bansilal ramnath agarwal charitable trust’s … · nonparametric methods: periodogram, modified...

Bansilal Ramnath Agarwal Charitable Trust’s VISHWAKARMA INSTITUTE OF TECHNOLOGY – PUNE

(An autonomous Institute affiliated to Savitribai Phule Pune University) 666, Upper Indiranagar, Bibwewadi, Pune – 411 037.

Structure and syllabus of M.Tech. E&TC Engineering. Pattern A-16 Revised, A.Y. 2017-18 out of 44

Bansilal Ramnath Agarwal Charitable Trust’s

Vishwakarma Institute of Technology (An Autonomous Institute affiliated to Savitribai Phule Pune University)

Structure & Syllabus of

M. Tech.

(Electronics &Telecommunication

Engineering) Pattern ‘A_16 Revised’

Academic Year 2017-18

Prepared by: - Board of Studies in E&TC Engineering

Approved by: - Academic Board, Vishwakarma Institute of Technology, Pune

Signed by

Chairman – BOS Chairman – Academic Board




Program Outcomes:

Engineering Graduates will be able to:

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering

problems.

2. Problem analysis: Identify, formulate, review research literature, and analyze complex

engineering problems reaching substantiated conclusions using first principles of

mathematics, natural sciences, and engineering sciences.

3. Design/development of solutions: Design solutions for complex engineering problems and

design system components or processes that meet the specified needs with appropriate

consideration for the public health and safety, and the cultural, societal, and environmental

considerations.

4. Conduct investigations of complex problems: Use research-based knowledge and research

methods including design of experiments, analysis and interpretation of data, and synthesis of

the information to provide valid conclusions.

5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern

engineering and IT tools including prediction and modeling to complex engineering activities

with an understanding of the limitations.

6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess

societal, health, safety, legal and cultural issues and the consequent responsibilities relevant

to the professional engineering practice.

7. Environment and sustainability: Understand the impact of the professional engineering

solutions in societal and environmental contexts, and demonstrate the knowledge of, and

need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and

norms of the engineering practice.

9. Individual and team work: Function effectively as an individual, and as a member or leader

in diverse teams, and in multidisciplinary settings.




10. Communication: Communicate effectively on complex engineering activities with the

engineering community and with society at large, such as, being able to comprehend and

write effective reports and design documentation, make effective presentations, and give and

receive clear instructions.

11. Project management and finance: Demonstrate knowledge and understanding of the

engineering and management principles and apply these to one‘s own work, as a member and

leader in a team, to manage projects and in multidisciplinary environments.

12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in

independent and life-long learning in the broadest context of technological change.

Program Educational Objectives (PEOs)

i. Have comprehensive knowledge of signal processing fundamentals to challenges of real

life complex problems

ii. Be professionals imbibed with a spirit of leadership, ethical behavior and societal

Commitment

iii. Be compliant to constantly evolving technology through lifelong learning

Program Specific Objectives (PSOs)

1. Use the knowledge of signal processing to develop algorithms and implement them on

embedded platforms.

2. Critically and systematically integrate knowledge to analyze, estimate and

solve complex problems and meet the challenges in the signal processing domain




Sr. No CONTENTS Page No

Structure 5-9

1 Smester -I 10-20

1.1 Adaptive Signal Processing ET501THL 10-11

1.2 Data Communication Networks ET501THP 12-13

1.3 Digital Integrated Circuit Design ET502THP 14-15

1.4 Linear Algebra and Statistics ES501TH 16-17

1.5 Quantitative Aptitude-I HS551TH 18

1.6 Research Methodology ES502TH 19-20

2 Semester -II 22-42

2.1 Advances in Digital Communication ET503THP 22-23

2.2 Mobile Communication ET507THP 24-25

2.3 Elective I ET5XXTHP 26-31

2.4 Elective II ET5XXTHP 32-39

2.5 Quantitative Aptitude -2 HS552TH 40

2.6 Engineering Economics HS553TH 41-42

3 Semester -II

3.1 Internship/ Dissertation I ET601INT/ET601PRJ 43

4 Semester IV

4.1 Internship/ Dissertation II ET601INT/ET602PRJ 44




Structure for M.Tech.E&TC Engineering

Academic Year – 2017-18

Semester I

Course Code Course Name Course

Type

Contact Hours /

Week

Credits

Th. Lab. Proj

ET501THL Adaptive Signal

Processing

THL 3 2 4

ET501THP Data

Communication

Networks

THP 3 2 4

ET502THP Digital Integrated

Circuit Design

THP 3 2 4

ES501TH Linear Algebra

and Statistics

TH 4 4

HS551TH Quantitative

Aptitude-I

TH 2 2

ES502TH Research

Methodology

TH 2 2

TOTAL 17 2 4 20




Structure for M.Tech.E&TC Engineering


Semester II


Type

Contact Hours /

Week

Credits

Th. Lab. Proj

ET503THP Advances in

Digital

Communication

THP 3 2 4

ET507THP Mobile

Communication

THP 3 2 4

ET5XXTHP Elective I THP 3 2 4

ET5XXTHP Elective II THP 3 2 4

HS552TH Quantitative

Aptitude -2

TH 2 2

HS553TH Engineering

Economics

TH 2 2

TOTAL 16 8 20




Elective –I Course Code Course Name

ET510THP Computer Vision

ET511THP Cloud computing and application development

ET508THP Speech Processing

Elective-II ET504THP Wavelet Theory and applications

ET505THP Design of Experiments

ET506THP Automotive Electronics

ET509THP Artificial Intelligence




Structure for M.Tech. E&TC Engineering


Semester III

Course Code Course

Name

Course

Type

Credits

ET601INT/ET601PRJ Internship/

Dissertation I

20

TOTAL 20




Structure for M.Tech. E&TC Engineering


Semester IV


Type

Credits

ET601INT/ET602PRJ Internship/ Dissertation II 20

TOTAL 20




FF No.: 654

ET501THL: Adaptive Signal Processing

Credits: 4 Teaching Scheme: 3 Hours / Week

Laboratory: 2 Hours/ Week

Unit 1: Stochastic Processes (7 Hours)

Random variables: ensemble averages, jointly distributed random variables, joint moments,

independent, uncorrelated and orthogonal random variables, linear mean square

estimation.Random processes: ensemble averages, stationary processes, autocorrelation and

auto-covariance matrices, ergodicity.

Unit 2: Signal Modeling (7 Hours)

Least square method, Pade approximation, Prony‘s method. Finite data records: autocorrelation

and covariance method, Stochastic Models: Autoregressive moving average models,

autoregressive models, moving average models.

Unit 3: Linear Prediction (7 Hours)

Forward linear prediction, backward linear prediction, Levinson-Durbin algorithm, lattice filter,

predictive modeling of speech

Unit 4: Wiener Filters (7 Hours)

Minimum mean square error (MMSE) and orthogonality principle, digital Wiener filter and

Wiener-Hopf equations. Applications: filtering, noise cancellation, linear prediction.

Unit 5: Adaptive filtering (7 Hours)

FIR adaptive filters: the steepest descent adaptive filter, Lease-Mean-Square (LMS) adaptive

filters, convergence of LMS algorithm, normalized LMS. Applications: noise cancellation,

channel equalization, adaptive recursive filters.

Unit 6: Spectrum Estimation (5 Hours)

Nonparametric methods: Periodogram, modified periodogram, Bartlett‘s method, Welch‘s

method. Parametric methods: autoregressive spectrum estimation, moving average spectrum

estimation, autoregressive moving average spectrum estimation

List of Practicals:

1. Random processes checking, whether ergodic or not?

2. Computing the probability density function of a Gaussian random sequence with specified

mean and variance.

3. Deconvolution using Wiener Filter.

4. Simulation of LMS algorithm for adaptive noise cancellation.

5. Simulation of RLS algorithm for adaptive noise cancellation.

6. Estimation of PSD of a noisy signal using periodogram and modified periodogram.




7. Estimation of power spectrum using Bartlett and Welch methods. Text Books:

1. Simon Haykin, “Adaptive Filter Theory”, 4th edition, Pearson Education

2. Monson Hayes, “Statistical Digital Signal Processing and Modeling”, Wiley India Edition

Reference Books:

1. Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon, “Statistical and Adaptive Signal

Processing: Spectral Estimation, Signal Modeling, Adaptive Filtering and Array

Processing”, McGrawHill, 2000

2. Bernard Widrow and Samuel Stearns, “Adaptive Signal Processing”, Pearson Education

Asia,2002

Course Outcomes:

The student will be able to –

1. Apply basic probability theory to model random signals in terms of Random Processes.

2. Find a model to provide an accurate estimation of the signal.

3. Represent speech signal using linear predictive coding (LPC) algorithm.

4. Formulate the Wiener filter as a constrained optimization problem.

5. Determine suitable LMS step size to trade off convergence time and misadjustment.

6. Derive the power spectrum of random signals.

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FF No.: 654

ET501THP: Data Communication and Networking


Project: 2 Hours/ Week

Unit I – Introduction ( 6 Hours)

Network architecture - Standards and underlying technologies - Internet Architecture:

Architectural concepts in ISO‘s OSI layered model, layering in the Internet. TCP/IP protocols

stack.

Unit II – Protocols ( 7 Hours)

Network, Transport and application layer protocols, addressing, routing, IPv4, IPv6, ICMP,

IGMP, ARP, RARP, TCP, UDP, WWW, HTTP, SMTP, FTP.

UNIT III - Local Area Network ( 7 Hours)

LAN standards, LAN design, approaches to improve LAN performance, Wireless LAN – IEEE

802.11 a, IEEE 802.12g , Bluetooth, WLAN design.

Unit IV: Network Management Standards and Models ( 7 Hours)

Network design issues, network and system management, - network management standards,

Network management model, organization model, information model, communication model,

and functional model.

Unit V: SNMP & RMON ( 6 Hours)

SNMPV1 Network Management, Major Changes in SNMP V2 and V3 SNMP Management,

RMON – Remote Monitoring, SMI & MIB, RMON1 and RMON2.

Unit VI: Network Management Tools and Systems (7 Hours)

Network Management Tools, Network Statistics Measurement Systems, Network

Management Systems, Commercial Network Management Systems, System Management,

Enterprise Management Solutions.

List of projects:

1. Prepare and test straight through and cross over cable

2. Implement a LAN

3. Implement a web application

4. Implement RSA algorithm




Text Book/Reference Books:

1. Data Communications and Networking – Behrouz A. Forouzan. TMH.

2. Mani Subramanian, ―Network Management principles and practice‖ 1st Edition,

Addison

Wesley, 1999.

3. William Stalling, ―SNMP – SNMPv2, SNMPv3 & RMON 1 and 2‖, 3rd Edition,

Addison

4. Data and Computer communication‘ – William Stallings, Eighth edition, PHI

Course Outcomes:

The students will be able to

1. Understand and describe underlined network technologies and architectures

2. Understand various protocols in data communication networks.

3. Design wired and wireless local area network

4. Understand the requirement of network management systems.

5. Understand SNMP based network and internetwork management.

6. Gain the information about current and evolving network management tools and standards




FF No. : 654

ET502THP : Digital Integrated Circuit Design



Unit I : Static CMOS Inverter (8 Hours)

MOS device physics, threshold voltage, body bias, I-V characteristics and design equations, the

actual device – secondary and short-channel effects, MOS SPICE models. The Static CMOS

Inverter — An Intuitive Perspective, The Static Behavior, Switching Threshold, Noise Margins,

static and dynamic power consumption, introduction to layout.

Unit II : Combinational Logic (9 Hours)

Static and Dynamic Design (9Hrs) CMOS Logic structures – Pseudo NMOS, Complementary

CMOS, Ratioed Logic, Pass-Transistor Logic, and Transmission Gate with examples, Dynamic

Logic – Basic Principle, DOMINO, NORA, Speed and power dissipation of dynamic logic gate,

Issues in dynamic design, cascading dynamic gates, layout of few combinational/sequential

circuits, Euler path to optimize layout

Unit III Sequential Logic Circuits ( 5 Hours )

Static and Dynamic Design (5Hrs) The bi-stability principle, CMOS Clocked Latches, CMOS

Multiplexer based Latches, Flip-flops, Clocked CMOS Logic (C2MOS), TSPC Latches,

pipelining of logic blocks.

Unit IV Integrated Memories (6 Hours)

Static Random-Access Memories, Static Random-Access 6-T Memory Cell, design equations,

DRAM cells, Sense Amplifier, Read-Only Memories- NAND and NOR structures, CAM

Unit V Logical Efforts (6 Hours)

Delay and Electrical Efforts of a gate, defining logical efforts, Multi-stage logic network,

Choosing path, applying logical efforts to design circuit for speed.

Unit VI Modern MOS Devices (6 Hours)

Technology node – concept and scaling, effect of scaling on performance of device and circuit,

LDD MOSFET, sub-micron MOSFET, SOI MOSFET, FinFET, Mutli-gate MOSFETs.

List of Projects

i. Simulate and analyze combinational logic circuit

ii. Simulate and analyze sequential logic circuit

iii. Simulate and analyze memory circuit




Text Books

1. ―Digital Integrated Circuits‖, Jan M. Rabaey, Anantha P. Chandrakasan, BorivojeNikolić,

Pearson Education, Second Edition2003

2. ―CMOS Digital Integrated Circuits‖, Kang Leblebici, McGraw-Hill

Course Outcomes:

1 Analyse CMOS inverter characteristics

2 To analyse combinational logic families

3 To find suitability of combinational and sequential circuits for pipeline operations

4 To distinguish logic levels for memory read and write operation

5 To calculate path delays and find optimum path

6 Compare modern techniques of MOS fabrication

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FF No. : 654

ES501TH :: LINEAR ALGEBRA AND STATISTICS

Credits: 04 Taching Scheme: Theory: 4 Hours/Week

Unit 1: Vector Spaces (6 Hours)

Rank of a matrix and solution of Linear Systems, Vectors in n-dimension, Vector spaces and

subspaces, Linear dependence and independence, Spanning set, Basis.

Unit 2: Linear Transformation (7 Hours)

Linear Transformation, Range and kernel of LT, Isomorphism, Column space, Row space, Null

space, Rank Nullity theorem, Orthogonal transformations and its geometrical interpretation.

Coordinate systems, Change of basis.

Unit 3: Inner product spaces (7 Hours)

Inner Product, length, and orthogonality, orthogonal sets, orthogonal projections, The Gram–

Schmidt process. Least square problem, Applications of linear models. Inner Product spaces,

Applications of Inner Product spaces.

Unit 4: Eigen values and Eigen vectors (6 Hours)

Eigen values and Eigen vectors. symmetric matrices, Complex eigen values, minimal

polynomial.

Unit 5: Applications of eigen values and eigen vectors (7 Hours)

Application of eigen values and eigen vectors to Discrete and continuous dynamical system.

Unit 6: Digonalization (7 Hours)

Diagonalization over real and complex field, canonical representation, spectral decomposition,

Quadratic forms, constrained optimization, The singular value decomposition, Applications to

image processing and statistics.

Text Books:

1. Ron Larson and David C. Falvo; Linear Algebra: An Introduction; First Indian reprint 2010;

Brooke/Cole, a part of Cengage Learning (Indian Edition).

2. B.S. Grewal; Higher Engineering Mathematics; 40th Edition 2007; Khanna Publishers.

3. Seymour Lipschutz, John Schiller; Introduction to Probability and statistics; 6th reprint 2008;

Schaum‘s Outline, Tata McGraw-Hill.




Reference Books:

1. Gilbert Strang; Linear Algebra and its Applications; 10th Indian reprint 2011;Cengage

Learning (Indian Edition).

2. David C. Lay ; Linear Algebra and its Applications; 12th impression 2011; Pearson Education

Inc,.

Course Outcomes:


1. Acquire the knowledge of vector spaces, linear transformations, Eigen values and eigen

vectors, complex numbers and random variables. (PO1,2,4,PSO2)

2. Set up, solve and interpret linear systems, use matrix transformations. (PO1,2,4,PSO2)

3. Apply knowledge of inner product spaces to compute length of a vector, angle, distance

between two vectors, to compute orthogonal basis using Gram-Schmidt process, compute

and apply the knowledge of eigen-values and eigenvectors. (PO1,2,4,PSO2)

4. Represent complex numbers algebraically and geometrically, find roots algebraic equations

and apply the knowledge of functions of complex numbers in problem solving.(PO1,2,4,PSO2)

5. Analyze and interpret probability distributions and perform regression analysis for

statistical data. (PO1,2,4,PSO2)




FF No. : 654

ES551TH :: QUANTITATIVE APTITUDE – I

Credits: 02 Teaching Scheme: Theory: 2 Hours/Week

Unit 1: Numbers, Surds and Indices & Logarithms (7 Hours)

Numbers, Average, Decimal fractions, Problem on ages, Simplification, Problems on numbers,

Square roots & cube roots, Logarithms, Surds and Indices, HCF and LCM of Numbers.

Unit 2: Time ,distance and work (7 Hours)

Time and distance, Problems on trains, Boats and Streams, Time and Work , Pipes and Cisterns,

Alligation or mixture

Unit 3: Measures of Statistical Data (6 Hours)

Percentage, Profit and loss, Ratio and Proportion, Simple interest, Compound interest,

Partnership, Chain Rule.

Unit 4: Logical Reasoning (7Hours)

Race and Games , Odd Man Out and Series, Number Series, Analogies, Logical Problems,

Letter and Symbol Series, Statement and Conclusion, Artificial Language

Unit 5: Area, Volume, Permutation and Combinations (7 Hours)

Area, Volume and Surface Areas, Calendar, Clocks, Permutations and Combinations,

Probability, Heights and Distances.

Unit 6: Data Interpretation (6 Hours)

Tabulations: Tabulations of Imports and Exports of Data, Analysis of Tabulated Data, Bar

Graphs: Vertical or Horizontal Bars, Pie Charts: Pie Graphs, Central angle, Line Graphs.

Text Books

1. Quantitative Aptitude For Competitive Examinations‖, Dr. R. S. Aggarwal, S. Chand.

2. ―How to Prepare for Quantitative Aptitude‖, Arun Sharma, Tata Mcgraw-Hill.

Reference Books

1. Quantitative Aptitude Quantum Cat Common Admission Test‖, K. Sarvesh Verma.,

Arihant.

2. ―Quantitative Aptitude for Competitive Examinations‖, Abhijit Guha, Fourth Quarter. Course Outcomes

The students will be able to:

1. improve their employability skills

2. improve aptitude, problem solving skills and reasoning ability

3. critically evaluate various real life situations by resorting to analysis of key issues and

factors.

4. demonstrate various principles involved in solving mathematical problems and thereby

reducing the time taken for performing job functions




FF No. : 654

ES502TH: Research Methodology

Credits: 02 Teaching Scheme:- Theory 2 Hrs/Week

Unit1: Formulating Research Problem and Literature Review (9 Hrs)

Overview: RE-Search, Definition, Research characteristics, Difference between methods

and methodology, Research categories, Overview of research process.

How to get new research ideas: Creating thinking, Preparations for improving thinking Defining

research problem statement: Need, What is a research problem, Sources of research problem,

research problem components

Literature Survey Overview: What is literature survey, Types of literature survey,

Sources of information, Types of technical papers, Publication and patent databases, How to read

a scientific paper, How to write scientific paper, writing technical papers in English – Grammar,

Punctuation, Tips for writing correct English, How to write a research proposal, How research is

funded, How to give a good research talk, Presentation tools Research Ethics and Legal Issues:

Intellectual Property rights, Patents, Copyrights, Plagiarism

Unit 2: Research Design and Data Collection (6 Hrs)

Research Design: What is research design, Research Design Parts, Research Design for

exploratory and Descriptive Research, Principals of Research design. Sampling Design: Steps in

sampling Design, Different Types of Sample Design

Unit 3: Data Collection and Analysis (6 Hrs)

Methods of data collection: Data types, Data Collection Types: Observation, Interview,

Questionnaire, Schedules, Collection of Secondary Data Analysis and Processing of Data:

Processing operations, Types of Analysis, statistics in Research, Measures of central Tendency,

Measures of Dispersion, Measures of Asymmetry, Measures of Relationship, simple regression

Analysis, Multiple correlation and regression, association in case of attributes

Unit 4: Hypothesis testing (9 Hrs)

Defining Hypothesis: What is hypothesis, Characteristics of hypothesis, Hypothesis Vs

Problem Statement Hypothesis Testing: Null hypothesis, Alternative Hypothesis, Level of

significance,Type I and Type II Errors, One tailed and two tailed hypothesis, Power of

hypothesis tests Parametric Tests: z-test, t-test, chi-square test, F-test, ANOVA

Total Contact Hours: 30

Text Books:

1. ‗Research Methodology: Methods and Trends‘, , by Dr. C. R. Kothari, 2nd revised edition,

New

Age International Limited Publishers, ISBN: 978-81-224-1522-3

2. ‗Research Methodology: An Introduction‘ by Wayne Goddard and Stuart Melville, 2nd

Edition,

Juta and Co. Ltd, ISBN: 0-70215660-4

Reference Books

1. ‗Research Methodology: A Step by Step Guide for Beginners‘, by Ranjit Kumar, 2nd Edition,

APH Publishing Corporation




2. ‗Research methodology: an introduction for science & engineering students‘, by Stuart

Melville

and Wayne Goddard

3. ‗Research Methodology‘ by Dr. Jayant Tatke, 2009, Symbiosis Centre for Distance Learning

4. ‗Operational Research‘ by Dr. S.D. Sharma, Kedar Nath Ram Nath & Co

5. Online material provided by the faculty




Semester – II




FF No. : 654

ET503THP : ADVANCES IN DIGITAL COMMUNICATION



Unit I: Introduction ( 4 Hrs)

Digital communication system (description of different modules of the block diagram), Complex

baseband representation of signals, Gram-Schmidt orthogonalization procedure. M-ary

orthogonal signals, bi-orthogonal signals, simplex signal waveforms.

Unit II Receiver in additive white Gaussian noise channels ( 8 Hrs) Coherent and non coherent demodulation-Matched filter, Correlator demodulator, square-law,

and envelope detection; Detector- Optimum rule for ML and MAP detection Performance- Bit-

error-rate, symbol error rate for coherent and non coherent schemes.

Unit III Synchronization ( 6 Hrs)

Different synchronization techniques (Early-Late Gate, MMSE, ML and spectral line methods).

Unit IV Equalization ( 8 Hrs) Linear equalization, Decision feedback equalization, Iterative equalization and decoding,

Adaptive equalization.

Unit V Multichannel and Multicarrier Systems ( 8 Hrs)

Multichannel Digital communications in AWGN channels-Binary signals & M-ary orthogonal

signals , Multicarrier communications-Capacity of Non ideal linear filter channel, an FFT-

basedmulticarrier system, minimizing peak to average ratio in the multicarrier systems

Unit VI Communication over fading channels ( 6 Hrs) Characteristics of fading channels, Rayleigh and Rician channels, receiver performance-average

SNR, outage probability, amount of fading and average bit/symbol error rate.

List of Projects

1. Simulation of binary communication system with antipodal/orthogonal signaling and plotting

the error probabilities.

2.Simulation of minimum mean - square error equalizer.

3.Simulation of carrier phase recovery for BPSK/QAM signals.

4.Simulation of multicarrier systems with fading channels.

Text Books ― Digital Communications‖, J. G. Proakis, Mc GrawHill, Edition 5

th, 2008.




Reference Books 1. ―Digital Communications‖, Simon Haykin, Wiley Publications, Fourth Edition.

2. ―Digital Communication‖, B.Sklar, Pearson, Second Edition .

3. ―Wireless Communications- Principle and practice‖, Theodore S, Rappaport, Second

edition, PHI.

Course Outcomes –

Students will be able to

1. represent signals using the Gram-Schmidt orthogonalization procedure

2. design and study performance characteristics of optimum receiver

for the various modulating methods.

3. derive carrier and symbol synchronization

4. analyze performance characteristics of equalizer algorithms .

5. analyze performance of multichannel and multicarrier systems

6. evaluate performance of digital signaling techniques for communication over fading

multipath channels




FF No. : 654

ET507THP : MOBILE COMMUNICATION



Unit I The Cellular Concept ( 6 Hrs)

Introduction, Frequency Reuse, Channel Assignment strategies, Hand off Strategies, interference

and system capacity, improving coverage and capability in Cellular Systems. Practical Link

Budget design using path loss models, Out door propagation models, Indoor propagation models,

Small Scale Multi path propagation, parameters of mobile multi path channels

Unit II Equalization & Diversity ( 6 Hrs)

Generic Adaptive Equalizers, Liner &nonlinear equalizers, Algorithms for equalizers, Diversity

techniques, Selection diversity improvement, Maximal ratio combining improvement, Space

diversity reception methods, RAKE receiver

Unit III Coding for Mobile Communication (7 Hrs)

Linear Block Codes: Syndrome and error detection, Error detection and correction capability,

Galois field, Primitive element & Primitive polynomial, Minimal polynomial and generator

polynomial, Description of Cyclic Codes, Generator matrix& Encoding for cyclic code,

Convolutional codes – Viterbi codes, TCM, BCH codes, RS codes. Coding for fading channel

Unit IV MIMO (7 Hrs)

MIMO, Physical Modelling of MIMO receiver, Modelling of MIMO fading channels, MIMO

System model & Zero forcing receiver, MIMO MMSE Receiver, SVD, SVD based optimal

MIMO transmission & capacity, V-BLAST receiver

Unit V Mobile Network & Transport Layer ( 7 Hrs)

TCP/IP Suite, Network Layer in the internet, TCP enhancement for wireless network,

implementation of wireless network, Mobile IP & SIP

Unit VI Standardized wireless Systems (7 Hrs)

Cognitive radio - spectrum sensing, management, sharing, GSM – overv iew, air interface,

channels, synchronization, coding, WiMAX/IEEE 802.16 – overview, modulation, channels,

Multiple Antenna Techniques. 3GPP LTE

List of Projects 1. Free space Propagation – Path Loss model to determine the free space loss and the power

received 2. Implementation of Cyclic Redundancy code using MATLAB/Simulink

3. Modeling a Decision feedback Equalizer




4. Model a fading channel based on Rayleigh & Rician Fading.

Text Books 1. Wireless Communications- Principle and practice, Theodore S, Rappaport, Second

edition, PHI

2.Mobile Communications, Jochen Schiller, Second Edition, Pearson Education.

Reference Books 1. David Tse, PramodWishwanath, ‗Fundamentals of Wireless Communications‘, Cambridge

University Press.

2. Vijay Garg, ‗Wireless Communications& networking‘, Morgan Kaufman Series in networking

3. Andreas Molisch, ‗Wireless Communications‘, Second Edition, Wiley Publications.

4. William C.Y. Lee, ‗Wireless & Cellular Telecommunication‘, McGraw Hill, 3rd

Edition

Course Outcomes:

Student will be able to

1. Determine the type and appropriate model of wireless fading channel based on the system

parameters and the property of the wireless medium.

2. Analyze different equalizers performance.

3. Spell the trade-offs among different forward error correction methods.

4. Understanding spectral efficiency & reliability gains from MIMO.

5. Differentiate between network layer of TCP/IP for mobile networking & traditional TCP/IP

suite.

6. describe different standard wireless systems




FF No. : 654

ET510THP: Computer Vision


Project 2 Hours/Week

Unit 1: Image Formation and Low-Level Processing (07 Hours)

Human Vision System, Computer Vision System: Overview and State-of-the-art,

Fundamentals of Image Formation, Transformation: Orthogonal, Euclidean, Affine,

Projective, Convolution and Filtering, Image Enhancement, Histogram Processing

Unit 2: Feature Extraction (07 Hours)

Edges - Canny, LOG, DOG, Line detectors (Hough Transform), Harris Corner detector,

SIFT, Scale-Space Analysis- Image Pyramids and Gaussian derivative filters, Feature

Matching and tracking

Unit 3: Image Segmentation (07 Hours)

Region Growing, Edge Based approaches to segmentation, Graph-Cut, Mean-Shift,

MRFs, Texture Segmentation

Unit 4: Object Recognition (07 Hours)

Global Methods, Active Contours, Split and Merge, Mode Finding, Normalized Cuts,

Histogram of Oriented Gradients

Unit 5: Classifiers (06 Hours)

Clustering: K-Means, Mixture of Gaussians, Classification: Discriminant Function,

Supervised, Un-supervised, Semi-supervised, Classifiers: Bayes, KNN, ANN models,

Dimensionality Reduction: PCA, LDA, ICA

Unit 6: Motion Estimation (06 Hours)

Triangulation, Two-frame structure from motion, Factorization, Bundle adjustment,

Translational alignment, Parametric motion, Spline-based motion, Optical flow,

Tracking.

List of Projects

1. Image Enhancement

2. Object or Human Detection using HOG.

3. Object or Human Detection using SIFT.

4. Line detection in video

Text Books:

1. Richard Szeliski, ―Computer Vision: Algorithms and Applications‖, Springer Publication.

2. Forsyth and Ponce, ―Computer Vision-A Modern Approach‖, 2nd

Edition, Pearson Education.




3. Bernd Jahne and Host HauBecker, ―Computer Vision and applications-A Guide for Students

and Practitioners‖, Elsevier.

Reference Books:

1. Milan Sonka, Vaclav Hlavac, Roger Boyle, ―Image Processing, Analysis, and Machine

Vision‖, Thomson Learning.

2. Robert Haralick and Linda Shapiro, "Computer and Robot Vision", Vol I, II, Addison-

Wesley, 1993.

3. Dana H Ballard and Christopher M. Brown, ―Computer Vision‖, Prentice Hall.

Course Outcomes:


1. Apply image enhancement techniques on images.

2. Develop feature vectors for object detection purpose.

3. Illustrate image segmentation algorithms.

4. Choose algorithm for object recognition.

5. Make use of classifies to classify the objects.

6. Demonstrate different motion estimation techniques.




FF No. : 654

ET511THP: Cloud Computing and Application Development


Project 2 Hours/Week

Unit 1: Introduction To cloud ( 6 Hrs)

Visualization concepts, Types of Visualization& its benefits, Introduction to Various

Virtualization OS, HA/DR using Virtualization, Moving VMs, Cloud Fundamentals, Cloud

Building Blocks, Understanding Public & Private cloud environments, Private Cloud

Environment, Public Cloud Environment, Managing Hybrid Cloud environment

Unit 2: The Cloud: Setting up ( 6 Hrs)

Setting up your own Cloud: Build private cloud using open source tools, Understanding various

cloud plugins, Setting up your own cloud environment: Autoprovisioning, Custom images,

Integrating tools like Nagios, Integration of Public and Private cloud

Unit 3: Iot and The Cloud ( 7 Hrs)

Introduction to cloud computing, Difference between Cloud Computing and IoT, Fog

Computing: The Next Evolution of Cloud Computing, Role of Cloud Computing in IoT. Living

on the Edge, An Abstract Edge Architecture Model, Connecting devices at the edge and to the

cloud.

Unit 4: Connecting IoT to Cloud ( 7 Hrs)

Various Real time applications of IoT, Connecting IoT to cloud, Cloud Storage for IoT . Cloud-

to-Device Connectivity, Device Ingress/Egress, Data Normalization and Protocol Translation,

Infrastructure, APIs, The Topology of the Cloud

Unit 5:Challenge in integration of IoT with Cloud ( 7 Hrs)

Security, Scalability, Reliability, Performance, Mobility, Resource Optimization & cost

efficiency, Cloud data management, cloud data monitoring, Cloud data Exchange, Infrastructure

Configuration & reconfiguration, IoT Overarching Challenges.

Unit 6: IoT Application Development ( 6 Hrs)

HTML (Documents, Tables, Hypertext And Link, Managing Forms, CSS), JAVASCRIPT (Basic

Syntax, Control Structures. Writing Functions, Working with Arrays, The Document Object

Model. Events Handling, Using Browser Objects. Object Oriented in JavaScript),

PHP(Variables, functions, conditional and lopping constructs, Web form using PHP, Validation,

session and cookie, user defined exception, client agent to send an email, File upload and

download, Web page filters, AJAX,JSON).




List of Projects

1. Create a Hamming Code in Binary Format Using Simulink and analyze error reduction.

2. Reduce the Error Rate Using a Hamming Code

3. Detect and Correct Errors in a BCH Code Using MATLAB/ Simulink

4. Design a Rate-2/3 Feedforward Encoder Using MATLAB/Simulink

Text Books

1.RanjanBose, ―Information Theory coding and Cryptography‖, McGraw-Hill Publication,

2ndEdition

2J C Moreira, P G Farrell, ―Essentials of Error-Control Coding‖, Wiley Student Edition.

Reference Books 1.BernadSklar, ―Digital Communication Fundamentals & applications‖, PearsonEducation.

Second Edition.

2.Simon Haykin, ―Communication Systems‖, John Wiley & Sons, Fourth Edition.

3.Shu lin and Daniel j, Cistellojr., ―Error control Coding‖ Pearson, 2NdEdition.

4.Todd Moon, ―Error Correction Coding : Mathematical Methods andAlgorithms‖, Wiley

Publication

5.Khalid Sayood, ―Introduction to Data compression‖, Morgan Kaufmann Publisher

Course Outcomes:

Student will be able to

1. compare performance of source coding theorem based on entropy

2. analyze & implement lossless compression techniques on information.

3. analyze linear block codes for error detection

4. decode cyclic code for error detection

5. analyze RS code

6. generate convolutional code word & decode using Viterbi decoding scheme




FF No. : 654

ET508THL: Speech Processing



Unit 1: Fundamentals of speech production (8 Hours)

Anatomy and physiology of speech production. Classification of phonemes used in American

English based on continuant/non-continuant properties. Acoustic theory of speech production,

sound propagation. Lossless tube model, multitube lossless model. Discrete time model for

speech production.

Unit 2: Human Auditory System (6Hours)

Peripheral auditory system, simplified model of cochlea. Sound pressure level and loudness.

Sound intensity and Decibel sound levels. Concept of critical band and introduction to auditory

system as a filter bank. Speech perception: vowel perception.

Unit 3: Time domain method of speech processing (6Hours)

Time-dependent speech processing. Short-time energy and average magnitude. Short-time

average zero crossing rate. Speech Vs. silence discrimination using energy and zero crossing

rate. Short-time autocorrelation function, short-time average magnitude difference function.

Pitch period estimation using autocorrelation function.

Unit 4: Linear prediction analysis (8 Hours)

Basic principles of linear predictive analysis. Autocorrelation method, covariance method.

Solution of LPC equations: Cholesky decomposition, Durbin‘s recursive solution, lattice

formulations and solutions. Frequency domain interpretation of LP analysis. Applications of

LPC parameters as pitch detection and formant analysis.

Unit 5: Cepstral Analysis (6 Hours)

Real Cestrum: Long-term real cepstrum, short-term real cepstrum, pitch estimation, format

estimation, Mel cepstrum. Complex cepstrum: Long-term complex cepstrum, short-term

complex cepstrum.

Unit 6: Speech processing Application (7Hours)

Speech recognition: complete system for an isolated word recognition with vector quantization

/DTW. Speaker recognition: Complete system for speaker identification, verification. Echo

cancellation: adaptive echo cancellation

List of Projects

1. 1. Development of speech enhancement system

2. Development of speaker verification system

3. Development of speaker identification system




4. Development of speech recognition system

5. Development of Speaker Diarization system

6. Development of speech de-reverberation system

7. Voice Pathology Detection

8. Audio Melody extraction

9. Speech enhancement using microphone array

Text Books:

1. Deller J. R. Proakis J. G. and Hanson J. H., “Discrete Time Processing of Speech Signals,”

Wiley Interscience

2. Ben Gold and Nelson Morgan, “Speech and audio signal processing,” Wiley

Reference Books:

1. L. R. Rabiner and S.W. Schafer, “Digital processing of speech signals,” Pearson Education.

2. Thomas F. Quateri , “Discrete-Time Speech Signal Processing: Principles and Practice,”

Pearson

3. Dr. Shaila Apte, “Speech and audio processing,” Wiley India Publication

4. L. R. Rabiner and B. H. Juang, “Fundamentals of speech recognition”

Course Outcomes:


1. Describe discrete time model of speech production system.

2. Detect voiced, unvoiced and silence part of a speech signal.

3. Implement algorithms for processing speech signals considering the properties of acoustic

signals and human hearing.

4. Analyze speech signal to extract the characteristic of vocal tract (formants) and vocal cords

(pitch).

5. Write a program for extracting LPC Parameters using Levinson Durbin algorithm.

6. Formulate and design a system for speech recognition and speaker recognition




FF No. : 654

ET504THP: WAVELET THEORY AND APPLICATIONS



Unit 1: Continuous Wavelet Transform (6 Hrs)

Introduction to wavelet transform, comparison with DFT and DCT. Introduction to

timefrequency analysis, Definition of CWT, properties of continuous wavelet transform, CWT as

correlation, CWT as an operator, Inverse CWT.

Unit 2: Discrete Wavelet Transform (6 Hrs)

Approximation of Vectors in linear vector subspaces, Basis for approximating sub spaces and

Haar Scaling function, Digital filter implementation of Haar Wavelet Decomposition.

Unit 3: Multi-resolution Analysis and Filter Banks (8 Hrs)

Definition of MRA, Construction of general orthonormal MRA,Wavelet Basis for MRA, Digital

Filtering Intepretation, Examplews of Orthogonal Basis-generating Wavelets, Interpreting

orthonormal MRAs for Discrete time Signal.

Unit 4: Different Families of Wavelets (8 Hrs)

Introduction to time frequency analysis Different families of wavelets, mathematical

preliminaries, windowed Fourier transform, short-time Fourier transform, Harr wavelet,

Daubechies Wavelets, Wavelet packet analysis, Harr wavelet packets, introduction to orthogonal

and bi-orthogonal wavelets.

Unit 5: Wavelet Transform and Data Compression (6 Hrs)

Transform Coding, Image compression using DTWT, Audio Compression, and Video Coding

using MRA.

Unit 6: Applications of Wavelet Transform (6 Hrs)

Application of wavelet theory to signal de-noising, transient detection, speckle removal, edge

detection and object isolation, image fusion, image enhancement, feature extraction,

communication applications like scaling functions as signaling pulses and multi-tone

modulation.




Project List

1. Implement Image compression

2. Edge Detection

3. Audio/Video Compression

4. Speech Processing

Text Books:

1. ―Wavelet Transforms: Introduction to Theory and Applications‖ by Raghuveer Rao and Ajit

Bopardikar, Pearson Education.

2. "Insight Into Wavelets - From Theory to Practice", by K P Soman, K I Ramchandran PHI

publication (2nd edition), Prentice Hall of India.

Reference Books:

1. Wavelet Analysis –by Springer Publication.

2. Ten lectures on wavelets –by Daubechies I (CBMS-NSF, SIAM, 1982).

3. ―Multirate Systems and Filter Banks‖, P. P. Vaidyanathan, Pearson Education.

Course Outcomes:

1. Students will be able to understand fundamentals of continuous and discrete wavelet

transform.

2. Students will be able to interpret multi resolution analysis.

3. Students will be made familiar with different wavelet families.

4. Students will be able to apply wavelet transform for various signal processing applications.

5. Students will be able to write a program for edge detection object isolation, image fusion,

image enhancement

6. Formulate and design a system for image compression




FF No. : 654

ET505 THP : DESIGN AND ANALYSIS OF EXPERIMENTS

Credits: 04 Teaching Scheme: Theory: 3 Hours / Week


Unit 1 : Statistical concepts ( 5 Hrs )

Strategy of experimentation, applications of experimental design, characterizing a process,

optimizing a process variable, principles of experimental design replication, randomization,

blocking, design guidelines, statistical techniques in experimentation

Unit 2 : Simple comparative experiments ( 8 Hrs )

Probability distributions, Mean, variance, expected values, sampling and sampling distributions,

properties of sample, mean, variance, degrees of freedom, normal distribution, standard normal

distribution, estimating sampling size, Chi square distribution, t distribution, f distribution,

important parametric tests, hypothesis testing, confidence intervals, limits of hypothesis tests,

case studies

Unit 3 : Experiments with single factor ( 7 Hrs )

Analysis of Variance (ANOVA), fixed effect and random effect model, analysis of fixed effect

model, decomposition of total sum of squares, Cochrans theorem, Normality assumption, model

adequacy checking, normal probability plot, plot of residuals in time sequence, plot of residuals

versus fitted values, Analysis of Covariance (ANCOVA), case studies

Unit 4 : Factorial design ( 8 Hrs )

Basic definitions and principles, advantages of factorials, two factor factorial design, statistical

analysis of fixed effect model, analysis of variance table for two factor factorial design fixed

effect model, degrees of freedom, Main effect plots, Interaction effect plots, interpretation of

parallelism, interpretation of p values, R squared static, case studies

Unit 5 : Regression models ( 6 Hrs )

Linear regression model, simple and multiple regression analysis, estimation of parameters,

predicted values, least squares fit, residuals and diagnostics , regression model for 2 k

factorial

design, Test for significance of regression




Unit 6 : Randomized block design (6 Hrs )

Basic definitions and principles, randomized complete block design, statistical analysis of

RCBD, model adequacy checking, balanced incomplete block design, balanced incomplete block

design, statistical analysis of BIBD, least square estimation of parameters

List OF Projects (Using Minitab):

1. Hypothesis Testing

2. Factorial Designs

3. Analysis of Variance

4. Model Adequacy checking

Text Books

1. Design and analysis of experiments, Douglas Montgomery, Wiley India, (2007)

2. Design and Analysis of Experiments, Das, M.N. and Giri, N, Wiley Eastern, New Delhi

Reference Books

3. Applied statistics and probability for engineers, Douglas Montgomery, Wiley India,

(2007)

Course Outcomes

Students will able to

1. Understand statistical concepts

2. Apply Hypothesis testing

3. Apply ANOVA test for analysis

4. Design Factorial Experiments

5. Create regression models

6. Create Randomized block designs




FF No. : 654

ET506 THP: AUTOMOTIVE ELECTRONICS



Unit I Automotive Systems Overview ( 6 Hours)

Overview of Automotive Industry: Role of technology in Automotive Electronics and

interdisciplinary design, Tools and processes. Introduction to Modern Automotive Systems and

need for electronics in automobiles and application areas of electronic systems in modern

automobiles, Automotive Vehicle Technology, Overview of Vehicle Categories.

Unit II Sensors and Actuators ( 6 Hours)

Sensors: Accelerometers, Wheel speed, Brake pressure, Seat occupancy, Engine speed, Steering

wheel angle, Vehicle speed, Throttle position, Temperature, Mass air flow (MAF) rate, Exhaust

gas oxygen concentration, Throttle plate angular position, Crankshaft angular position/RPM,

Manifold Absolute Pressure (MAP), Differential exhaust gas pressure and Air bag sensors.

Actuators: Solenoids, Various types of electric motors and piezoelectric force generators.

Unit III Communication protocols ( 7 Hours)

Overview of automotive communication protocols, CAN, LIN , Flex Ray, MOST , Ethernet,

D2B and DSI, Communication interface with ECUs, Interfacing techniques and Interfacing with

infotainment gadgets, Relevance of Protocols such as TCP/IP for automotive applications,

Wireless LAN standards such as Bluetooth, IEEE 802.11x communication protocols for

automotive applications. Infotainment Systems: Application of telematics in automotive domain,

Global positioning systems (GPS) and General packet radio service (GPRS).

Unit IV Automotive Control Systems ( 7 Hours)

Control system approach in Automotive Electronics, Analog and digital control methods,

modelling of linear systems, System responses, Modelling of Automotive Systems with simple

examples.

Model based Development: Introduction Simulink and SIMSCAPE tool boxes

Unit V Safety Systems in Automobiles ( 7 Hours)

Active Safety Systems: ABS, TCS, ESP, Brake assist, etc. Passive Safety Systems: Airbag

systems. Advanced Driver Assistance Systems (ADAS): Combining computer vision techniques

as pattern recognition, feature extraction, learning, tracking, 3D vision, etc. to develop real-time

algorithms able to assist the driving activity. Examples of Assistance Applications: Lane

Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection,

Headlights Control, Connected Cars technology and trends towards Autonomous vehicles.

Unit VI Automotive Diagnostics ( 7 Hours)

Diagnostics: Fundamentals of Diagnostics, Basic wiring system and Multiplex wiring system,

Preliminary checks and adjustments, Self-diagnostic system, Fault finding and corrective




measures, Electronic transmission checks and Diagnosis, Diagnostic procedures and sequences,

On-board and off-board diagnostics in Automobiles

List of projects

1. Implement a simple sensor for automotive application

2. Design and test regenerative braking system

3. Implement a automotive fault diagnosis system

Text Books

1. Williams. B. Ribbens: ―Understanding Automotive Electronics‖, 6th Edition, Elsevier

Science, Newnes Publication, 2003.

2. Robert Bosch: ―Automotive Electronics Handbook‖, John Wiley and Sons, 2004.

Reference Books

1. Ronald K Jurgen: ―Automotive Electronics Handbook‖, 2nd Edition, McGraw-Hill, 1999.

James D. Halderman: ―Automotive Electricity and Electronics", PHI Publication

Course Outcomes:

The students will be able to

1. Understand the concepts of Automotive Electronics and its evolution and trends.

2. Understand sensors and different signal conditioning techniques, interfacing techniques

and actuator mechanisms aligned to automotive systems.

3. Describe various communication systems, wired and wireless protocols used in vehicle

networking.

4. Understand, design and model various automotive control systems

5. Understand Safety standards, advances in towards autonomous vehicles.

6. Understand vehicle on board and off board diagnostics.




FF No. : 654

E509THP: Artificial Intelligence


Project : 2 Hours/ Week

Unit I Introduction to AI ( 6 Hrs)

Foundation of AI, history of AI, intelligent agents: agents and Environments, nature of

environment, structure of agent.

Unit II Problem Solving ( 6 Hrs)

Solving problem by searching, Informed search and exploration, constraints satisfaction problem

and adversarial search.

Unit III Knowledge and Reasoning ( 7 Hrs)

Logical agents, First order logic, Inferences in first order logic, knowledge representation.

Unit IV Planning and Learning ( 7 Hrs)

Planning, partial order planning. Uncertain Knowledge and Reasoning, Learning from

observations, knowledge in learning, statistical learning method.

Unit V Basics of Neural Networks ( 7 Hrs) Neural Network Representation, Computing a Neural Network's Output, Activation functions, Derivatives of

activation functions, Gradient descent for Neural Networks, Backpropagation

Unit VI Deep Neural Networks ( 7 Hrs) Deep L-layer neural network, Forward Propagation in a Deep Network, Building blocks of deep neural

networks, Forward and Backward Propagation, Parameters vs Hyperparameters, Building your Deep Neural

Network: Step by Step.

List of Projects

1. Self Driving Car

2. Character Recognition

3. Image Classification Using Deep Neural Network

Text Books

1. Artificial Intelligence: A Modern approach, Stuart Russell and Peter Norvig , Pearson,

second Edition

https://www.coursera.org/learn/neural-networks-deep-learning/lecture/7dP6E/deep-l-layer-neural-network




Course Outcomes

Students will able to-

1. Explain different part of artificial systems.

2. Explain how Artificial Intelligence enables capabilities applied to different applications such

as chess-playing computers, self- driving cars, robotic vacuum cleaners.

3. Implement classical Artificial Intelligence techniques, such as search algorithms, minimax

algorithm, neural networks, tracking, robot localization.

4. Ability to apply Artificial Intelligence techniques for problem solving.

5. Explain the limitations of current Artificial Intelligence techniques.

6. Explain different part Deep learning architecture




FF No. : 654

HS552TH :: QUANTITATIVE APTITUDE - II

Credits: 02 Teaching Scheme: Theory: 2 Hours/Week

Unit 1: Area, Volume, Permutation and Combinations (7 Hours)

Area, Volume and Surface Areas, Calendar, Clocks, Permutations and Combinations,

Probability,

Heights and Distances.

Unit 2: Data Interpretation (7 Hours)

Tabulations: Tabulations of Imports and Exports of Data, Analysis of Tabulated Data, Bar

Graphs: Vertical or Horizontal Bars, Pie Charts: Pie Graphs, Central angle, Line Graphs.

Unit 3: Probability (7 Hours)

Introduction to probability, Structure of probability, Results of probability, Revision of

probability: BAYES‘ RULE, and examples; Random variable and probability distribution:

Discrete and Continuous distribution, Expected value and variance of a distribution.

Unit 4: Correlation & Regression Analysis (7 Hours)

Regression analysis (Linear only), Correlation analysis, Karl Pearson‘s correlation coefficient,

Spearman‘s Rank correlation coefficient

Text Books

1. Quantitative Aptitude For Competitive Examinations‖, Dr. R. S. Aggarwal, S. Chand.

2. ―How to Prepare for Quantitative Aptitude‖, Arun Sharma, Tata Mcgraw-Hill.

3. Probability & Statistics for Engineers- Richard Johnson – Prentice Hall of India,

4. Statistics for Management- Richard Levin , Rubin - Prentice Hall of India,

Reference Books

1. Quantitative Aptitude Quantum Cat Common Admission Test‖, K. Sarvesh Verma., Arihant.

2. ―Quantitative Aptitude for Competitive Examinations‖, Abhijit Guha, Fourth Quarter. Course Outcomes

The students will be able to:

1. improve their employability skills

2. improve aptitude, problem solving skills and reasoning ability

3. critically evaluate various real life situations by resorting to analysis of key issues and

factors.

4. demonstrate various principles involved in solving mathematical problems and thereby

reducing the time taken for performing job functions




HS553TH :: ENGINEERING ECONOMICS Credits: 02 Teaching Scheme: Theory: 2 Hours/Week

Unit I (07 Hrs)

Engineering Economic Analysis

Introduction, Concept of Money – Its Functions & worth. Inflation – Concept, Causes,

Remedies to control inflation, Value of Currency, Factors governing exchange rates, Currency

Fluctuations. Concept of Taxes, Types of Taxes – Direct & Indirect, Depreciation. Effect of

above concepts on decision making.

Significance of above concept in real life decision making

Unit II (07 Hrs)

Time Value of Money& Life Cycle Costing

Concept of Interest, Time Value of Money – Basis for comparison of alternatives, Discount

Rate, Compound Rate, Present Worth, Future Worth, Annual Worth, Annuity, Perpetuity.

Life Cycle Costing - Introduction, methodology, applications of LCC in industrial world,

differentiation with traditional costing methods, Capital Budgeting: DCF & NDCF

Techniques: Payback, Discounted Payback, ARR, IRR, NPV, Annual Worth, Cost Benefit

Ratio

Numerical Applications on Time Value of Money

Unit III (07 Hrs)

Concept of Demand and Supply

Law of Demand & Supply: Meaning and Determinants of Demand. Demand Function. Law of

Demand, Market Demand, Elasticity of demand. Types of elasticity. Measurement of

elasticity. Significance and uses of the elasticity. Meaning and Determinants of Supply, Law

of supply. Equilibrium of demand and supply i.e. price determination.

Exceptions of Law of Demand & Supply

Unit IV (07 Hrs)

Concept of Utility, Competition

Law of Diminishing Marginal Utility – Concept, Law of Diminishing Marginal Utility Price

Determination, Competition – Concept, Types (Monopoly, Oligopoly, etc.), Benefits to Buyer

& Seller, Economies of Scales, Law of Variable Proportions

Cases related with above concepts

Text Books

1. Theusen H.G., Engineering Economic Analysis, Prentice Hall of India

2. Henry M. Steiner, Engineering Economic Principles, McGraw Hill

3. S.M. Mahajan, Engineering Economics, Everest Publishing House, Pune

4. Samuelson PA, Nordhaus WD, Economics, Tata McGraw Hill

Reference Books

1. Colin Drury, ―Management and Cost Accounting‖, English Language Book Society,

Chapman and Hall London.

2. Khan M. Y., Jain P. K., ―Financial Management”, Tata McGraw Hill




Course Outcomes:

Our students will be able to:

1. Analyze the effect of inflation, currency fluctuations, and taxes on decision making

2. Compare and select investment alternatives based on costs and time value of money

3. Analyze the impact of demand and supply on pricing of product and competition

4. Understand the concept of utility and competition and its relevance in business environment




Guidelines for M.Tech Dissertation I

In the first semester students are expected to complete the following sub-components of

Thesis and present it to panel of examiners. Hard copy of the semester I report should

include the following.

1. Motivation behind the Research

2. Need of the Research

3. Information Gathering Survey Report

4. Scope of the Thesis

5. Problem specification with System Requirement Specification (SRS).

6. System Analysis and Feasibility study Report covering feasibility in terms of

implementation, usability.




Guidelines for M.Tech Dissertation II

In the semester-IV students are expected to complete the following sub-components of

Thesis and present it to panel of examiners.

1. System Design

2. Implementation

3. Testing

It is mandatory to publish the at least one Journal/Conference paper before the submission of

Thesis.

Guidelines for M.Tech Internship

Students pursuing internship should undergo a one semester training from a reputed research organization

or an Electronics and Telecommunication based industry. Students are required to present their work

upon successful completion of the internship and submit a hard copy of the reporst to the department.