y. Tang, T. Wang, and D. Fu, “Multicell switched-inductor/switchedcapacitor combined active-network converters,” IEEE Trans. Power Electron., vol. 30, no. 4, pp. 2063–2072, Apr. 2015.

15. Y. Tang, D. Fu, T. Wang, and Z. Xu, “Hybrid switched-inductor converters for high step-up conversion,” IEEE Trans. Ind.

Electron., vol. 62, no. 3, pp. 1480–1490, Mar. 2015. 16. F. Mohammadzadeh Shahir, E. Babaei and M. Farsadi, "Voltage-Lift Technique Based Nonisolated Boost DC–DC Converter:

Analysis and Design," in IEEE Transactions on Power Electronics, vol. 33, no. 7, pp. 5917-5926, July 2018.

17. Pandiarajan N, Muthu R (2011) Mathematical modeling of photovoltaic module with Simulink. International Conference on Electrical Energy Systems(ICEES 2011), p6.

18. Tu H-LT, Su Y-J (2008) Development of generalized photovoltaic model usingMATLAB/SIMULINK. Proc World Congr Eng

Comput Sci 2008:6 19. Salmi T, Bouzguenda M, Gastli A, Masmoudi A (2012) Matlab/simulink basedmodelling of solar photovoltaic cell. Int J Renew

Energy Res 2(2):6

20. Mihnea Rosu Hamzescu, Sergiu Oprea" Practical Guide to Implementing Solar Panel MPPT Algorithms" Microchip Technology

Inc., 2013.

29.

Authors: Abhinav Kumar, Ashish Agrawal

Paper Title: Effect of Substrate Layer Thickness on the AC Losses in Stacked Superconducting Pancake Coils

using Direct H-formulations

Abstract: Advanced electric aircrafts are in their design phase and superconducting machines are going to

be the part of such fascinating technology. In order to diminish the losses involved due to conventional copper

conductors, superconductors are proposed for the electric aircraft applications by the American research

agencies like NASA and AFRL.

Usually, Pancake coils are frequently used in various electric aircraft power applications including high speed

motors, generators, transformers and solenoid magnets. Coils are generally bound with high temperature

superconducting (HTS) tapes like BSCCO and YBCO. Mostly, 2nd generation coated conductors (YBCO) are

employed in power applications due to their merits over BSCCO (1st generation tapes). A superconducting tape

manufactured by SuperPower through iBAD manufacturing technique generally consist copper stabilizer, silver

over-layer, YBCO layer, buffer layer, substrate material followed by copper stabilizer. The volume fraction of

the substrate material and copper stabilizer is more than 90% in the proposed tape. In the present work, the

thickness of the substrate material has been varied to evaluate the AC losses involved in the above mentioned

applications due to time-varying magnetic fields. A current of 270 A (Ic=330 A) is flowing through a coil of 108

turns. AC loss has been evaluated for various thicknesses 30 µm to 90 µm at a frequency of 50 Hz. The

simulations are done using COMSOL MultiPhysics® commercial software package.

Keyword: Substrate Layer, Superconductors, Pancake coils, H-formulations, YBCO, iBAD manufacturing

technique. References:

1. J. Sliwinski, A. Gardi, M. Marino, and R. Sabatini, “Hybrid-electric propulsion integration in unmanned aircraft,” Energy, vol.

140, pp. 1407–1416, 2017.

2. C. E. D. Riboldi, “An optimal approach to the preliminary design of small hybrid-electric aircraft,” Aerosp. Sci. Technol., vol.

81, pp. 14–31, 2018.

3. C. Pornet and A. T. Isikveren, “Conceptual design of hybrid-electric transport aircraft,” Prog. Aerosp. Sci., vol. 79, pp. 114–135,

2015. 4. E. Frosina, C. Caputo, G. Marinaro, A. Senatore, C. Pascarella, and G. Di Lorenzo, “Modelling of a Hybrid-Electric Light

Aircraft,” Energy Procedia, vol. 126, pp. 1155–1162, 2017.

5. P. C. Vratny and M. Hornung, “Sizing Considerations of an Electric Ducted Fan for Hybrid Energy Aircraft,” Transp. Res. Procedia, vol. 29, pp. 410–426, 2018.

6. S. Ma, S. Wang, C. Zhang, and S. Zhang, “A method to improve the efficiency of an electric aircraft propulsion system,” Energy,

vol. 140, pp. 436–443, 2017. 7. Kumar, A. Agrawal, and J. M. L. Jeyan, “A Numerical Model Comprising the Effect of Number of Turns on AC Losses in 2G

HTS Coated Conductor at 77K using H-formulations,” in 2019 IEEE 2nd International Conference on Power and Energy

Applications (ICPEA), 2019, pp. 115–118. A. Kumar and R. Kaur, “Electromagnetic analysis of 1MJ class of high temperature superconducting magnetic energy storage

(SMES) coil to be used in power applications,” vol. 050003, p. 050003, 2018.

A. Kumar, J. V. M. L. Jeyan, and A. Agarwal, “Numerical analysis on 10 MJ solenoidal high temperature superconducting magnetic energy storage system to evaluate magnetic flux and Lorentz force distribution,” Phys. C Supercond. its Appl., vol. 558, pp. 17–

24, 2019.

8. T. J. Haugan, “Development of Superconducting and Cryogenic Power Systems and Impact for Aircraft Propulsion,” Air Force Res. Lab., no. April, 2015.

9. N. Amemiya, T. Tsukamoto, M. Nii, T. Komeda, T. Nakamura, and Z. Jiang, “Alternating current loss characteristics of a Roebel

cable consisting of coated conductors and a three-dimensional structure,” Supercond. Sci. Technol., vol. 27, no. 3, p. 35007, 2014.

10. N. J. Long, R. A. Badcock, K. Hamilton, A. Wright, Z. Jiang, and L. S. Lakshmi, “Development of YBCO Roebel cables for high

current transport and low AC loss applications,” vol. 022021, no. 11, pp. 1–9, 2010. 11. Brambilla, F. Grilli, and L. Martini, “Development of an edge-element model for AC loss computation of high-temperature

superconductors,” vol. 16, 2007. 12. Z. Hong, A. M. Campbell, and T. A. Coombs, “Numerical solution of critical state in superconductivity by finite element

software,” Supercond. Sci. Technol., vol. 19, no. 12, p. 1246, 2006.

13. F. Gömöry, M. Vojenčiak, E. Pardo, and J. Šouc, “Magnetic flux penetration and AC loss in a composite superconducting wire

150-154

with ferromagnetic parts,” Supercond. Sci. Technol., vol. 22, no. 3, p. 34017, 2009. 14. Stenvall and T. Tarhasaari, “An eddy current vector potential formulation for estimating hysteresis losses of superconductors with

FEM,” Supercond. Sci. Technol., vol. 23, no. 12, p. 125013, 2010.

15. N. Amemiya, S. Murasawa, N. Banno, and K. Miyamoto, “Numerical modelings of superconducting wires for AC loss calculations,” Phys. C Supercond., vol. 310, no. 1–4, pp. 16–29, 1998.

16. N. Enomoto and N. Amemiya, “Electromagnetic field analysis of rectangular high Tc superconductor with large aspect ratio,”

Phys. C Supercond., vol. 412, pp. 1050–1055, 2004. 17. K. P. Thakur, A. Raj, E. H. Brandt, J. Kvitkovic, and S. V Pamidi, “Frequency-dependent critical current and transport ac loss of

superconductor strip and Roebel cable,” vol. 065024, 2011.

30.

Authors: Ashish Agrawal, Abhinav Kumar

Paper Title: Effect of Self-field and Loosening of Stacked Superconducting Tapes on Critical Current of a Single

Pancake Coil

Abstract: Research on superconducting systems is getting pace due to the increasing demand of efficient

machines to fulfill the need of the society. Applications like motors, transformers and magnetic energy storage

systems involve the pancake coils tightly bounded with the superconducting tapes. Moreover, all such devices

are sensitive to the number of turns and operating current. Critical current of the superconducting tape is most

important parameter while designing such machines as if operating current exceeds this value it will turn to a

normal conductor. Critical current of the tape is further depending upon the operating temperature, external and

self-field. In this work, effect of both self-field and degree of tightness or looseness on the critical current of the

tape has been studied. The results showed that the critical current of the tape is significantly affected by both

self-field and the inter-distance among the adjacent tapes.

Keyword: Superconducting tape, critical current, pancake coils, Superconductivity, High temperature

superconductors. References:

1. M. Park et al., “Conceptual Design of HTS Magnet for a 5 MJ Class SMES,” vol. 18, no. 2, pp. 750–753, 2008. 2. S. Kwak et al., “Design of HTS Magnets for a 2 . 5 MJ SMES,” vol. 19, no. 3, pp. 1985–1988, 2009.

3. Kumar and R. Kaur, “Electromagnetic analysis of 1MJ class of high temperature superconducting magnetic energy storage

(SMES) coil to be used in power applications,” vol. 050003, p. 050003, 2018. A. Morandi et al., “Design and Comparison of a 1-MW / 5-s HTS SMES With Toroidal and Solenoidal Geometry,” vol. 26, no. 4,

pp. 1–6, 2016.

4. T. Takematsu et al., “Degradation of the performance of a YBCO-coated conductor double pancake coil due to epoxy impregnation,” Phys. C Supercond. its Appl., vol. 470, no. 17–18, pp. 674–677, 2010.

5. F. Grilli, M. Vojenˇ, A. Kario, and V. Zerme, “Estimation of Self-Field Critical Current and Transport-Magnetization AC Losses

of Roebel Cables,” pp. 1–5, 2015. 6. Q. Wang et al., “High Temperature Superconducting YBCO Insert for 25 T Full Superconducting Magnet,” vol. 25, no. 3, pp. 3–

7, 2015.

7. V. M. R. Zermeno, F. Grilli, and F. Sirois, “A full 3D time-dependent electromagnetic model for Roebel cables,” Supercond. Sci. Technol., vol. 26, no. 5, p. 52001, 2013.

8. Z. Jiang et al., “Magnetization Loss in REBCO Roebel Cables,” vol. 28, no. 3, 2018.

9. E. Pardo and F. Grilli, “Numerical simulations of the angular dependence of magnetization AC losses : coated conductors , Roebel cables and,” vol. 014008, 2012.

10. W. Goldacker, F. Grilli, E. Pardo, and A. Kario, “Roebel cables from REBCO coated conductors : a one-century-old concept for

the superconductivity of the future,” vol. 093001. 11. V. M. R. Zermeno, F. Grilli, and F. Sirois, “A full 3D time-dependent electromagnetic model for Roebel cables,” Supercond. Sci.

Technol., vol. 26, no. 5, 2013.

12. F. Grilli, M. Vojenˇ, A. Kario, and V. Zerme, “HTS Roebel Cables : Self-Field Critical Current and AC Losses under Simultaneous Application of Transport Current and Magnetic Field,” vol. 8223, no. c, pp. 1–5, 2016.

13. F. Grilli, V. M. R. Zerme, E. Pardo, M. Vojenˇ, and A. Kario, “Self-field Effects and AC Losses in Pancake Coils Assembled

from Coated Conductor Roebel Cables,” pp. 1–5.

14. M. Furuse, M. Yoshikawa, Y. Itoh, S. Fukui, and T. Nakamura, “Fabrication and Testing of Racetrack-Shaped Double-Pancake

Coil for Stator Windings of Induction-Synchronous Motor,” vol. 25, no. 3, 2015.

15. W. Y. Li, X. J. Niu, H. Su, W. Chen, S. S. Peng, and J. Zheng, “A Study on HTS Double Pancake Coil for Electric Machine,” pp. 177–179, 2013.

16. K. Nagao, T. Nakamura, H. Sugimoto, and T. Morishita, “Synchronous motor with HTS-2G wires.”

17. K. C. Seong et al., “Development of a 600 kJ HTS SMES,” vol. 468, pp. 2091–2095, 2008. 18. Morandi, M. Fabbri, B. Gholizad, F. Grilli, F. Sirois, and V. M. R. Zermeño, “Design and Comparison of a 1-MW/5-s HTS

SMES With Toroidal and Solenoidal Geometry,” IEEE Trans. Appl. Supercond., vol. 26, no. 4, pp. 1–6, 2016.

19. Kumar, J. V. M. L. Jeyan, and A. Agarwal, “Numerical analysis on 10 MJ solenoidal high temperature superconducting magnetic energy storage system to evaluate magnetic flux and Lorentz force distribution,” Phys. C Supercond. its Appl., vol. 558, pp. 17–

24, 2019.

20. F. Grilli, F. Sirois, S. Member, V. M. R. Zermeño, and M. Vojenˇ, “Self-Consistent Modeling of the I c of HTS Devices : How Accurate do Models Really Need to Be ?,” vol. 24, no. 6, 2014.

21. V. Zermeño, F. Sirois, M. Takayasu, M. Vojenciak, A. Kario, and F. Grilli, “A self-consistent model for estimating the critical

current of superconducting devices,” Supercond. Sci. Technol., vol. 28, no. 8, p. 85004. 22. J. Fleiter, A. Ballarino, L. Bottura, and P. Tixador, “Electrical characterization of REBCO Roebel cables,” Supercond. Sci.

Technol., vol. 26, no. 6, p. 65014, 2013.

23. K. P. Thakur, A. Raj, E. H. Brandt, J. Kvitkovic, and S. V Pamidi, “Frequency-dependent critical current and transport ac loss of superconductor strip and Roebel cable,” vol. 065024, 2011.

24. W. Yuan, A. M. Campbell, and T. A. Coombs, “A model for calculating the AC losses of second-generation high temperature

superconductor pancake coils,” Supercond. Sci. Technol., vol. 22, no. 7, p. 75028, 2009. 25. E. Pardo, J. Šouc, and J. Kováč, “AC loss in ReBCO pancake coils and stacks of them: modelling and measurement,” Supercond.

Sci. Technol., vol. 25, no. 3, p. 35003, 2012.

26. W. Yuan, A. M. Campbell, and T. A. Coombs, “Ac losses and field and current density distribution during a full cycle of a stack of superconducting tapes,” J. Appl. Phys., vol. 107, no. 9, p. 93909, 2010.

27. Kumar, A. Agrawal, and J. M. L. Jeyan, “A Numerical Model Comprising the Effect of Number of Turns on AC Losses in 2G HTS Coated Conductor at 77K using H-formulations,” in 2019 IEEE 2nd International Conference on Power and Energy

Applications (ICPEA), 2019, pp. 115–118.

155-158

28. S. H. T. Superconductor, “SuperPower ® 2G HTS Wire Specifications,” pp. 2–5, 2000.

31.

Authors: Abhilash Unnikrishnan, Abraham Sudharson Ponraj

Paper Title: Genetic Algorithm for Effective Fall Detection with Wrist Wearable Device

Abstract: Falls have always been a major cause of injury related deaths among the old aged population in

our country. It causes mental trauma and severe fractures to the bones and spine which impacts their quality of

life. Therefore a proper fall prediction and alert system along with a timely rapid response could enable us to

tackle such serious fall events and decrease the fatality. Various sensors and embedded controllers are used in

conjunction with various machine learning classifiers to help us predict and optimize the falls effectively. This

work presents a wrist wearable device using MPU-6050 sensor and raspberry-pi controller with help of machine

learn algorithm which help us to predict the falls. Five different supervised learning algorithms and one

unsupervised algorithm was implemented and evaluated on the basis of their accuracy, sensitivity and

specificity. Out of all these classifiers, the decision tree with an accuracy of 85% was implemented in the system

which classified the fall from the real time non-fall data sets. Further the performance of system was increased

using genetic algorithm which gave better classification results unlike the normal decision tree classifier. Once

the falls are predicted we can give a real-time response which can be an added feature to this system.

Keyword: Decision tree, Fall Detection, Genetic algorithm, Machine learning. References:

1. https://www.who.int/news-room/fact-sheets/detail/falls What the Internet of Things (IoT) Needs to Become a Reality by

Freescale 2. de Quadros, Thiago, Andre Eugenio Lazzaretti, and Fábio Kürt Schneider. "A movement decomposition and machine learning-

based fall detection system using wrist wearable device." IEEE Sensors Journal 18.12 (2018): 5082-5089.

3. Sposaro, Frank, and Gary Tyson. "iFall: an Android application for fall monitoring and response." Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. IEEE, 2009.

4. Mao, Aihua, et al. "Highly portable, sensor-based system for human fall monitoring." Sensors 17.9 (2017): 2096. 5. Liu, Chien-Liang, Chia-Hoang Lee, and Ping-Min Lin. "A fall detection system using k-nearest neighbor classifier." Expert

systems with applications 37.10 (2010): 7174-7181.

6. Aziz, Omar, et al. "A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials." Medical & biological engineering

& computing 55.1 (2017): 45-55.

7. Guo, Han Wen, et al. "A threshold-based algorithm of fall detection using a wearable device with tri-axial accelerometer and

gyroscope." Intelligent Informatics and Biomedical Sciences (ICIIBMS), 2015 International Conference on. IEEE, 2015.

8. Vallabh, Pranesh, et al. "Fall detection using machine learning algorithms." SoftCOM. 2016.

9. Huynh, Quoc T., et al. "Optimization of an accelerometer and gyroscope-based fall detection algorithm." Journal of Sensors 2015 (2015).

10. Aguiar, Bruno, et al. "Accelerometer-based fall detection for smartphones." Medical Measurements and Applications (MeMeA),

2014 IEEE International Symposium on. IEEE, 2014. 11. Zhang, Tong, et al. "Fall detection by wearable sensor and one-class SVM algorithm." Intelligent computing in signal processing

and pattern recognition. Springer, Berlin, Heidelberg, 2006. 858-863.

12. Santiago, Joseph, et al. "Fall detection system for the elderly." Computing and Communication Workshop and Conference (CCWC), 2017 IEEE 7th Annual. IEEE, 2017.

13. Luo, Jing, Bocheng Zhong, and Dinghao Lv. "Fall Monitoring Device for Old People based on Tri-Axial Accelerometer."

IJACSA) Int J Adv Comput Sci Appl 6.5 (2015). 14. De Cillis, Francesca, et al. "Fall-detection solution for mobile platforms using accelerometer and gyroscope data." Engineering in

Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE, 2015.

15. Yuan, Jian, et al. "Power-efficient interrupt-driven algorithms for fall detection and classification of activities of daily living." IEEE Sensors Journal 15.3 (2015): 1377-1387.

16. Kerdegari, Hamideh, et al. "Evaluation of fall detection classification approaches." Intelligent and Advanced Systems (ICIAS),

2012 4th International Conference on. Vol. 1. IEEE, 2012. 17. Sheryl Oliver A et Al. Optimized Low Computational Algorithm For Elderly Fall Detection Based On Machine Learning

Techniques.

18. https://www.neuraldesigner.com/blog/genetic_algorithms_for_feature_selection

19. https://scikit-learn.org/stable/

20. https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/

21. https://github.com/renatoosousa/GeneticAlgorithmForFeatureSelection.

159-164

32.

Authors: Abhinav Kumar, Umar Mushtaq, Muskan Raikwar, Anil Chamoli, Shahid Khan

Paper Title: Properties of Kerosene-Aluminium Nanofluid used to Estimate the Overall Heat Transfer Rates

during Regenerative/Film Cooling of Thrust Chambers

Abstract: Large heat transfer rates are always desired for rocket propulsion applications as high heat

loads are associated at the nozzle exit. Different strategies have been employed in order to have high heat

transfer coefficients including use of liquid nitrogen, spray cooling etc. ISRO has planned to use aluminium

based nano-particles with kerosene in order to cool launching vehicles including GSLV Mk III as it is the

heaviest rocket that can carry large payloads. Recently, ISRO has announced to install its own International

Space Station (ISS) in future and in such applications larger payloads are to be carried by the rocket. In this

work, an analytical study on the thermodynamic properties of the aluminium nano-particles based kerosene

nanofluid has been done and an attempt has also been made to develop a temperature and pressure dependent

correlation that can be used in computational analysis of thrust chambers while film/regenerative cooling.

Keyword: Film cooling, kerosene, dodecane, thrust chambers, GSLV Mk III.

References:

165-169

1. J.-G. Kim, K.-J. Lee, S. Seo, Y.-M. Han, H.-J. Kim, and H.-S. Choi, “Film cooling effects on wall heat flux of a liquid propellant combustion chamber,” in 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2006, p. 5196.

2. R. Arnold, D. I. Suslov, and O. J. Haidn, “Film cooling in a high-pressure subscale combustion chamber,” J. Propuls. power, vol.

26, no. 3, pp. 428–438, 2010. 3. J. Song and B. Sun, “Thermal-structural analysis of regeneratively- cooled thrust chamber wall in reusable LOX / Methane rocket

engines,” Chinese J. Aeronaut., vol. 30, no. 3, pp. 1043–1053, 2017.

4. S. R. Shine and S. S. Nidhi, “Review on fi lm cooling of liquid rocket engines,” Propuls. Power Res., vol. 7, no. 1, pp. 1–18, 2018.

5. T.-S. Wang, “Transient three-dimensional startup side load analysis of a regeneratively cooled nozzle,” Shock Waves, vol. 19, no.

3, pp. 251–264, 2009. 6. H. Marchi, F. Laroca, A. F. C. da Silva, and J. N. Hinckel, “Numerical solutions of flows in rocket engines with regenerative

cooling,” Numer. Heat Transf. Part A Appl., vol. 45, no. 7, pp. 699–717, 2004.

7. W. Mirand, M. H. Naraghi, and I. Introduction, “Analysis of Film Cooling and Heat Transfer in Rocket Thrust Chamber and Nozzle,” no. January, pp. 1–14, 2011.

8. K. Agarwal, A. Vaidyanathan, and S. S. Kumar, “Investigation on convective heat transfer behaviour of kerosene-Al2O3

nanofluid,” Appl. Therm. Eng., vol. 84, pp. 64–73, 2015. 9. J. Song and B. Sun, “Damage localization effects of the regeneratively- cooled thrust chamber wall in LOX / methane rocket

engines,” Chinese J. Aeronaut., vol. 31, no. 8, pp. 1667–1678, 2018.

10. J. A. Eastman, S. R. Phillpot, S. U. S. Choi, and P. Keblinski, “Thermal transport in nanofluids,” Annu. Rev. Mater. Res., vol. 34, pp. 219–246, 2004.

11. W. Yu, D. M. France, J. L. Routbort, and S. U. S. Choi, “Review and comparison of nanofluid thermal conductivity and heat

transfer enhancements,” Heat Transf. Eng., vol. 29, no. 5, pp. 432–460, 2008.

12. L. Fedele, L. Colla, and S. Bobbo, “Viscosity and thermal conductivity measurements of water-based nanofluids containing

titanium oxide nanoparticles,” Int. J. Refrig., vol. 35, no. 5, pp. 1359–1366, 2012.

13. Y. Xuan and W. Roetzel, “Conceptions for heat transfer correlation of nanofluids,” Int. J. Heat Mass Transf., vol. 43, no. 19, pp. 3701–3707, 2000.

14. L. Spernarth and S. Magdassi, “Preparation of ethyl cellulose nanoparticles from nano-emulsion obtained by inversion at constant

temperature,” Micro Nano Lett., vol. 2, no. 4, pp. 90–95, 2007. 15. M. Shahrul, I. M. Mahbubul, S. S. Khaleduzzaman, R. Saidur, and M. F. M. Sabri, “A comparative review on the specific heat of

nanofluids for energy perspective,” Renew. Sustain. Energy Rev., vol. 38, pp. 88–98, 2014.

33.

Authors: Praveena Rachel Kamala S., Justus S.

Paper Title: A Knowledge Representation Model using Concept-Relation Graph

Abstract: Huge volume of relevant and irrelevant information is available from different sources for

gaining knowledge about a system. If the required data is in a structured form, then the fact can be easily

understood otherwise the process becomes laborious and results are vague after intense analysis. In this paper,

we are proposing a framework for fetching knowledge from unstructured source of data. The algorithms

proposed identifies the concepts and separates the concepts and relation words, enables to add new concepts and

also modify the old concept word with new concept and locate the concept. This Concept-Relation Model

enables the system to work according to the users’ connivance and deliveries accurate knowledge. This model

does not manipulate or interpret the information provided but only represent and share the desired knowledge.

Keyword: Knowledge Representation, Logic, Visualization.

References: 1. Frank van Harmelen, Vladimir Lifschitz and Bruce Porter,“Handbook of Knowledge Representation”. 1st Edition: 2007; Volume

1.

2. Madalina Croitoru et al,“Graphical norms via conceptual graphs”. Knowledge-Based Systems. 2012; 29: pp. 31–43.

3. John F Sowa, “Knowledge Representation: Logical, Philosophical and Computational Foundations”. New York, PWS Publishing Co., 2000.

4. S. Praveena Rachel Kamala and Dr. S. Justus,“Towards MORK: Model for Representing Knowledge”. I. J. Modern Education

and Computer Science. 2016; pp.45-53. 5. Chuntao Jiang et al,“Text classification using graph mining-based feature extraction”. Knowledge-Based Systems. 2010; 23: pp.

302–308.

6. Praveena Rachel Kamala S and Justus S, “Concept – Relation Constructs for Knowledge Representation”. International Journal of Control Theory and Applications. 2016; 9: pp. 463 - 473.

7. Tiago A. Almeida et al,“Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering”.

Knowledge-Based Systems. 2016; 108: pp. 25–32. 8. Julia Hoxha et al, “Automated learning of domain taxonomies from text using background knowledge”. Journal of Biomedical

Informatics. 2016; 63: pp. 295–306.

9. Miao Fana et al,“Distributed representation learning for knowledge graphs with entity descriptions”. Pattern Recognition Letters. 2016; 000: pp. 1–7.

10. Praveena Rachel Kamala S and Justus S, “Concept Relation Logic for Visualizing File Behavior in a Knowledge Representation

Model”. Inernational Journal of Engineering and Advanced Technology. 2019; 8: pp. 1732 -1740. 11. Misael Mongiovì et al,“Merging open knowledge extracted from text with MERGILO”. Knowledge-Based Systems. 2016; 108:

pp. 155–167.

12. Antonio Jimeno Yepes et al, “Knowledge based word-concept model estimation and refinement for biomedical text mining”. Journal of Biomedical Informatics. 2015; 53: pp. 300–307.

13. Alberto Tonon et al,“Contextualized ranking of entity types based on knowledge graphs”. Web Semantics: Science.Services and

Agents on the World Wide Web. 2016; 37–38: pp. 170–183.

170-175

34.

Authors: Abhinav Kumar, Anil Chamoli, Shahid Khan, Arfaj Ahamed Anwar, J. V. Muruga Lal Jeyan

Paper Title: Density of Kerosene Aluminium Nanofluid used for Regenerative Cooling Applications of Thrust

Chambers

Abstract: Efficient cooling of thrust chamber is always remaining the key area of research due to the

increase in the interest of space exploration programmes. Agencies like NASA, ISRO, SpeceX, BlueOrigin are

planning to start commercial flights into the space for common people. To achieve this accuracy and safety both

are required and efficient cooling of the thrust chambers is one of them as it has to handle large heat loads twice,

176-180

first at the time of take-off and secondly, when the vehicle has to land on the ground. ISRO has planned to use

kerosene with aluminium nanoparticles for the cooling purposes of the thrust chambers. To achieve higher

accuracies, the property variations as a function of temperature and pressure are required so that point to point

variation can be visualized comparatively easily. In the present work, density of kerosene and its nanofluid has

been studied and in order to use this property variation directly into the simulation software, curve fitting has

been done for density of the nanofluid as a function of temperature.

Keyword: Thrust chambers, kerosene, nanofluid, heat transfer, pressure drop, regenerative cooling. References:

1. J.-G. Kim, K.-J. Lee, S. Seo, Y.-M. Han, H.-J. Kim, and H.-S. Choi, “Film cooling effects on wall heat flux of a liquid propellant

combustion chamber,” in 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 2006, p. 5196. 2. R. Arnold, D. I. Suslov, and O. J. Haidn, “Film cooling in a high-pressure subscale combustion chamber,” J. Propuls. power, vol.

26, no. 3, pp. 428–438, 2010.

3. J. Song and B. Sun, “Thermal-structural analysis of regeneratively- cooled thrust chamber wall in reusable LOX / Methane rocket engines,” Chinese J. Aeronaut., vol. 30, no. 3, pp. 1043–1053, 2017.

4. S. R. Shine and S. S. Nidhi, “Review on fi lm cooling of liquid rocket engines,” Propuls. Power Res., vol. 7, no. 1, pp. 1–18,

2018. 5. T.-S. Wang, “Transient three-dimensional startup side load analysis of a regeneratively cooled nozzle,” Shock Waves, vol. 19, no.

3, pp. 251–264, 2009.

6. H. Marchi, F. Laroca, A. F. C. da Silva, and J. N. Hinckel, “Numerical solutions of flows in rocket engines with regenerative cooling,” Numer. Heat Transf. Part A Appl., vol. 45, no. 7, pp. 699–717, 2004.

7. W. Mirand, M. H. Naraghi, and I. Introduction, “Analysis of Film Cooling and Heat Transfer in Rocket Thrust Chamber and

Nozzle,” no. January, pp. 1–14, 2011. 8. M. Pizzarelli, F. Nasuti, and M. Onofri, “Trade-off analysis of high-aspect-ratio-cooling-channels for rocket engines,” Int. J. Heat

Fluid Flow, vol. 44, pp. 458–467, 2013.

9. K. Agarwal, A. Vaidyanathan, and S. S. Kumar, “Investigation on convective heat transfer behaviour of kerosene-Al2O3 nanofluid,” Appl. Therm. Eng., vol. 84, pp. 64–73, 2015.

10. P. Taylor and S. M. S. Murshed, “Determination of effective specific heat of nanofluids,” no. June 2012, pp. 37–41, 2011.

35.

Authors: Nikhil Chakravarthy Mallela, Arun krishna Chitturi, Swarnalatha Purushotham

Paper Title: Implementation of a Secured Authentication System using a Policy Generator with Email

Notifications

Abstract: For securing the login, passwords of users from intruders and hackers, the website owners and

administrators are providing certain guidelines to the users to create secure and strong passwords using a

mechanism called Password Checkers. These guidelines which are provided helps the users to create strong

passwords, these guidelines are also becoming the raw input for the hackers as they clearly show based on which

policy the password was generated which increases the risk for brute force attacking with more ease. There by

increasing the success rate probability for the brute force attackers. To overcome and to decrease the success

probability for brute force attacking the Dynamic Password Policy Generator is being devised.The profiles of

users are built and maintained by the system automatically bases on the interaction with the monitored database

in training phase. This DBSAFE system will help both the administrator as well as the users to feel secured in

terms with their data security. Also whenever, an unsuccessful attempts leaving a notification through an email

will always add a extra layer of security to the system. When the system’s critical files were all under watch and

someone try to access those, concerned people will be intimated to verify the system security keeping the system

and database safe and healthy.

Keyword: Randomized structure generation, User favored password, Password monitor, Passwords anomalies References:

1. Sivaji, N., & Yuvaraj, K. S. (2018). Improving Usability of Password Management with Storage Optimized Honeyword

Generation. 2. Yang, S., Ji, S., & Beyah, R. (2017). DPPG: A Dynamic Password Policy Generation System. IEEE Transactions on Information

Forensics and Security, 13(3), 545-558.

3. Zhang, S., Zeng, J., & Zhang, Z. (2017, October). Password guessing time based on guessing entropy and long-tailed password distribution in the large-scale password dataset. In 2017 11th IEEE International Conference on Anti-counterfeiting, Security, and

Identification (ASID) (pp. 6-10). IEEE.

4. Sallam, A., Bertino, E., Hussain, S. R., Landers, D., Lefler, R. M., & Steiner, D. (2015). DBSAFE—an anomaly detection system to protect databases from exfiltration attempts. IEEE Systems Journal, 11(2), 483-493.

5. Zheng, Wantong, and Chunfu Jia. "CombinedPWD: A New Password Authentication Mechanism Using Separators Between

Keystrokes." 2017 13th International Conference on Computational Intelligence and Security (CIS). IEEE, 2017. 6. Biswas, Subhradeep, and Sudipa Biswas. "Password security system with 2-way authentication." 2017 Third International

Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2017.

7. Wang, Ding, et al. "Targeted online password guessing: An underestimated threat." Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. ACM, 2016.

8. Wang, Ding, et al. "Understanding passwords of chinese users: characteristics, security and implications." CACR Report,

Presented at ChinaCrypt (2015). 9. Hussain, Syed Rafiul, Asmaa M. Sallam, and Elisa Bertino. "Detanom: Detecting anomalous database transactions by insiders."

Proceedings of the 5th ACM Conference on Data and Application Security and Privacy. ACM, 2015. 10. Balaji, R., and V. Roopak. "DPASS—Dynamic password authentication and security system using grid analysis." 2011 3rd

International Conference on Electronics Computer Technology. Vol. 2. IEEE, 2011

11. Freeburne, Alexander B. "System and method of enterprise administrative password generation and control." U.S. Patent No. 8,775,820. 8 Jul. 2014.

12. De Carnavalet, Xavier De Carné, and Mohammad Mannan. "From Very Weak to Very Strong: Analyzing Password-Strength

Meters." NDSS. Vol. 14. 2014. 13. Ganesan, Ravi. "Method and system for generating pronounceable security passwords." U.S. Patent No. 5,588,056. 24 Dec. 1996.

14. Yangqing, Zhu, et al. "Design of a new web database security model." 2009 Second International Symposium on Electronic

181-186

Commerce and Security. Vol. 1. IEEE, 2009. 15. Bodavula, Vikram. "Real time password generation apparatus and method." U.S. Patent No. 8,984,599. 17 Mar. 2015.

36.

Authors: S. Renuga Devi

Paper Title: Rainfall Prediction using Extreme Learning Machine for Coonoor Region

Abstract: Rainfall time-series forecasting is an important research area which has applications in several

fields like flood forecasting, drought prediction, water resource planning and management, precision agriculture

and disaster management, to name a few. This paper discusses about a machine learning method called the

Extreme Learning Machine (ELM) for predicting rainfall. The study area is Coonoor region, Tamil Nadu, India,

which is prone to rainfall induced landslides. Two data sets have been used in this study. Data set 1 comprises of

daily rainfall data of Coonoor, meteorological parameters like temperature, wind speed, relative humidity cloud

cover and month, for the period 2004-2013. Data set 2 consists rainfall data of 14 rain gauge stations and month.

A comparative study between the data sets is performed to show that only rainfall data is sufficient to accurately

predict the rainfall in the given region. This is substantiated by performing sensitivity analysis on both the data

sets. Sensitivity analysis also provides the most important predictor that contributes to accurate prediction of

rainfall.

Keyword: Extreme learning machine, rainfall prediction, sensitivity analysis, single hidden layer feed

forward network. References:

1. G. B. Huang , L. Chen, and C. K. Siew, “Universal approximation using incremental networks with random hidden computation nodes,” IEEE Trans. Neural Networks, vol. 17, 2006.

2. H. Aksoy, A. Guven, A. Aytek, M. Yuce and N. E. Unal, “Discussion of generalized regression neural networks for

evapotranspiration modelling,” Hydrol. Sci. J., vol. 52, pp. 825–828, 2007. 3. Koutsoyiannis, “Discussion of ‘‘generalized regression neural networks for evapotranspiration modelling,’’Hydrol. Sci. J., vol.

52, pp. 832–835, 2007. 4. S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, “Extreme learning machine: algorithm, theory and applications,” Artificial

Intelligence Review, pp. 1-13, 2013.

5. S. Yu, C. Miao, and X. Wang, “Application of extreme learning machine neural network to forecast of short-term precipitation,” Journal of Yunnan University, 2013.

6. R. Singh, S. Balasundaram, “Application of Extreme Learning Machine Method for Time Series Analysis,” Intl. J. of Intelligent

Technology, Vol.2, No.4, pp. 256-262, 2007.

7. S. Chattopadhyay, and G. Chattopadhyay, “Identification of the best hidden layer size for three layered neural net in predicting

monsoon rainfall in India,” J. of Hydroinformatics, 2008.

8. Z. Sun, T. S. Choi, K. Au, and Y. Yu, “Sales forecasting using extreme learning machine with applications in fashion retailing,” Decision Support System, 2008.

9. W. Zong, G. B. Huang, “Face recognition based on extreme learning machine,” J. of Neurocomputing, 2011.

187-192

37.

Authors: G. Suganeshwari, S.P Syed Ibrahim

Paper Title: A Movie Recommendation using Common Genre Relation on User-Item Subgroup

Abstract: Movie recommendation system has played a vital role in retrieving the movies that are of

interest to the user. Most of the traditional methods provide a unified recommendation without considering the

individual preference of the user. To address this challenge, various recommender methods are currently

employing side information like location, time, gender, and genre to provide a personalized recommendation. In

this paper, we propose —Common Genre Relations (COGS), which incorporates the information on genre

relationships between the movies. Meanwhile, the method reduces the search space for each user and helps to

mitigate the sparsity problem. To improve the scalability, the methods are executed on user-item subgroups.

Extensive experiments are conducted on a real-world dataset. The empirical analysis shows that the proposed

method based on the graph model excels the accuracy at top-k than the state-of-art collaborative filtering

methods.

Keyword: Collaborative filtering, data sparsity, genre relation, movie recommendation system, random walk.

References: 1. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and

possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, Jun. 2005.

2. Desrosiers and G. Karypis, “A comprehensive survey of neighborhood-based recommendation methods,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. New York: Springer-Verlag, 2011, ch. 4, pp. 107–

144.

3. Jiang, Shuhui, Xueming Qian, Tao Mei, and Yun Fu. “Personalized travel sequence recommendation on multi-source big social media,” IEEE Trans. Big Data 2, no. 1, pp. 43-56, 2016.

4. Jiajun Bu, Shulong Tan, Chun Chen, Can Wang, Hao Wu, Lijun Zhang, and Xiaofei He. “Music recommendation by unified

hypergraph: Combining social media information and music content,” In Proc. Int. Conf. on MM. ACM, New York, NY, 391–400, 2010.

5. Harper F Maxwell, and Joseph A Konstan. 2016. “The movie lens datasets: History and context,”. ACM Tran. interactive

intelligent sys. (tiis) 5, no. 4: 19. 6. J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” in Proc. 14th

Conf. Uncertainty Artif. Intell., pp. 43–52, 1998.

7. Sarwar, G. Karypis, and J. Konstan, “Recommender systems for largescale e-commerce: Scalable neighborhood formation using clustering,” in Proc. 5th Int. Conf. Comput. Inf. Technol., 2002, pp. 1–6.

8. X. Su and T. Khoshgoftaar, “Collaborative filtering for multi-class data using belief nets algorithms,” in Proc. IEEE 18th ICTAI,

pp. 497–504, Nov. 2006.

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10. Lemire and A. Maclachlan, “Slope one predictor for online rating based collaborative filtering,” in Proc. SDM, pp. 1–5, 2005.

11. Billsus and M. Pazzani, “Learning collaborative information filters,” in Proc. 15th Int. Conf. Mach. Learn., pp. 46–54, 1998. 12. Y. Koren, “Factorization meets the neighborhood: A multifaceted collaborative filtering model,” in Proc. SIGKDD, pp. 426–434,

2008.

13. David Liben-Nowell and Jon Kleinberg, “The link prediction problem for social networks,” in Proc. of the 12th Int. Conf. Inf. Knowledge Management (CIKM’03). ACM, New York, NY, 556–559, 2003.

14. Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose, “On social networks and collaborative recommendation,” in Proc.

32nd Int. ACMSIGIR Conf. Research and Dev. Inf. Retrieval (SIGIR’09). ACM, New York, NY, 195–202, 2009. 15. Kensuke Onuma, Hanghang Tong, and Christos Faloutsos. “TANGENT: A novel, surprise me, recommendation algorithm,” in

Proc. of the 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’09). ACM, New York, NY, 657–666,

2009. 16. Marco Gori and Augusto Pucci, “ItemRank: A random-walk based scoring algorithm for recommender engines,” in Proc. of the

20th Int. Joint Conf. on AI. Morgan Kaufmann, San Francisco, CA, 2766–2771, 2007.

17. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proc. 10th Int. Conf. WorldWideWeb, pp. 285–295, 2001.

18. Badrul M. Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Application of dimensionality reduction in recommender

system-a case study,” in Proc. ACM WebKDD Web Mining for E-Commerce Workshop. 19. Mnih, A and Salakhutdinov, R, “Probabilistic matrix factorization,” in Adv. in neural inf. Processing, sys, pp. 1257-1264, 2008.

20. Lee, D. D., and Seung, H. S, “Algorithms for non-negative matrix factorization,” in Adv. Neural inf. Processing Systems, pp.

556-562, 2000.

21. Chen, J, Fang, J., Liu, W., Tang, T., Chen, X., and Yang, C, “Efficient and portable ALS matrix factorization for recommender

systems,” in IEEE Int. Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 409-418, May 2017.

22. Songjie Gong, “An efficient collaborative recommendation algorithm based on item clustering,” in Adv. in Wireless Networks and Inf. Systems. Springer, 381–387, 2010.

23. Thomas George and Srujana Merugu, “A scalable collaborative filtering framework based on coclustering,” in 5th IEEE Int.

Conf. Data Mining. IEEE. 625–628, 2005. 24. Long, B, Zhang, Z. M and Yu, P. S, “Co-clustering by block value decomposition,” in Proc. ACM SIGKDD int. conf. Knowledge

discovery in data mining, pp. 635-640, August 2005.

25. Shafiei, M. M., and Milios, E. E, “Latent Dirichlet co-clustering,” in Sixth Int. Conf. on Data Mining (ICDM'06), pp. 542-551, 2006.

26. Yildirim, H, and Krishnamoorthy, M. S., “A random walk method for alleviating the sparsity problem in collaborative filtering,”

in Proc. 2008 ACM conf. On Recommender Systems, pp. 131-138, October 2008. 27. M. Jamali and M. Ester, “Trustwalker: a random walk model for combining trust-based and item-based recommendation,” in

Proc. of the 15th ACM SIGKDD Int. Conf. Knowledge discovery and data mining, pp. 397-406, June 2009.

28. M. Clements, Vries A. P, and Reinders M. J, “The task-dependent effect of tags and ratings on social media access,” ACM Trans. Inf. Systems (TOIS), 28(4), 21, 2010.

29. J. Bu, S. Tan, C. Chen, C. Wang, H. Wu, L. Zhang, and X. He, “Music recommendation by unified hypergraph: combining social

media information and music content,” in Proc. 18th ACM Int. Conf. Multimedia pp. 391-400, October 2010.

30. H. Wang, F. Nie, H. Huang, and F. Makedon, “Fast nonnegative matrix tri-factorization for large-scale data co-clustering,” in

Twenty-Second Int. Joint Conf. A.I. June 2011. 31. Bell R. M and Koren, Y, “Lessons from the Netflix prize challenge”, SiGKDD Explorations, 9(2), 75-79, 2007.

32. Xia, H. Liu, I. Lee, and L. Cao, “Scientific article recommendation: Exploiting common author relations and historical

preferences, “IEEE Trans. Big Data, 2(2), pp. 101-112, 2016.

38.

Authors: B. S. Kiruthika Devi, T. Subbulakshmi

Paper Title: Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in

Cloud Computing Environment

Abstract: Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to

target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects

the functionalities of the cloud services and affects its core capabilities of cloud such as availability and

reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of

immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack

flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of

Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol

attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies

security requirements and can identify the different DDoS security attacks. The proposed system provides

promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good

alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert

reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target

virtual instance and protects the cloud applications from DDoS attacks.

Keyword: DDoS, cloud computing, IDS, virtual instance, detection, defense. References:

1. E. Brown, “NIST issues cloud computing guidelines for managing security and privacy,” National Institute of Standards and

Technology Special Publication 800-144, 2012.

2. Top Threats Working Group, “The Notorious Nine Cloud Computing Top Threats in 2013, Cloud Security Alliance”, 2013.

[Online].

Available:https://downloads.cloudsecurityalliance.org/initiatives/top_threats/The_Notorious_Nine_Cloud_Computing_Top_Threats_in_2013.pdf

3. Darren Anstee, “The consequences of DDoS attacks are rising”, 2018.

[Online].Available:https://www.scmagazineuk.com/consequences-ddos-attacks-rising/article/1490354. 4. Ahmad Nassiri, “5 most famous DDoS attacks”, 2018. [Online].Available:https://www.a10networks.com/resources/articles/5-

most-famous-ddos-attacks.

5. CSA, “CSA-Guidance/Domain13-Security as a service .md at master”, 2017. [Online]. Available:https://github.com/cloudsecurityalliance.

6. S.Potteti and N.Parati, “An innovative intrusion detection system using SNORT for cloud environment”, International Journal of

200-205

Innovative Research in Computer and Communication Engineering, vol.3, no.6, June 2015. 7. A.Bakshi and B.Yogesh, “Securing cloud from DDoS attacks using intrusion detection system in virtual machine”, In Second

International Conference on Communication Software and Networks, pp. 260-264, 2010.

8. C.Mazzariello, R. Bifulco and R.Canonoco, “Integrating a network IDS into an Open source Cloud computing”, In Sixth International conference on Information Assurance and Security (IAS), pp. 265-270, 2010.

9. S. V. Sandar and S. Shenai, “Economic Denial of Sustainability (EDoS) in Cloud Services using HTTP and XML based DDoS

Attacks”, International Journal of Computer Applications, vol.41, no. 20, pp. 11-16, 2012. 10. K.Patel and R.Srivastava, “Classification of Cloud Data using Bayesian Classification”, International Journal of Science and

Research, vol. 2, no. 6, June 2013.

11. D.Singh, D.Patel, B.Borisaniya and C.Modi, “Collaborative IDS Framework for Cloud”, International Journal of Network Security, vol.18, no.4, pp.699-709, July 2016.

12. J.Choi, C.Choi, B.Ko, D. Choi, and P. Kim, “Detecting Web based DDoS Attack using MapReduce operations in Cloud

Computing Environment”, Journal of Internet Services and Information Security, vol.3, np. 3/4, pp. 28-37. 13. D.Dittrich, “The DoS projects trinoo distributed denial of service attack

tool”,1999.[Online].Available:https://staff.washington.edu/dittrich/misc/trinoo.analysis.txt

14. D.Dittrich, “The tribe flood network distributed denial of service attack tool”,1999.[Online].Available:https://staff.washington.edu/dittrich/misc/tfn.analysis.txt

15. J.Barlow and W.Thrower, “TFN2K- an analysis, axent security team”,

2000.[Online].Available:https://packetstormsecurity.com/distributed/TFN2k_Analysis-1.3.txt 16. D.Dittrich, “The stacheldraht distributed denial of service attack tool”, 2016. 2018. [Online]. Available:

https://staff.washington.edu/dittrich/misc/stacheldraht.analysis.txt

17. D.Dittrich, G.Weaver, S.Dietrich, and N.Long, “The Mstream distributed denial of service attack tool”, 2000. [Online].

Available: https://staff.washington.edu/dittrich/misc/ mstream.analysis.txt

18. Bysin. “Knight.c sourcecode”, 2001. [Online]. Available: http://packetstormsecurity.nl/distributed/knight.c

19. Rsnake, “Slowloris HTTP DoS”, 2016. [Online]. Available https://download.pureftpd.org/misc/slowloris.pl 20. Hping3 network tool, 2019. [Online]. Available: linux.die.net/man/8/hping3

21. SNORT network intrusion detection system, 2019 [Online]. Available: www.snort.org

22. IPtables, 2019. [Online]. Available: linux.die.net/man/8/iptables

39.

Authors: Subhajit Dhar, Vivek Maik, Mayank Srivastava

Paper Title: Regularized Deblurring using Directional Prior with Sparse Representation

Abstract: Blind deconvolution defined as simultaneous estimation and removal of blur is an ill-posed

problem that can be solved with well-posed priors. In this paper we focus on directional edge prior based on

orientation of gradients. Then the deconvolution problem is modeled as L2-regularized optimization problem

which seeks a solution through constraint optimization. The constrained optimization problem is done in

frequency domain with an Augmented Lagrangian Method (ALM). The proposed algorithm is tested on various

synthetic as well as real data taken from various sources and the performance comparison is carried out with

other state of the art existing methods.

Keyword: Deblurring, Restoration, Sparse .prior, gradient angle prior. References:

1. Filip Sroubek and PeymanMilanfar., “Robust Multichannel Blind Deconvolution via Fast Alternating Minimization”. IEEE tran. Image Processing, vol. 21, no. 4, April 2012.

2. Zhang Hongyinget. al., “Variational Image Deblurring Using Modified Hopfield Neural Network”. IEEE 2006.

3. F. Guichard, L. Moisan, and J. M. Morel, "A Review of PDE Models in Image Processing and Image Analysis," Journal de Physique IV,France. Vol. 12, pp. 1-18, 2002.

4. Jain Feng Caiet.al., “Blind motion deblurring from a single image using sparse approximation”. IEEE paper, 2009.

5. J. Cai, S. Osher, and Z. Shen. “Linearized bregman iterations for frame-based image deblurring”. UCLA CAM Reports (08-52), 2008.

6. N. Joshi, R. Szeliski, and D. Kriegman. PSF estimation usingsharp edge prediction. In CVPR, 2008.

7. Zhaosong Lu, Yong Zhang., “An augmented Lagrangian approach for sparseprincipal component analysis”. Mathematical

optimization society 2011.

8. GuoW.Wei., “Generalized Perona-Malik Equation for Image Restoration”. IEEE signal processing letters, vol.6, no.7,july 1999. 9. Carlos Bazan and Peter Blomgen., “ Image Smoothing and Edge Detection by Nonlinear Diifusion and Bilateral Filter”. Digital

processing conference, 2009.

10. SarmilaPadhy and Ratnakar Dash., ”Improved Spatilly Adaptive Denoising Algorithm to Suppress Gaussion Noise in an Image”. International Journal of Computer Applications, vol. 67, no. 17, April 2013.

11. Neel Joshi, Richard Szeliski, and David J.Krieman., “PSF estimation using Sharp Edge Prediction”, 2009.

12. R. Fegus et al. Removing camera shake from a single photograph. ACM Transactions on Graphics, 27(3):787–794, August 2006. 13. Y. Tai, H. Du, M. S. Brown, and S. Lin. “Image/video deblurringusing a hybrid camera”. In CVPR, 2008.

14. Gupta, N. Joshi, C.L. Zitnick, M.F. Cohen. “Single Image Deblurring using Motion Desity functions. IN ECCV, pages 171-184,

2010. 15. TomerPeleg and Michael Elad. “A Statistical Prediction Model based on Sparse Representation for Single Image Super-

Restoration”. IEEE trans. On image processing, vol. 23, no. 6, June 2014. 16. T. Peleg, Y. C. Eldar, and M. Elad, “Exploiting statistical dependencies in sparse representations for signal recovery,” IEEE

Trans. Signal Process., vol. 60, no. 5, pp. 2286–2303, May 2012.

17. J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution as sparse representation of raw image patches,” in Proc. IEEE

Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1–8.

18. Naoto Katsumata, Yasuo Matsuyama.” Similar –Image Retrival Systems Using ICA and PCA Bases”. IJC on neural Netwoks,

Montreal, Canada, August 2005.

19. Haichao Zhang, David Wipf and Yanning Zhang. “Multi Image Blind Deblurring Using a Coupled Adaptive Sparse Prior”. IEEE on Computer Vision & Pattern Recognition. 2013.

20. D. P.Wipf, B. D. Rao, and S. S. Nagarajan. Latent variable Bayesian models for promoting sparsity. IEEE Trans. on Information

Theory, 57(9):6236–6255, 2011. 21. G. Giannakis and R. Heath, “Blind identification of multichannel FIR blurs and perfect image restoration,” IEEE Trans. Image

Process., vol. 9, no. 11, pp. 1877–1896, Nov. 2000.

22. H.-T. Pai and A. Bovik, “On eigenstructure-based direct multichannel blind image restoration,” IEEE Trans. Image Process., vol. 10, no. 10, pp. 1434–1446, Oct. 2001. .

23. M. Haindl and S. Šimberová, “Model-based restoration of short-exposure solar images,” in Frontiers in Artificial Intelligence and

206-210

Applications L. Jain and R. Howlett, Eds. Amsterdam, The Netherlands: IOS Press, 2002, pp. 697–706. 24. V. Katkovnik, D. Paliy, K. Egiazarian, and J. Astola, “Frequency do-main blind deconvolution in multiframe imaging using

anisotropic spa-tially-adaptive denoising,” in Proc. 14th EUSIPCO, Sep. 2006, pp. 1–5.

25. G. Panci, P. Campisi, S. Colonnese, and G. Scarano, “Multichannel blind image deconvolution using the bussgang algorithm: Spatial and multiresolution approaches,” IEEE Trans. Image Process., vol. 12, no.11, pp. 1324–1337, Nov. 2003.

40.

Authors: Ashish Agrawal, Abhinav Kumar

Paper Title: Numerical Model to Calculate Magnetization AC Losses for Superconducting Strip used for Current

Transport Applications in Electric Aircrafts

Abstract: High Temperature Superconducting (HTS) tapes are being proposed in the current transportation

applications in electric aircrafts due to their capacities to carry large currents with low losses and higher

efficiencies. Many systems are involved in the aircraft power distribution units and each component has its own

magnetic field which may affect the working of surrounding systems. It has been found from many studies that

perpendicular field has significant effect on the critical current of the HTS tape. In the present study, effort has

been made to develop a numerical code through which magnetization AC losses due to external magnetic field

are evaluated for YBCO superconductor. Calculated values are compared with Halse-Brandt model and it has

been found that with the increase in the index value ‘n’, the results are approaching the Halse-Brandt model.

Keyword: AC Losses, Superconducting Strip, Current Transport, Numerical Modelling, Roebel Cables. References:

1. S. C. Clarke, “Aircraft Electric Propulsion Systems: Applied Research at NASA,” pp. 1–37, 2015. 2. T. J. Haugan, “Development of Superconducting and Cryogenic Power Systems and Impact for Aircraft Propulsion,” Air Force

Res. Lab., no. April, 2015.

3. S. S. Fetisov et al., “Development and Characterization of a 2G HTS Roebel Cable for Aircraft Power Systems,” IEEE Trans. Appl. Supercond., vol. 26, no. 3, pp. 1–4, 2016.

4. Kario, M. Vojenciak, A. Kling, B. Ringsdorf, A. Jung, and W. Goldacker, “Superconducting properties of Roebel coated conductor cable from Superpower and SuperOx tapes with different transposition length,” in International Workshop on Coated

Conductors Applications-CCA2014, Jeju, Korea, 2014, vol. 30.

5. S. Lee et al., “Development and production of second generation high Tc superconducting tapes at SuperOx and first tests of model cables,” Supercond. Sci. Technol., vol. 27, no. 4, p. 44022, 2014.

6. M. Solovyov, J. Šouc, and F. Gömöry, “AC loss properties of single-layer CORC cables,” in Journal of Physics: Conference

Series, 2014, vol. 507, no. 2, p. 22034. 7. S. Elschner et al., “New experimental method for investigating AC losses in concentric HTS power cables,” IEEE Trans. Appl.

Supercond., vol. 25, no. 3, pp. 1–5, 2014.

8. T. Yamaguchi, Y. Shingai, M. Konishi, M. Ohya, Y. Ashibe, and H. Yumura, “Large current and low AC loss high temperature superconducting power cable using REBCO wires,” SEI Tech. Rev., vol. 78, p. 79, 2014.

9. Davies, P. Norman, C. Jones, S. Galloway, and M. Husband, “A review of Turboelectric Distributed Propulsion technologies for

N+3 aircraft electrical systems,” in 2013 48th International Universities’ Power Engineering Conference (UPEC), 2013, pp. 1–5. 10. H. Alafnan et al., “Application of SMES-FCL in Electric Aircraft for Stability Improvement,” IEEE Trans. Appl. Supercond.,

vol. 29, no. 5, pp. 1–6, 2019.

11. Y. Sogabe, M. Nii, T. Tsukamoto, T. Nakamura, and N. Amemiya, “Electromagnetic Field Analyses of REBCO Roebel Cables Wound into Coil Configurations,” IEEE Trans. Appl. Supercond., vol. 24, no. 3, pp. 1–5, 2014.

12. T. Tsukamoto, T. Mifune, Y. Sogabe, Z. Jiang, T. Nakamura, and N. Amemiya, “Influence of Geometrical Configurations of

HTS Roebel Cables on Their AC Losses,” IEEE Trans. Appl. Supercond., vol. 25, no. 3, pp. 1–5, 2015. 13. S. Lakshmi et al., “Magnetic and Transport AC Losses in HTS Roebel Cable,” IEEE Trans. Appl. Supercond., vol. 21, no. 3, pp.

3311–3315, 2011.

14. T. Komeda et al., “Experimental Comparison of AC Loss in REBCO Roebel Cables Consisting of Six Strands and Ten Strands,” IEEE Trans. Appl. Supercond., vol. 24, no. 3, pp. 1–5, 2014.

15. Z. Jiang et al., “Transport AC Loss Characteristics of a Five Strand YBCO Roebel Cable With Magnetic Substrate,” IEEE Trans.

Appl. Supercond., vol. 21, no. 3, pp. 3289–3292, 2011. 16. M. R. Halse, “AC face field losses in a type II superconductor,” J. Phys. D. Appl. Phys., vol. 3, no. 5, p. 717, 1970.

17. E. H. Brandt and M. Indenbom, “Type-II-superconductor strip with current in a perpendicular magnetic field,” Phys. Rev. B, vol.

48, no. 17, p. 12893, 1993. 18. J. Otten, Characterisation of REBCO Roebel cables, vol. 24. KIT Scientific Publishing, 2019.

211-215

41.

Authors: Pattabiraman Venkatasubbu, Mukkesh Ganesh

Paper Title: Used Cars Price Prediction using Supervised Learning Techniquesa

Abstract: The production of cars has been steadily increasing in the past decade, with over 70 million

passenger cars being produced in the year 2016. This has given rise to the used car market, which on its own has

become a booming industry. The recent advent of online portals has facilitated the need for both the customer

and the seller to be better informed about the trends and patterns that determine the value of a used car in the

market. Using Machine Learning Algorithms such as Lasso Regression, Multiple Regression and Regression

trees, we will try to develop a statistical model which will be able to predict the price of a used car, based on

previous consumer data and a given set of features. We will also be comparing the prediction accuracy of these

models to determine the optimal one.

Keyword: ANOVA, Lasso Regression, Regression Tree, Tukey’s Test

References: 1. Shonda Kuiper (2008) Introduction to Multiple Regression: How Much Is Your Car Worth?, Journal of Statistics Education, 16:3,

DOI: 10.1080/10691898.2008.11889579 2. Geurts P. (2009) Bias vs Variance Decomposition for Regression and Classification. In: Maimon O., Rokach L. (eds) Data

Mining and Knowledge Discovery Handbook. Springer, Boston, MA

3. Robert T. (1996) Regression Shrinkage and Selection Via the Lasso. In: Journal of the Royal Statistical Society: Series B (Methodological) Volume 58, Issue 1

216-223

4. Hastie, Trevor, and Daryl Pregibon. Shrinking trees. AT & T Bell Laboratories, 1990. 5. Kim, Tae Kyun. "Understanding one-way ANOVA using conceptual figures." Korean journal of anesthesiology 70.1 (2017): 22.

6. Haynes W. (2013) Tukey’s Test. In: Dubitzky W., Wolkenhauer O., Cho KH., Yokota H. (eds) Encyclopedia of Systems Biology.

Springer, New York, NY 7. Jaccard, James, Michael A. Becker, and Gregory Wood. "Pairwise multiple comparison procedures: A review." Psychological

Bulletin 96.3 (1984): 589.

8. Dupac, Václac, ed. Sampling from a finite population. Marcel Dekker, Incorporated, 1981.

42.

Authors: S. Abijah Roseline, S. Geetha

Paper Title: An Efficient Malware Detection System using Hybrid Feature Selection Methods

Abstract: Malware is a serious threat to individuals and users. The security researchers present various

solutions, striving to achieve efficient malware detection. Malware attackers devise detection avoidance

techniques to escape from detection systems. The key challenge is that growth of malware increases every hour,

leading to large damages to users’ privacy. The training process takes much longer time, mining the unnecessary

features. Feature Selection is effective in achieving unique feature set in detecting malware. In this paper, we

propose a malware detection system using hybrid feature selection approach to detect malware efficiently with a

reduced feature set. Machine learning based classification is performed on eight classifiers with two malware

datasets. The experiments were done without and with feature selection. The empirical results show that the

classification using selected feature set and XGB classifier identifies malware efficiently with an accuracy of

98.9% and 99.26% for the two datasets. Keyword: Malware, Malware Features, Malware Detection, Malware Feature Selection, PE Files. References:

1. Moskovitch, R., Stopel, D., Feher, C., Nissim, N., Japkowicz, N., and Elovici, Y, “Unknown Malcode Detection and the

Imbalance Problem,” Journal in Computer Virology, Springer. 5: 295-308, 2009. 2. O'Kane, P., Sezer, S., McLaughlin, K. and Im, E.G., “SVM training phase reduction using dataset feature filtering for malware

detection,” IEEE transactions on information forensics and security, 8(3), pp.500-509, 2013.

3. Chih-Ta Lin, Nai-Jian Wang, Han Xiao and Claudia Eckert, “Feature Selection and Extraction for Malware Classification,” National Taiwan University of Science and Technology, Taipei, Taiwan, 2015.

4. Nataraj, L., Karthikeyan, S., Jacob, G. and Manjunath, B, “Malware Images: Visualization and Automatic Classification,”

Proceedings of the 8th International Symposium on Visualization for Cyber Security, Article No. 4, 2011. 5. Kumar, A., Kuppusamy, K.S. and Aghila, G., “A learning model to detect maliciousness of portable executable using integrated

feature set,” Journal of King Saud University-Computer and Information Sciences, 2017.

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2009.

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based unknown malware detection", Information Sciences, 231, pp. 64-82, 2013.

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sequence alignment and visualization,’’ in Cluster Computing. New York, NY, USA: Springer-Verlag, 2017, pp. 1–9. [Online]. Available: http://dx.doi.org/10.1007/s10586-017-1110-2

19. M. Imran, M. T. Afzal, and M. A. Qadir, ‘‘Similarity-based malware classification using hidden Markov model,’’ in Proc. 4th

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43.

Authors: S. Viswanatha Rao, Sakuntala S. Pillai, Shiny G.

Paper Title: Sustaining the Life of Wireless Sensor Node with Energy Harvesting

Abstract: Wireless Sensor Networks (WSN) play a significant role in a number of sensing and monitoring

applications. It has also become a key enabling technology for Internet of Things (IoT). Most of the earlier

works were focused on extending the battery life by reducing the energy consumption of the wireless node. 229-234

Availability of small footprint devices capable of ambient energy harvesting has triggered renewed interest in

enhancing the performance of WSNs. In this paper we propose a novel method that can be applied to duty cycle

based Media Access Control (MAC) protocols in energy harvesting WSNs to sustain the life of the node. Our

approach takes into consideration the solar energy available at any instant of time so as to maximize the

performance. We have conducted detailed measurements and analysis of solar energy harvested under different

conditions. Our results show significant enhancement in throughput achieved while sustaining the life of the

node.

Keyword: duty cycle, media access control, solar energy harvesting, wireless sensor networks.

References: 1. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “A survey on sensor networks,” IEEE Communications Magazine, Vol.

40, No. 8, pp. 102-114, August 2002. 2. David Culler, Deborah Estrin, Mani Srivastava, “Overview of Sensor Networks,” Computer, August 2004.

3. Demirkol, C. Ersoy, and F. Alagoz, “Energy efficient medium access control protocols for wireless sensor networks and its state-

of-art,” IEEE International Symposium on Industrial Electronics, Vol. 1, pp. 669-674, May 2004. 4. Octarina Nur Samijayani, Hamzah Firdaus, Anwar Mujadin, “Solar Energy Harvesting for Wireless Sensor Networks Node,”

IEEE International Symposium on Electronics and Smart Devices (ISESD), 17-19 Oct. 2017, Yogyakarta, Indonesia.

5. Greg Jackson; Simona Ciocoiu; Julie A. McCann, “Solar Energy Harvesting Optimization for Wireless Sensor Networks,” 2017 IEEE Global Communications Conference, 4-8 Dec. 2017 Singapore.

6. Amzar Omairi, Zool H. Ismail, Kumeresan A. Danapalasingam, Mohd Ibrahim, “Power Harvesting in Wireless Sensor Networks

and its adaptation with Maximum Power Point Tracking: Current Technology and Future Directions,” IEEE Internet of Things Journal Year: 2017, Volume: 4, Issue: 6.

7. Ou Yang, Wendi B Heinzelmzn, “Modeling and performance analysis for duty-cycled MAC protocols with applications to

SMAC and XMAC,” IEEE Transactions in Mobile Computing, vol. 11, No. 6, June 2012. 8. Navid Tadayon, Sasan Khoshroo, Elaheh Askari, Honggang Wang, Howard Michel, “Power Management in SMAC-based

Energy Harvesting Wireless Sensor Networks using Queuing Analysis,” Journal of Network and Computer Applications 36(3),

1008-1017, 2013. 9. Wei Ye, John Heidemann, Deborah Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, Proceedings of

the 21st International Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM 2002), New

York, NY, USA, June, 2002.

44.

Authors: Jothi Prabha A, Bhargavi R, Harish B

Paper Title: Predictive Model for Dyslexia from Eye Fixation Events

Abstract: Dyslexia is a specific learning disorder where the individual often find difficulty in spelling and

reading words fluently. Dyslexia is non-curable but with right remedial support, dyslexics can become highly

successful in academics and life. Eye movement patterns during reading process can provide an in-depth

understanding about reading disorders caused by dyslexia. Eye movements can be captured using eye-tracker,

from which the relationship between how eyes move with respect to the words they read can be understood. In

this work, a set of binocular fixation and saccade features were extracted from raw eye tracking data based on

statistical measures. Machine learning algorithms such as Random Forest Classifier (RF), Support Vector

Machine (SVM) for classification and K-Nearest Neighbor (KNN) were analyzed to output classification models

for prediction of dyslexia. KNN gave higher levels of accuracy of 95% compared to SVM and RF over a small

feature set of features related to fixations and saccades. These eye features can be used as a basis for developing

screening means for prediction of dyslexia. Prediction of dyslexia at an early stage can help children to go for

remediation which helps them for academic excellence.

Keyword: Dyslexia, Eye movements, KNN, RF, SVM References:

1. Christo, J. M. Davis, and E.B. Stephen, Identifying, assessing, and treating dyslexia at school. Springer Science & Business Media, 2009.

2. P. Ott, Teaching children with dyslexia: A practical guide. Routledge, 2007.

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Ghesquière, "Intact but less accessible phonetic representations in adults with dyslexia." Science , vol. 342, pp. 1251-1254, 2013 5. S.E Shaywitz, Overcoming dyslexia: A new and complete science-based program for reading problems at any level. Knopf, 2003.

6. M. Iwabuchi, R. Hirabayashi, K. Nakamura & N. K. Dim, Machine Learning Based Evaluation of Reading and Writing

Difficulties. Studies in health technology and informatics, vol. 242, pp. 1001-1004, 2017 7. Billard and F. D. Pinton, “Clinique de la dyslexie. Archives de pédiatrie”, vol. 17, pp.1734-1743, 2010.

8. Prado, M. Dubois and S. Valdois, “The eye movements of dyslexic children during reading and visual search: impact of the visual

attention span. Vision research”, vol. 47, pp. 2521-2530, 2007. 9. J. Pan, M. Yan, J. Laubrock, H. Shu, & R. Kliegl, “Saccade-target selection of dyslexic children when reading Chinese”, Vision

research, vol. 97, pp. 24-30, 2014.

10. Fischer, M. Biscaldi and P. Otto, “Saccadic eye movements of dyslexic adult subjects”. Neuropsychologia, vol. 31, pp. 887-906,

1993

11. L. Rello and M. Ballesteros, “Detecting readers with dyslexia using machine learning with eye tracking measures” in Proceedings

of the 12th Web for All Conference, ACM, pp. 16., May 2015. 12. J. Lustig, J. “Identifying dyslectic gaze pattern: Comparison of methods for identifying dyslectic readers based on eye movement

patterns”, 2016.

13. J. Bingel, M. Barrett & S. Klerke, “Predicting misreadings from gaze in children with reading difficulties” in Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications , pp. 24-34, 2018.

14. S. Kim & R. Wiseheart, “Exploring Text and Icon Graph Interpretation in Students with Dyslexia: An Eye‐tracking

Study”. Dyslexia, vol. 23, pp. 24-41, 2017.

15. K. Lukasova, M. P. Nucci, R. M. N de Azevedo, G. Vieira, J.R. Sato & E. Amaro, “Predictive saccades in children and adults: A combined fMRI and eye tracking study”. PloS one, vol . 13, 2018.

235-240

16. H. Jarodzka and S. Brand‐Gruwel, “Tracking the reading eye: towards a model of real‐world reading.”, Journal of Computer

Assisted Learning, vol. 33, pp. 193-201, 2017.

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cognition and cognitive development?” Developmental cognitive neuroscience, vol. 25, pp. 69-91, 2017.

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cognitive impairment” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , pp. 1016-1026, 2017.

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45.

Authors: Joxy John, Swapna Davies

Paper Title: Image Dehazing Based on Physical Model and Gray Projection

Abstract: The climatic scattering and ingestion offer climb to the ordinary marvel of obscurity, which truly

impacts the detectable quality of view. Dehazing is the technique used to expel the dimness. In late year, various

works have been done to improve the detectable quality of picture taken under horrible climate. The images that

are taken under overcast conditions experience the evil impacts of shading contortion and attenuation. The

proposed strategy is in light of the Dark Channel Prior speculation and gray projection. The transmission map is

resolved using the determined estimation of atmospheric light. It uses box filter to lessen the complexity and to

improve the computing speed. This computation can restore image with incredible quality and the speed of

image computation is high. The proposed strategy is differentiated with other image enhancement strategies and

image restoration techniques. It is likewise exceptionally proficient technique since it can process huge images

within less time.

241-245

Keyword: Dark Projection, Image dehazing, Physical Model, Transmission estimation References:

1. Wencheng Wang, Tao Ji, Faliang Chang, “A Fast Single-Dehazing Method Based on Physical Model and Gray Projection”, IEEE

Access, vol. 6, no. 3, Feb.2018.

2. K. He, J. Sun, X. Tang “Single image haze removal using dark channel prior”, IEEE Trans. Pattern Anal. Mach. Intell, vol. 33, no.

12, Dec.2011.

3. R. Fattal, “Single image dehazing”, ACM Trans. Graph, vol. 27, no. 3.p.72, Aug.2008.

4. H. Luetal, “Depth map reconstruction for underwater Kinect camera using inpainting and local image mode filtering”, IEEE Access vol. 5, pp. 71157122, Apr. 2017.

5. C. J. Q. Zhu, J. Mai, L. Shao “A fast single image haze removal algorithm using color attenuation prior”, IEEE Trans. Image

Process vol. 24, no. 11, Nov.2015. 6. J. Jiang, T. Hou, M. Qi “Improved algorithm on image haze removal using dark channel prior”, J. Circuits Syst vol. 16, no. 2,

pp.12, 2011.

7. T. J. Cooper, F. A. Baqai “Analysis and extensions of the Frankle- McCann Retinex algorithm”, J. Electron. Image., vol. 13, no. 1, pp. 8592, Jan.2004.

8. M.-J. Seow, V. K. Asari, “Ratio rule and homomorphic filter for enhancement of digital color image”, Neurocomputing, vol. 69,

nos. 79, pp.954958, Mar. 2006. 9. J. P. Oakley, B. L. Satherley “Image quality in poor visibility conditions using a physical model for contrast degradation”, IEEE

Trans. Image Process, vol. 7, no. 2, pp. 167179, Feb. 1998.

10. S. G. Narasimhan , S. K. Nayar, “Interactive (de) weathering of an image using physical models”, IEEEWorkshop Color Photometric Methods Comput. Vis., Paris, France, Oct. 2003, pp. 18.

11. S. K. Nayar, S. G. Narasimhan, “Vision in bad weather”, IEEE Int. Conf. Comput. Vis., Kerkyra, Greece, Sep. 1999, pp. 820827.

12. Y. Y. Schechner, S. G. Narasimhan, S. K. Nayar, “Instant dehazing of images using polarization”, IEEE Conf. Comput. Vis. Pattern Recognit., Kauai, HI, USA, Dec. 2001, pp. 325332.

13. G. Meng, Y. Wang, J. Duan, S. Xiang, C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization”,

IEEE Int. Conf. Comput. Vis., Sydney, NSW, Australia, Dec. 2013,pp. 617624. 14. S.-C. Huang, B.-H. Chen, W.-J.Wang, ‘Restoration of single hazy images captured in real-world weather conditions”, IEEE

Trans.Circuits Syst. Video Technol, vol. 24, no. 10, pp. 18141824, Oct. 2014.

15. H. Yuan, C. Liu, Z. Guo, and Z. Sun, “A region-wised medium transmission based image dehazing method”, IEEE Access, vol. 5, pp. 17351742, Jan. 2017.

46.

Authors: Farhan Rahman, Masooma Aliraza Suleman

Paper Title: Soft Computing Methods for Fault Detection in Power Transmission Lines

Abstract: Communication is a major aspect of our day to day life and for maintaining the transmission of

the data; electric power transmission lines play a major role in acting as the medium for this transmission. The

transmission lines can further be differentiated as an overhead transmission line and underground transmission

line. But the transmission is often hindered by the physical factors or generally known as faults. In the past few

years, the implementation of the underground cable has seen an upsurge as these cables are not easily affected

by the physical factors as the overhead cables are, as a result, there have been various methods adopted by the

engineers for the analysis, detection and control of these faulty lines. Depending on the supply range of a

particular nation different materials are used for the transmission lines. Different fault detection methods are

used for the exact location of the fault and implementing that in a digitized way is the optimum solution.

Whenever there is a fault the entire transmission line is affected, to ensure that the safety of the transmission line

a governing system has been implied in our proposed work. Locating a fault requires various detection methods,

one such method is the time domain reflectometry (TDR) which we have inculcated in our analysis of fault lines.

This technique incorporates the transmission of a pulse down the cable, any change in the characteristics

impedance will cause a part of the incident pulse to reflect back, this knowledge is helpful for locating

discontinuities in a system.

Keyword: Fault detection, Short circuit fault, Time Domain Reflectometry, Cascaded System. References:

1. Clegg, Underground Cable Fault Location. New York: McGraw Hill, 1993. 2. Saha, M.M., Rosolowski, E. and Izykowski, J., “A fault location algorithm for series compensated transmission lines incorporated

in current differential protective relays” The International Conference on Advanced Power System Automation and Protection,

pp. 706-711, 2011. 3. Apostolov AP, George W. Protecting NYSEG’s six-phase transmission line. IEEE Comput Appl Power.1992;5(4):33–

36.doi:10.1109/67.160044.

4. Sharma, R., Ahmad, A. and Shailendra, K. S., “Protection of Transmission Lines using Discrete Wavelet Transform,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 3, Issue-1, June, 2013.

5. Koley E, Yadav A, Thoke AS. A new single-ended artificial neural network-based protection scheme for shunt faults in six-phase

transmission line. Int Trans Electr Energy Syst. 2014. 6. Tze Mei Kuan, Azrul Mohd. Ariffin, Maria Madelina Bemmynser Sedau. Advancement of TDR Technique for Locating Power

Cable Insulation Degradation Vol.7 (2017) No. 6 ISSN: 2088-5334

7. Katsumi Uchida, Yoichi Kato, Masahiko Nakade, Daisuke Inoue, Hiroyuki Sakakibara, and Hideo Tanaka, “Estimating the Remaining Life of Water-Treed XLPE Cable by VLF Voltage Withstand Tests,” Asia Pasific IEEE PES Transmission and

Distribution Conference and Exhibition, Vol. 3, October 2002, pp. 1879-1884.

8. T. M. Kuan, S. Sulaiman, A. M. Ariffin and W. M. S. W. Shamsuddin, “MATLAB/Simulink Power Cable Modelling for Cable Defects Assessment,” Journal of Fundamental and Applied Sciences, Vol. 10 (5S), 22 March 2018,

pp. 571-588. 9. Moon Kang Jung, Yong June Shin and Jin Bae Park, “Application of Time-Frequency Domain Reflectometry based on Multi-

band Signal for Detection and Localization of Fault.

10. Qinghai Shi, Uwe Troeltzsch and Olfa Kanoun, “Detection and Localization of Cable Faults by Time and Frequency Domain

Measurements,” 2010 7th International Multi-Conference on Systems, Signals and Devices, Amman, pp. 1-6, 27-30 June 2010.

11. Dr R.K.Jena , Power System Protection Lecture notes.

12. David L. McKinnon, “Insulation Resistance Profile (IRP) and Its Use for Assessing Insulation Systems,” IEEE International

Symposium on Electrical Insulation (ISEI), San Diego, June 2010, pp. 1-4.

246-250

13. Yan Li, Paul Wagenaars, Peter A.A.F. Wouters, Peter C.J.M. van der Wielen and E. Fred Steennis. Power Cable Joint Model: Based on Lumped Components and Cascaded Transmission line approach International Journal on Electrical Engineering and

Informatics – Volume 4, Number 4, December 2012 14. Y. Tian, P. Lewin, and A. Davies, “Comparison of on-line partial discharge detection methods for hv cable joints,” IEEE

Transactions on Dielectrics and Electrical Insulation, vol. 9, no. 4, pp. 604–615, Aug 2002

47.

Authors: Reena Tandon

Paper Title: Bianchi Type V Universe and Bulk Viscous Models with Time Dependent Gravitational Constant

and Cosmological Constant in General Relativity

Abstract: This paper deals with Bulk Viscous Models and Bianchi type-𝑽universe in the standard general

relativity theory by assuming 𝝃(𝒕) = 𝝃𝟎 𝝆𝒎 where 𝝃𝟎 𝒂𝒏𝒅 𝒎 𝒂𝒓𝒆 constants, 𝝆 is the density. Einstein field

equations (𝑬𝑭𝑬𝒔) have been solved in the presence of a variable gravitational constant as well as a variable

cosmological constant together with a bulk viscous fluid. In order to find a deterministic solution of EFEs, a

simple form of Hubble parameter being constant when 𝑯(𝑹) = 𝒂(𝑹−𝒏 + 𝟏) where 𝒂 > 𝟎 , 𝒏 > 𝟏, has been

considered here that shows the signature flip from early deceleration to present acceleration. Moreover, the bulk

viscous coefficient is allowed to vary with the density as𝝆. The physical and geometrical behavior are described

in detail for the obtained model. The model obtained here is in good agreement with the present cosmological

observations.

Keyword: Bulk Viscous Models, Bianchi Type - Models, Cosmological constants, Hubble constants and

gravitational constant. References:

1. P. A. M. DIRAC, “The cosmological constants,” DIRAC, P. A. M. Cosmol. Constants. Nature, vol. 158, 1937, p. 323.

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1088. 4. Sistero Roberto F., “Cosmology with G and A Coupling Scalars,” Gen. Relativ. Gravit., vol. 23, no. 11, 1991, pp. 1265–1278.

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1992, pp. 351–357.

7. Abdussattar and R. G. Vishwakarma, “Some FRW models with variable G and Λ,” Class. Quantum Gravity, vol. 14, no. 4, 1997, pp. 945–953.

8. H. A. Borges and S. Carneiro, “Friedmann cosmology with decaying vacuum density,” Gen. Relativ. Gravit., vol. 37, no. 8,2005,

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48.

Authors: Jason Jeyasingh, Justus S

Paper Title: Validating the Knowledge Acquisition Process Metrics in Content Management Systems

Abstract: Measuring the processes involved in knowledge engineering for designing and building an

intelligent system has taken significant role. Out of the four basic processes involved in knowledge engineering,

this paper deals with the knowledge acquisition process and the metrics necessary for measuring the process

itself. Three metrics are proposed for the knowledge acquisition process based on the entailment procedures, its

length and complexity, and the cohesion and coupling attributes of the collection of knowledge units. These

three metrics are formalized based on the Briand’s mathematical properties for validating software metrics.

These metrics are indicative in the way it gives an insight on the design and the development of a

knowledgebase. In addition to these metrics, newer metrics can also be proposed for knowledge representation

and knowledge sharing processes.

Keyword: Knowledge Engineering, Knowledge Acquisition, Software Metrics, Metrics validation.

References:

1. Arisha, A. and Ragab, M. (2013).The MinK Framework: Developing Metrics for the Measurement of Individual Knowledge. KIM2013 Knowledge & Information Management Conference, UK, 4 – 5 June, 2013.

2. Briand, L.C., Morasca, S., Basili, V.R., (1996) Property-Based Software Engineering Measurement. IEEE Transactions on

Software Engineering, 22(1): 68-85. 3. Calero C., Piattini M., Genero M. (2001) Defining Complexity Metrics for Object-Relational Databases. In Patel D., Choudhury

I., Patel S., de Cesare S. [eds], Object Oriented Information System. London: Springer 4. Chidamber, S.R., and Kemerer, C.F. (1994)A Metrics Suite for Object-Oriented Design IEEE Transactions on Software

Engineering 20(6): 476-493.

5. Christophe F, Bernard A, Coatanéa E. (2010) RFBS: A Model for Knowledge Representation of Conceptual Design. CIRP Annals–Manufacturing Technology, 59(1): 155–158.

6. Churcher, N. I., and M. J. Shepperd.(1995). Towards a Conceptual Framework for Object-Oriented Metrics.ACM Software

Engineering Notes. 20(2): 69–76. 7. Daniela Geanina Luca Cososchi, Alina Luca, LuminiţaMihaelaLupu, IonuţViorelHerghiligiu. (2018). Indicators System For

Assessing The Organizational Knowledge Acquisition Process. Environmental Engineering and Management Journal, 17( 4):

937-950 8. Francis Rousseaux, Stéphane Cormier, (2016) Knowledge Acquisition at the Time of Big Data. Federated Conference on

Computer Science and Information Systems, September 11-14 (Poland: FedCSIS), 1343-1348.

9. Hepsiba Mabel V, Justus Selwyn. (2016) A Review on the Knowledge Representation Models and its Implications. International Journal of Information Technology and Computer Science 8(10): 72-81.

10. John F. Sowa, 2001. Conceptual Graphs, draft proposed ISO standard for conceptual graphs.

http://www.jfsowa.com/cgcgstand.htm. 11. Justus. S, Iyakutti. K. (2011) An Empirical Validation of the Suite of Metrics for Object-Relational Data Modelling. International

Journal of Intelligent Information and Database Systems 5(1): 49-80.

12. KorcanKavusan, Niels G. Noorderhaven and Geert Duysters. (2016) Knowledge acquisition and complementary specialization in alliances: The impact of technological overlap and alliance experience. Research Policy, 45(10): 2153-2165.

13. Kunal Chopra, Monika Sachdeva. 2015. Evaluation Of Software Metrics For Software Projects. International Journal of

Computers & Technology.14(6). 14. Manik Sharma, Gurvinder Singh. 2011. Predictive Metric- A Comparative Study. International Journal of Computer Science and

Technology (IJCST). 2 (1)

15. Martin Lorenz et. al. (2017) Object-Relational Mapping Revisited - A Quantitative Study on the Impact of Database Technology on O/R Mapping Strategies. International Conference on System Sciences.

16. Maryam Dehghani, PeymanAkhavan, (2017)An experimental investigation of knowledge acquisition techniques. Journal of

Management Development, 36(4):493-514. 17. Ming Chang Lee, To Chang. 2013. Software Measurement and Software Metrics in Software Quality. International Journal of

Software Engineering and Its Applications 7, (4).

18. Ohwada, Hayato, Yoshida, Kenichi, (2016) Knowledge Management and Acquisition for Intelligent Systems. Lecture Notes in Artificial Intelligence. 14th Pacific Rim Knowledge Acquisition Workshop. August 22-23 (Springer).

19. Prabhjot Kaur. (2016). A Review of Software Metric and Measurement.International Journal of Computer Applications &

Information Technology.9(2). 20. Rani Geetika, Paramvir Singh. 2014.Dynamic Coupling Metrics for Object Oriented Software Systems- A Survey. ACM

257-262

SIGSOFT Software Engineering Notes. 39 (2) 21. Srinivasan. K.P., T. Devi. (2014) Software Metrics Validation Methodologies in Software Engineering. International Journal of

Software Engineering & Applications, 5(6): 87-102

22. Ronald J. B, Hector J. L. (2004) Knowledge Representation and Reasoning. (San Francisco: Morgan Kaufmann Publishers) 23. Tse-Hsun Chen et. al. (2016) An Empirical Study on the Practice of Maintaining Object-Relational Mapping Code in Java

Systems. 13th International Conference on Mining Software Repositories, May, 2016.

24. Tullawat P &VichitaVathanophas R (2012). Knowledge acquisition: the roles of perceived value of knowledge content and source. Journal of Knowledge Management 16, 724-739.

25. The Hindu, the online daily newspaper, (http://www.thehindu.com/news/national/tamil-nadu/tamil-nadu-where-climate-

paradoxes-are-becoming-the-norm/article17530209.ece), visited in August 2017.

49.

Authors: A. Abdul Rahman

Paper Title: A Quality Communication Framework for Open Source Softwares

Abstract: Open Source Software (OSS) is an emerging technology of software development in present

era, careful implementation and procurement can gives us more benefits for OSS- adapting organizations. But

any failure in design, development and procurement will leads to major impact to OSS- developer organization

(community) to stop their further release and completely destroy the adapted system. Success of any software’s

based on it’s time to delivery, development cost and effectiveness. Open source software can fulfill these factors

due to its availability of source code, openness and sustainability of Technical quality among development

community. At the same time, more number of developers in a community of any OSS leads to delay in

development. But it has chance to improve software effectiveness in terms of defect free, since software can be

reviewed using various testing methodology, tools and plans in different perception. An OSS has more issues

such as lack of architecture design, co-ordination, organizational structure and documentation, removing these

issues will strengthen the quality of OSS. This paper includes the quality model and framework for organized

communication the quality characteristics in an appropriate aspects is required since unorganized

communications wherever people involved can leads to high quality risk.

Keyword: Quality Requirements Process, Quality Factors, Framework for Organized Communications,

Contribution validation process.

References:

1. Mark Aberdour., Opensourcetesting.org, “Achieving Quality in Open Source Software”,IEEE Computer Society,2007.

2. AtiehKhanjani, RizaSulaiman, “The Process of Quality Assurance Under Open Source Software development”, IEEE computer Society,2011.

3. RuedigerGlott, Arne-KristianGroven, Kristen Haaland, Anna Tannenberg, “Quality model for Free/Libre Open Source Software-

towards the “Silver Buttet”? “,IEEE,2010. 4. PerryDonham , “Ten Rules for Evaluating Open Source Software”,www.collaborative Consulting,2004.

5. TabiasOtte, RobetMoreton, Heinz D.Knonell,| “Development of a Quality Assurance Framework for the Open Source

development Model”, IEEE Computer Society, 2008. 6. AbdulRahman.A, “Guidelines for Software Quality Model Using Domain-Values, User and Organizational Perception”,

International Journal of Communication and Engineering (IJCE), Volume5, Issue: 01, 2012.

7. HimaniGoyal and Er. JasbirKour, “Design Method of Open Source Software”, International Journal of Computing and Applications 6(1),2011,PP.75-82.

8. Vieri Del Bianco, Luigi Lavazza, SandroMorasca and DavideTaibi, “Quality of Open Source Software : the QualiPSo

Trustworthiness Model”, International Federation for Information Processing, 2009.,pp-199-212. 9. T.RMadanmohan and Rahul De, “Open Source Reuse in Commercial Firms”, IEEE Computer Society,2004.

10. ChristophLattenmann, Stefan Stieglitz “Framework for Governance in Open Source Communities”, IEEE , 2005, Proceeding of

38th Hawaii International Conference on System Science. 11. Won Jun Sung, JiHyeok Kim and Sung YulRhew., “A Quality Model for Open Source Software Selection”, IEEE Computer

Society, 2007.

12. Lect. Marius Popa., “Processes of Quality management in Open Source Software”, Open Source Scientific Journal, Vol.1 No. 1,2009.

13. Pekka Maki-Asiala, Mari Matinlassi ., “ Quality Assurance of Open Source Components: Integrator Point of View “,IEEE

Computer Society,2006. 14. AnasTawileh, Omer Ranna, “Free and Open Source Software Quality Assurance”, IEEE Computer Society, 2006.

263-267

50.

Authors: Vandana Mansur, Sujatha R

Paper Title: Hyperledger Sawtooth Blockchain-Iot E-Provenance Platform for Pharmaceuticals

Abstract: This paper investigates the Blockchain traceability framework necessary to develop supply chain

application for pharmaceuticals, particularly for life saving drugs which need special logistic conditions. The

thorough knowledge of the application specific design assists in the development phase. A solution framework

has been proposed for the e-provenance platform for medicines addressing the issue of data streaming between

the IoT and the Sawtooth Blockchain unit. A distinct three layered Blockchain-IoT architecture presented, is

well suited for this design application. A classification of the Assets in this context distinguishes the properties

of each asset type. The pharmaceutical supply chain flow indicates the modules to be deployed along with the

transaction between the concerned parties. The application specific system design presents the platform

requirements of both the IoT data acquisition and Blockchain data processing units. The theme of the paper

namely provenance, can be realized only after investigating the data payload structure that distinguishes the

ownership of the IoT sensors and clients namely manufacturer, distributor, retailer and consumer. The overview

of the Docker containers illustrates the programming requirements for the specific application, giving the

deployment needs at the application side. Finally, application deployment methodology indicates the Blockchain

IoT designer, which software modules and hardware components are required to develop the design.

268-273

Keyword: Blockchain, IoT, provenance, Hyperledger Sawtooth, Smart contract, asset References:

1. S. V. Sathyanarayana, M. A. Kumar, K. N. Hari Bhat “Symmetric key image encryption scheme with key sequences derived

from random sequence of cyclic elliptic curve Points,” International Journal of Network Security, vol. 12, no. 3, pp. 137-150, May 2011

2. Dridi Manel, Ouni Raouf, Haddaji Ramzi, Mtibaa Abdellatif, “ Hash Function and Digital Signature ECDSA,” 14th International

Conference on Sciences and Techniques of Automation Control and Computer Engineering- STA 2013, pp. 1-6, Dec. 2013

3. Y. Yuan and F. Wang, “Towards blockchain-based intelligent transportation systems,” 2016 IEEE 19th International Conference

on Intelligent Transportation Systems (ITSC), Rio de Janeiro, pp. 2663-2668, Aug. 2016

4. Arshdeep Bahga, Vijay K. Madisetti, “Blockchain Platform for Industrial Internet of Things,” Journal of Software Engineering and Applications, vol. 9, pp. 533-546, Oct. 2016

5. Andy Melichar, “IoT Data Management with the TIG Stack Telegraph, InfluxdB and Grafana (TIG),” Technical Report, Scrum

Master, pp. 1-4, Feb. 2017 6. V. Ramesh, M. Sankaramahalingam, M. S. D. Bharathy, R. Aksha, “Remote temperature monitoring and control using IoT,”

International Conference on Computing Methodologies and Communication (ICCMC), Erode, pp. 1059-1063, May 2017

7. Feng Tian, “A supply chain traceability system for food safety based on HACCP, blockchain & Internet of things,” 2017 International Conference on Service Systems and Service Management, Dalian, pp. 1-6, Jun. 2017

8. Anoop MS, “Elliptic Curve Cryptography An Implementation Guide,” Infosec Writers, pp. 1-9, Jul. 2017

9. Z. Huang, X. Su, Y. Zhang, C. Shi, H. Zhang, L. Xie, “A decentralized solution for IoT data trusted exchange based-on

blockchain,” 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, pp. 1180-1184, Aug.

2017

10. Kelly Olson, Mic Bowman, James Mitchell, Shawn Amundson, Dan Middleton, Cian Montgomery, “Sawtooth: An Introduction,” Hyperledger Blockchain Technologies for Business, White papers, pp. 1-7, Jan. 2018

11. T. M. Fernandez-Carames, P. Fraga-Lamas, “A Review on the Use of Blockchain for the Internet of Things,” in IEEE Access,

vol. 6, pp. 72-78, Jan. 2018 12. M. P. Caro, M. S. Ali, M. Vecchio and R. Giaffreda, “Blockchain-based traceability in Agri-Food supply chain management:

A practical implementation,” 2018 IoT Vertical and Topical Summit on Agriculture Tuscany (IOT Tuscany), Tuscany, pp. 1-4,

Apr. 2018 13. Sidra Malik, Salil S Kanhere, Raja Jurdak, “ Product Chain: Scalable Blockchain Framework to Support Provenance in Supply

Chains,” 17th International Symposium on Network Computing and Applications (NCA), IEEE, pp. 1–10, Apr. 2018

14. Tharun Mohan, “Improve Food Supply Chain Traceability using Blockchain,” Technical report, The Pennsylvania University, pp. 10-16, May 2018

15. Jun Lin, Zhiqi Shen, Antinh Zhang, Yueting Chai, “Blockchain and IoT based Food Traceability for Smart Agriculture,” ICCSE’

18 3rd International Conference on Crowd Science and Engineering, Singapore, vol 3., pp. 1-6, Jul. 2018. 16. Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, Ji Wang, “Untangling Blockchain: A Data Processing

View of Blockchain Systems,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 7, Jul. 2018

17. Alfanso Panarello, Nachiket Tapas, Giovanni Merlino, Francesco Longo, Antonio Puliafito, “Blockchain and IoT Integration: A Systematic Survey,” Sensors, Semantic Scholar, Aug. 2018.

18. Varun Raj, “Hyperledger sawtooth Series,” Discussion Forum by Skcript, Discourse, pp. 1-10, Nov. 2018 19. Gavina Baralla , Andrea Pinna , Corrias Giacomo, “Ensure Traceability in European Food Supply Chain by using a blockchain

System,” Technical report, University of Cagliari, Italy, pp. 1-5, Mar. 2019

20. Rahul R., “Hyperledger Sawtooth-Introductory Tutorial,” Technical Report Graduate Algorithms, WordPress, USA, pp. 2-5, Mar. 2019

21. Report Committee, “Hyperledger Architecture: Smart Contracts,” Technical Report Forfim Academy, Varese, Italy, pp.1-4,

Apr. 2019

51.

Authors: Plasin Francis Dias, R. M. Banakar

Paper Title: Entropy Based Target Identification in Synthetic Aperture Radar Polarimetry

Abstract: Synthetic aperture radar is used for polarimetric target identification. It is most prominent

imaging radar. This radar covers the widest ranges of earth crust with high resolution images. It captures images

day and night. It is suitable for any seasonal weather conditions. The polarization data contains information, on

scattering mechanism related to different objects. The objects are land, ocean, glaceries, snow and dense forest

which are natural distributed targets. By the use of scattering mechanism the different objects are classified.

Scattering mechanism is measured by scattering elements of the matrix. The full polarization of synthetic

aperture radar data classifies the obtained image. This paper analyses an entropy based target identification

related to synthetic aperture radar polarimetry. The method is also the outcome of Eigen decomposition analysis.

The paper also gives broader view of identification of target using physical property and analytical model. The

method is helpful for system level design and scattering process considerations.

Keyword: Synthetic Aperture Radar, Polarimetry, Eigen, Decomposition, Entropy, Coherency matrix References:

1. Kazuo Ouchi, “Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics,” Review Remote Sensing ISSN

2072 -4292 , vol. 1, no. 1, pp. 716-765, Feb. 2013.

2. M.Ouarzeddine, B. Souissi, A. Belhadj-Aissa, “Classification of Polarimetric SAR images based on scattering mechanisms,”

University of Science and Technology Houri Boumediene, vol. 1, no. 1, pp.1-6, Jan. 2007.

3. Manuel E. Arrigada, “Performance of scattering matrix decomposition and color space for synthetic aperture radar imagery,”

Master Thesis for degree of Master of Science, pp. 1–73, Mar. 2010. 4. Maurizio Sarti, Lucio Mascolo, “An investigation of different polarimetric decomposition techniques for soil moisture

estimation,” IEEE,vol. 1, no. 1, pp. 209-213, Sept. 2012.

5. Shenglong GUO, Yang, Wen HONG, Jianfeng Wang and Xiaoyang Guo, “Model based target decomposition with π/4 mode compact polarimetry data,” Science China Information Sciences, vol. 59, no. 1, pp.1-10, Jun. 2016.

6. Hongbo Sun,Masanobu Shimada and Feng Xu, “Recent Advances in Synthetic Aperture Radar Remote Sensing-Systems, Data

Processing and Applications,” IEEE Geoscience Remote Sensing Letter, vol. 14, no. 11, pp. 2013-2016, Nov. 2017. 7. Shashikumar, “Advances in Polarimetry,” SPIE Asia Pacific Remote sensing APRS Symposium tutorial, vol. 1, no.1, pp. 1-23,

Apr. 2016.

274-279

8. Jacob Van Zyl and Yunjin Kim, “Synthetic Aperture Radar Polarimetry,” Jet Propulsion Laboratory, California Information Technology, pp. 88-100, Dec. 2010.

9. Si-Wei Chen, Yong-Zhen Li, Xue-Song Wang , Shan-Ping Xiao and Motoyuki Sato, “Modelling and interpretation of scattering

mechanism in polarimetric synthetic aperture radar,” IEEE signal processing magazine, vol. 1, no. 1, pp. 79-89, Jul. 2014.

10. Sang‐Hoon Hong and Shimon Wdowinski, “Revising vegetation scattering theories: Adding a rotated dihedral double bounce

scattering to explain cross‐polarimetric SAR observations over wetlands,” Proceedings, ‘Fringe 2011 Workshop’, Frascati, Italy,

pp. 1-7, Sep. 2011. 11. Eric Pottier, “Recent advances in the development of the open source toolbox for polarimetric and interferometric polarimetric

SAR data processing: The POLSARPRO v4.1.5 software,” IEEE, vol. 1, no. 1, pp. 2527-2530, Aug. 2010.

12. Jong sen Lee and Thomas L. Ainsworth, “An Overview of Recent Advances In Polarimetric SAR information Extraction: Algorithms and Applications,” IEEE International Geoscience and Remote sensing Symposium, pp. 851-854, Jul. 2010.

13. Alberto Moreira, Pau Prats–Iraola, Marwan Younis, Gerhard Krieger,Iren Hajnsek and Konstantinos P.

Papathanassiou,Microwave and Radar institute of the German Aerospace Center (DLR), Germany, “A Tutorial on Synthetic Aperture Radar,” IEEE Geoscience and remote sensing magazine, pp. 6-38, Mar. 2013.

14. C.P. Schwegmann, W. Kleynhans, B. P. Salmon, “The Development of Deep learning in Synthetic Aperture Radar Imagery,”

International workshop on remote sensing with Intelligent Processing, vol.1, no.1, pp.1-6, May. 2017. 15. Technical Committee, “Advanced Radar Polarimetry Tutorial-Radar polarimetry,” Technical report, Canada centre for remote

sensing, pp. 1-24, Nov. 2015.

16. Yonglei Chang , Jie Yang , Pingxiang Li , Lingli Zhao , Lei Shi , “Sample extraction based on helix scattering for polarimetric sar calibration,” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information

Sciences, vol. XLII-2/W7, pp. 1-4, Sep. 2017.

17. Sverre Holm, “ Synthetic Aperture Radar and Sonar-SAR and SAS,” Lecture notes, Department of informatics, University of

OSLO, pp.1-19, May. 2015.

18. Yoshio Yamaguchi, Toshifumi Moriyama, Motoi Ishido, and Hiroyoshi Yamada, “Four-Component Scattering Model for

Polarimetric SAR Image Decomposition,” IEEE Transactions on Geoscience and remote sensing, vol. 43, no. 8, Aug. 2005. 19. Yoshio Yamaguchi, Akinobu Sato, Wolfgang-Martin Boerner, Ryoichi and Hiroyoshi Yamada, “ Four-Component Scattering

Power Decomposition With Rotation of Coherency Matrix,” IEEE Transactions on Geoscience and Remote sensing, vol. 49, no.

6, Jun. 2011. 20. Shane Robert Cloude and Eric Pottier, “A review of target decomposition theorems in radar polarimetry ,” IEEE Transactions on

Geoscience and Remote sensing, vol. 34, no. 2, pp. 1-4, Mar. 1996.

21. Thuy LeToan , “SAR Image classification content scattering physics,” ESA most dragon programme Advanced training course in Land Remote sensing , Beijing, pp. 1-4, Oct. 2005.

22. Muhtar Qong, Takeo Tadono, Hiroyuki Wakabayashi and Masanobu Shimada, “Muller Matrix Based Classification of

Polarimetric SAR Data,” Earth Observation Research Center , National Space Development Agency of Japan, IEEE, vol. 1, no. 1, pp. 375-377, Dec. 2000.

23. Kohei Arai , “ Polarimetric SAR Image Classification with High Frequency Component Derived from Wavelet Multi Resolution

Analysis:MRA,” International Journal of Advanced Computer Science and Applications(IJACSA), vol. 2, no. 9, pp. 37-42, Dec. 2011.

24. Camilla Brekke, Cathleen E. Jones, Stine Skrunes , Benjamin Holt, Martine Espeseth and Torbjorn Eltoft , “ Cross Correlation

between Polarization Channels in SAR Imagery over Oceanographic Features,” IEEE Geoscience and remote sensing letters. vol.

1, no. 1, pp. 1-5, Jan. 2011.

52.

Authors: Namratha Gonuguntla, Swarnalatha P

Paper Title: Data Mining Technology and Techniques in Medical Science Field Application

Abstract: The electronic and the written records both played an equally significant part in the daily lives

of the health department staffs. However, after the globalised version of change in the healthcare department, it

has been recorded by the researcher in his examination a portion of the fundamental focuses that demonstrate the

genuine advantage of the information and the dangers. Consequently in this report, the analysts will show

unmistakably the Data mining procedure and its different focuses. The primary focal point of the investigation is

needy upon the Nursing work, and in this manner various health care centres and the nurses will be taken under

consideration. The report additionally examines about the data mining subject's experience and how it rose in the

therapeutic science field of capacity. The information that are dealt with in the framework has changed into a

making care each. It has been noticed that information mining is considered to all computer-based information

stockpiling and recovering method. The approach of the digital form of data gathering will also be explained and

along with it will also showcase some critical points that are co-related with the findings and analysis. In the

end, the researcher will provide its own recommendation for the betterment of the nursing in the medical science

field of capacity.

Keyword: Administration, analytics, diagnosis, HealthCare and nursing. References: T. P.N, Introduction to data mining. Pearson Education India, 2018.

1. W. I.H, F. E., H. M.A and P. C.J, Data Mining: Practical machine learning tools and techniques. United States: Morgan Kaufmann, 2016.

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Care Plan Data", Western Journal of Nursing Research, vol. 39, no. 1, pp. 20-41, 2016. Available: 10.1177/0193945916672828.

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Research", Western Journal of Nursing Research, vol. 37, no. 10, pp. 1284-1294, 2015. Available: 10.1177/0193945915576778. 6. S. Henly et al., "Integrating emerging areas of nursing science into PhD programs", Nursing Outlook, vol. 63, no. 4, pp. 408-416,

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8. S. Brown, S. White and N. Power, "Introductory anatomy and physiology in an undergraduate nursing curriculum", Advances in

Physiology Education, vol. 41, no. 1, pp. 56-61, 2017. Available: 10.1152/advan.00112.2016. 9. Leary, B. Tomai, A. Swift, A. Woodward and K. Hurst, "Nurse staffing levels and outcomes – mining the UK national data sets

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10. M. Kazemi, S. Hajian and N. Kiani, "Quality Control and Classification of Steel Plates Faults Using Data Mining", Applied

Mathematics & Information Sciences Letters, vol. 6, no. 2, pp. 59-67, 2018. Available: 10.18576/amisl/060202. 11. S. Henly et al., "Emerging areas of science: Recommendations for Nursing Science Education from the Council for the

Advancement of Nursing Science Idea Festival", Nursing Outlook, vol. 63, no. 4, pp. 398-407, 2015. Available:

10.1016/j.outlook.2015.04.007. 12. H. Lach, K. Buckwalter and L. Aaronson, "Midwest Nursing Research Society News", Western Journal of Nursing Research, vol.

37, no. 7, pp. 1005-1008, 2015. Available: 10.1177/0193945915586899.

13. J. Xu, Y. Zhang, P. Zhang, A. Mahmood, Y. Li and S. Khatoon, "Data Mining on ICU Mortality Prediction Using Early Temporal Data: A Survey", International Journal of Information Technology & Decision Making, vol. 16, no. 01, pp. 117-159,

2017. Available: 10.1142/s0219622016300020.

14. N. N. and R. Kulkarni, "Appraisal Management System using Data mining Classification Technique", International Journal of Computer Applications, vol. 135, no. 12, pp. 45-50, 2016. Available: 10.5120/ijca2016908596.

15. L. McKenna, E. Halcomb, R. Lane, N. Zwar and G. Russell, "An investigation of barriers and enablers to advanced nursing roles

in Australian general practice", Collegian, vol. 22, no. 2, pp. 183-189, 2015. Available: 10.1016/j.colegn.2015.02.003. 16. S. Inoue, N. Ueda, Y. Nohara and N. Nakashima, "Recognizing and Understanding Nursing Activities for a Whole Day with a

Big Dataset", Journal of Information Processing, vol. 24, no. 6, pp. 853-866, 2016. Available: 10.2197/ipsjjip.24.853.

17. S. Henly et al., "Emerging areas of nursing science and PhD education for the 21st century: Response to commentaries", Nursing Outlook, vol. 63, no. 4, pp. 439-445, 2015. Available: 10.1016/j.outlook.2015.05.003.

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53.

Authors: Senthil Athiban M*, Mehazin Shaju, M M Sravani, Dr.S Ananiah Durai

Paper Title: Architectural Enhancement of Network on Chip

Abstract: This paper gives a new architectural design suggestion of NoC, with efficient way of

communication. Firstly, to create a serial data communication architecture in competence with the existing

widely used parallel form of data transmission and reception [1]. Secondly to enable simultaneous transmission

and reception between more than one module at the same time. Thirdly to create the architecture that is

modifiable as per the need of user. The theoretical data rate calculated was 300 MBps. The throughput we

achieved after the completion is 250MBps.

Keyword: Architecture, NoC, SoC, Topology. References:

1. W. J. Dally and B. Towles, “Route packets, not wires: on-chip interconnection networks,” Proceedings of the 38th annual Design

Automation Conference. Acm, pp. 684–689, 2001. 2. R. R. Dobkin, A. Morgenshtein, A. Kolodny, and R. Ginosar, “Parallel vs. serial on-chip communication,” in Proceedings of the

2008 international workshop on System level interconnect prediction. ACM,pp. 43–50, 2008.

3. S. Kumar et al., “A network on chip architecture and design methodology”,Proceedings IEEE Computer Society Annual Symposium on VLSI. New Paradigms for VLSI Systems Design. ISVLSI 2002, Pittsburgh, PA, USA, pp. 117-124, 2002.

4. P. P. Pande, C. Grecu, M. Jones, A. Ivanov, and R. Saleh, “Performance evaluation and design trade-offs for network-on-chip

interconnect architectures,”IEEE transactions on Computers, vol. 54, no. 8, pp. 1025–1040, 2005. 5. H. Ye, Q. Wang, P. Yang, W. Li and Z. Yu, "The Design and Implementation of a NoC System Based on SoCKit," 2016

International Conference on Networking and Network Applications (NaNA), Hakodate, pp. 145-148, 2016.

6. F. Karim, A. Nguyen, and S. Dey, “An interconnect architecture fornetworking systems on chips,” IEEE micro, vol. 22, no. 5, pp. 36–45,2002.

7. K.-C. Chang, M. Liao, and B.-Y. Shiu, “Design and implementation of a noc supporting priority-based communications for

many-core socs,”International Computer Symposium (ICS2010). IEEE, pp.483–488, 2010 8. H. Luo, S. Wei, D. Chen, and D. Guo, “Hybrid circuit-switched noc for low cost on-chip communication,” Anti-counterfeiting,

Security, and Identification. IEEE,pp. 1–5,2012.

9. P. P. Pande, C. Grecu, A. Ivanov, and R. Saleh, “Design of a switch fornetwork on chip applications,” Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS’03., vol. 5. IEEE, pp. V–V,2003.

10. X.-T. Tran, T. Nguyen, H.-P. Phan, and D.-H. Bui, “Axi-noc: Highperformance adaptation unit for arm processors in network-on-

chip architectures,” IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences, vol. 100, no. 8, pp. 1650–1660, 2017.

11. X.-T. Tran, T. Nguyen, H.-P. Phan, and D.-H. Bui, “Axi-noc: Highperformance adaptation unit for arm processors in network-on-

chip architectures,” IEICE Transactions on Fundamentals of Electronics,Communications and Computer Sciences, vol. 100, no.

8, pp. 1650–1660, 2017.

H.-K. Hsin, E.-J. Chang, C.-H. Chao, and A.-Y. Wu, “Regional aco based routing for load-balancing in noc systems,” Second

World Congress on Nature and Biologically Inspired Computing (NaBIC).IEEE, pp. 370–376, 2010.

287-290

54.

Authors: John J. Lee, Youssef Souryal, Darren Tam, Dongsoo Kim, Kyubyung Kang, Dan D. Koo

Paper Title: Building a Private LoRaWAN Platform

Abstract: LoRaWAN technology has been here for several years as one of LPWAN technologies. It consists

of various components such as end nodes, a gateway, a network server, and an application server at the 291-295

minimum. The servers have been exclusive products of commercial companies, and not many experimental or

academic ones are available. Recently one such software has been developed. However, few fully functional

academic ones have been reported. In this study, we implement a fully functional private independent

LoRaWAN platform for the academic research of LPWAN Internet of Things (IoT) and demonstrate that our

platform can support not only end-to-end LoRaWAN communication but also graphical user interface on an

embedded and limited computing power system.

Keyword: LoRaWAN, LPWAN, Gateway, Network Server, Application Server. References:

1. LoRa Alliance, Available: https://lora-alliance.org/

2. Sigfox, Available: https://www.sigfox.com/en 3. Wi-SUN, Available: https://www.wi-sun.org/

4. The Things Network (TTN), Available: https://www.thethingsnetwork.org/

5. ChirpStack, Available: https://www.chirpstack.io/, 2019. 6. FreeRTOS, Barry, Richard. “FreeRTOS,” Internet, Oct (2008), https://www.freertos.org/

7. Dunkels, B. Gronvall, and T. Voigt, “Contiki-a lightweight and flexible operating system for tiny networked sensors,” 29th

annual IEEE international conference on local computer networks, pp. 455-462, 2004. 8. LoRaGo PORT, Available: https://sandboxelectronics.com/?product=lorago-port-multi-channel-lorawan-gateway

9. Semtech UDP Packet Forwarder, Available: https://github.com/Lora-net/packet_forwarder

10. gRPC, https://grpc.io/ 11. Google, “protocol-buffers,” Available: https://developers.google.com/protocol-buffers/

12. PostgreSQL, Available: https://www.postgresql.org/

13. SODAQ, Available: https://support.sodaq.com/sodaq-one/explorer/ 14. Marvin, Available: https://github.com/iotacademy/marvin

15. STM32 B-L072Z-LRWAN1, Available: https://www.st.com/en/evaluation-tools/b-l072z-lrwan1.html

16. Arduino IDE, https://www.arduino.cc/en/Main/Software 17. System Workbench IDE, Available: https://www.st.com/en/development-tools/sw4stm32.html

18. ARM Mbed, Available: https://www.mbed.com/en/

19. Node-RED, Available: https://nodered.org/ 20. Cayenne LPP, Available: https://community.mydevices.com/t/cayenne-lpp-2-0/7510

55.

Authors: Gopireddy Sirisha, Parmar Harshdeepsinh, Konduri Subrahmanya Hemanth,

Sathiya Narayanan

Paper Title: Machine Vision Based Autonomous Green AgriBot

Abstract: Automated agriculture bot offers effective solutions to modern agriculture. This paper explains

the proposed multi-purpose automatic bot using machine vison-based navigation system powered by sun

tracking solar panel with battery charger system with an overview on newly combined methodologies for the

efficient improvement in production as well as in multiple agricultural operations like levelling, seed distribution

and spraying, with effective power management system. This proposed idea will make the process sustainable

and eco-friendly.

Keyword: Agricultural Bot, Row guidance system, Solar Power, Battery-management, Maximum Power

Point Tracking, Internet of things. References:

1. Palepu V Santhi, Nellore Kapileswar, Vijay K. R. Chenchelaand Venkata Siva Prasad, “Sensor and Vision based Autonomous AGRIBOT for Sowing seeds,” International Conference on Energy, Communication, Data Analytics and Soft Computing

(ICECDS), 2017.

2. Snehal S Warekar and B. T. Salokhe, “Solar powered robotic vehicle,” International Journal of Advanced Research in Computer

and Commu- nication Engineering Vol. 4, Issue 5, May 2015.

3. R. C. Prasad, “Design and Implementation of MPPT Algorithm for Solar Energy System,” International Journal of Advanced

Research in Computer Ccience and Software Engineering, Vol. 3, Issue 10, October 2013. 4. Theodore Amissah Ocran, Cao Juny, Cao Binggang, and Sun Xinghua, “Artificial Neural Network Maximum Power Point

Tracker for Solar Electric Vehicle,” Tsinghua Science and Technology, Vol. 10, No. 2, pp. 204-208, April 2005.

5. Tom as de J. Mateo Sanguino and Justo E. Gonz alez Ramos, “Smart Host Micro controller for Optimal Battery Charging in a Solar-Powered Robotic Vehicle,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 3, June 2013.

6. N Kemal Ure, Girish Chowdhary, Tuna Toksoz, Jonathan P How, Matthew A. Vavrina, and John Vian, “An Automated Battery

Man- agement System to Enable Persistent Missions With Multiple Aerial Vehicles,” IEEE/ASME Transactions on Mechatronics, 2013.

296-299

56.

Authors: Vanisri Muralisankar , S. Graceline Jasmine

Paper Title: Object Detection and Identification in Surveillance Images using Image Processing

Abstract: The goal of object detection and identification in surveillance images using image processing is

to detect a particular part of the image from surveillance camera like an object’s position, movement, and its

sequence. Object tracking and recognition deal with recognizing the image of video which can differ in color,

range, size, illumination changes with time and some cluttered images. As this paper has been surveying and an

algorithm has been proposed and implemented, the identified object has freed from the shadow, clutter,

illumination changes were detected and eliminated appropriately.

Keyword: Background Subtraction, Illumination, Shadow, Surveillance Image References:

1. Shaikh, S.H., Saeed, K. and Chaki, N., 2014. Moving object detection approaches challenges and object tracking. Moving Object

300-305

Detection Using Background Subtraction (pp. 5-14). Springer, Cham. 2. Jalled, F. and Voronkov, I., 2016. Object Detection Using Image Processing. arXiv preprint arXiv:1611.07791.

3. Wang, Y.Q., 2014. An analysis of the Viola-Jones face detection algorithm. Image Processing on Line, 4, pp.128-148.

4. Joshi, K.A. and Thakore, D.G., 2012. A survey on moving object detection and tracking in a video surveillance system. International Journal of Soft Computing and Engineering, 2(3), pp.44-48.

5. Hadi, R.A., Sulong, G. and George, L.E., 2014. Vehicle detection and tracking techniques: a concise review. ArXiv preprint

arXiv: 1410.5894. 6. Ray, K.S. and Chakra borty, S., 2017. An Efficient Approach for Object Detection and Tracking of Objects in a Video with

Variable Background. arXiv preprint arXiv:1706.02672.

7. Kulawansa, W.H.H.L., Indradasa, S.T., Epitakaduwa, G.N.G., Karthika, A. and Chinthaka, J., 2016. Police EYE: Parking Violation Detection System. Imperial Journal of Interdisciplinary Research, 2(12).

8. Li, M., Wu, X., Liu, J. and GUo, Z., 2018, October. Restoration of Unevenly Illuminated Images. In 2018 25th IEEE International

Conference on Image Processing (ICIP) (pp. 1118-1122). IEEE. 9. Brutzer, S., Höferlin, B. and Heidemann, G., 2011, June. Evaluation of background subtraction techniques for video surveillance.

In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 1937-1944). IEEE.

10. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

11. Finlayson, G.D., Drew, M.S. and Fredembach, C., Apple Inc, 2013. Detecting illumination in images. U.S. Patent 8,385,648.

12. Guo, R., Dai, Q. and Hoiem, D., 2011, June. Single-image shadow detection and removal using paired regions. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 2033-2040). IEEE.

13. Wang, J.M., Chung, Y.C., Chang, C.L. and Chen, S.W., 2004, March. Shadow detection and removal for traffic images. In IEEE

International Conference on Networking, Sensing and Control, 2004 (Vol. 1, pp. 649-654). IEEE.

57.

Authors: Sai Nikhil Alisetti, Swarnalatha Purushotham, Lav Mahtani

Paper Title: Guiding and Navigation for the Blind using Deep Convolutional Neural Network

Based Predictive Object Tracking

Abstract: Indoor and outdoor Navigation is a tough task for a visually impaired person and they would

most of the time require human assistance. Existing solutions to this problem are in the form of smart canes and

wearable. Both of which use sensors like on - board proximity and obstacle detection, as well as a haptic or

auditory feedback system to warn the user of stationary or incoming obstacles so that they do not collide with

any of them as they move. This approach has many drawbacks as it is not yet a stand - alone reliable device for

the user to trust when navigating, and when frequently triggered in crowded areas, the feedback system will

confuse the user with too many requests resulting in loss of actual information.

Our Goal here is to create a Personalized assistant to the user, which they can interact naturally with their

voice to mimic the aid of an actual human assistance while they are on the move. It works by using its object

detection module with a high reliability training accuracy to detect the boundaries of objects in motion per frame

and once the bounding box crosses the threshold accuracy, recognize the object in the box and pass the

information to the system core, where it verifies if the information needed to be passed onto the user or not, if

yes it passes the converted speech information to the voice interaction model. The voice interaction model is

consent-based, it would accept and respond to navigation queries from the user and will intelligently inform

them about the obstacle which needs to be avoided. This ensures only the essential information in the form of

voice requests is passed onto the user, which they can use to navigate and also interact with the assistant for

more information.

Keyword: Vision Processing, Medical Aid, Voice Assistant, Real Time Object Detection, YOLO2000 Model

References: 1. Ray, Kumar S., Sayandip Dutta, and Anit Chakraborty. ”Detection, Recognition and Tracking of Moving Objects from Real-time

Video via

2. SP Theory of Intelligence and Species Inspired PSO.” arXiv preprint arXiv:1704.07312 (2017).

3. Anwar, Ashraf, and Sultan Aljahdali. ”A smart stick for assisting blind

4. people.” IOSR Journal of Computer Engineering 19.3 (2017): 86-90.

5. Motwani, Tanvi S., and Raymond J. Mooney. ”Improving Video Activity Recognition using Object Recognition and Text

Mining.” ECAI. Vol. 1.2012. 6. Kocaleva, Mirjana, et al. ”Pattern Recognition and Natural Language Processing: State of the Art.” TEM Journal 5.2 (2016): 236-

240.

7. Yang, Yezhou, et al. ”Robots with language: Multi-label visual recognition using NLP.” Robotics and Automation (ICRA), 2013 IEEE

8. International Conference on. IEEE, 2013.

9. Agarwal, Ankit, Deepak Kumar, and Abhishek Bhardwaj. ”Ultrasonic stick for blind.” International journal of engineering and computer science 4.4 (2015): 11375-11378.

10. Sharma, Sharang, et al. ”Multiple distance sensors based smart stick for visually impaired people.” 2017 IEEE 7th Annual

Computing and Communication Workshop and Conference (CCWC). IEEE, 2017. 11. Sharma, Tushar, et al. ”Smart Cane: Better Walking Experience for Blind People.” 2017 3rd International Conference on

Computational Intelligence and Networks (CINE). IEEE, 2017.

12. Agarwal, Namita, et al. ”Electronic guidance system for the visually impaired—A framework.” 2015 International Conference on Technologies for Sustainable Development (ICTSD). IEEE, 2015.

13. Jiang, Rui, and Qian Lin Li. ”Let Blind People See: Real-Time Visual Recognition with Results Converted to 3D Audio.” Proc.

International Conference on Computer Vision. 2015. 14. Redmon, Joseph, and Ali Farhadi. ”YOLO9000: better, faster, stronger.” Proceedings of the IEEE conference on computer vision

and pattern recognition. 2017.

306-313

58.

Authors: Vashisht Marhwal, Piyush Bamel, Tanay Agarwal

Paper Title: Predicting Crisis in Global Trade Network: An Enhanced Decision Tree Based Methods

Abstract: International Trade Relations represent a natural Social Information Network that has been

extensively analyzed for various purposes like monitoring the global economy. The aim is to use the Global

Trade Network to predict the occurrence of natural disasters or financial crisis based on the fact that the trade

relations tax a hit in their patterns. The Global Network compromises of Export-Import Relations between the

countries in the form of a Weighted Social Network. Predicting Trade relations help us effectively predict any

future crisis and prepare for the same. An analysis of the Global Trade Network would discuss the centrality

measures and Degree strengths. Using a list of crises which has occurred in the past and with the help of an

efficient Machine Learning Model and Sampling Technique the aim is to improve the accuracy and precision of

our prediction and discuss the implications on the network.

Keyword: Crisis Prediction, Decision Tree, Global Trade Network, Social Network Analysis. References:

1. He, J., & Deem, M. W. (2010). Structure and response in the world trade network. Physical review letters, 105(19), 198701. 2. Kumar, P., & Zhang, K. (2007, October). Social network analysis of online marketplaces. In IEEE International Conference on e-

Business Engineering (ICEBE'07) (pp. 363-367). IEEE.

3. Kim, S., & Shin, E. H. (2002). A longitudinal analysis of globalization and regionalization in international trade: A social network approach. Social forces, 81(2), 445-468.

4. Christina Kao, Lili Yang, Ye Yuan (2015, December). Predicting Crisis in Global Trade Network, Stanford University

(Unpublished).

5. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal

of statistical mechanics: theory and experiment, 2008(10), P10008.

6. Schönfeld, Mirco & Pfeffer, Juergen. (2019). Fruchterman/Reingold (1991): Graph Drawing by Force-Directed Placement.

10.1007/978-3-658-21742-6_49.

7. Hagberg, A., Swart, P., & S Chult, D. (2008). Exploring network structure, dynamics, and function using NetworkX(No. LA-UR-08-05495; LA-UR-08-5495). Los Alamos National Lab.(LANL), Los Alamos, NM (United States)

8. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.

9. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, 321-357.

10. Goyal, S., Chauhan, R. K., & Parveen, S. (2016, December). Spam detection using KNN and decision tree mechanism in social

network. In 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 522-526). IEEE.

314-318

59.

Authors: Balaji M, Vishal Gundavarapu, Sasipriya P, Kanchana Bhaaskaran V S

Paper Title: Low Power Multiplier using Approximate Compressor for Error Tolerant Applications

Abstract: Approximate or inexact computing has gained a significant amount of attention for error tolerant

systems such as signal processing and image processing applications. In this paper, a comprehensive analysis

and evaluation of multipliers realized using the existing approximate 4-2 compressors towards achieving low

power has been presented. 8-bit Dadda multiplier has been chosen and the power consumption comparison has

been performed. The exact multiplier has also been realized to enable the calculation of power savings for the

approximate multipliers. An image compression algorithm using approximate multipliers has been implemented

to analyze the operability of the approximate multipliers. Accuracy of the approximate multipliers has also been

computed by means of Normalized Error Distance (NED) and PSNR. All the circuits are designed using 45nm

CMOS process technology and simulations are carried out using Cadence® Virtuoso design tools.

Keyword: 4-2 compressor, Approximate compressor, Dadda multiplier, Low power multiplier. References:

1. Chang, J. Gu, M. Zhang, “Ultra Low-Voltage Low- Power CMOS 4-2 and 5-2 Compressors for Fast Arithmetic Circuits,” IEEE

Transactions on Circuits & Systems, Vol. 51, No. 10, pp. 1985-1997, Oct. 2004.

2. Momeni, J. Han, P.Montuschi, and F. Lombardi, “Design and Analysis of Approximate Compressors for Multiplication”, IEEE Transactions on Computers, Volume: 64 , Issue: 4 , April 2015.

3. Chia-Hao Lin and Ing-Chao Lin, “High Accuracy Approximate Multiplier With Error Correction”, 2013 IEEE 31st International

Conference on Computer Design, 6-9 October 2013. 4. Zhixi Yang, Jie Han, Fabrizio Lombardi, “Approximate Compressors for Error-Resilient Multiplier Design”, 2015 IEEE

International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS), 12-14 October 2015.

5. Perneti Balasreekanth Reddy and V. S. Kanchana Bhaaskaran, “Design of Adiabatic Tree Adder Structures for Low Power”. 6. Jinghang Liang, Jie Han and Fabrizio Lombardi, “New Metrics for the Reliability of Approximate and Probabilistic Adders”,

IEEE Transactions On Computers, 62(9), 1760-1771.

7. Transactions on Computers, Volume: 62 , Issue: 9 , Sept. 2013.

319-324

60.

Authors: Sufola Das Chagas Silva Araujo, V S Malemath, K. Meenakshi Sundaram

Paper Title: Disease Identification in Chilli Leaves using Machine Learning Techniques

Abstract: Crop diseases reduce the yield of the crop or may even kill it. Over the past two years, as per the

I.C.A.R, the production of chilies in the state of Goa has reduced drastically due to the presence of virus. Most

of the plants flower very less or stop flowering completely. In rare cases when a plant manages to flower, the

yield is substantially low. Proposed model detects the presence of disease in crops by examining the symptoms.

The model uses an object detection algorithm and supervised image recognition and feature extraction using

convolutional neural network to classify crops as infected or healthy. Google machine learning libraries,

TensorFlow and Keras are used to build neural network models. An Android application is developed around the

model for the ease of using the disease detection system.

325-329

Keyword: Component, formatting, Plant, symptoms. References:

1. J. R. a. A. Farhadi, "YOLO9000: Better, Faster, Stronger," in 2017 IEEE Conference on Computer Vision and Pattern

Recognition (CVPR), Honolulu, HI, 2017. 2. S. D. Khirade and A. B. Patil, "Plant Disease Detection Using Image Processing," in International Conference on Computing

Communication Control and Automation, Pune, 2015

3. S. G, Dhivya., R, Latha.S and R.Rajesh, "PLANT DISEASE DETECTION AND ITS SOLUTION USING IMAGE CLASSIFICATION," International Journal of Pure and Applied Mathematics, vol. 119, no. 14, pp. 879-883, 2018.

4. K. Singh, M. &. Chetia, S. &. Singh and Malti, "Detection and Classification of Plant Leaf Diseases in Image Processing using

MATLAB," International Journal of Life sciences Research, vol. 5, no. 1, pp. 120-124, 2017. 5. K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv: 1409.1556v6

[cs.CV], 2014.

6. J. Redmon, S. Divvala and R. G. a. A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," J. Redmon, S. Divvala and R. G. a. A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on

Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016.

7. Y. Lecun, L. Bottou and Y. B. a. P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov 1998.

8. S. Ren, K. He and R. G. a. J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE

Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017.

9. R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015.

10. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy, “Comparative Analysis of K-Means and K-Nearest

Neighbor Image Segmentation Techniques” in IEEE 6th International Conference on Advanced Computing (IACC)Year:February2016,DOI: 10.1109/IACC.2016.27,IEEE Conference Publications.

11. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy "Vegetable-Fruit Identification Based on Intensity and

Texture Segmentation" in special issue of SCOPUS INDEX JOURNAL titled "International Journal of Control Theory and Applications (2016)”.(ISSN : 0974-5572), 12th October, 2016.

12. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy “An optimized approach for enhancement of

13. medical images” in Biomedical Research 7th September 2016, “An International Journal of Medical Sciences”, THOMPSON REUTERS INDEX JOURNAL,(ISSN 0970-938X),Special Issue: S283-S286’

14. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy “Chest CT Scans Screening of COPD based Fuzzy Rule

Classifier Approach “ in International Conference on “Advance in Signal Processing and Communication-SPC 2016”,July 9-10,2016,Proceedings published by WALTER De GRUYTER

15. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy “Recognition and Detection of Object Using Graph-Cut

Segmentation “ in “ 7th World Conference on Applied Sciences, Engineering & Management (WCSEM)” organized by American Business School of Paris, France during 26-27 October 2018. Proceedings ISBN 13: 978-81-930222-3-8 ,SCOPUS

Indexed International Journal of Engineering and Technology, 2227-524X

16. Sufola Das Chagas Silva Araujo, Meenakshi Sundaram Karuppaswamy,V. S. Malemath “Detection and Segmentation of Blood Cells Based on Supervised Learning”

in“IndianJournalofScienceandTechnology,Vol12(20),DOI:10.17485/ijst/2019/v12i20/145114, May 2019

61.

Authors:

Paper Title:

Abstract:

Keyword: References:

330-336

62.

Authors: Ch. Suvarna Ragini

Paper Title: Do Horoscopes Influence the Process of Language Learning? A Model-Based Research Report

Theme: Astronomy

Abstract: As teachers of language it is our common experience that we have to incorporate a plethora of

teaching methodologies in our classroom situations. One specific way of teaching may not be suitable for the

learners with different learning preferences and it may not always be receiving bouquets but brickbats too.

Sometimes we even go to such a stage of contemplation that whether it is destiny which decides upon their

learning potentiality.The main aim of the current study is to explore the relevance between the learners’

horoscopes and learning abilities indicated in Reid’s Perceptual Styles Questionnaire. And, in doing so it studied

the variation in learners’ preferences and their attitudes towards Language learning based upon the predictions

made in their horoscopes.The parameters taken into consideration for testing their language abilities were

Communication, Reading skills and Writing skills. Standardized Model Tests (GRE, TOEFL) were conducted to

evaluate their performances andlater their performancescores were matched with the predictions made in their

horoscopes. Interestingly 90% of the results indicated that the students’ performances matched with the learning

styles and preferences predicted in their horoscopes.Though the study needs further implications, it is believed

that it providesthe teachers with the ability of classifying a mixed-ability classroom and designing relevant tasks

to suit the learners’ interests.

Keyword: Learning abilities, horoscopes, predictions, Perceptual Styles. References:

1. C.I.T.E. Learning Styles Instrument, Murdoch Teacher Center, Wichita, Kansas 67208.)

2. [2] Gavrilov’s (2009) https://projecteuclid.org/euclid.aoas/1239888367

3. Jones, Leo (2007). The student-centered classroom. New York, NY: Cambridge University Press. 4. Linda Geddes (2019) Chasing the Sunhttps://profilebooks.com/chasing-the-sun.html

337-341

5. https://www.narcity.com/ca/on/ottawa/lifestyle/type-student-based-zodiac-sign 6. Premji, Rishad, President- NASSCOM- The National association of Software and Services Companies

7. Ragini, Suvarna(2019) How to develop accuracy in speech and writing skills at tertiary level? A study report, International

journal www.languageinindia.com Vol.19:2 February 2019. 8. Reid’s Perceptual Learning Style Preference Questionnaire

9. (Copyright 1984, by Joy Reid.Explanation of learning styles was adapted from the C.I.T.E. Learning Styles Instrument, Murdoch

Teacher Center, Wichita, Kansas 67208 ) 10. Sheorey, Ravi (1991). An examination of language learning strategy use in the setting of an indigenized variety of

English.System Vol. 27 . 2.

11. Shrivastava, Archana and J. Sundarsingh (2010). Coping with the Problems of Mixed Ability Students.Language in India Vol.

10.1.

63.

Authors: P. Punithavathi, S. Geetha*

Paper Title: Cancelable Biometric Transformations for E-Passport Security

Abstract: E-Passport or Electronic Passport is comprised of a microprocessor chip which contains

biometric data used to authenticate the identity of passport holder. Facial image, fingerprint image and palm-

print image are some of the currently standardized biometric modalities, embedded in the E-Passports. The

privacy of the biometric data becomes a problem during storage and transmission. The biometric data cannot be

revoked or re-issued unlike tokens or passwords, if compromised. The possibility of producing cancelable

templates from the biometric features of user has been studied, thus eliminating threats to privacy and security of

the biometric features. Cancelable template is obtained from original biometric image after intentional/repeated

distortions (based on user-specific key), and thereby providing security to the templates. The distortions can be

implemented either at signal or at feature level. The cancelable templates can be revoked or re-issued whenever

required just by changing the user-specific key. The probability of applying cancelable transformations to secure

the privacy of the biometric features in E-passport has been explored in the article.

Keyword: Biometrics, Cancelable biometrics, E-passport, Template Security, Transformed Template

References: 1. “ICAO,” [Online]. Available: https://www.icao.int/Pages/default.aspx.

2. “EAC,” [Online]. Available:

https://www.bsi.bund.de/EN/Topics/ElectrIDDocuments/SecurityMechanisms/securEAC/eac_node.html. 3. “Wikipedia,” [Online]. Available: https://en.wikipedia.org/wiki/Biometric_passport.

4. V. Pasupathinathan, J. Pieprzyk and H. Wang, “An on-line secure e-passport protocol,” Information Security Practice and

Experience, vol. 4991, pp. 14-28, 2008. 5. A. Juels, D. Molnar and D. Wagner, “Security and privacy issues in e-passports,” in First International Conference on

Security and Privacy for Emerging Areas in Communications Networks, 2005.

6. G. Avoine, K. Kalach and J. J. Quisquater, “E-Passport: securing international contacts with contactless chips,” in International Conference on Financial Cryptography and Data Security, 2008.

7. R. Chaabouni and S. Vaudenay, “The extended access control for machine readable travel documents,” in Proceedings of

the Special Interest Group on Biometrics and Electronic Signatures (No. LASEC-CONF-2009-016), 2009. 8. S. Geetha, P. Punithavathi, A. M. Infanteena and S. S. S. Sindhu, “A Literature Review on Image Encryption Techniques,”

International Journal of Information Security and Privacy, vol. 12, no. 3, pp. 42-83, 2018.

9. C. Soutar, A. Roberge and B. Vijaya Kumar, “ Biometric Encryption using Image Processing,” SPIE, pp. 17-188, 1998. 10. N. Ratha, S. Chikkerur, J. Connell and R. Bolle, “Generating cancelable fingerprint,” IEEE Transactions on Pattern

Analysis and Machine Intelligence, vol. 29, pp. 561-572, 2007. 11. “ISO/IEC FCD 24745,” [Online]. Available: https://www.iso.org/standard/52946.html.

12. P. Punithavathi, S. Geetha, M. Karuppiah, S. H. Islam, M. M. Hassan and K. K .R. Choo. “A lightweight machine learning-

based authentication framework for smart IoT devices”. Information Sciences, 484, 255-268, 2019. 13. P. Punithavathi and S. Geetha, “ (2017). Random Projection-based Cancelable Template Generation for Sparsely

Distributed Biometric Patterns. I, 7(3).,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 7, no. 3,

2017. 14. S. Wang and J. Hu, “Design of alignment-free cancelable fingerprint templates via curtailed circular convolution,” Pattern

Recognition, vol. 47, no. 3, pp. 1321-1329, 2014.

15. F. V. Competition, 2002. [Online]. Available: http://bias.csr.unibo.it/fvc2002/. 16. “Yale face database,” [Online]. Available: http://cvc.yale.

342-346

64.

Authors: Geetha R, Geetha S

Paper Title: Multi-Layered ‘Odd-Even’ Reversible Embedding for Encrypted Images

Abstract: Reversible Data Hiding (RDH) has been under research for the past two decades. Recently RDH

in encrypted images (RDH-EI) draws more attention among the researchers. Since it is used effectively in

privacy protection and cloud computing. In this paper, a RDH-EI using LSB based odd-even embedding

technique scheme is proposed. Initially the cover image is encrypted using block and pixel scrambling and bit

plane mix ordering. Block and pixel scrambling is done through four random walks/Space Filling Curves

(SFCs). Each block and pixel is tested for randomness of information source through each random walk.

Finally the secret message is embedded in each block of image based on testing the pixel value and position

value in the block using LSB embedding. This method avoids overflow/underflow issues that usually happens

in RDH/LSB embedding.

Keyword: encryption, histogram, PSNR, reversible data hiding. References:

1. I. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker, Digital Watermarking and Steganography. San Mateo, CA, USA: Morgan

Kaufmann,2007.

347-351

2. Y. Q. Shi, Z. Ni, D. Zou, C. Liang, and G. Xuan, ``Lossless data hiding:Fundamentals, algorithms and applications,'' in Proc. IEEE Int. Symp.Circuits Syst., vol. 2. May 2004, pp. 33_36.

3. R. Caldelli, F. Filippini, and R. Becarelli, ``Reversible watermarkingtechniques: An overview and a classi_cation,'' EURASIP J.

Inf. Secur.,vol. 2010, 2010, Art. no. 134546. 4. Y. Q. Shi, ``Reversible data hiding,'' in Proc. Int. Workshop Digit.Watermarking, 2004, pp. 1_12.

5. F. Bao, R.-H. Deng, B.-C. Ooi, and Y. Yang, ``Tailored reversible watermarking schemes for authentication of electronic clinical

atlas,''IEEE Trans. Inf. Technol. Biomed., vol. 9, no. 4, pp. 554_563, Dec. 2005. 6. W.Zhang, B.Chen, N.Yu, Improving various reversible data hiding schemes via optimal codes for binarycover, IEEE Trans.Image

Process.21(6) (2012) 2991 – 3003.

7. J. Fridrich, Steganography in Digital Media: Principles, Algorithms, and applications. Cambridge, U.K.: Cambridge Univ. Press, 2009.

8. Tian J (2003) Reversible data embedding using a difference expansion. IEEE Trans. Circuits Syst., 13(8):890–896.

9. Z. Ni, Y.-Q. Shi, N. Ansari, andW. Su, ``Reversible data hiding,'' in Proc .IEEE Int. Symp. Circuits Syst., May 2003, pp. II-912_II-915.

10. D. M. Thodi and J. J. Rodriguez, ``Expansion embedding techniques for reversible watermarking,'' IEEE Trans. Image Process.,

vol. 16, no. 3, pp. 721_730, Mar. 2007 11. D. Coltuc and J. M.Chassery, ``Very fast watermarking by reversible contrast mapping,'' IEEE Signal Process. Lett., vol. 14, no.

4, pp. 255_258, Apr. 2007.

12. S. Weng, Y. Zhao, J.-S. Pan, and R. Ni, ``Reversible watermarking based on invariability and adjustment on pixel pairs,'' IEEE Signal Process. Lett., vol. 15, pp. 721_724, 2008.

13. L. Kamstra and H. J. A. M. Heijmans, ``Reversible data embedding into images using wavelet techniques and sorting,'' IEEE

Trans. Image Process., vol. 14, no. 12, pp. 2082_2090, Dec. 2005.

14. K. Ma, W. Zhang, X. Zhao, N. Yu, and F. Li, ``Reversible data hidingin encrypted images by reserving room before encryption,''

IEEE Trans. Inf. Forensics Security, vol. 8, no. 3, pp. 553_562, Mar. 2013.

15. W. Zhang, K. Ma, and N. Yu, ``Reversibility improved data hidingin encrypted images,'' Signal Process., vol. 94, no. 1, pp. 118_127, Jan. 2014.

16. X. Zhang, ``Reversible data hiding in encrypted image,'' IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255_258, Apr. 2011.

17. S. Lian, Z. Liu, Z. Ren, and H. Wang, ``Commutative encryption and watermarking in video compression,'' IEEE Trans. Circuits Syst. Video Technol., vol. 17, no. 6, pp. 774_778, Jun. 2007.

18. M. Cancellaro, F. Battisti, M. Carli, G. Boato, F. G. B. De Natale, and A. Neri, ``A commutative digital image watermarking and

encryption method in the tree structured Haar transform domain,'' Signal Process., Image Commun., vol. 26, no. 1, pp. 1_12, 2011.

19. R. Schmitz, S. Li, C. Grecos, and X. Zhang, ``A new approach to commutative watermarking-encryption,'' in Proc. 13th Joint

IFIP TC6/TC11Conf. Commun. Multimedia Secur., 2012, pp. 117_130. 20. Jung K, Yoo K (2009) Data hiding method using image interpolation. Comput. Stand. Interfaces, vol: 31 PP: 465–470

21. Luo L, Chen Z, Chen M, Zeng X, Xiong Z (2010) Reversible image watermarking using interpolation technique. IEEE Trans. Inf.

Forensics Secur., 5 (1): 187–193. 22. Abadi MAM, Danyali H, Helfroush MS (2010) Reversible watermarking based on interpolation error histogram shifting. 5th

International Symposium on Telecommunications (IST), Kish Island, Iran, pp. 840– 845

23. Lee CF, Huang YL (2012) An efficient image interpolation increasing payload in reversible data hiding. Expert Systems with

Applications, Elsevier, vol: 39, pp: 6712-6719.

24. Geetha R, Geetha S (2018) Improved Reversible Data Embedding In Medical Images Using I-IWT and Pairwise Pixel Difference Expansion. Smart and Innovative Trends in NGCT 2017, CCIS 828, Springer Nature.pp 601-611

25. R. Geetha and S. Geetha, "Multilevel RDH scheme using image interpolation," 2016 International Conference on Advances in

Computing, Communications and Informatics (ICACCI), Jaipur, 2016, pp. 1952-1956. 26. R. Geetha and S. Geetha, “Embedding Electronic Patient Information in Clinical Images: An Improved and Efficient Reversible

Data Hiding Technique” Multimedia Tools and Applications -Springer Nature (Accepted for publication) DOI: 10.1007/s11042-

019-08484-2

65.

Authors: B. J. Balamurugan, R. Madura Meenakshi

Paper Title: Zumkeller Labeling of Complete Graphs

Abstract: Let G (V, E) be a graph with vertex set V and edge set E. The process of assigning natural

numbers to the vertices of G such that the product of the numbers of adjacent vertices of G is a Zumkeller

number on the edges of G is known as Zumkeller labeling of G. This can be achieved by defining an appropriate

vertex function of G. In this article, we show the existence of this labeling to complete graph and fan graph.

Keyword: Zumkeller Number, Labeling, Injective Function and Complete Graph, Fan Graph. References:

1. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: Strongly multiplicative Zumkeller labeling of graphs. In International

Conference on Information and Mathematical Sciences. Elsevier (2013) 349−354.

2. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: Strongly multiplicative Zumkeller labeling for acyclic graphs.

Proceedings of International Conference on Emerging Trends in Science, Engineering, Business and Disaster Management

(ICBDM - 2014). to appear in IEEE Digital Library. 3. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: Zumkeller labeling of some cycle related graphs. Proceedings of

International Conferenc on Mathematical Sciences (ICMS - 2014). Elsevier (2014) 549−553.

4. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: Zumkeller labeling algorithms for complete bipartite graphs and wheel

graphs. Advances in Intelligent Systems and Computing. Springer. 324 (2015) 405−413.

5. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: Algorithms for Zumkeller labeling of full binary trees and square grids.

Advances in Intelligent Systems and Computing, Springer. 325 (2015) 183−192.

6. Balamurugan, B.J., Thirusangu, K., Thomas, D.G.: k-Zumkeller labeling for twig graphs. Electronic Notes in Discrete

Mathematics, Elsevier, 48(2015) 119 -126. 7. Gallian, J.A.: A dynamic survey of graph labeling. Electronic Journal of Combinatorics. 17 (DS6) (2013).

8. Gary S. Bloom and Solomon W.Golomb.: Applications of numbered undirected graphs. Proceedings of the IEEE, Vol. 65,

No.4, (April 1977) 562-570. 9. Harary, F.: Graph theory. Addison-Wesley. Reading Mass (1972).

10. Jonathann Webb, Fernando Docemmilli, Makhailn Bonin,: Graph Theeory Applications in network Security. Cyber

Security & Digital Forencis Workshops, Australia (September 2015), 11. Krishnnappa H. K, N.K.Srinath and P.Ramakanth Kumar, Vertex Magic Total Labelings of complete graphs, IJCMSA., Vol

352-355

4, No 1-2(2010) 157-169 12. Krishnnappa H. K, N.K.Srinath and P.Ramakanth Kumar, Vertex Magic Total Labelings of complete graphs and their

application for public-key cryptosystem, IJIRCCE, Vol. 1, Issue 2, (April 2013) 379-387

13. Rosa, A.: On certain valuations of the vertices of a graph. In N.B. Gordan and Dunad, editors, Theory of graphs.

International Symposium. Paris (1966) 349−359.

14. Yuejian Peng, Bhaskara Rao, K.P.S.: On Zumkeller numbers. Journal of Number Theory. 133(4) (2013) 1135−1155.

15. Zongheng Zhou, S. Das and H. Gupta.: Connected k-coverage problem in sensor networks, Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969), Chicago, IL, (2004) 373-378

66.

Authors: S. Sreenivasan, B. J. Balamurugan

Paper Title: On Computing Cluster Centers of Trapezoidal Fuzzy Numbers

Abstract: In this paper we compute cluster centers of trapezoidal fuzzy numbers through fuzzy c means

clustering algorithm and kernel based fuzzy c means clustering algorithm. A new complete metric distance

between the trapezoidal fuzzy numbers is used to compute the cluster centers on the set of trapezoidal fuzzy

numbers.

Keyword: Fuzzy clustering, Kernel function, Trapezoidal fuzzy numbers, Fuzzy C number clustering

algorithms. References:

1. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, (Plenum Press, New York, 1981). 2. D Dubois and H Prade. Fuzzy sets and systems: Theory and applications, (Academic Press, New York, 1980).

3. J. C. Dunn, “A fuzzy relative of the ISODATA process and Its Use in Detecting Compact”, well-Separated Cluster, J. Cybernet,

1974, 3, pp. 32 - 57. 4. Hadi Sadoghi Yazdi, Mehri Sadoghi Yazdi , Abedin Vahedian, “Fuzzy Bayesian Classification of LR Fuzzy Numbers”, IACSIT

International Journal of Computer Theory and Engineering, Vol. 1, No. 5, December 2009.

5. R Herbrich, Learning Kernel Classifier, (MIT Press, Cambridge, MA 2002). 6. W. L. Hung and M. S. Yang, “Fuzzy Clustering on LR-Type fuzzy numbers With an Application in Taiwanese Tea Evaluation”,

Fuzzy sets and systems, 2005, 150(3), pp. 561 - 577.

7. Meena Tushir, Smriti Srivastava, “A New Kernel based Hybrid c Means Clustering model”, IEEE international Fuzzy systems Conference, 2007.

8. K R Muller, S Mika, G Ratsch, K Tsuda, B Scholkopf, “An introduction to kernel-based learning algorithms”, IEEE Transaction

on Neural Networks 12(2), 2001, pp. 181-201. 9. Rong Lan, Jiu-Iun Fan, “A Fuzzy C Means Type Clustring Algorithm on triangular Fuzzy Numbers”, Sixth international

conference on Fuzzy Systems and Knowledge Discovery, 2009.

10. S Sreenivasan and B J Balamurugan, “Computing Cluster Centers of Trapezoidal Fuzzy Numbers Through Fuzzy C Means and Kernel Based Fuzzy C Means Clustering Algorithms with Two Metric Distances Using Matlab”, International Journal of Civil

Engineering and Technology (IJCIET) 9(10), 2018, pp. 1322–1330.

11. M S Yang, C H Ko, “On a Class of fuzzy c Numbers Clustering Procedure for Fuzzy Data”, Fuzzy sets and systems, 1996, 84(1), 49 - 60.

12. D Zhang, S Chen, “Fuzzy clustering kernel method”, in: Proc of the Internat. Conf. on Control and Automation, 2002, pp. 123-

127. 13. S Zhou, J Gan, “Mercer kernel fuzzy c means algorithm and prototypes of clusters”, in: Proc. of Conf. on Internat. Data

Engineering and Automated Learning, Vol 3177, 2004, pp. 613-618.

356-359

67.

Authors: S Venkatesh, B J Balamurugan

Paper Title: Narayana Prime Cordial Labeling of Complete Graph and Complete Bipartite Graph

Abstract: Complete bipartite graph and complete graph are very important graphs and they find vital

applications in the field of computer science. In this paper we compute the labels 0 and 1 to the edges of these

graphs by satisfying the cordiality condition using prime numbers and Narayana numbers. We use divisibility

concepts while computing the NP-cordial graphs.

Keyword: Narayana numbers, Prime numbers, NP cordial graph. References:

1. B.D. Acharya, S.M. Hegde, Arithmetic Graphs, J. Graph Theory, 14(3) (1990), 275–299.

2. B.J. Balamurugan, K. Thirusangu, B.J. Murali, J.Venkateswara Rao, J.(2019), Computation of Narayana Prime Cordial Labeling

of Book Graphs, Applied Mathematics and Scientific Computing. 3. J.A. Gallian, A Dynamic Survey of Graph Labeling, Electronic Journal of Combinatorics, DS6 (2018).

4. F. Harary, Graph Theory, Addison-Wesley, Reading Mass (1972).

5. N. Lakshmi Prasana, K. Saravanthi, Nagalla Sudhakar, Applications of Graph Labeling in Major Areas of Computer Science, International Journal of Research in Computer and Communication Technology, 3(8) (2014).

6. Miklos Bona, Bruce E. Sagan, On Divisibility of Narayana Numbers by Primes, Journal of Integer Sequences, 8 (2005).

7. B.J. Murali, K. Thirusangu, B.J. Balamurugan, Narayana Prime Cordial Labeling of Graphs, International Journal of Pure and Applied Mathematics. 117(13), (2017) 1-8.

8. M. Randic, D. Morales, O. Arauji, Higher-order Lucas Numbers, Divulgaciones Mathematicas, 16(2) (2008), 275–283.

9. Rosa, On Certain Valuations of the Vertices of a Graph, In Theory of Graphs (Internat. Sympos. Rome. 1966), Gordan and Breach. Newyork. Dunod. Paris (1967), 349–359.

10. Thomas Koshy, Catalan Numbers with Applications, Oxford University Press (2009).

360-365

68.

Authors: Raunav Chitkara, John Rajan A

Paper Title: BDCPS — A Framework for Smart Manufacturing Systems using Blockchain Technology

Abstract: Blockchain is going to be the most fundamental technology, and will change the world —

going forward. In fact, the revolution has already begun. The birth of Industry 366-378

4.0 aka the Fourth Industrial Relution (I4.0), has created a need for autonomous and integrated, secure

manufacturing systems. The current smart systems lack the decentralized decision making and real-time

communication infrastructure, which is a condition for adaptive, smart manufacturing systems. In this paper, an

autonomous, secure and collaborative platform based on Blockchain technology, is presented to adapt to such

results. In support with Internet of Things (IoT) and cloud services, a Blockchain Driven Cyber Physical

Production System (BDCPS) architecture is designed to communicate with machines, users, devices, suppliers

and other peers.

Using the Smart Contracts feature and trust-less peer-to-peer decentralized ledger feature, BDCPS will validate

the claim with a small-scale real-life Blockchain with IoT system. This implementation case study will be

running a private Blockchain on a single board computer, and bridged to a microcontroller containing IoT

sensors. The applications of this system in automotive manufacturing industry are presented, to proceed towards

Industry 4.0.

Keyword: Automotive, Blockchain, Industry 4.0, Internet of Things, Manufacturing, Smart Manufacturing References:

1. No Title, (n.d.). https://bitcoin.org/bitcoin .pdf. 2. D. Revan, No Title, (n.d.). https://mediu m.com/@darthrevan344/blockchain-ethereum-io t-poc-machine-

maintenance-part-i-272524c16edf.

3. N. Mohamed, J. Al-jaroodi, Applying Blockchain in Industry 4 4. . 0 Applications, 2019 IEEE 9th Annu. Comput. Commun. Work. Conf. (n.d.) 852–858. doi:10.1109/CCWC.2019.8666558.

5. No Title, (n.d.). https://developer.ibm.co m/tutorials/cl- blockchain-basics-intro-bluemix-trs/.

6. K.D. Jebessa, Decentralization of Power and Local Autonomy in Ethiopian Federal System: A Look at Two Decades Experiment, Http://Www.Sciencepublishinggroup.Com. 1 (2016) 45. doi:10.11648/J.URP.20160103.11.

7. No Title, (n.d.). https://medium.com/@siddharthram/simplest

8. -explanation-of-blockchain-technology-in-10-minutes-1-2-77cb42c 3eb9c. 9. No Title, (n.d.). https://hackernoon.com/merkle-trees-181cb4bc 30b4.

10. No Title, (n.d.). https://hackernoon.com/consensus-mechanisms

11. -explained-pow-vs-pos-89951c66ae10. 12. No Title, (n.d.). https://coolwallet.io/ethmining/.

13. No Title, (n.d.). https://searchsqlserver.techtarget.com/definiti on/hashing.

14. No Title, (n.d.). https://medium.com/@abhibvp003/smart-cont racts-on-the-blockchain-adeep-dive-in-to-smart-contracts-9616ad2 6428c.

15. No Title, (n.d.). https://en.wikipedia.org/wiki/Cyber-physical_ system.

16. W.G. April, 001.Recommendations for implementing the strategic, Acatech. (2013) 4–7. doi:10.13140/RG.2.2.14480.20485.

17. No Title, (n.d.). https://medium.com/air pod-blog/airpod-is- taking-part-in-industry-4-0-ee 35ddeadc5f.

18. P. Pinheiro, R. Barbosa, Highlights of Practical Applications of Heterogeneous Multi- Agent Systems. The PAAMS Collection, 430 (2014) 149–160. doi:10.1007/978-3-319-07767-3.

19. Deloitte, Accelerating technology disruption in the automotive market. Blockchain in the automotive industry, (2018). https://www

2.deloi 20. tte.com/uk/en/pages/consumer-industrial- products/articles/automotive-blockchain.html.

21. C. Lehmann, C. Schock, R. Technology, Requirements for Blockchain Applications in Manufacturing Small and Medium Sized

Enterprises, 24th Int. Conf. Prod. Res. (ICPR 2017). (2017) 155–158. 22. G. Management, M. Consulting, C. White, Blockchain - Technologies for the Automotive Industry, (2017). https://www.gin

kgo.com/wp-c ontent/uploads/2017/06/WP_Blockchain-online .pdf.

23. How Blockchain Can Slash the Manufacturing “ Trust Tax ,” (n.d.). 24. P. Zheng, H. Wang, Z. Sang, R.Y. Zhong, Y. Liu, C. Liu, K. Mubarok, S. Yu, X. Xu, Smart manufacturing systems for Industry

4.0: Conceptual

25. framework, scenarios, and future perspectives, High. Educ. Springer-Verlag. 13 (2018) 137–150. doi:10.1007/s11465-018- 26. 0499-5.

27. A. Bahga, V.K. Madisetti, Blockchain Platform for Industrial Internet of Things, J. Softw. Eng. Appl. 09 (2016) 533–546.

doi:10.4236/jsea.2016.910036.

28. M. Marques, C. Agostinho, G. Zacharewicz, R. Jardim- Gonçalves, Decentralized decision support for intelligent manufacturing

in Industry 4.0, J. Ambient Intell. Smart Environ. 9 (2017) 299–313. doi:10.3233/AIS-170436.

29. Z. Li, A.V. Barenji, G.Q. Huang, Toward a blockchain cloud manufacturing system as a peer to peer distributed network platform, Robot. Comput. Integr. Manuf. 54 (2018) 133–144. doi:10.1016/j.rcim.2018.05.011.

30. M. Isaja, J. Soldatos, Distributed ledger technology for decentralization of manufacturing processes, 2018 IEEE

Ind. Cyber-Physical Syst. (2018) 696–701. doi:10.1109/ICPHYS.2018.8390792. 31. V. Dieterich, M. Ivanovic, T. Meier, S. Zäpfel, M. Utz,

32. P. Sandner, Application of Blockchain Technology in the Manufacturing Industry, (2017) 1–23. www.twitter.com/fsblo

ckchain%0Awww.facebook.de/fsblockchain. 33. K. Subbiah, B. Ferrarini, J. Maupin, M. Hinojales,

34. S. Kulshrestha, R. Guhathakurta, D. Wright, The Age of Blockchain: A Collection of Articles, (2018) 1–28.

doi:10.5281/ZENODO.1202390. 35. N.M. Kumar, P.K. Mallick, Blockchain technology for security issues and challenges in IoT, Procedia Comput. Sci. 132 (2018)

1815–1823. doi:10.1016/j.procs.2018.05.140.

36. G. Halse, What is the future of blockchain in manufacturing?, IT Manuf. (2018). http://www.instrumentation.co.za/9104a.

69.

Authors: Priscilla Rajadurai, M. Karthi, C. Niroshini Infantia, V. Neelambary, Lokeshwar Kumar Tabjula

Paper Title: Detection of Copy Right Infringement of Audio in on-Demand Systems using Audio Fingerprinting

Abstract: Today, with an evolution of large public media databases, repositories and services such as Sound

Cloud, Spotify, Wynk, YouTube and Saavn, the chances of intentional and unintentional plagiarism is been

increasing at an exponential rate. The paper proposed a solution which is used to detect copy right infringement

of audio in real time systems using the technique called Audio Fingerprinting. The resulting software will be

able to process an audio file, generate an audio fingerprint that can be stored and searched to find the matched

values in a database. 379-382

Keyword: copy right, audio fingerprint, digital signal processing, music, hash function, plagiarism detection,

license, audio database. References:

1. S. Chu, S. Narayanan and C. C. J. Kuo, “Environmental Sound Recognition With Time-Frequency Audio Features,” in IEEE

Transactions on Audio, Speech, and Language Processing, vol. 17, no.6, pp. 1142-1158, Aug. 2009. 2. P. Cano et al., “A review of Audio Fingerprinting”, J.VLSI Signal Process., vol 41.,no.3 2005

3. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1999.

4. S.Subramanya, R.Simba, B. Narahari, and A.Youssef, “Transform-based indexing of audio data for multimedia databases.”, in Proc. Of Int. Conf. on Computational Intelligence and Multimedia Applications, New Delhi, India, Sept. 1999.

5. A.Kimura, K. Kashino, T. Kurozumi and H. Murase, “Very quick audio searching: introducing global pruning to the time-series

active search” in Proc. Of Int. Conf. on Computational Intelligence and Multimedia Applications, Salt Lake City, Utah, May 2001.

6. Yu Liu, Hwan Sik Yun, and Nam Soo Kim, “Audio Fingerprinting Based on Multiple Hashing in DCT Domain”, IEEE signal Processing letters, vol., 16,no,6,June 2009.

7. Priscilla, R., and M. Karthi. "Usage of Bioinformatic Data for Remote Authentication in Wireless Networks." ICTACT Journal on

Image & Video Processing 9, no. 1 (2018). 8. Gladence LM, Ravi K, Karthi M. An Enhanced Method For Detecting Congestive Heart Failure -Automatic Classifier.

Ramanathapuram: IEEE International Conference on Advanced Communication Control and Computing Technologies,

ICACCCT-2014. 2014; p. 586-90

9. Shumeet Baluja and Michele Covell, “ Content Fingerprinting Using Wavelets” in Proceedings of 3rd European conference on

Visual Media Production(CVM), London UK, 2006,pp,198-207.

10. J.Haitsma, T. Kalker, and J. Oostven, “Robust audio hashing for content identification,” in Procs. Of the International Workshop on Content-Based Multimedia Indexing, oct-2001.

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70.

Authors: C. Sridevi, M.Kannan

Paper Title: Lung Segmentation and Iterative Weighted Averaging Smoothing Technique on Chest Ct Images

Abstract: Computed Tomography (CT) is one of the most commonly used imaging modalities for tumour

detection and diagnosis, due to its high spatial resolution, fast imaging speed and wide availability. Nodules of

the lungs and pathological residues with varied diameter can be comfortably viewed by computed tomography

and can be categorized as benign or malignant. The key intention of this segmentation and smoothing is to

develop an efficient methodology for an automated lung tumour diagnosis. Segmentation is the quantitative pre-

processing step used in the chest CT analysis. The iterative weighted averaging technique is used in addressing

the issues related to the segmentation and smoothing method employed in this paper. The methodology

incorporates the accurate Lung Segmentation, enclosure of Juxtapleural nodules, the proper insertion of the

bronchial vessels for enhancing the smoothness of the segmented Lung area. The steps involved in this paper

include Image preprocessing by nonlinear anisotropic diffusion filtering, Thorax Extraction, Lung segmentation

and iterative weighted averaging technique for smoothing the contours. The main feature in choosing the

nonlinear anisotropic diffusion filtering is for properly preserving the regions unaffected with cancer and also to

differentiate the artefacts involved due to the subject movements. In the fuzzy c- means clustering algorithm, the

lung parenchyma is identified from the thorax region based on the fuzzy membership value and the affected

regions are attached. The standard execution time for segmentation process of one dataset is 1.91s, it is the more

rapid process than the manual segmentation.

Keyword: Lung Segmentation, Lung Nodules, Fuzzy c-means Clustering, Iterative Weighted Averaging. References:

1. J. Jeya Caleb, M. Kannan, Design of an Improved K-Means Algorithm for the Clustering Of Data, Journal of Chemical and Pharmaceutical Sciences, Volume 10 Issue 3, ISSN: 0974-2115, July – September 2017.

2. J. Jeya Caleb, M. Kannan, VLSI Implementation of Constructive Neural Network for Skin Cancer Detection, Journal of

Computational and Theoretical Nanoscience, Vol. 15, 485–492, 2018. 3. Ratishchandra Huidrom, Yambem Jina Chanu, Khumanthem Manglem Singh, A Fast Automated Lung Segmentation Method for

the Diagnosis of Lung Cancer, Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, November 5-8, 2017, 1499 - 1502.

4. Bhagyarekha U. Dhaware, Anjali C. Pise, Lung Cancer Detection Using Bayasein Classifier and FCM Segmentation, 2016

International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), 978-1-5090-2080-5/16, 2016 170 -174.

5. Shengjun Zhou, Yuanzhi Cheng, Shinichi Tamura, Automated lung segmentation and smoothing techniques for inclusion of

juxtapleural nodules and pulmonary vessels on chest CT images, journal on Biomedical Signal Processing and Control, Volume 13 , Pages 62-70, September 2014.

6. P.R. Varshini, S. Baskar, S. Alagappan, An improved adaptive border marching algorithm for inclusion of juxtapleural nodule in

lung segmentation of CT-images, IEEE Int. Conf. Image Process. (2012) 230–235. 7. S. Sivakumar, C. Chandrasekar, Lungs image segmentation through weighted FCM, in: IEEE International Conference on Recent

Advances in Computing and Software Systems, 2012, pp. 109–113.

8. Q. Li, F. Li, K. Suzuki, J. Shiraishi, H. Abe, R. Engelmann, Y. Nie, H. MacMahon, K.Doi, Computer-aided diagnosis in thoracic CT, Seminars in Ultrasound, CT, MRI 26 (5) (2005) 357–363.

9. Cancer Facts & Figures, American Cancer Society: Cancer Statistics, 2012 http://www.cancer.org.

10. C.I. Henschke, D.I. McCauley, D.F. Yankelevitz, D.P. Naidich, G. McGuinness, O.S.Miettinen, D.M. Libby,M.W. Pasmantier,J.Koizumi, N.K.Altorki,J.P. Smith, Early lung cancer action project: overall design and findings from baseline

screening, Lancet 354 (9173) (1999) 99–105.

11. S.G. Armato, W.F. Sensakovic, Automated lung segmentation for thoracic CT: impact on computer-aided diagnosis, Acad. Radiol. 11 (9) (2004) 1011–1021.

12. R. Bellotti, F. De Carlo, G. Gargano, S. Tangaro, D. Cascio, E. Catanzariti, P. Cerello,S.C. Cheran, P. Delogu, I. De Mitri, C.

Fulcheri, D. Grosso, A. Retico, S. Squarcia, E.Tommasi, B. Golosio, A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model, Med. Phys. 34 (12) (2007) 4901–4910.

383-387

13. J. Pu, J. Roos, C.A. Yi, S. Napel, G.D. Rubin, D.S. Paik, Adaptive border marching algorithm: automatic lung segmentation on chest CT images, Comput. Med.Imaging Graph. 32 (6) (2008) 452–462.

14. E.M.Van Rikxoort, B. de Hoop, M.A. Viergever, M. Prokop, B. van Ginneken,Automatic lung segmentation fromthoracic

computed tomography scans using a hybrid approach with error detection, Med. Phys. 36 (7) (2009) 2934–2947. 15. Y. Yim, H. Hong, J. BeomSeo, N. Kim, E. Jin Chae, Y. Gil Shin, Correction of lung boundary using the gradient and intensity

distribution, Comput. Biol. Med. 39(3) (2009) 239–250.

16. [16] Shakouri G., Hamed & B Menhaj, M & Moradmand, Hashem, Introduction to an Iterative Weighted Mean Smoothing Filter Based on a Simple Fuzzy Rule. Journal of Amirkabir University of Technology. 2006. 27-33.

71.

Authors: Mohammed Shoaib Syed, Deshmukh Saira Khanam, Shaik Abdul Khader Jilani, Jeevan Kumar

Rapole and Adeel Ahmad

Paper Title: Physical Properties of Surrogate’s Blood

Abstract: The present paper discusses methods to determine Coefficient of Viscosity, Volume flow rate,

Surface Tension, Turbidity, Size and shape of Surrogate’s Red Blood Cells with the help of different techniques

like “Capillary tube method” & “Laser diffraction technique” designed and developed in our Biophysics

Laboratory and comparison with the normal pregnant women’s blood. Significant variations are observed in the

trails. This variation may be attributed to the number of factors including past reproductive history, maternal

age, reason of fertility and life style factors of surrogate’s and normal pregnant women. This investigation

identifies grounds of several adverse effects/conditions being associated with Surrogacy and compare with

Normal pregnancy. Further discussed the more interesting results along with future directions.

Keyword: Human Blood, Viscosity, Volume flow rate, Surface Tension, Size and shape of Red blood cell,

Turbidity, Surrogacy, Capillary tube method, and Laser Diffraction Technique. References:

1. Imrie, Susan; Jadva, Vasanti (4 July 2014). "The longterm experiences of surrogates: relationships and contact with surrogacy families in genetic and gestational surrogacy arrangements”. Reproductive BioMedicine Online. 29 (4): 424–435.

doi:10.1016/j.rbmo.2014.06.004.

2. http://www.people.com/people/archive/article/0,,20096199,00.html 3. Merino, Faith (2010). Adoption and Surrogate Pregnancy. New York: Infobase Publishing.

4. See Tong, Rosemarie (2011). "Surrogate Parenting" (http://www.iep.utm.edu/surrpar/). Internet Encyclopedia of Philosophy.

5. Longhurst, Geometrical and physical optics, Longmans Green & Co. Ltd., London, 1964. 6. Gopala Krishna G, Kaleem Ahmad Jaleeli and Adeel Ahmad “National Symposium on Cellular and Molecular Biophysics, 1995,

Nizam College, (Autonomous) Osmania University, Hyderabad.

7. Gopala Krishna G, Anwer Ali AKW and Adeel Ahmad, Indian J. Exp. Bio. Vol. 21, 383- 385, 1983a.

8. J. Calthrope, Advanced experiments in practical optics, William Heinemann Ltd., London, 1952.

9. Charles, F. Mayer, The diffraction of light x-ray and material particles, University of Chicago Press, Chicago, Illinois, 1934.

10. Syed Mohammed Shoaib et al 3rd International Conference on Medical Physics in Radiation Oncology and Imaging (ICMPROI-2018), Dhaka, Bangladesh.

388-391

72.

Authors: C. Pavithra, W. Madhuri, S. Roopas Kiran, N. Arunai Nambi Raj, K. V. Siva Kumar

Paper Title: Elastic and Anelastic Behavior of Microwave Sintered BCT-BST Ceramics

Abstract: Lead free 0.55(Ba0.9Ca0.1) TiO3-0.45Ba (Sn0.2Ti0.8) O3 (BCT-BST) ceramic is synthesized

by three different techniques viz solid state method, sol-gel method and molten-salt method. The prepared BCT-

BST ceramic samples exhibited cubic crystal structure on X-ray powder diffraction analysis. The morphology of

the samples is analyzed using transmission electron microscope. The lattice interactions with ultrasonic waves

and morphotropic phase boundary at 70oC is confirmed from elastic and anelastic studies of the ceramic. High

piezoelectric coefficient d33 of 623 pC/N is achieved in the prepared BCT-BST ceramics.

Keyword: Sol-gel method, Solid state method, Molten-salt method, internal friction, longitudinal modulus. References:

1. Hans Jeffe (1958) “Piezoelectric ceramics” Journal of American Ceramic society, 41 (11), 299-506

2. Kailun Zoua, Yu Dan, Haojie Xu, Qing Feng Zhang, YinmeiLu, Haitao Huang, Yunbin He (2019) “Recent advances in lead-free

dielectric materials for energy storage” Materials research bulletin 113, 190 - 201 3. Zhu, L. F., Zhang, B. P., Zhao, L., Li, S., Zhou, Y., Shi, X. C., Wang, N. (2016). 'Large Piezoelectric Effect of (Ba,Ca)TiO3 – X

Ba(Sn,Ti)O3 Lead-Free Ceramics'. Journal of the European Ceramic 36, 1017–24

4. Mason, P. W. (1985), “Physical Acoustics and the Properties of Solids”, Van Nostrand, Princeton, New Jersey 5. Love, H. E. A. (1934), “Theory of Elasticity”, Cambridge University Press, London and New York.

6. Kolsky, H. (1953), “Stress Waves in Solids”, Clarendon Press, Oxford.

7. Prager, W. (1961), “Introduction of Mechanics of Continua”, Ginna nd journalsco., Boston, Massachussetts.

8. Sommerfeld, A. (1950), “Mechanics of Deformble Bodies”, Academic Press, New York. 9. Bhimsenachar, J. (1962),” Internal Friction Behavior”, Journal of Annamalai University, XXII, 14. 10. Manson, P. W. (1965), (Ed.), “Physical Acoustics, Principles and Methods”, Part B. Academic Press, New York, 3.

11. Manson, P. W. (1966), (Ed.), “Physical Acoustics, Principles and Methods”, Part A. Academic Press, New York, 3. 12. Truel, R., Elbaum, C. and Chick, B. B. (1969), “Ultrasonic Methods in Solid State Physics”, Academic Press, New York.

13. Seitz, F. and Turnbull, D. (1958), (Ed.) “Solid State Physics, Advances in Research and Applications”, Academic Press, New

York, 7. 14. Puškár, A. (2001), ‘Internal Friction of Materials’, Cambridge International Science Publishing.

15. Ramana, M. V., and Reddy, N. R. (2010), ‘Internal friction and dielectric permittivity studies: barium lead titanate ferroelectric

ceramics’, Physica Scripta, 82(6), 065601. 16. Zhao, C., Hui, W., Jie, X. and Jiagang W. (2016), 'Composition-Driven Phase Boundary and Electrical Properties in

(Ba0.94Ca0.06)(Ti1–xMx)O3 (M = Sn, Hf, Zr) Lead-Free Ceramics'. Dalton Transactions 45, 6466–80.

17. Yuvaraj, S., Nithya, V. D., Fathima, K. S., Sanjeeviraja, C., Selvan, G. K., Arumugam, S., Selvan, R. K. (2013), 'Investigations on the Temperature Dependent Electrical and Magnetic Properties of NiTiO3 by Molten Salt Synthesis'. Materials Research

392-396

Bulletin 48(3), 1110–16. 18. S.A. Gridnev, I. I. Popov (2019), ‘Effect of electronic subsystem on elastic and anelastic properties of ceramic Ba0.2Sr0.8TiO3’,

Ferroelectrics, 543, 130-136.

19. V. S. Postnikov, S. A. Gridnev, B. M. Darinskii and I. N. Sharshakov (1976) ‘Internal friction at first order phase transition in solids’, Il Nouvo Cimento B, 33B(1), 324-337.

20. Gridnev, S. A., (2007), ‘Low-Frequency Shear Elasticity and Mechanical Losses in Ferroelastics’, Ferroelectrics, 360,1–24.

73.

Authors: Anju Varghese, Anusha.S.R, A. Anita Angeline,.Kanchana Bhaaskaran.V.S

Paper Title: Clock Delayed Dual Keeper Domino-Logic Design with Reduced Switching

Abstract: Clock Delayed Dual Keeper domino logic style with Static Switching mechanism (CDDK_SS)

using delayed enabling of the keeper circuit and modified discharge path has been proposed in this paper. In

CDDK domino circuit, the principle of delayed enabling of keeper circuit offers reduced contention between

keeper circuit and Pull Down Network (PDN). The modified discharge path at the output node eradicates the

switching at the output node for identical TRUE inputs during the pre-charge phase. This facilitates in obtaining

static like output in contrast with conventional domino logic. The simulation results of Arithmetic and Logic

Unit (ALU) subsystems demonstrate 17.7% reduction in dynamic power consumption while compared to

conventional domino logic. Furthermore, 62% enhancement in speed performance has been achieved with good

robustness. Design and simulation have been executed using Cadence® Virtuoso, with UMC 90nm technology

node library.

Keyword: Domino logic, Keeper transistor, Static Switching Mechanism, High speed, Low Power

Consumption References:

1. Jan M RabaeyAnanthaChandrakasanBorivoje Nikolic Digital Integrated Circuits- A Design Perspective,2nd edition, Prentice Hall,

2003

2. Krambeck, R. H., Charles M. Lee, and H-FS Law, ‘High-Speed Compact Circuits with CMOS’," IEEE Journal of Solid-State Circuits, (1982),17.3: 614-619.

3. Moradi F, Cao TV, Vatajelu EI, Peiravi A, Mahmoodi H, Wisland DT, ‘Domino Logic Designs for High- Performance and

Leakage-Tolerant Applications’, Integration, The VLSI Journal, 2013 Jun 30;46(3):247-54. 4. Sujeet Kumar, SanchitSingal, Amit-Kumar Pandey and R. K..Nagaria, Design and Simulation of Low Power Dynamic Logic

Circuits-Using-Footed Diode Domino Logic,IEEE SCES(2013)

5. Angeline, A.A. and Bhaaskaran, V.K., ‘High Performance Domino Logic Circuit Design by Contention Reduction’, VLSI

Design: Circuits, Systems and Applications Springer, Singapore, 2018, (pp. 161-168).

6. Anita Angeline, A., and V. S. Kanchana Bhaaskaran. "High speed wide fan‐in designs using clock controlled dual keeper domino

logic circuits." ETRI Journal (2019).

7. Garg, Sandeep, and Tarun Kumar Gupta, ‘Low Power Domino Logic Circuits in Deep-Submicron Technology using CMOS’, Engineering Science and Technology, an International Journal(2018), 625-638.

8. Angeline, A. A., & Bhaaskaran, V. K. (2018). High Performance Domino Logic Circuit Design by Contention Reduction. In

VLSI Design: Circuits, Systems and Applications (pp. 161-168). Springer, Singapore. 9. Fang Tang, Amine Bermak, ZhoyueGue, Low Power Pseudo Dynamic buffer, Integration The VLSI Journal 45(2012) 395-404.

10. Belluomini, W. A., & Saha, A. M. (2009). U.S. Patent No. 7,598,774. Washington, DC: U.S. Patent and Trademark Office.

11. Siva Kumar Akurati ,A. Anita Angeline , V S Kanchana Bhaaskaran ALU design using Pseudo Dynamic Buffer based domino logic, IEEE ICNETS2(2017) 289-295.

12. Computer Arithmetic: Algorithms and Hardware Designs, Oxford University Press, New York, 2000 , by Behrooz Parhami.

397-402

74.

Authors: Latha R, Vetrivelan P

Paper Title: Patient History-driven Framework for Healthcare Analytics

Abstract: Wireless body are network (WBAN) is evolving more rapidly due to the development of internet

of things (IoT). Decision making is the main concern in medical field which leads to optimization. Medical

evidences in patient care improve optimization in patient care. Partially observable markov decision process

(POMDP) helps in making accurate decisions with the help of observations and past actions in medical field.

Hence dynamic decision making makes it possible. In POMDP, the incremental method is designed to

incorporate any immediate change and immediately send updates. In this paper, process mining is applied in

finding the history of patients who are travelling from one country to another for in search of job or for doing a

major clinical operation. Event data is very much important for handling patient’s history. Event data stores the

date and time at which the patient gets consultation. Electronic medical records (EMR) are nothing but storage

of all the event data of patients visiting the hospital. Event data gives the evidence of patients when they had a

consultation with a doctor. Event data is present anywhere. Partially Observable Markov Decision Process for

Patient-history and Careflow mining Algorithm for Heuristic Comparison are presented in this paper. Process

mining gives a direct relationship by step by step evaluation and improvement of the process. It also exhibits

patient care by identifying the execution errors, understanding the process heterogeneity. The online process

mining tool, PROM helps finding the history of patient.

Keyword: Decision making, IoT, POMDP, PROM, WBAN References:

1. R. Mans, W. van der Aalst, R.J. Vanwersch, “Process Mining in Healthcare: Evaluating and Exploiting Operational Healthcare Processes”, Springer, 2015, pp. 17-26.

2. P. Homayounfar, “Process mining challenges in hospital information systems”, Federated Conference on Computer Science and

Information Systems (FedCSIS), IEEE, 2012, pp. 1135-1140. 3. M. Jansen-Vullers, H.A. Reijers, “Business process redesign in healthcare: towards a structured approach”, Inform. Syst. Oper.

403-408

Res., 2005, pp. 321-339. 4. R. Grol, J. Grimshaw, “Evidence-based implementation of evidence-based medicine”, Joint Commiss. J. Qual. Improve., 1999,

pp. 503-513.

5. Z.J. Radnor, M. Holweg, J. Waring, “Lean in healthcare: the unfilled promise?” Soc. Sci. Med., 2012, pp. 364-371. 6. W. Van Der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer Science &

Business Media, vol. 2, 2011.

7. W.M.P. van der Aalst, A.J.M.M. Weijters, “Process mining: a research agenda”, Comput. Ind., 2004, pp. 231-244. 8. R. Mans, W.M. van der Aalst, N.C. Russell, P.J. Bakker, A.J. Moleman, “Process-aware information system development for the

healthcare domain-consistency, reliability, and effectiveness”, Business Process Management Workshops, Springer, 2009,

pp. 635-646. 9. Diego Calvanese, Marco Montali, Alifah Syamsiyah, Wil M.P. van der Aalst. “Ontology-Driven Extraction of Event Logs from

Relational Databases”. In Business Process Management Workshops, 2015.

10. Leemans, Maikel, van der Aalst, Wil M.P. “Process mining in software systems: Discovering real-life business transactions and process models from distributed systems”. In International Conference on Model Driven Engineering Languages and Systems,

2015.

11. Patrick Mukala, Joos Buijs, Maikel Leemans, Wil van der Aalst. “Learning Analytics on Coursera Event Data: A Process Mining Approach”. In 5th International Symposium on Data-driven Process Discovery and Analysis, 2015.

12. Alfredo Bolt, Massimiliano de Leoni, Wil van der Aalst, “Pierre Gorissen. Exploiting Process Cubes, Analytic Workflows and

Process Mining for Business Process Reporting: A Case Study in Education”. In International Symposium on Data-driven Process Discovery and Analysis, 2015.

13. Prabhakar Dixit, Joos Buijs, Wil van der Aalst, Bart Hompes, Hans Buurman. “Enhancing Process Mining Results using Domain

Knowledge”. In International Symposium on Data-driven Process Discovery and Analysis, 2015.

14. Verbeek, H.M.W. & Aalst, W.M.P. van der. “Decomposed process mining: the ILP case”. In Business Process Management

Workshops, Eindhoven, the Netherlands, Haifa, Israel. 2014.

75.

Authors: S. Sriram, R. Govindarajan, K. Thirusangu

Paper Title: Pell Labeling of Joins of Square of Path Graph

Abstract: A graph is composed of p vertices and q edges. A Pell labeling graph is the one with

( )u V G being distinct. Label ( )f u from 0,1,2, … p-1 in a such a way that each edge is labelled with

( )* :f E G N→ such that ( ) ( ) ( )* 2f uv f u f v= + are distinct. In this paper we study Square of Path

graph 2

nP and attach an edge to form a join to the square of path graph2

nP and prove the join of square of path

graph 2

nP is Pell labelling graph and further study on some interesting results connecting them.

Keyword: Square of Path graph ,Pell labelling , Pell labelling graph, Joins of Square of path graph References:

1. Gallian . J.A , A Dynamic Survey of Graph Labeling,2018;Twenty first edition 2. Rosa.A, On certain valuation of the vertices of a graph in Theory of Graphs, (Intl.Symp.Rome.1966), Gordon and Breach, Dunod

Paris,1967,349-355. 3. Shiama .J , Pell Labeling of some graphs, Asian Journal of Current Engineering and Maths, Vol 2, No 4 , 2013

4. Sriram .S, Govindarajan R, Super Harmonic Mean Labeling of Joins of Square of Path Graph, International Journal of Research

in Advent Technology, Vol.7, No.3, March , 2019 5. Sharon Philomena V, Thirusangu K, 3-Total Product Cordial and Pell Labeling on Tree <K 1,n : 2>, International Journal of Pure

and Applied Mathematics, Vol. 101 No.5, pp.747-752, 2015

6. Muthuramakrishnan D and Sutha S, Pell graceful labeling of graphs, Malaya Journal of Matematik, Vol.7, No.3, 508-512,2019 7. Avudainayaki R, Selvam B, Harmonious and Pell Labeling of Some Extended Duplicate Graph, International Journal of Scientific

Research in Mathematics and Statistical Sciences, Vol.5, Issue.6, pp.152-156,2018

8. Gross.J and .Yellen.J, Handbook of graph theory, CRC, Press: 2004

409-413

76.

Authors: M. Kalaimathi, B. J. Balamurugan

Paper Title: Computation of Even-Odd Harmonious Labeling of Certain Family of Acyclic Graphs

Abstract: Let ( ),G V E be a graph with p number of vertices and q number of edges. An injective

function : 1,3,5, ,2 1→ −f V         p is called an even-odd harmonious labeling of the graph G if there exists an

induced edge function ( ) : 0,2, ,2 1→ −*f E       q such that

*f is bijective function

( ) ( ) ( )( )( )2= = +*f e uv f u f v mod  q

The graph obtained from this labeling is called even-odd harmonious graph.

Keyword: Graphs, Even-Odd Harmonious Labeling, Injective Function, Bijective Function. References: 1. B.D. Acharya , S.M Hegde, Arithmetic Graphs. J. Graph Theory, Vol. 14, No.3, 275-299, (1990).

2. N. Adalin Beatress, P.B Sarasija, Even-Odd Harmonious Graphs, International Journal of Mathematics and Soft Computing, Vol. 5, No. 1, 23-29, (2015).

3. R.L Graham, N.J.A Sloane, On Additive Bases and Harmonious Graphs, SIAM Journal on Algebraic and Discrete Methods, Vol. 1,

No. 4, 382-404, (1980). 4. F. Harary, Graph Theory, Addison-Wesley, Reading Mass, (1972).

414-419

5. Joseph A. Gallian, A Dynamic Survey of Graph Labeling, The Electronic Journal of Combinatorics, DS6, (2018). 6. M.Kalaimathi, B.J Balamurugan, Computation of Even-Odd Harmonious Labeling of Graphs Obtained by Graph Operations, Recent

Trends in Pure and Applied Mathematics, AIP Conf. Proc. 2177, 020030-1 - 020030-9, (2019).

7. N. Lakshmi Prasana, K. Saravanthi, Nagalla Sudhakar, Applications of Graph Labeling in Major Areas of Computing Science. International Journal of Research in Computer and Communication Technology, Vol. 3, No. 8, (2014).

8. Z. Liang, Z. Bai Z, On The Odd Harmonious Graphs with Applications, J. Appl. Math. Comput., 29, 105-116, (2009).

9. Rosa, On Certin Valuations of the Vertices of a Graph, In Theory of Graphs(Internat.Sympos. Rome. 1966), Gordan and Breach, Newyork, Dunod, Paris, 349-359, (1967).

10. P. B. Sarasija, R. Binthiya, Even Harmonious Graphs with Applications, International Journal of Computer Science and Information

Security, Vol. 9, No. 7, 161-163, (2011). 11. D. B. West, Introduction to Graph Theory, PHI Learning Private Limited, 2nd Edition, (2009).

77.

Authors: Aravindh Kolanchinathan, Arockia Selvakumar A*

Paper Title: Cloud based IoT Applications for Industrial and Home Automation

Abstract: IoT for Industrial and Home Automation is emerging with a big bang, has huge potential for

every field to be used. When there is a need for efficient means to seek IoT interface, a cloud server is what

strikes in every design and applications. There are numerous aspects in building a real-time IoT interface, but

IoT through cloud can be a source of multiple gains in contrast to its peers such as edge computing [1].

Industrial and Home Automation involve an excellent delivery protocol for an error-free effective transmission

in the internet. MQTT protocol is a better option to facilitate the requirements of IoT through its smooth

implementation, Quality of Service and data delivery. In today’s world, IoT cloud providers compete to provide

reasonable and precise IoT based utilities. Despite extensive engagement of these IoT clouds, we have not

initiated standard regularization or few comparative analytical investigations across the research databases. An

IoT application calls for diverse resources of a particular cloud and hence it calls for a survey on IoT cloud

concerning Latency, interval for subsequent update, user-friendliness, IFTTT compatibility, data handling,

processing data, storage limits, servers used and security. An analyses of five of the most eminent clouds

(Adafruit IO, Amazon Web Service (AWS), Blynk, Thingspeak and Ubidots) based on the above-described

specifications are the factors of motivation for this paper and hence matches the best cloud suited to serve

specific purpose and applications.

Keyword: Internet of Things (IoT), Message Queuing Telemetry Transport (MQTT), IFTTT (If This Then

That). References:

1. Abdur Rahim Biswas, Raffaele Hiaffreda: IoT and Cloud Convergence: Opportunities and challenges in IEEE world forum on

internet of things (WF-IoT), pp. 375 - 376, 24 April 2014.

2. Partha Pratimray; A survey of IoT cloud platforms in Future computing and informatics journal, pp. 35-46, December 2016.

3. Luciano Barreto, Antonio Celesti, Massimo Villari, Maria Fazio, Antonio Puliafito in An Authentication model for IoT clouds,

IEEE/ACM International conference on Advances in Social networks Analysis and Mining (ASONAM), 11 February 2016.

4. Ran Canetti, Ben Riva, Guy N. Rothblum, Practical Delegation of Computation using Multiple servers, proceedings of the 18th

ACM conference on Computer and Communications Security, pp. 445-454, 21 October, 2011.

5. Boyi Xu, Lihong Jiang, Athanasios V, Vasilakos, IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and

Challenges in IEEE Internet of Things Journal, pp. 75 - 87, 20 October 2016.

6. Nirandika Wanigasekara, Jenny Schmalfuss Darren Carlson, David S. Rosenblum, A bandit approach for intelligent IoT service

composition across Heterogeneous smart spaces in proceedings of the 6th International conference on IoT, pp. 121-129, 7

November 2016.

7. Yuang Chen, Thomas Kunz; performance evaluation of IoT Protocols under a Constrained Wireless Access Network in

International conference on selected topics in Mobile & Wireless networking (MOWNET), pp. 1 – 7, 23 June 2016.

8. Davide Mulfari, Antonio Celesti, Massimo Villari, A Computer System Architecture providing a User-Friendly Man machine

interface for accessing Assistive Technology in Cloud computing in Journal of Systems and Software, pp. 129-138, February

2015.

9. Steven Ovadia, Automate the Internet with “If This Then That” (IFTTT) in Behavioral & Social Sciences Librarian, vol. 33, 2014,

pp. 208-211, 10 November 2014.

10. Qi Zhang, Lu Cheng, Raouf Boutaba, Cloud computing: state-of-the-art and research challenges, in Journal of Internet Services

and Applications, pp. 7–18, 20 April 2010.

11. Zia ur Rehman ; Farookh K. Hussain ; Omar K. Hussain, Towards Multi-criteria Cloud Service Selection in 2011 Fifth

International Conference on Innovative Mobile and Internet Services, Ubiquitous Computing, pp. 44 – 48, 4 August 2011.

12. Savio Sciancalepore, Angelo Capossele, Giuseppe Piro, Gennaro Boggia, Giuseppe Bianchi, Key Management Protocol with

Implicit Certificates for IoT systems in IoT-Sys '15 Proceedings of the 2015 Workshop on IoT challenges in Mobile and Industrial

Systems, pp. 37-42, May 18, 2015;

13. Yosra Ben Saied, Alexis Olivereau; HIP Tiny Exchange (TEX): A distributed key exchange scheme for HIP-based Internet of

Things in Third International Conference on Communications and Networking, pp. 1 – 8, April 1, 2012.

14. Mouza Bani Shemaili, Chan Yeob Yeun, Khalid Mubarak, Mohamed Jamal Zemerly, A new lightweight hybrid cryptographic

algorithm for the IoT 2012, International Conference for Internet Technology & Secured Transactions, pp. 87 – 92, 12 December

2012.

15. Sachin Babar, Parikshit Mahalle, Antonietta Stango, Neeli Prasad, Ramjee Prasad, Recent Trends in Network Security and

Applications in International Conference on Network Security and Applications, CNSA 2010, pp. 420-429, 17 November 2016.

16. Urs Hunkeler, Hong Linh Truong, Andy Stanford-Clark, MQTT-S - A publish/subscribe protocol for Wireless Sensor Networks in

3rd International Conference on Communication Systems Software & Middleware and Workshops, pp. 791–798, 27 June 2008.

17. Jayavardhana Gubbi, RajkumarBuyya, SlavenMarusic, MarimuthuPalaniswami, Internet of Things (IoT): A vision, architectural

elements, and future directions in Future Generation Computer Systems, pp. 1645-1660, September 2013.

18. Jalamkar, D., Krishnakumar, A. A. Selvakumar. "Implementation of Internet of things in a mobile Humanoid robot: A base for

future of IOT enabled robotics applications" in International Journal of Engineering & Technology 7, vol. 4, pp. 386-389, 2018.

19. Jalamkar, D., and A. A. Selvakumar. "Use of internet of things in a humanoid robot-a review." in Advances in Robotics &

Automation 5, vol. 2, pp 2-5, 2016.

420-427

20. Antonio Celesti, Maria Fazio, Maurizio Giacobbe, Antonio Puliafito, Massimo Villari; Characterizing Cloud Federation in IoT;

30th International Conference on Advanced Information Networking & Applications Workshops (WAINA), pp. 93–98, 19 May

2016.

21. G.M. Lee, The Internet of Things - Concept and Problem Statement in Institute TELECOM, pp. 21, 2012.

78.

Authors: S. Shankar Narayanan, Arockia Selvakumar A*, Michael Short

Paper Title: IoT Based Multi-Terrain Rover for Urban Search and Rescue Application

Abstract: This paper presents the development of a semi-autonomous exploration perspective (approach)

for Urban search and rescue environments (USAR). The developed rescue robot consists of a 2- wheel drive

with a traction system capable of traversing in various terrain within a 47-degree inclination. A 2-DOF

articulated end-effector is attached to robot, which can reach to a height of 45 cm from the ground, hold and lift

the object up to 20 Kg. The robot movement is controlled by RFID and Wi-Fi for low latency audio and video

feed by a mobile unit. The rover has a night mode with less noise capabilities to aid rescue in dark among

various sensors for topography mapping. The objective of the robot is to maneuver with in the narrow-spaces

where there is zero-visibility and create space by removing the possible obstacles in the path using the arm. The

rover was tested in places with strong EM interference and was found to be viable.

Keyword: IoT, Rover, RFID, Rescue, Vision, Wi-Fi. References: 1. Al-khawaldah, M., Livatino, S., and Lee, D. (2010). Reduced Overlap Frontier-based Exploration with Two Cooperating Mobile

Robots. In Proc. of the IEEE Int. Symp. on Ind. El. (ISIE ’10), Italy.

2. Jacoff, A & Messina, Elena & Weiss, B.A. & Tadokoro, S & Nakagawa, Y. (2003). Test Arenas and Performance Metrics for Urban

Search and Rescue Robots. 3. 3396 - 3403 vol.3. 10.1109/IROS.2003.1249681. 3. Tanaka, J., Suzumori, K., Takata, M., Kanda, T., & Mori, M. (2005). A mobile jack robot for rescue operation. IEEE International

Safety, Security and Rescue Rototics, Workshop, 2005., 99-104.

4. Himoto, A & Aoyama, H & Fuchiwaki, O & Misaki, Daigo & Sumrall, Ted. (2005). Development of micro rescue robot-human detection. 526 - 531. 10.1109/ICMECH.2005.1529313.

5. Zang, Xizhe & Liu, Yixiang & LIN, Zhenkun & ZHANG, Can & Iqbal, Sajid. (2016). Two multi- linked rescue robots: Design,

construction and field tests. Journal of Advanced Mechanical Design, Systems, and Manufacturing. JAMDSM0089-JAMDSM0089. 10.1299/jamdsm.2016jamdsm0089.

6. W. Burgard, M. Moors, C. Stachniss, & F. Schneider, "Coordinated multi-robot exploration," IEEE Transactions on Robotics. 21(3): p.

376-386, 2005. 7. M. Al-khawaldah, and D. Lee, "Cooperative robot exploration with Line-of-Sight technique," International Conference on Information

and Communication Systems: Jordan/Amman. to appear, 2009.

8. W. Sheng, Q. Yang, J. Tan, & N. Xi "Distributed multi-robot coordination in area exploration," Robotics and Autonomous Systems, 54(12): p. 945-955, 2006.

9. R.Misumi, S.Kurata and H.Aoyama: "Design and Development of Micro Hopping Robots for Victim Detection under Rubble", in

Proceedings of 6th Jpn-Franc Cong. on Mechatronics, 2003, pp.386-391 10. Blitch,J., Murphy R.R. and Durkin, T., "Mobile Semiautonomous Robots for Urban Search and Rescue" IEEE Transaction on Systems,

Man and Cybernetics, 2002, pp.211-224.

11. Jalamkar, D., and A. A. Selvakumar. "Use of internet of things in a humanoid robot-a review." Advances in Robotics & Automation 5, no. 2, 2-5, 2016.

12. Jalamkar, D., Krishnakumar, A. A. Selvakumar. " Implementation of Internet of things in a mobile Humanoid robot: A base for future of IOT enabled robotics applications" International Journal of Engineering & Technology 7, no. 4,386-389, 2018.

428-432

79.

Authors: Sathish Kumar K, Venkatesh S,

Paper Title: Detection of Tumor Objects from MRI Brain Images using Thresholding Segmentation

Abstract: Nowadays identification of brain tumor is a critical and challenging work in the research field.

We describe the detection of brain tumor by using thresholding segmentation. Segmentation of brain tumor in

MRI is developing research works. It is used to identify the tumor shape, size, and exact location. Our proposed

system has three stages to detect the brain tumor in MRI. Thresholding technique is developed to detect the brain

tumor in MRI. The first stage is to collection MRI data from the web database. The second stage for pre-

processing and finally post-processing. The proposed system is less time consuming while compared to other

techniques and the medical experts are also easily identify the exact tumor location.

Keyword: MRI (Magnetic Resonance Imaging), Brain tumor, Thresholding Segmentation, Edge Detection. References:

1. A.Sivaramakrishnan And Dr.M.Karnan “A Novel Based Approach For Extraction Of Brain Tumor In Mri Images Using Soft Computing Techniques”, International Journal Of Advanced Research In Computer And Communication Engineering, Vol. 2,

Issue 4, April 2013. 2. M. Rakesh1, T. Ravi, 2012. “Image Segmentation and Detection of Tumor Objects in MR Brain Images Using FUZZY C-

MEANS (FCM) Algorithm”. International Journal of Engineering Research and Applications (IJERA)

3. Anupurba Nandi, 2015. “Detection of human brain tumor using MRI image segmentation and morphological operators”. IEEE International Conference on Computer Graphics, Vision and Information Security

4. Swapnil R. Telrandhe, Amit Pimpalkar, 2016. “Detection of brain tumor from MRI images by using segmentation & SVM”.

World Conference on Futuristic Trends in Research and Innovation for Social Welfare W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

5. K.Srinivas, July-2012.“A Scientific Approach for Segmentation and Clustering Technique of Improved K-Means and Neural

Networks”. International Journal of Advanced Research in Computer Science and Software Engineering.

6. Varsha Kshirsagar*, Prof.Jagruti Panchal, 2014. “Segmentation of Brain Tumour and Its Area Calculation”. International Journal

of Advanced Research in Computer Science and Software Engineering Volume 4, Issue 5.

433-437

80.

Authors: Rajkamal P, Ramkumar E, Prathiba A., Kanchana Bhaaskaran V. S.

Paper Title: Authenticated Encryption using Lightweight Cryptographic Primitives for Wireless Sensor

Networks

Abstract: Wireless Sensor Networks (WSN) transmits sensitive data from physical environment to digital

medium. Thus, security is essential to transmit these vulnerable data. Authenticated Encryption aim to provide

security in WSN with integrity, confidentiality, and authentication of data transmitted. Block cipher modes of

operations provide different cipher text for identical plain text which further enhances the security. Sensor nodes

have its own operational constraint which makes security algorithm to be light in terms of area and low power

consumption. In this paper novel architecture of authenticated encryption of lightweight symmetric cipher

PRESENT and lightweight hash function SPONGENT in OFB mode is proposed. Proposed design is evaluated

in terms of Gate Equivalent (GE), operating frequency and power consumption using Cadence Genus® tool

using 180 nm technology library.

Keyword: Authenticated Encryption, low area, low power, PRESENT, SPONGENT. References:

1. T. Eisenbarth, C. Paar, A. Poschmann, S. Kumar, and L. Uhsadel, “A Survey of Lightweight Cryptography Implementations,”

IEEE Des. Test, vol. 24, no. 6, pp. 522–533, Nov. 2007. 2. D. Maimut and K. Ouafi, “Lightweight Cryptography for RFID Tags,” IEEE Security & Privacy, vol. 10, no. 2, pp. 76–79, Mar

2012.

3. A. Bogdanov et al., “PRESENT: An ultra-lightweight block cipher,” in Cryptographic Hardware and Embedded Systems (Lecture Notes in Computer Science), vol. 4727. Berlin, Germany: Springer, 2007, pp. 450–466.

4. “Specification for the Advanced Encryption Standard (AES),” in Federal Information Processing Standards Publication, vol. 197,

2001. 5. M. J. Dworkin, “Recommendation for Block Cipher Modes of Operation: The CCM Mode for Authentication and

Confidentiality,” National Institute of Standards and Technology NIST SP 800-38c, 2007. 6. M. Bellare and C. Namprempre, “Authenticated encryption: relations among notions and analysis of the generic composition

paradigm,” Journal of Cryptology. The Journal of the International Association for Cryptologic Research, vol. 21, no. 4,pp. 469–

491, 2008. 7. C. A. Lara-Nino, M. Morales-Sandoval, and A. Diaz-Perez, “Novel FPGA-based low-cost hardware architecture for the

PRESENT block cipher,” in Proc. Euromicro Conf. Digit. Syst. Design, Aug./Sep. 2016,pp. 646–650.

8. C. A. Lara-Nino, M. Morales-Sandoval, and A. Diaz-Perez, “Small lightweight hash functions in FPGA,” in Proceedings of the 2018 IEEE 9th Latin American Symposium on Circuits & Systems (LASCAS), pp. 1–4, Puerto Vallarta, Feburary 2018.

9. X. Zhang, H. M.Heys, andC. Li, “Energy efficiency of encryption schemes applied to wireless sensor networks,” Security and

Communication Networks, vol. 5, no. 7, pp. 789–808, 2012. 10. K. Bok, Y. Lee, J. Park, and J. Yoo, “An energy-efficient secure scheme in wireless sensor networks,” Journal of Sensors, vol.

2016, 2016.

11. P. Rogaway, M. Bellare, and R. S. Ferguson, “OCB: a blockcipher mode of operation for efficient authenticated encryption,” ACM Transactions on Information and System Security, vol. 6, no. 3, pp. 365–403, 2003.

438-443

81.

Authors: Shantakumar B. Patil, Premjyoti Patil, Sreerajavenkatareddy Velagala, Harshini Eggoni

Paper Title: Low Cost Automated Washing Machine

Abstract: Before purchasing any equipment, everybody will ask two things about the products. i. The cost

of the equipment’s. ii. Efficiency of the products, both never go together. But in our proposed system we present

such a system which fulfill both the conditions (low cost and efficient). Presently all hostess (House Wife) is

using a smart phone. By making use of these smart phones we are controlling low cost non-automated washing

machine and we use it like smart washing machine. The designed circuit used in the washing machine is simple

and it consumes less power which in turn dissipate less heat and consumes less space.

Keyword: Smart Phone, Washing Machine.

References: 1. Carl Hamacher, Zvonko Vranesic, Safwat Zaky: “Computer Organization”, TMH 2. 2. William Stallings: “Computer Organization and Architecture”, PHI, Pearson Education, Delhi, 10th Edition, 2016, ISBN:

9780134101613.

3. David. A. Patterson, John L. Hennessy: “Computer Organization and Design – The Hardware / Software Interface”, ARM Edition, 5th Edition, Elsevier, 2014, ISBN: 97801240776263.

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8. Muhammad Ali Mazidi, Janice Gillespie Mazidi, Rollin D. McKinley: “The 8051 Microcontroller and Embedded Systems – using

assembly and C”, Pearson Publication, New Delhi, revised 2nd Edition, 2011, ISBN: 978-81-317-5899-1. 9. 9.John Davies: “MSP430 Microcontroller Basics”, Elsevier, 2008, ISBN: 978-0-7506-8276-X.

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444-446

82.

Authors: Ishan Yash, Samir Ahmed Talkal, Karapurkar Shivani Prashant

Paper Title: Iov Hybrid Architecture – Inter-Vehicular Communication and Data Transmission

Abstract: The Internet of Vehicle has an Intelligent Vehicular Grid consisting of the sensor platform, which

continuously processes the data gathered from the surrounding of the vehicle and feeds it to the infrastructure.

This data is further utilized by other vehicles to assist in traffic management, vehicle localization, pollution

control and safe navigation. In this paper, we have suggested the ways through which we could attain a high data

rate, seamless connectivity, security, improved quality of service and scalability. The first section of the paper

consists of the proposed architecture and its benefits, and the latter consists of the scope of improvement with

results using sensor dataset its application and challenges. Communication between the infrastructure and other

vehicles can create privacy and security violations; hence, we also address ways to ensure the location privacy of

the user.

Keyword: IoV (Internet of Vehicles), VANET (Vehicular ad-hoc network), V2X, IoT (Internet of Things),

SVM (support vector machine), RAU (Radio Access unit) References:

1. White Paper of Internet of Vehicles (IOV) - Edition 2. Link: https://www.apec.org/-/media/Files/Groups/PPSTI/The-2nd-APEC-White-Paper-on-the-Internet-of-Vehicles-Edition-2.pdf

2. Internet of Vehicles and Autonomous Connected Car - Privacy and Security Issues. By Joshua Joy and Mario Gerla, IEEE 2017

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8. Sherazi, H. H. R., Khan, Z. A., Iqbal, R., Rizwan, S., Imran, M. A., & Awan, K. (2019). A Heterogeneous IoV Architecture for Data Forwarding in Vehicle to Infrastructure Communication. Mobile Information Systems, 2019.

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12. Yang, Fangchun, et al. "An overview of internet of vehicles." China Communications 11.10 (2014): 1-15.

13. Joy, Joshua, and Mario Gerla. "Internet of vehicles and autonomous connected car-privacy and security issues." 2017 26th International Conference on Computer Communication and Networks (ICCCN). IEEE, 2017.

14. Chennikara-Varghese, Jasmine, et al. "Survey of routing protocols for inter-vehicle communications." 2006 Third Annual International Conference on Mobile and Ubiquitous Systems: Networking & Services. IEEE, 2006

15. Y. Xia and B. Cheng, “A Vehicle Routing Problem based on intelligent batteries transfer management for the EV network,” Communications, China, 5, vol. 11, pp. 160-170, 2014.

16. V. Namboodiri and G. Lixin, “Prediction-Based Routing for Vehicular <emphasis emphasistype=”italic”>Ad Hoc</emphasis> Networks,” Vehicular Technology, IEEE Transactions on, 4, vol. 56, pp. 2332-2345, 2007.

17. J. Toutouh, J. Garcia-Nieto, and E. Alba, “Intelligent OLSR Routing Protocol Optimization for VANETs,” Vehicular Technology, IEEE Transactions on, 4, vol. 61, pp. 1884-1894, 2012.

18. M. Bakhouya, J. Gaber, and P. Lorenz, “An adaptive approach for information dissemination in Vehicular Ad hoc Networks,” Journal of Network and Computer Applications, 6, vol. 34, pp. 19711978, 11// 2011

19. J. Nzouonta, N. Rajgure, W. Guiling, and C. Borcea, “VANET Routing on City Roads Using Real-Time Vehicular Traffic Information,” Vehicular Technology, IEEE Transactions on, 7, vol. 58, pp. 3609-3626, 2009.

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83.

Authors: Prithwiraj Das, Ria Pathak, P. Augusta Sophy Beulet

Paper Title: Low Power Implementation Of Ternary Content Addressable Memory (TCAM)

Abstract: In network routers, Ternary Content Addressable Memory (TCAM)[1] based search engines

take an important role. One of the improved versions of Content Addressable Memory (CAM) is TCAM. For

high speed and broader searching operation TCAM is used. Unlike normal CAM, TCAM has 3 logic states: 0, 1,

‘X’. In TCAM within one single clock cycle, search operation can be performed. That is why it is called special

type of memory. Also, quick search ability is one of the popular features of TCAM. To compare the search and

stored data, TCAM array acts parallel in every location. But high power dissipation is the main disadvantage of

TCAM. To overcome this power dissipation in this paper we proposed a low power TCAM implementation by

using Reversible logic.[2] Reversible logic has less heat dissipating characteristics property with respect to

irreversible gate. Also, Reversible logic has ultra-low power characteristics feature. In recent past it has been

proved that reversible gates can implement any Boolean function.

Keyword: TCAM, CAM, Reversible Logic, Fredkin Gate, Feynman Gate, Peres Gate, Taffoli Gate References:

1. Inayat Ullah, Zahid Ullah, Jeong-A Lee. "EE-TCAM: An Energy-Efficient SRAM-Based TCAM on FPGA." Electronics, MDPI

journal, publised 10 septenber 2018 2. Kostas Pagiamzis, Ali Sheikholeslami, “Content-Addressable Memory (CAM) Circuits andArchitectures: A Tutorial and

Survey”, IEEE JOURNAL OF SOLID-STATE CIRCUITS VOL.41, NO. 3, MARCH 2006

3. Md. Selim Al Mamun, David Menville “Quantum Cost Optimization for Reversible Sequential Circuit” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol.4, No. 12,2013

4. “Array using a Novel Reversible Logic Gate and Decoder”, 2011 11th IEEE International Conference on Nanotechnology

Portland Marriott August 15-18, 2011, Portland, Oregon, USA. 5. S Dinesh Kumar Noor Mahammad SK, “A Novel Ternary Content-Addressable Memory (TCAM) Design Using Reversible

Logic” , 2015 28th International Conference on VLSI Design and 2015 14th InternationalConference on Embedded systems.

6. B.Raghu kanth, B.Murali Krishna “A DISTINGUISH BETWEEN REVERSIBLE AND CONVENTIONAL LOGIC GATES”

International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 2, Mar-

Apr 2012, pp.148 7. C. Bennett, “Logical reversibility of computation,” IBM Journal of Research and Development, vol. 17, no. 6, pp. 525-532, Nov

1973

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