basaveshwar engineering college (autonomous) department … syllabus i-iv semester.pdf · aided...

BASAVESHWAR ENGINEERING COLLEGE (AUTONOMOUS)

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING [TEQIP LEAD INSTITUTE, GOVT. AIDED INSTITUTION, AICTE RECOGNIZED, AFFILIATED TO VTU BELAGAVI]

BAGALKOT-587103, KARNATAKA, INDIA

SCHEME OF TEACHING AND EXAMINATION FOR 2015-16 ONWARDS

Semester-I:

Sl. No

Subject Code

Subjects Name Hours / Week

C Exam Marks

L T P S CIE SEE Total

1 PCS121C Advanced Algorithms 3 0 1 0 4 50 50 100

2 PCS122C Advances in Operating Systems 3 0 1 0 4 50 50 100

3 PCS124C Big Data Analytics 4 0 0 0 4 50 50 100

4 PCSXXXE Elective-I 4 0 0 0 4 50 50 100

5 PCSXXXE Elective-II 4 0 0 0 4 50 50 100

6 PCSXXXE Elective-III 4 0 0 0 4 50 50 100

7 PCS124S Seminar 0 2 0 0 2 50 50 100

8 PCS120M Introduction to Computer Science* 4 0 0 0 0 50 50 100

Total 26 02 02 0 26 400 400 800

* Bridge course only for the students coming from non CSE / ISE branches.

Semester-II:

Sl. No

Subject Code


C Exam Marks


1 PCS221C High Performance Computing and Programming

3 0 2 0 4 50 50 100

2 PCS222C Software Architecture 4 0 0 0 4 50 50 100

3 PCS223C Advanced Computer Networks 3 0 2 0 4 50 50 100

4 PCSXXXE Elective-IV 4 0 0 0 4 50 50 100

5 PCSXXXE Elective-V 4 0 0 0 4 50 50 100

6 PCSXXXE Elective-VI 4 0 0 0 4 50 50 100

7 PCS224T Term Paper 0 0 2 0 2 50 50 100

Total 22 0 06 0 26 350 350 700

Semester-III:

Sl. No

Subject Code


C Exam Marks


1 PCS321C Soft Computing & Intelligent Systems 4 0 0 0 4 50 50 100

2 PCS322C Database Technologies 4 0 0 0 4 50 50 100

3 PCS323C Effective Teaching Learning Practices 2 0 2 4 4 50 50 100

4 PCS324M Industrial Workshop (Mandatory) 0 4 4 4 0 50 - 50

5 PCSXXXE Elective-VII 4 0 0 0 4 50 50 100

6 PCS325P Project Phase-I 0 0 4 4 8 50 50 100

Total 14 4 10 12 24 300 250 550

Semester-IV:

Sl. No

Subject Code


C Exam Marks


1 PCS421P Project Phase-II 0 0 14 14 24 50 50 100

Total 0 0 14 14 24 50 50 100

LIST OF ELECTIVE COURSES FOR I, II AND III SEMESTER OF M.TECH. (CSE) 2009-2010 ONWARDS

Sl. No. Subject Code Subjects L T P C

1 PCS002E Computer Graphics and Visualization 4 0 0 4

2 PCS003E Digital Image Processing 4 0 0 4

3 PCS004E Object Technology 4 0 0 4

4 PCS005E Digital Signal Processing 4 0 0 4

5 PCS006E Machine Learning 4 0 0 4

6 PCS007E Distributed Systems 4 0 0 4

7 PCS009E Cryptography & Network Security 4 0 0 4

8 PCS010E Pattern Recognition 4 0 0 4

9 PCS014E Multi core Programming 4 0 0 4

10 PCS017E Soft Computing 4 0 0 4

11 PCS018E Computer vision 4 0 0 4

12 PCS019E System software and Compiler Design 4 0 0 4

13 PCS020E Software Quality Engineering 4 0 0 4

14 PCS021E Pervasive Computing 4 0 0 4

15 PCS022E Database Management Systems 4 0 0 4

16 PCS023E Storage area Networks 4 0 0 4

17 PCS024E Grid and cluster Computing 4 0 0 4

18 PCS025E Parallel Computing 4 0 0 4

19 PCS026E Data Mining 4 0 0 4

20 PCS027E Algorithmic Graph Theory 4 0 0 4

21 PCS028E Wireless and Mobile Communication 4 0 0 4

22 PCS029E Real Time Systems 4 0 0 4

23 PCS030E Multimedia Computing 4 0 0 4

24 PCS031E Finite Automata and Formal Languages 4 0 0 4

25 PCS032E Mobile Computing 4 0 0 4

26 PCS033E Compiler Design 4 0 0 4

27 PCS034E Cloud Computing 4 0 0 4

28 PCS035E Web Services 4 0 0 4

ADVANCED ALGORITHMS

Sub Code : PCS121C Credits : 04 Hours/Week : 04 CIE Marks : 50 Total Hours : 48 SEE Marks : 50

COURSE OUTCOMES

After the completion of the course student should be able to,

Analyze randomized algorithms. Employ indicator random variables and linearity of expectation to perform the analyses. Recite analyses of algorithms that employ this method of analysis.

Understand the different methods of amortized analysis (aggregate analysis, accounting and potential method). Perform amortized analysis.

Know major string matching algorithms and compare efficiencies of different algorithms.

Know the wide range of advanced algorithmic problems, their relations and variants, and application to real-world problems.

Identify the different ways to analyze randomized algorithms (expected running time, probability of error). Recite algorithms that employ randomization.

UNIT-I (12 HOURS)

Amortized Analysis: Aggregate, Accounting and Potential Methods. Graph Algorithms: Bellman - Ford Algorithm; Johnson’s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson method; Maximum bipartite matching. Polynomials and the FFT Representation of polynomials; the DFT and FFT; Efficient implementation of FFT

UNIT-II (12 HOURS) Number -Theoretic Algorithms Elementary notions; GCD; Modular Arithmetic; Solving modular linear equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing; Integer factorization. String-Matching Algorithms Naïve string Matching; Rabin - Karp algorithm; String matching with finite automata; Knuth-Morris-Pratt algorithm Boyer – Moore algorithms.

UNIT-III (12 HOURS)

Probabilistic and Randomized Algorithms Probabilistic Algorithms, randomizing deterministic algorithms, Monte Carlo and Las Vegas Algorithms, Probabilistic numerical algorithms, Probabilistic parallel algorithms NP-Complete Problems The classes P and NP, Reducibility, NP- complete problems: Cook’s theorem, Sample NP-complete problems, the class co-NP, The Classes NC and P-Complete Approximation Algorithms Bin Packing, The Steiner tree problem, the facility location problem

UNIT-IV (12 HOURS) Introduction to parallel algorithms and architectures Approaches to the design of parallel algorithms, Architectural constraints and design of parallel algorithms, Performance measures of parallel algorithms, parallel sorting Internet algorithms Search Engines, Ranking web pages, Hashing, Caching, content delivery and consistent hashing, Message security algorithms. Note: A list of assignments will be provided in the beginning of semester and evaluated for 20 Marks TEXT BOOKS: 1) T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 2nd Edition,

Prentice-Hall of India, 2002. 2) Kenneth A. Berman and Jerome L. Paul: “Algorithms”, Cengage Learning, 2002. REFERENCE BOOKS: 1) Ellis Horowitz, Sartaj Sahni, S.Rajasekharan: “Fundamentals of Computer Algorithms”, 2nd

Edition, University Press, 2007. 2) Alfred V. Aho,John E. Hopcroft, J.D.Ullman: “The Design and Analysis of Computer Algorithms”,

Addison-Wesley, 1974.

ADVANCES IN OPERATING SYSTEMS


COURSE OUTCOMES

After the completion of the course student should be able to,

Know functions, structures and history of operating systems Master understanding of design issues with operating system.

Identify various process management concepts including scheduling, synchronization and deadlocks.

Apply multithreading, concepts of memory management including virtual memory master system resources sharing among the users.

Explore issues related to file system interface and implementation, disk management be familiar with protection and security mechanisms.

UNIT-I (12 HOURS)

Operating System Overview: Operating System Objectives and Functions, The Evolution of Operating Systems, Major Achievements, Developments Leading to Modern Operating Systems, Microsoft Windows Overview, Traditional UNIX Systems, Modern UNIX Systems, Linux. Process Description and Control; What is a Process? Process States, Process Description, Execution of the Operating System, Security Issues, UNIX SVR4 Process Management, Threads, SMP, and Microkernels; Processes and Threads, Symmetric Multiprocessing (SMP), Microkernels, Windows Vista Thread and SMP Management, Linux Process and Thread Managements.

UNIT-II (12 HOURS) Concurrency: Mutual Exclusion and Synchronization: Principles of Concurrency, Mutual Execution: Hardware Support, Samaphores, Monitors, Message Passing, Readers/Writers Problem, Concurrency: Deadlock and Starvation; Principles of Deadlock, Deadlock Prevention, Deadlock Avoidance, Deadlock Detection, An Integrated Deadlock Strategy, Dining Philosophers Problems, Dining Philosophers Problems, Linux Kernel Concurrency Mechanisms, Windows Vista Concurrency Mechanisms

UNIT-III (12 HOURS) Uniprocessor Scheduling: Types of Scheduling, Scheduling Algorithms, Traditional UNIX Scheduling, Multiprocessor and Real-Time Scheduling; Multiprocessor Scheduling, Real-Time Scheduling, Linux Scheduling, UNIX FreeBSD Scheduling, Windows Vista Scheduling, Embedded Operating Systems; Embedded Systems, Characteristics of Embedded Operating Systems, eCOS, TinyOS.

UNIT-IV (12 HOURS)

Computer Security Threats: Computer security concepts, Threats, Attacks, and Assets, Intruders, Malicious software overview, Viruses, worms, and bots, Rootkits, Computer Security Techniques; Authentication, Access Control, Intrusion Detection, Malware Defense, Dealing with Buffer Overflow

Attacks, Windows Vista Security. Distributed Processing, Client/server and Clusters; Client/server Computing, Distributed Message Passing, Remote Procedure, Clusters, Windows Vista Clusters Server, Sun Cluster. TEXT BOOK: 1) William Stallings, “Operating Systems: Internals Design and Principles”, 6th edition, Longman,

2009. REFERENCES: 1) Gary Nut, “Operating Systems”, Third Edition, Pearson Education. 2006.

BIG DATA AND ANALYTICS


COURSE OUTCOMES

Pre-requisites: Basic knowledge of Relational Database Management System concepts

Understand the need to integrate structured, semi-structured and unstructured data.

Understand the significance, characteristics and challenges of big data.

Comprehend the reasons behind the popularity of Hadoop Ecosystem and its versions.

Identify the significance of NoSQL and NewSQL databases.

Comprehend MapReduce and MongoDB framework.

UNIT- I (12 HOURS) (Types of digital data):Types of Digital Data, Structured: Sources of structured data, Ease with Structured data, Semi-Structured: Sources of semi-structured data, Unstructured: Sources of unstructured data, Issues with terminology, Dealing with unstructured data,(Big Data): Characteristics of data, What big data? Definitions and Challenges of big data, other characteristics of data which are not definitional traits of big data, Why big data? Are we just an information consumer or do we also produce information? Traditional Business Intelligence(BI) versus Big data, A typical BI environment, A big data environment, Big data stack, What is changing in the realms of big data? (Big data analytics) Where do we begin? What is big data analytics? What big data analytics isn’t? Why this sudden hype around big data analytics? Classification of analytics Top challenges facing big data, Why is big data analytics important? Greatest challenges that prevent businesses from capitalizing on big data, what kind of technologies are we looking towards to help meet the challenges posed by big data? Data science, Data Scientist – your new best friend!!!, Terminologies used in big data environment, In memory analytics, In database processing, Massively parallel processing, Parallel versus distributed systems, Shared Memory architecture, Consistency, Availability, Partition Tolerance (CAP) theorem explained, Basically Available Soft State Eventual Consistency (BASE), Few top Analytics tools, Introduction to Jasper Report using Jasper Soft Studio

UNIT- II (12 HOURS) (The big data technology landscape): NoSQL, Where is it used? What is it? Types of NoSQL databases, Why NoSQL? Advantages of NoSQL, What we miss with NoSQL? NoSQL Vendors, SQL Versus NoSQL , NewSQL, Comparison of SQL, NoSQL and NewSQL, Hadoop: Features of Hadoop, Key advantages of Hadoop,Versions of Hadoop, Hadoop 1.0, Hadoop 2.0, Overview of Hadoop Ecosystems, Hadoop Versus, SQL, Integrated Hadoop systems offered by leading market vendors, Cloud based Hadoop solutions. (Hadoop) : Introducing Hadoop, Why not RDBMS, Distributed Computing Challenges, Brief History of Hadoop, Hadoop Overview, Hadoop Components, High Level Architecture of Hadoop, Hadoop Distributed File System(HDFS), HDFS Architecture, Daemons Related to HDFS, Working with HDFS Command, Special Features of Hadoop, Processing Data With

Hadoop,Introduction, How Map Reduce Works? Map Reduce Example, Word Count Example using Java, Managing Resources and Applications with YARN, Introduction, Limitation of Hadoop 1.0, Hadoop 2: HDFS, Hadoop 2: YARN, Interacting with Hadoop EcoSystem, Hive,Pig, HBASE, Sqoop, Business Intelligence on Hadoop.

UNIT- III (12 HOURS) (NoSQL - MongoDB): What is MongoDB? Why MongoDB? Using JSON, Creating or generating a unique key, Support for dynamic queries, Storing binary data, Replication, Sharding, Updating information in-place, Terms used in RDBMS and MongoDB, Data types in MongoDB, MongoDB - CRUD (Insert(), Update(), Save(), Remove(), find()), MongoDB- Arrays, Java Scripts, Cursors, Map Reduce Programming, Aggregations. (NoSQL - Cassandra): What is Cassandra? Why Cassandra? Peer to peer network, Gossip and Failure detection, Anti-Entropy & Read Repair, Writes in Cassandra, Hinted handoffs, Tunable consistency,Cassandra- CQLSH - CRUD, Counter, List, Set, Map, Tracing.

UNIT- IV (12 HOURS) (Hadoop Hive): Introduction to Hive - The Problem, Solution - Hive Use Case, Data Growth, Schema Flexibility and Evolution, Extensibility, What is Hive, History of Hive and Recent Releases of Hive, Hive Features, Hive Integration and Work Flow, Hive Data Units, Hive Architecture, Hive Primitive Data Types and Collection Types, Hive File Formats, Hive Query Language - Statements, DDL , DML, Hive Partitions, Bucketing, Views, Sub Query, Joins, Hive User Defined Function, Aggregations in Hive, Group by and Having, Serialization and Deserialization, Hive Analytic Functions. (Hadoop - Pig): Introducing Pig, History and Anatomy of Pig, Pig on Hadoop, Pig Features, Pig Philosophy, Word count example using Pig, Use Case for Pig, Pig Primitive Data Types, Colletion Types and NULL, Pig Latin Overview, Pig Latin Grammar - Comments, Keywords, Identifiers, Case sensitivity in Pig, Common Operators in Pig, Pig Statements, LOAD, STORE, DUMP, Interactive Shell - GRUNT, FILTER,SORT,GROUP BY,ORDER BY, JOIN, LIMIT, Pig Latin Script, Local Mode,Map Reduce Mode, Running Pig Script,Working with Field, Tuple, Bag,User Defined Function, Parameters in Pig. TEXT BOOK: 1) Big Data and Analytics, Seema Acharya and Subhashini C – Wiley India, 2015. REFERENCE BOOKS: 1) Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley and SAS Business

Series, 2012. 2) Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007. 3) Paul Zikopoulos, Dirk deRoos, Krishnan Parasuraman, Thomas Deutsch , James Giles, David

Corrigan, “Harness the Power of Big data – The big data platform”, McGraw Hill, 2012.

SEMINAR

Sub Code : PCS124S Credits : 02 Hours/Week : CIE Marks : 50 Total Hours : SEE Marks : 50

COURSE OUTCOMES

Access information in a variety of ways appropriate to a discipline, including locating and using library collections and services and other search tools and databases.

Obtain, select, store, create, use and organize support materials appropriately.

Demonstrate effective writing skills and processes by employing the rhetorical techniques of academic writing, including invention, research and critical analysis.

Identify and critically evaluate the quality of claims, explanation, support, and document it in professional way.

A student doing research under supervision of a professor for his/her project work discusses his/her survey, findings, conclusions and new technologies with professor and presents it to complete class. The learning’s of seminar course introduced in our curriculum meets the objectives of programme (i,iii,v,ix,x,xi) and bring in collection, distributing, analyzing and communication capability into learner.

HIGH PERFORMANCE COMPUTING AND PROGRAMMING


COURSE OUTCOMES

Analyze a given problem for possibilities of parallel computations

Identify algorithms and hardware for the solution of high performance projects program computers with shared and distributed memory architectures

Use appropriate programming languages efficiently for scientific computations

Apply parallel programs on different hardware architectures and software environments

Assess the performance and power efficiency of implementations.

Identify modern computing architectures for high performance computing with architectural prospective.

UNIT I (12 HOURS)

Introduction: Stored program computer architecture, General purpose cache based microprocessor architecture, Performance metrics and benchmarks ,Transistors galore: Moore’s low, Pipelining, Superscalarity, SIMD. Memory hierarchies:, Cache, Cache mapping, Prefetch Multicore processors, Multithreaded processors, Vector processors, Design principles, Maximum performance estimates. Parallel Computers Taxonomy of parallel computing paradigms, Shared memory computers, Cache coherence UMA, ccNUMA, Distributed memory computers, Hierarchical (hybrid) systems Interconnection Network, Basic performance characteristics of networks, Buses, Switched and fat tree networks, Mesh networks, Hybrid Networks

UNIT II (12 HOURS)

Basic of parallelization: Parallelism, Data parallelism, Functional parallelism, Parallel scalability Simple scalability laws, Parallel efficiency, Serial performance versus strong scalability Refined performance models, Choosing the right scaling baseline, Case study, Load imbalance GPUs as Parallel Computers: Architecture of a Modern GPU, Why More Speed or Parallelism, Parallel Programming Languages and Models, Overarching Goals Evolution of Graphics Pipelines, the Era of Fixed-Function Graphics Pipelines, Evolution of Programmable Real-Time Graphics, Unified Graphics and Computing Processors, GPGPU: An Intermediate Step in GPU Computing Scalable GPUs.

UNIT III (12 HOURS) A Brief Introduction To Opencl Background, Data Parallelism Model, Device Architecture Kernel Functions , Device Management and Kernel Launch, Electrostatic Potential Map in OpenCL Open CL device architecture, Basic open CL examples, concurrency model, CPU/GPU implementation , Open CL profiling ,Case studies, Introduction to Web CL Fundamental Limitations Facing Parallel Computing, Bandwidth Limitations, Latency Limitations Latency Hiding/Tolerating Techniques and their limitations.

UNIT IV (12 HOURS)

Power-Aware Computing and Communication, Power-aware Processing Techniques,Power aware Memory Design, Power-aware Interconnect Design, Software Power Management High Performance architecture examples IBM CELL BE, Nvidia Tesla GPU, Intel Larrabee Microarchitecture and Intel Nehalem microarchitecture Advanced Topics in computing (a) Petascale Computing (b) Optics in Parallel Computing (c) Quantum Computers (d) Recent developments in Nanotechnology and its impact on HPC Note: A list of assignments will be provided in the beginning of semester and evaluated for 20 Marks TEXT BOOKS: 1. Introduction to High Performance Computing for Scientists and Engineers by Georg Hager

Gerhard Wellein 2. Programming Massively Parallel Processors: A Hands-on Approach by David Kirk and Wen-mei

Hwu 3. Heterogeneous Computing with Open CL by Benedict R. Gaster REFERENCE BOOKS: 1. “Advanced Computer Architecture: Parallelism, Scalability, Programmability”, by Kai Hwang,

McGraw Hill 1993. 2. “Parallel Computer Architecture: A hardware/Software Approach”, by David Culler Jaswinder Pal

Singh, Morgan Kaufmann, 1999 3. “Scalable Parallel Computing”, by Kai Hwang, McGraw Hill 1998. 4. “Principles and Practices on Interconnection Networks”, by William James Dally and Brian

Towles, Morgan Kauffman 2004. 5. GPU Gems 3 --- by Hubert Nguyen (Chapter 29 to Chapter 41) 6. Introduction to Parallel Computing, Ananth Grama, Anshul Gupta, George Karypis, and Vipin

Kumar, 2nd edition, Addison-Welsey, © 2003. 7. Petascale Computing: Algorithms and Applications, David A. Bader (Ed.), Chapman & Hall/CRC

Computational Science Series, © 2007.

SOFTWARE ARCHITECTURES


COURSE OUTCOMES

Understand the key elements of software architecture Argue the importance and role of software architecture in large scale software systems.

Design and motivate software architecture for large scale software systems.

Know a variety of architectural styles and how they may be combined in a single system.

Identify software architecture design for a non-trivial system.

Understand software architecture aids different stages of the software lifecycle.

UNIT-I (12 HOURS) Review of Basic Concepts: What is a pattern? What makes a pattern? Pattern Categories; Relationships between patterns; Pattern description; Patterns and software architecture; What software architecture is and what it is not; Other points of view; Architectural patterns, reference models and reference architectures; Importance of software architecture; Architectural structures and views. Designing the Architecture: Architecture in the life cycle; Designing the architecture; Forming the team structure; Creating a skeletal system.

UNIT-II (12 HOURS) Reconstructing Software Architectures: Introduction; Informal extraction; Database construction; View fusion; Reconstruction; Examples. Software Product Lines: Introduction; What makes software product lines work? Scoping; Architectures for product lines; What makes software product lines difficult?

UNIT-III (12 HOURS) Building Systems from Off-the-Shelf Components: Impact of components on architecture; Architectural mismatch; Component-based design as search; ASEILM example. Some Design Patterns: Introduction; Management: Command processor, View handler; Communication: Forwarder-Receiver, Client-Dispatcher-Receiver, Publisher-Subscriber.

UNIT-IV (12 HOURS) Pattern Systems: What is a Pattern System? Pattern classification; Pattern selection; Pattern systems as implementation guidelines; The evolution of pattern systems. Case Studies: Key Word In Context; Instrumentation Software; Mobile Robotics; Cruise Control; The World Wide Web: A case study in interoperability; J2ee / EJB: A case study in industry-standard computing infrastructure.

TEXT BOOKS: 1. Len Bass, Paul Clements, Rick Kazman: Software Architecture in Practice, 2nd Edition, Pearson

Education, 2003. 2. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal: Pattern-

Oriented Software Architecture, A System of Patterns, Volume 1, John Wiley and Sons, 2007. 3. Mary Shaw and David Garlan: Software Architecture-Perspectives on an Emerging Discipline, PHI

Learning, 2007. REFERENCE BOOKS: 1. E. Gamma, R. Helm, R. Johnson, J. Vlissides: Design Patterns-Elements of Reusable Object-

Oriented Software, Pearson Education, 1995. 2. Web site for Patterns: http://www.hillside.net/patterns/

ADVANCED COMPUTER NETWORKS


COURSE OUTCOMES

Know the details of encoding, error detection, and reliable transmission in direct link networks.

Understand the functioning of switching and forwarding of packets in ATM and packet switching network.

Explore the routing algorithms in internetwork.

Identify the functions of end to end protocols: TCP, UDP.

Know the details of congestion control and resource allocation in network.

Explore the end to end data representation and compression.

Identify the wireless networks, link level design and channel access.

UNIT – I (12 HOURS) Review of Basic Concepts: (Self Study) Direct link networks: Hardware Building Blocks-nodes, links; Encoding, Framing (Self Study), Error Detection- Two-Dimensional Parity, Internet checksum Algorithm, cyclic Redundancy Check; reliable Transmission- Stop-and-Wait, Sliding Window, Concurrent Logical Channels; Ethernet (802.3), Rings (802.5, FDDI) – Token Ring Media Access Control, Token Ring Maintenance, FDDI (Self Study), Wireless. Packet Switching: Switching and forwarding – Datagrams, Virtual Circuit Switching, Source Routing; Bridges and LAN Switches – Learning Bridges, Spanning Tree Algorithm, Broadcast and Multicast, Limitations of Bridges; cell switching (ATM) – Cells, Segmentation and Reassembly, Virtual Paths, Physical Layers for ATM, Implementation and Performance.

UNIT – II (12 HOURS) Internetworking: Simple internetworking (IP) – What is an Internetwork?, Service Model, Global Address, Datagram Forwarding in IP, Address Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual Networks and Tunnels; Routing – Network as a Graph, Distance Vector(RIP), Link State(OSPF), Metrics, Routing for Mobile Hosts, Global Internet – Subnetting, Classless Routing(CIDR), Interdomain Routing(BGP), Routing Areas, IP Version 6(IPv6), Multiprotocol Label Switching - Destination-Based Forwarding, Explicit Routing, Virtual Private Networks and Tunnels. End-to-End Protocols: Simple Demultiplexer (UDP); Reliable byte stream (TCP) – End-to-End Issues, Segment Format, Connection Establishment and Termination, Sliding Window Revisited, Triggering Transmission, Adaptive Retransmission, Record Boundaries, TCP Extensions, Alternative Design Choices, Remote Procedure Call – RPC Fundamentals, RPC Implementaions (SunRPC, DCE)

UNIT – III (12 HOURS) Congestion Control and Resource Allocation: Issues in resource allocation – Network Model, Taxonomy, Evaluation Criteria; Queuing discipline – FIFO, Fair Queuing; TCP Congestion Control – Additive Increase/Multiplicative Decrease, Slow Start, Fast Retransmit and Fast Recovery; Congestion – Avoidance mechanisms – DECbit, Random Early Detection (RED), Source-Based

Congestion Control, Quality of Service – Application Requirements, Integrated Services (RSVP), Differentiated Services, Equation-Based Congestion Control. End – to – End Data: Presentation Formatting – Taxonomy, Examples (XDR, ASN.1, NDR), Markup Languages (XML), Data Compression – Lossless Compression Algorithms, Image Compression (JPEG), Video Compression (MPEG), Transmitting MPEG over a Network, Audio Compression (MP3) Applications: Traditional applications – Electronic Mail (SMTP, MIME, IMAP), World Wide Web (HTTP), Name Service (DNS), Network management (SNMP); Web services – Custom application Protocols (WSDL, SOAP), A Generic application Protocol (REST) (Self Study), Multimedia Applications – Session Control and Call control, Resource Allocation for Multimedia Applications, Overlay Networks – Routing Overlays, Peer-to-Peer Networks, Content Distribution Networks.

UNIT – IV (12 HOURS) Wireless networks: Introduction; The wireless channel; Link Level Design; Channel access; Network design; Wireless Networks Today; Future Systems and Standards. Optical Networks: Optical Links; WDM systems; Optical Cross-Connects; Optical LANs; Optical Paths and Networks. TEXT BOOKS: 1. Larry L. Peterson and Bruce S. David: Computer Networks – A Systems Approach, 4th Edition,

Elsevier, 2007. 2. J. Walrand and P. Varaya, “High Performance Communication Networks”, Harcourt Asia

(Morgan Kaufmann), 2000 REFERENCE BOOKS: 1. Behrouz A. Forouzan: Data Communications and Networking, 4th Edition, Tata McGraw Hill,

2006. 2. William Stallings: Data and Computer Communication, 8th Edition, Pearson Education, 2007. 3. Alberto Leon-Garcia and Indra Widjaja: Communication Networks -Fundamental Concepts and

Key Architectures, 2nd Edition Tata McGraw-Hill, 2004.

TERM PAPER

Sub Code : PCS224T Credits : 02 Hours/Week : CIE Marks : 50 Total Hours : SEE Marks : 50

COURSE OUTCOMES

Access information in a variety of ways appropriate to a discipline, including locating and using library collections and services and other search tools and databases.

Obtain, select, store, create, use and organize support materials appropriately.

Demonstrate effective writing skills and processes by employing the rhetorical techniques of academic writing, including invention, research and critical analysis.

Identify and critically evaluate the quality of claims, explanation, support, and document it in professional way.

A term paper is a research paper written by students over an academic term or semester which accounts for a large amount of a grade and makes up much of the course. Term papers are generally intended to describe an event or concept or argue a point. There is much overlap between the terms "research paper" and "term paper". The phrase "term paper" was originally used to describe a paper (usually a research based paper) that was due at the end of the "term" - either a semester or quarter, depending on which unit of measure a school used. However, the term has fallen out of favor. Common usage has "term paper" and "research paper" as interchangeable, but this is not completely accurate. Not all term papers involve academic research, and not all research papers are term papers.

SOFT COMPUTING AND INTELLIGENT SYSTEM


COURSE OUTCOMES

Upon completion of the course, Student should be able to:

Identify and describe soft computing techniques and their roles in building intelligent machines

Recognize the feasibility of applying a soft computing methodology for a particular problem.

Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems.

Apply neural networks to pattern classification and regression problems.

Effectively use existing software tools to solve real problems using a soft computing approach.

Evaluate and compare solutions by various soft computing approaches for a given problem.

UNIT I (12 HOURS)

Introduction to intelligent systems and soft computing: Introduction, Intelligent systems, Knowledge-based systems, Knowledge representation and processing, soft computing, Problems. Fundamentals of fuzzy logic systems: Introduction ,Background , Fuzzy sets, Fuzzy logic operations , Generalized fuzzy operations, Generalized fuzzy complement , Implication (if-then),Some definitions , Fuzziness and fuzzy resolution , Fuzzy relations, Composition and inference , Considerations of fuzzy decision-making, Problems.

UNIT II (12 HOURS)

Fuzzy logic control: Introduction, Background, Basics of fuzzy control, Fuzzy control architectures, Properties of fuzzy control, Robustness and stability, Problems. Fundamentals of artificial neural networks: Introduction, Learning and acquisition of knowledge, Features of artificial neural networks, Fundamentals of connectionist modeling, Problems.

UNIT III (12 HOURS) Major classes of neural networks: Introduction ,The multilayer perceptron , Radial basis function networks, Kohonen's self-organizing network , The Hopfield network , Industrial and commercial applications of ANN , Problems , Introduction to Support vector machines, Problems. Neuro-fuzzy systems: Introduction, Background, Architectures of neuro-fuzzy systems, Construction of neuro-fuzzy systems, Problems.

UNIT IV (12 HOURS) Soft computing for smart machine design: Introduction, Controller tuning, Supervisory control system, Problems.

Tools of soft computing in real-world applications: Case study: Soft computing tools for solving a class of facilities layout planning problem, Mobile position estimation using an RBF network in CDMA cellular systems, Learning-based resource optimization in ATM networks. TEXT BOOK: 1. Fakhreddine O.Karray, Clarence De Silva, “Soft Computing and Intelligent Systems Design

Theory, Tools and Application”, Pearson Education. 2. Vojislav Kecman, “ Learning and Soft Computing: Support Vector Machines, Neural Networks,

and Fuzzy Logic Models”, Pearson Education (Asia) Pte. Ltd. 2004. REFERENCE BOOK(S): 1. Naresh sinha, Madan Gupta “Soft computing and Intelligent Systems- theory and application”,

Addison Wesley. 2. Timothy J.Ross, "Fuzzy Logic with Engineering Applications", McGraw-Hill, 1997.

DATABASE TECHNOLOGY


COURSE OUTCOMES

Understand relational model concepts, constraints and schemas.

Know update operations, transactions and constraint violations.

List and distinguish the types of database system architectures.

Know the need for parallel databases. Define I/O parallelism.

Identify Interoperation and Interoperation parallelism.

UNIT – I (12 HOURS)

Review of Relational Data Model and Relational Database Constraints: Relational model concepts; Relational model constraints and relational database schemas; Update operations, transactions and dealing with constraint violations. Database-System Architectures: Centralized and Client-Server Architecture; Server System Architectures; Parallel Databases; Distributed Systems; Network Types. Parallel Databases: Introduction; I/O Parallelism, Interquery Parallelism; Intraquery Parallelism; Intraoperation Parallelism; Interoperation Parallelism; Query Optimization.

UNIT – II (12 HOURS) Concepts of Object Databases: Overview of Object-Oriented Concepts; Objects, identity, Object Structure and Type Constructors; Encapsulation of operations, methods, and persistence; Type and class hierarchies, and inheritance; complex objects. Object Database Standards, Languages and Design: Overview of Object model of ODMG; Object definition Language ODL; Object Query Language OQL; Conceptual design of Object database.

UNIT – III (12 HOURS) Distributed Databases: Homogeneous and heterogeneous Databases; Distributed Data storage; Distributed transactions; Commit Protocols; Concurrency Control in Distributed Databases; Availability; Distributed Query Processing; Heterogeneous Distributed Databases. Data Warehousing and Mining: Decision-Support Systems; Data Warehousing; Data Mining; Classification; Association Rules; Other Types of Associations; Clustering; Other Forms of Data Mining.

UNIT – IV (12 HOURS)

Enhanced Data Models for Some Advanced Applications: Active database concepts and triggers; Temporal, Spatial, and Deductive Databases – Basic concepts. More Recent Applications: Mobile databases; Multimedia databases, Geographical information system and Genome database management. The concept of big data.

TEXT BOOKS: 1. Rameez Elmashri, Shamakant B Navathe, ‘Fundamentals of Database Systems’, Fifth Edition,

Pearson Education. (5.1-5.3 (except 5.2.5 & 5.3.4); 20.1-20.5; 21.1- 21.5(except21.4); 24.1-24.4(except 24.3.2 & 24.4.5-24.4.8); 30.1-30.4(except 30.2.3).

2. Abraham Silberschatz, Henry. F. Korth and S.Sudharsan, “Database System Concepts”, Sixth Edition, Tata McGraw Hill, 2011. (17.1- 17.5; 18.1-18.7; 19.1-19.8 (except 19.4.3, 19.6.3-19.6.6); 20.1- 20.8 (except 20.4.2 – 20.4.4).

REFERENCE BOOKS: 1. Raghu Ramakrishnan and Johannes Gehrke: Database Management Systems, 3rd Edition,

McGraw-Hill, 2003. 2. Connolly and Begg: Database Systems, 4th Edition, Pearson Publications, 2009.

EFFECTIVE TEACHING-LEARNING PRACTICES

Sub Code : PCS323C Credits : 04 (Theory : 2. Practical : 2) Hours/Week : 04 CIE Marks : 50 Total Hours : 48 SEE Marks : 50

COURSE OUTCOMES

Design / Plan Instruction.

Create / Maintain Learning Climates.

Implement / Manage Instructions.

Assesses and Communicates Learning Results.

Reflects / Evaluates Teaching / Learning skills.

Engage in Professional Development. To organize Knowledge of Content.

UNIT I (6 HOURS)

Scientific Basis of art of Teaching, andragogy Analysis of Adult learning, Cognitive and Social Learning Theory, Profile of Adult learning. Content Analysis: Categories of Content, facts concepts and theory of content analysis Preparation of Content map on context. Instructional Objectives and Phases of teaching.

UNIT II (6 HOURS) Instructional Planning, Writing Instructional objectives, preparing instructional resources. Types of Objectives : program, course, and classroom objectives. Establishing specific objectives. Planning by level of Instructions. Blooms Taxonomy.

UNIT III (6 HOURS) Skills of Teaching : Introduction, Purpose of Introduction. Ways of Introducing a topic. Motivation. Role of motivation, in learning. Skill of explanation, Types of explanation and their requirements. Skill of questioning in class. Reasons of questioning in the class. Teaching in concept of generalization, with illustration of example, types of examples, sequencing of examples

UNIT IV (6 HOURS) Presentation skills, various methods of teaching, Ausubels advanced organization strategy, Bruner’s concept of attainment strategy. Academic standards and student assessment and evaluation : Standards, evaluate student learning , criteria for selecting tests Standardized and non standardized tests strengths and limitation, ABET Standard: For engineering teachers Reference.

TEXT BOOK: 1. Strategies for Effective Teaching, Allan C. Ornstein, McGrawHill REFERENCE BOOKS: 2. A Taxonomy for Learning Teaching and Assessing, Lorin W. Anderson and David R. Krathwohl,

Pearson Education 3. Andrew W Apple, 1997,Modern Compiler Implementation in C –Cambridge Theory - 2 Credits Practical (Delivery + Material preparation) - 2 Credits Allotment of topics/subject/delivery sessions:

A student is associated with a subject teacher.

The subject teacher will assign 8 sessions (1 per week)

The teacher will assess the teaching material (including handouts) prior to the delivery.

The teacher will evaluate his delivery for every session and keep records.

The teacher will submit internal evaluation report at the end of semester Conduction of CIE:

Two CIE’s based on theory (30 Marks)

Internal evaluation of teachers (20 Marks 15 CIE Presentation + 5 Marks assignments) Conduction of SEE:

Theory (50% of Marks) Examination for 100 Marks to be reduced to 25 Marks.

Presentation + report submission (50% of Marks) 50 Marks to be reduced to 25 Marks.

INDUSTRIAL WORKSHOP

Sub Code : PCS324M Credits : 00 Hours/Week : CIE Marks : 50 Total Hours : SEE Marks : 00

COURSE OUTCOMES

Know the industry practice for system design.

Understand the new technology for design of software systems.

Identify the new field of innovation.

Interact with industry people. Every student has to attend a training conducted by the department in collaboration with industrial experts. CIE will be conducted during the training program. Every student has to submit a report on training. SEE involves Presentation to Departmental evaluation committee.

PROJECT PHASE-I

Sub Code : PCS325P Credits : 08 Hours/Week : CIE Marks : 50 Total Hours : SEE Marks : 50

COURSE OUTCOMES

Identification of the issues and challenges in various computing domains to formulate the problem and propose a solution.

Design and implement the solution by employing the tools and frameworks adhering to various software engineering processes.

Analysis for performance parameters and comparison with existing and contemporary solutions.

Incorporate and document appropriate sources in accordance with the formatting style proper to the discipline.

THE PHASE-I INCLUDE: 1) Deciding the broad area for project work. 2) Sufficient literature Survey (Minimum of 10-15 literatures includes Research papers, technical

reports, white papers, manuals and survey reports. 3) Identification of Issues and defining problem. 4) A report containing summary of survey made covering issues and problem definition with print

outs of all literature documents. 5) Presentation on survey made.

SCHEME OF EVALUATION FOR PROJECT PHASE-I CIE Evaluation: By Guide for Report writing : 50 Marks SEE Evaluation: Report Evaluation (Survey of minimum of 25 papers of relevant research area) : 25 Marks Seminar (Presentation) : 25 Marks TOTAL : 100 Marks

PROJECT PHASE-II

Sub Code : PCS421P Credits : 24 Hours/Week : CIE Marks : 50 Total Hours : SEE Marks : 50

COURSE OUTCOMES

Identification of the issues and challenges in various computing domains to formulate the problem and propose a solution.

Design and implement the solution by employing the tools and frameworks adhering to various software engineering processes.

Analysis for performance parameters and comparison with existing and contemporary solutions.

Incorporate and document appropriate sources in accordance with the formatting style proper to the discipline

GUIDELINES FOR PROJECT PHASE II AND SCHEME OF EVALUATION

CIE: 50 Marks

Project progress Presentation I 10 Marks

Project progress Presentation II 10 Marks

Project progress Presentation III 10 Marks

Project Presentation IV 10 Marks

Report Writing 10 Marks

TOTAL 50 Marks

SEE out of 100 Marks: Report Evaluation and Viva voce Examination by three examiners 1) Internal examiner 2) External Examiner 3) HOD/Nominee 70 Marks for Report Evaluation (Average of Marks given by three examiners) 30 Marks for Viva Voce examination (Joint evaluation by three examiners) Total 100 Marks. Final Marks= CIE Marks out of 50+ 50% of SEE (50% of out of 100)

COMPUTER GRAPHICS AND VISUALIZATION

Sub Code : PCS002E Credits : 04 Hours/Week : 04 CIE Marks : 50 Total Hours : 48 SEE Marks : 50

COURSE OUTCOMES

* Know the architecture of graphics systems. Understand graphics algorithms for drawing 2D

primitives. * Explore the functions of 2D and 3D transformations. * Know the details of 3D object representations. * Explore the open GL API related to color, viewing, primitive. * Identify the event driven input using open GL programming API. * Illustrate viewing with a computer. Explore the lighting and shading models, the shading model

using case study on sphere model.

UNIT I (12 HOURS) Introduction: Application of computer graphics, Elements of pictures created in computer graphics, graphics display devices, Basic raster graphics algorithms for drawing 2D primitives: Midpoint line & circle drawing algorithm, scan-line polygon filling algorithm, antialising, 2D geometric transformations: Basic transformations,

UNIT II (12 HOURS)

2D transformation matrix representations and homogeneous coordinates, composite transformations, window-to-view port coordinate transformation, clipping operations- Cohen Sutherland line clipping, Sutherland-Hogeman polygon clipping, 3D object representations: polygon surfaces, curved lines and surfaces, quadric surfaces, spline representations, Bezier curves and surfaces, B-spline curves and surfaces

UNIT III (12 HOURS)

THE OPENGL: The OpenGL API; Primitives and attributes; Color; Viewing; Control functions; The Gasket program; Polygons and recursion; The three-dimensional gasket; Plotting implicit functions. INPUT AND INTERACTION: Interaction; Input devices; Clients and servers; Display lists; Display lists and modeling; Programming event-driven input; Menus; A simple CAD program; Building interactive models; Animating interactive programs; Design of interactive programs; Logic operations.

UNIT IV (12 HOURS)

VIEWING: Classical and computer viewing; Viewing with a computer; Positioning of the camera; Simple projections; Projections in OpenGL; Hidden-surface removal; Interactive mesh displays; Parallel-projection matrices; Perspective-projection matrices; LIGHTING AND SHADING: Light and matter; Light sources; The Phong lighting model; Computation of vectors; Polygonal shading; Approximation of a sphere by recursive subdivisions; Light sources in OpenGL; Specification of materials in OpenGL; Shading of the sphere model; Global illumination.

TEXT BOOKS: 1) Computer Graphics - OpenGL Version – Donald Hearn and Pauline Baker, 2nd Edition, Pearson

Education, 2003 2) Interactive Computer Graphics A Top-Down Approach with OpenGL -Edward Angel, 5th Edition,

Addison-Wesley, 2008. REFERENCE BOOKS: 1) Computer Graphics – James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes,

Addison-wesley 1997. 2) Computer Graphics Using OpenGL – F.S. Hill,Jr. 2nd Edition, Pearson Education, 2001.

DIGITAL IMAGE PROCESSING


COURSE OUTCOMES

Know the fundamentals of digital images and their representations in spatial and frequency domains.

Develop new algorithms to improve the quality of digital images by applying enhancement and restoration techniques.

Analyze the properties of discreet transforms and their importance role in digital image processing.

Identify various image enhancement techniques in spatial as well as frequency domains.

Identify various color models to represent color images and transformation among the models.

Analyze the techniques available in morphological image processing. .

UNIT I (12 HOURS) 1. Introduction:

What is digital image processing? Origins of Digital Image Processing, Fundamental Steps in Digital Image Processing, Components of an Image Processing System A Simple Image Formation Model, Basic Concepts in Sampling and Quantization, Representing Digital Images, Zooming and Shrinking Digital Images, Some Basic Relationships Between Pixels, Linear and Nonlinear Operations

2. Image Enhancement in the Spatial Domain: Some Basic Gray Level Transformations, Histogram Processing, Enhancement Using Arithmetic/Logic Operations, Basics of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters, Combining Spatial Enhancement Methods.

UNIT II (12 HOURS)

3. Image Enhancement in the Frequency Domain:

Background, Introduction to the Fourier Transform and the Frequency, Domain, Smoothing Frequency-Domain Filters, Sharpening Frequency Domain Filters, Homomorphic Filtering.

4. Image Restoration: A Model of the Image degradation/Restoration process, Noise Models, Restoration in the Presence of Noise Only–Spatial Filtering, Periodic Noise Reduction by Frequency Domain Filtering, Linear, Position-Invariant Degradations , Estimating the Degradation Function, Inverse Filtering ,Minimum Mean Square Error (Wiener) Filtering.

UNIT III (12 HOURS)

5. Color Fundamentals:

Color Models, Pseudocolor Image Processing, Basics of Full-Color Image Processing, Color Transformations, Smoothing and Sharpening, Color Segmentation, Noise in Color Images.

6. Morphological Image Processing: Preliminaries, Dilation and Erosion, Opening and Closing, The Hit-or-Miss Transformation, Some Basic Morphological Algorithms

UNIT IV (12 HOURS)

7. Image Segmentation:

Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, Region-Based Segmentation

8. Representation and Description: Representation, boundary descriptors, regional descriptors, use of principal components and description.

9. Object Recognition: Patterns and Pattern Classes, Recognition Based on Decision-Theoretic Methods, Structural Methods

TEXT BOOKS: 1. Rafel C Gonzalez and Richard E. Woods, "Digital Image Processing", PHI 2nd Edition 2005. 2. Scott.E.Umbaugh, "Computer Vision and Image Processing", Prentice Hall, 1997

DISTRIBUTED SYSTEMS


COURSE OUTCOMES

Define distributed systems (DS) and list the examples of DS such as Internet, intranet, mobile and ubiquitous computing and need of resource sharing in the web.

Analyze the challenges of DS, architectures in DS, Internet protocols, and inter-process communication.

Know the distributed objects will communicate and how the remote method invocation takes place in distributed objects.

Explore the support of operating system for communication through processes and threads and list the security techniques used for safe operation of distributed communication.

Know the working of distributed file servers and distributed shared memory.

UNIT I (12 HOURS)

Characterization of Distributed Systems and System Models: Introduction, Examples of distributed systems, Resource sharing and the Web, Challenges, Architectural models, Fundamental models. Networking and Internetworking: Types of Networks, Networks principles, Internet protocols, Network case Studies (Ethernet, wireless LAN and ATM). Interprocess Communication: Introduction, The API for the Internet protocols, External data representation and marshalling, Client-Server communication, Group communication

UNIT II (12 HOURS)

Distributed Objects and Remote Invocation : Communication between distributed objects, Remote procedure call, events and notifications, JAVA RMI case study. Operating System Support and Security: The Operating system layer, protection, processes and threads, communication and invocation , operating system architecture, overview of security techniques, cryptographic algorithms, digital signatures, cryptography pragmatics, case studies: Needham-Schroeder, Kerberos, SSL and Millicent.

UNIT III (12 HOURS) Distributed File Systems : File service architecture, Sun Network file system, Andrew file system, Recent advances. Transactions and Concurrency Control : Transactions, nested transactions, locks, optimistic concurrency control, timestamp ordering, comparison of methods for concurrency control.

UNIT IV (12 HOURS) Distributed Transactions :Flat and nested distributed transactions, atomic commit protocols, concurrency control in distributed transactions, distributed deadlocks, transaction recovery. Distributed Shared Memory:Design and Implementation issues, sequential consistency and Ivy, Release consistency and Munin, other consistency models CASE Studies: COBRA , Mach

TEXT BOOK: 1. George Coulouris, Jean Dollimore, Tim Kindberg: “Distributed Systems, Concept and Design”, 3rd

edition, Pearson Education, 2005. REFERENCE: 1. Andrew S. Tanenbaum & Marten van Steen, Distributed Systems – Principles and Paradigms, PHI,

2002.

CRYPTOGRAPHY AND NETWORK SECURITY


COURSE OUTCOMES

State the principles and practices of cryptography and network security.

Understand, analyze and use various cryptographic techniques.

Understand the practical applications (such as in email, IP and the web) that have been implemented and are in use to provide network security.

Apply correct cryptographic and authentication techniques to provide required security services to their applications

UNIT-I (12 HOURS)

Symmetric Ciphers: Overview: Services, Mechanisms and Attacks, The OSI Security Architecture, A Model of Network Security. Classical Encryption Techniques: Symmetric Cipher Model, Substitution Techniques, Transposition Techniques, Rotor Machines, Steganography. Block Cipher and the Data Encryption Standard: Simplified DES, Block Cipher Principles.

UNIT-II (12 HOURS) The Data Encryption Standard, The Strength of DES, Differential and Linear Cryptanalysis. Symmetric Ciphers: Triple DES, Blowfish. Confidentiality Using Conventional Encryption: Placement of Encryption Function, Traffic Confidentiality, Key Distribution, Random Number Generation. Public-Key Encryption, Digital signatures and Authentication Protocols:Number Theory: Prime Numbers, Format’s and Euler's Theorems, Testing for Primality. Public-Key Cryptography and RSA: Principles of Public Key Cryptosystems, The RSA Algorithm, Key Management, Diffie Hellman Key Exchange.

UNIT-III (12 HOURS) Message Authentication: Authentication Requirements, Authentication Functions, Message Authentication Codes, MDS Message Digest Algorithm. Digital Signatures and Authentication Protocols: Digital Signatures, Authentication Protocols, Digital Signature Standard. Network Security: Authentication Applications: Kerberos, XS09 Directory Authentication Service. Electronic Mail Security: Pretty Good Privacy.

UNIT-IV (12 HOURS) IP Security: Overview, IP Security Architecture, Authentication Header, Encapsulation Security Payload. Web Security: Web Security Requirements, Secure Sockets Layer and Transport Layer Security, Secure Electronic Transaction. TEXT BOOK: 1. William Stallings, Cryptography and Network Security.

STORAGE AREA NETWORKS


COURSE OUTCOMES

Know the architecture of basic components in storage area networks.

Identify the functioning of the protocols used in disk subsystem.

Explore the functions of storage network components.

Identify the fiber communication in storage networks.

Know the details of network attached storage.

Explore the file system in storage area networks.

UNIT I (12 HOURS) Introduction: Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data Access problem; The Battle for size and access Intelligent Disk Subsystems Architecture of Intelligent Disk Subsystems; Hard disks and Internal I/O Channels; JBOD, Storage virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk Access; Intelligent disk subsystems, Availability of disk subsystems.

UNIT II (12 HOURS)

I/O Techniques: The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol Stack: FC0,FC1,FC2,FC3,FC4, Fibre Channel SAN: Point-to-ponit topology, Fabric topology, Arbitrated loop, Hardware components, InetrSANs, IP Storage File System and NAS:Local File Systems; Network file Systems and file servers; Shared Disk file systems; Comparison of fibre Channel and NAS.

UNIT III (12 HOURS) Storage Virtualization: Definition of Storage virtualization ; Implementation Considerations; Storage virtualization on Block or file level; Storage virtualization on various levels of the storage Network; Symmetric and Asymmetric storage virtualization in the Network SAN Architecture and Hardware devices: Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch; Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.

UNIT IV (12 HOURS) Software Components of SAN The switch’s Operating system; Device Drivers; Supporting the switch’s components; Configuration options for SANs. Management: Planning Business Continuity; Managing availability; Managing Serviceability; Capacity planning; Security considerations.

TEXT BOOKS: 1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: “Storage Networks Explained”, Wiley India,

2007 2. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2003. REFERENCE BOOKS: 1. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to

understanding and Implementing SANs”, John Wiley India, 2002.

DATA MINING


UNIT – I (12 Hours)

INTRODUCTION, DATA – 1: What is Data Mining? Motivating Challenges; The origins of data mining; Data Mining Tasks. Types of Data; Data Quality. DATA – 2: Data Preprocessing; Measures of Similarity and Dissimilarity

UNIT – II (12 HOURS)

CLASSIFICATION: Preliminaries; General approach to solving a classification problem; Decision tree induction; Rule-based classifier; Nearest-neighbor classifier. ASSOCIATION ANALYSIS – 1: Problem Definition; Frequent Itemset generation; Rule Generation; Compact representation of frequent itemsets; Alternative methods for generating frequent itemsets.

UNIT – III (12 HOURS) ASSOCIATION ANALYSIS – 2: FP-Growth algorithm, Evaluation of association patterns; Effect of skewed support distribution; Sequential patterns. CLUSTER ANALYSIS: Overview, K-means, Agglomerative hierarchical clustering, DBSCAN, Overview of Cluster Evaluation.


FURTHER TOPICS IN DATA MINING: Multidimensional analysis and descriptive mining of complex data objects; Spatial data mining; Multimedia data mining; Text mining; Mining the WWW. Outlier analysis. APPLICATIONS: Data mining applications; Data mining system products and research prototypes; Additional themes on Data mining; Social impact of Data mining; Trends in Data mining. TEXT BOOKS: 1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar , Introduction to Data Mining, Pearson

Education, 2007 2. Jiawei Han and Micheline Kamber, Data Mining – Concepts and Techniques , 2nd Edition,

Morgan Kaufmann, 2006. REFERENCE BOOKS: 1. K.P.Soman, Shyam Diwakar, V.Ajay, Insight into Data Mining – Theory and Practice, PHI

publishers, 2006.

REAL TIME SYSTEMS


COURSE OUTCOMES

Model real-time systems using formal models of timed behaviour.

State properties of timed system behaviour.

Explain various principles underlying automated verification.

Use tools to assist verification of real-time properties.

Understand selected verfication techniques and know their advantages and limitations.

To use different abstraction levels during real-time systems development.

Analyze scheduling aspects of real-time systems.

Implement real-time programs on real-time operating systems

UNIT –I (12 HOURS)

Basic Real-Time Concepts: Basic Computer Architecture-Bus Transfer Mechanism, Input and Output, Memory, CPU Operation; Some Terminology- Software Concepts, System Concepts, Real-Time Definitions, Events and Determinism, Synchronous and Asynchronous Events, Determinism, Time-Loading; Real-Time Design Issues; Example Real-Time Systems; Brief History-Software, Hardware. Language Issues : Language features:parameter passing,recursion,dynamic allocation,typing,exception handling,abstract data typing,modularity Real- Time Specification And Design Techniques Natural Languages; Mathematical Specification; Flowcharts; Structure Charts; Pseudocode and Programming Design Languages; Finite State Automata; Data Flow Diagrarns- DeMarco's Rules, Hatley and Pribhai's Extensions; Petri Nets; Warnier-Orr Notation- Indexed Loop; Statecharts- Depth, Orthogonality, Broadcast Communication; Sanity in Using Graphical Techniques.

UNIT –II (12 HOURS) Real- Time Kernels Polled Loop System- Polled Loop with Interrupts; Phase/State- Driven Code; Coroutines; Interrupt-Driven Systems- Context Switching, Round-Robin Systems, Preemptive Priority Systems, Major and Minor Cycles, Hybrid Systems; Foreground/Background Systems- Background Processing, Initialization, Real- Time operation; Full-Featured Real Time Operating Systems- Task- Control Block Model; Build or Buy? POSIT. Intertask Communication And Synchronization Buffering Data- Time-Relative Buffering, Ring Buffers; Mailboxes Mailbox Implementation, Other Operations on Mailboxes, Queues; Critical Regions; Semaphores- Mailboxes and Semaphores, Counting Semaphores, Problems with Semaphores, The Test- and- Set Instruction; Event Flags and Signals; Deadlock- Avoidance, Detect and Recover. Real-Time Memory Management Process Stack Management- Task-Control Block Model, Managing the Stack, Run-Time Ring Buffer, Maximum Stack Size, Multiple Stack Arrangements, Task-Control Block Model;

UNIT –III (12 HOURS) Real-Time Memory Management : contd :Dynamic Allocation-Swapping, Overlays, MFT, MVT, Demand Paging, Working Sets, Real Time Garbage Collection, Contiguous File Systems; Static Schemes. System Performance Analysis And Optimization Response-Time Calculation- Polled Loops, Coroutines / Phase- Driven Code, Interrupt Systems; Interrupt Latency- Propagation Delay, Macroinstruction Execution Times, Interrupts Disabled, Preemption, Low Priority Interrupts High; Time-Loading and Its Measurement Using a Logic Analyzer, Instruction Counting, Pictorial Representation, Instruction Execution Time Simulators, Deterministic Performance; Scheduling Is NP-Complete; Reducing Response Times and Time Loading- Compute at Slowest Cycle, Scaled Arithmetic, Binary Angular Measurement, Look-Up Tables, Basic Optimization Theory, Other Optimization Techniques, Combination Effects, Speculative Execution; Analysis of Memory Requirements- Memory-Mapped I/O and DMA Memory, Program Area, RAM Area, Stack Area, Memory Management Schemes; Reducing Memory-Loading- Variable Selection, Reuse Variables, Memory Fragmentation, Self-Modifying Code; I/O Performance. QUEUING MODELS Probability Functions- Continuous; Discrete; Basic Buffer Size Calculation- Handling Bursts of Data, Variable Buffer Size Calculation; Classical Queuing Theory- The M/M/1 Queue, Service and Production Rates, More Buffer Calculations, Response- Time Modeling, Other Queuing Models; Little's Law; Erlang's Formula.

UNIT –IV (12 HOURS)

Reliability, Testing, And Fault Tolerance "Faults, Failures, Bugs and Effects; Reliability- Formal Definition, Calculating System Reliability; Testing- Unit Level Testing, System Level Testing, Statistically Based Testing, Cleanroom Testing, Stress Testing; Fault Tolerance- General Problems Handling, N-Version Programming, Built-In-Test Software, CPU Testing, Memory Testing, Spurious and Missed Interrupts, Dealing with Bit Failures. Hardware /Software Integration Goals of Real-Time System Integration- System Unification, System Validation; Tools- Millimeters, Oscilloscope, Logic Analyzer, In-Circuit Emulator, Software Simulators, Hardware Prototypes/ Simulators, Debuggers; Methodology- Establishing a Baseline, Backoff Method, Patching; The Software Heisenberg Uncertainty Principle- Real-World Analogies, The Software Heisenberg Uncertainty Principle, Testing of Software, Time- and Memory-Loading, Other Implications. REAL-TIME APPLICATIONS Real-Time Systems as Complex Systems; The First Real-Time Application; Real-Time Databases; Real-Time Image Processing- Virtual Reality, Multimedia; Real-Time UNIX; Building Real-Time Applications with Real-Time Programming Languages. TEXT BOOK: 1. Phillip A. Laplante,' Real- Time Systems Design and Analysis- An Engineer's Handbook,' PHI

Publications, Second Edition, 2000 (Chapters 1,3,5; 6,7,8,9,10,11,13,14)

COMPILER DESIGN


COURSE OUTCOMES

Know scanning and lexical analysis processes.

Understand the principles of parsing and application to real-world compilation techniques.

Know intermediate code representations and generation.

Explore to code optimization techniques.

Explore the theory and practice of compilation Understand the underlying concepts of design of compilers

UNIT – I (12 HOURS)

INTRODUCTION, LEXICAL ANALYSIS: Language processors; The structure of a Compilers; The evolution of programming languages; The science of building a compiler; Applications of Compiler technology; Lexical analysis: The Role of Lexical Analyzer; Input Buffering; Specifications of Tokens; Recognition of Tokens. SYNTAX ANALYSIS: Introduction; Context-free Grammars; Writing a Grammar; Top-down Parsing. Bottom-up Parsing; Introduction to LR Parsing: Simple LR.,Parser Generators.


SYNTAX-DIRECTED TRANSLATION: Syntax-Directed definitions; Evaluation order for SDDs; Applications of Syntax-directed translation; INTERMEDIATE CODE GENERATION: Variants of syntax trees; Three-address code; Types and declarations; Translation of expressions; Type checking;

UNIT – III (12 HOURS)

INTERMEDIATE CODE GENERATION: Control flow; Back patching. RUN-TIME ENVIRONMENTS: Storage Organization; Stack allocation of space. Access to non-local data on the stack; Heap management; Introduction to garbage collection.


CODE GENERATION: Issues in the design of Code Generator; The Target language; Addresses in the target code; Basic blocks and Flow graphs; Optimization of basic blocks; A Simple Code Generator. INSTRUCTION-LEVEL PARALLELISM: Processor Architectures; Code-Scheduling Constraints; Basic-Block Scheduling; Global Code Scheduling; Software Pipelining. TEXT BOOK: 1. Alfred V Aho, Monica S. Lam, Ravi Sethi, Jeffrey D Ullman, 2007,Compilers- Principles,

Techniques and Tools –– 2nd Edition, Addison-Wesley. (chapter 1:1.1 -1.5,chapter 3:3.1 to 3.4,Chapter 4:4.1-4.6.4,Chapter 5:5.1-5.4.4,Chapter 6:6.1 -6.5.2,6.7-6.7.3,Chapter 7:7.1-7.5,Chapter 8:8.1-8.6)

REFERENCE BOOKS: 1. Charles N. Fischer, Richard J. leBlanc, Jr, 1991, Crafting a Compiler with C –, Pearson Education. 2. Andrew W Apple, 1997,Modern Compiler Implementation in C –Cambridge University Press. 3. Kenneth C Louden,1997, Compiler Construction Principles & Practice, Thomson Education.

CLOUD COMPUTING


COURSE OUTCOMES

Understand the significance, characteristics and challenges of cloud computing.

Comprehend the reasons behind the popularity of Cloud Computing Technology, Accessing the Clouds and Developing Applications

Explore the Local Clouds and Thin Clients, Best practices and the future of Cloud computing.

Know Cloud Computing might evolve.

Comprehend different cloud computing platforms and their architectural features.

Identify cloud services and its usage for individuals

Able to differentiate between cloud services and cloud platforms and its features

UNIT – I (12 HOURS) Cloud Computing Basics: Overview, Applications, Intranet and the Cloud, First Movers in the Cloud; The Use of Cloud Computing, Benefits, Security concerns, regulatory issues; Overview of different cloud computing applications that are implemented; Business case for implementing a Cloud: Cloud Computing Services, Applications help to the business, deleting the datacenter, Saleforce.com, Thomson Reuters.


Cloud Computing Technology: Hardware and Infrastructure: Clients, Security, Network, Services; Accessing the Clouds: Platforms, Web applications, Web APIs, Web Browsers; Cloud Storage: Overview, Cloud Storage providers, Standards: Applications, Client, Infrastructure, Service.

UNIT – III (12 HOURS)

Cloud Computing at Work: Software as a service: Overview, Driving Forces, Company offerings, Industries; Software plus services: Overview, Mobile Device Integration, Providers, Microsoft Online; Developing Applications: Google, Microsoft, Intuit QuickBase, Cast Iron Cloud, Bungee Connect, Development: Google, Sales Force, Azure.

UNIT – IV (12 HOURS) Local Clouds and Thin Clients: Virtualization, server solutions, Thin Clients; Migrating to the clouds: Cloud services for individuals, Cloud services aimed at Mid-market, and Enterprise-Class, Migration; Best practices and the future of Cloud computing: analyzing the services, Best practices, How Cloud Computing might evolve.

TEXT BOOKS:

1. Cloud Computing a Practical approach, Anthony T Velte, Toby J Velte, Robert Elsenpeter, Tata

McGraw-HILL, 2010 Edition

REFERENCE BOOKS:

1. Mastering Cloud Computing, Rajkumar Buyya, Christian Vecchiola, S. Thamari Selvi, McGraw Hill Education (India) Private Limited.

2. Distributed and Cloud Computing, Kai Hwang, Geoffrey C. Fox, Jack J. Dongarra, Morgan Kaufmann Publishers 2012.

3. Cloud computing, Barrie Sosinsky, Wiley India. 4. Cloud Computing, Kumar Saurabh, 2nd Edition, Wiley, India

WEB SERVICE


COURSE OUTCOMES

Know the Web Services languages and protocols.

Able to write XML document using web Services.

Explore the SOAP protocol to provide web Services.

Implement Web Service conversations using WSCL (Web Services Conversations Language).

Explore Web Service Transaction Protocol for enterprise application.

Identify Web Service Security and QoS Importance for Web Services.

UNIT I (12 HOURS) Introduction to Web Services: SOAP (Simple Object Access Protocol), WSDL(Web Service Description Langauge), UDDI(Universal Description, Discovery and Integration), Importance of Web Services, Web services and Enterprises. XML Fundamentals: XML-The Lingua Franca of Web Services, XML documents, XML Namespaces, XML Schema, Processing XML: Extensible stylesheet Transformation (XSLT) and XML path Language (XPATH).

UNIT II (12 HOURS) SOAP and WSDL: The SOAP Model, SOAP messages, SOAP Encoding, SOAP RPC, Alternative SOAP Encodings. Document, RPC, Literal, Encoded. SOAP-Web Services and the REST Architecture. SOAP 1.1. WSDL, Using SOAP and WSDL, Using SOAP and WSDL. UDDI: UDDI Analogies, The UDDI Business Registry, UDDI under the Covers, Accessing UDDI, UDDI management.

UNIT III (12 HOURS) Conversations: Conversations overview, WSCL(Web Services Conversations Language), WSCL Interface Components, The Bar Scenario Conversation, Relationship between WSCL and WSDL. Workflow: Business Process Management, Workflows and Workflow Management systems, Business Process Execution Language for Web Services(BPEL) , BPEL 1.1 and OASIS WSBPEL, BPEL and its Relationship to BPML, WSCL, WSFL, Xlang and others.

UNIT IV (12 HOURS) Transactions: ACID(Atomic, consistent, Isolate Durable) Transactions, Distributed Transactions and Two-Phase Commit, Dealing with Heuristic Outcomes, OASIS Business Transaction Protocol. Security: Everyday Security Basics, Security as End-to-End process, Web Service Security Issues, WS-Security elements. QoS: QoS Importance for Web Services, QoS Metrics for Web Services. Building QoS into Web Services. TEXT BOOK: 1. Sandeep Chatterjee , James Webber “Developing Enterprise Web Services” Pearson Education.

REFERENCE BOOK: 1. B. V. Kumar, S. V. Subrahmanya “Web Services” Tata McGraw-Hill. 2. Gustavo Alonso, Fabio Casati, Harumi Kuno, Vijay Machiraju: Web Services (Concepts,

Architectures and Applications ),` Springer International Edition 2009.

basaveshwar engineering college (autonomous) department … syllabus i-iv semester.pdf · aided...

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