SRI KRISHNA COLLEGE OF TECHNOLOGY …skct.edu.in/SKCT-CSE/docs/PG CSE -R2015.pdf · 15PCS524 Cloud Computing ... Job sequencing with deadlines- Optimal storage on tapes. Course Code

M.E AUTONOMOUS CURRICULUM - 2015 Regulations

SEMESTER I

S.No Course Code Course Title L/T/P Contact

Hrs/ Wk Credits Category

1. 15PCS105

Theoretical Foundations of

Computer Science 3/2/0 5 4 BS

2. 15PCS301

Advanced Data structures and

Algorithms 3/0/0 3 3 PC

3. 15PCS302 Computer Networks Engineering

and Management 3/0/0

3 3 PC

4. 15PCS5XX

Elective I 3/0/0 3 3 PE

5. 15PCS5XX Elective II

3/0/0 3 3 PE

6. 15PCS5XX Elective III

3/0/0 3 3 PE

7. 15PCS303 Data Structures and Algorithms

Laboratory 0/0/4 4

2 PC

8. 15PCS304 Network Programming

Laboratory 0/0/4 4

2 PC

Total 28 23

SEMESTER II

S.No Course

Code Course Title L/T/P

Contact


1. 15PCS305 Advanced Databases

3/1/0 4 4 PC

2. 15PCS306

Software Testing and Quality

Assurance 3/0/0 3 3

PC

3. 15PCS307 Advanced Operating Systems

3/1/0 4 4 PC

4. 15PCS5XX Elective IV

3/0/0 3 3 PE

5. 15PCS5XX Elective V

4/0/0 3 3 PE

6. 15PCS5XX Elective VI

3/0/0 3 3 PE

7. 15PCS308

Database Management and

Operating System Laboratory 0/0/4 4 2

PC

Total 24 22

SRI KRISHNA COLLEGE OF TECHNOLOGY

KOVAIPUDUR, COIMBATORE-42.

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

Can be completed in any of the semesters from I to II

S.No Name of the Course L/T/P Contact

Hours/Wk Credits

1. Three 1 Credit

Course Application

Development Field Study

3

SEMESTER III

S.No Course


Contact


1. 15PCS5XX Elective VII

3/0/0 3 3 PE

2. 15PCS5XX Elective VIII

3/0/0 3 3 PE

3. 15PCS5XX Elective IX

3/0/0 3 3 PE

4 15PCS309 Project Phase I 0/0/6 6 6 PW

Total 15 15

SEMESTER IV

S.No Course


Contact


1. 15PCS310 Project Phase II 0/0/24 24 12 PW

Total 24 12

MANDATORY COURSES

S.No Course Code Course Title

1 15CS801 Intellectual Property Rights

2 15CS802 Research Methodology

PROFESSIONAL ELECTIVES

S.No Course code Course Title

1. 15PCS501 Agent Based Intelligent Systems

2. 15PCS502 Mobile Application Development

3. 15PCS503 Soft Computing

4. 15PCS504 Cyber Security

5. 15PCS505 Visualization Techniques

6. 15PCS506 XML and Web Services

7. 15PCS507 Data Sciences

8. 15PCS508 Bio Informatics

9. 15PCS509 Multimedia Systems

10. 15PCS510 Software Project Management

11. 15PCS511 Virtualization Techniques

12. 15PCS512 Semantic Web

13. 15PCS513 Wireless Sensor Networks

14. 15PCS514 Natural Language Processing

15. 15PCS515 Service Oriented Architecture

16. 15PCS516 Mobile and Pervasive computing

17. 15PCS517 Mining Tools and Techniques

18. 15PCS518 Image Processing Techniques

19. 15PCS519 Grid Computing

20. 15PCS520 Object Oriented System Engineering

21. 15PCS521 Big Data Analytics

22. 15PCS522 Internet of Things

23. 15PCS523 Advanced Computer Architecture

24. 15PCS524 Cloud Computing

25. 15PCS525 Principles of Compiler Design

26. 15PCS526 Information Security

27. 15PCS527 Pattern Recognition

OPEN ELECTIVES

S.No Course code Course Title

1 15PCS601 Robotics

2 15PCS602 Nano Computing

3 15PCS603 Internet Marketing

4 15PCS604 Optimization Techniques

5 15PCS605 E – Commerce

6 15PCS606 Green Computing

M.E CSE AUTONOMOUS SYLLABUS – 2015 REGULATIONS

SEMESTER I

1. COURSE OBJECTIVES

To acquire the knowledge of the concepts needed to test the logic of a program.

To gain mathematical knowledge this has application in expert system, in data base and a basic for the

prolog language.

To identify patterns on many levels.

To have good understanding about discrete distributions and its applications

UNIT I FUNDAMENTAL STRUCTURES 9

Set theory-Relationships between sets-Operations on sets-set identities- Principle of inclusion and exclusion –

Minsets, Relations- binary relations – Partial orderings-Equivalence relations. Functions – Properties of Functions –

composition of functions - Inverse Functions – Permutation functions.

UNIT II LOGIC

9

Proposition logic-Logical connectives-Truth tables-Normal forms (conjunctive and disjunctive)-Predicate Logic-

Universal and existential quantifiers- Proof techniques-direct and indirect-Proof by contradiction-Mathematical

Induction.

UNIT III COMBINATORICS

9

Basics of counting-Counting arguments-pigeonhole Principle-Permutations and Combinations –Recursion and

Recurrence relations-Generating functions.

UNIT IV MODELING COMPUTATION AND LANGUAGES 9

Finite state machines-Deterministic and Non-deterministic finite state machines-Turing Machines-Formal Languages-

Classes of Grammars-Type 0-Context Sensitive-Context Free-Regular grammars –Ambiguity.

UNIT V NUMBER THEORY 9

Divisibility - Prime Numbers – Fundamental Theorem of Arithmetic – The Sieve of Eratosthenes – Division

Procedure – Greatest Common Division – Alternative definition of GCD (a,b) – Least Common Multiple –

Congruence - Congruence Class modulo m – Linear Congruence – The Chinese Remainder Theorem

Course Code Course Name Contact Hours

L T P C

15PCS105 Theoretical Foundations of Computer Science 3 2 0 4

STATE OF THE ART (NOT FOR EXAMINATION)

Applications of Logical languages-Algebraic structures-Boolean Algebra

TUTORIALS: 15

TOTAL: 60

2. COURSE OUTCOMES

At the end of this course student should

use the concept of Discrete structures in software design and development.

design and implement combinational circuits.

design and implement various sequential logic circuits.

demonstrate their knowledge of Number theory in transmission, coding and manipulation of

numerical data.

REFERENCES

1 Kenneth.H.Rosen.“ Discrete Mathematics and Applications”, 7th Edition ,TMH ,2012.

2 Judith L.Gersting, “Mathematical structures for Computer Science”,7th Edition, W.H.Freeman and

Company,NY,2006.

3 M.K.Venkataraman, N.Sridharan and N.Chandrasekaran, “Discrete Mathematics”, The National

Publishing company, 2004.

4 T.Veerarajan, “Discrete Mathematics with Graph Theory and Combinatorics” Tata McGraw-Hill

Publishing Company Limited, 2008.


To understand the mathematical aspects of analyzing algorithms

To understand the complexity of an algorithm and choose the best

To study different linear and non-linear data structures

To learn different sorting structures

To know about different problem solving techniques

UNIT I COMPLEXITY ANALYSIS & ELEMENTARY DATA STRUCTURES 9

Algorithm: Analysis - Asymptotic notations - Properties - Time-space tradeoff; Iterative and recursive algorithms;

Recurrence equations - Solving recurrence equations; ADT - Array and Pointer Implementation - List - Stack - Queue.

UNIT II HEAP STRUCTURES 8

Min-max heaps - Deaps - Leftist heaps - Binomial heaps - Skew heaps.

UNIT III SEARCH STRUCTURES 8

Binary Search Trees - AVL Trees - Splay Trees - 2-3 Trees - 2-3-4 Trees - Red-Black Trees - B-Trees - Tries.

UNIT IV DIVIDE AND CONQUER & GREEDY 10

Divide and Conquer: General Method - Binary Search - Merge Sort- Quick Sort - Strassen’s matrix multiplication -

Greedy Algorithms: General Method - Container Loading - Knapsack Problem - Job sequencing with deadlines-

Optimal storage on tapes.


L T P C

15PCS301 ADVANCED DATA STRUCTURES AND ALGORITHMS 3 2 0 4

UNIT V DYNAMIC PROGRAMMING AND BACKTRACKING 10

Dynamic Programming - Multistage graphs - Optimal binary search trees - 0/1 Knapsack - Travelling Salesperson

Problem - Backtracking: General Method - 8 Queens Problem - Sum of subsets - Graph coloring - Hamiltonian

problem - Knapsack problem.


R-trees - Dynamic trees - Finger search trees - Treaps - Maximum and Minimum cost flow - Optimization and Graph

Clustering

TOTAL: 45

2. COURSE OUTCOMES

At the end of the course the students should be able to

Design and analyze the performance of algorithms

Choose suitable data structures for different applications

Use suitable searching structure based on the problem’s need

Solve problems involving data structures using different techniques

Use the applications of data structures in different fields of Engineering and Science

REFERENCES

1. E. Horowitz, S. Sahni and Dinesh Mehta, Fundamentals of Data structures in C++, Galgotia, Second

Edition, 2006.

2. E. Horowitz, S. Sahni and S. Rajasekaran, Computer Algorithms / C++, Galgotia, Second Edition, 2009.

3. Adam Drozdex, Data Structures and algorithms in C++, Fourth Edition, Thomson learning - Vikas

Publishing House, 2013.

4. G. Brassard and P. Bratley, Algorithmics: Theory and Practice, Prentice - Hall, 1996.

5. Thomas H. Corman, Charles E. Leiserson, Ronald L. Rivest, Introduction to Algorithms, Second Edition,

PHI 2003.


To understand network layering basics

To know the traffic and QOS characteristics

To study and understand high performance and switching networks

To understand network management protocols and systems

UNIT I FOUNDATIONS OF NETWORKING 8

Communication Networks – Network Elements – Switched Networks and Shared media Networks –Datagrams and

Virtual Circuits – Multiplexing – Switching - Error and Flow Control – Congestion Control – Layered Architecture.-

Service Integration-Network Externalities.


L T P C

15PCS302 COMPUTER NETWORKS ENGINEERING AND

MANAGEMENT 3 0 0 3

UNIT II QUALITY OF SERVICE 10

Traffic Characteristics and Descriptors – Quality of Service and Metrics – Best Effort model and Guaranteed Service

Model – Limitations of IP networks – Scheduling and Dropping policies for BE and GS models – Traffic Shaping

algorithms – Possible improvements in TCP – Significance of UDP in inelastic traffic

UNIT III HIGH PERFORMANCE NETWORKS 9

Integrated Services Architecture – Components and Services – Differentiated Services Networks – Per Hop Behaviour

– Admission Control – MPLS Networks – Principles and Mechanisms – Label Stacking – RSVP – RTP/RTCP

UNIT IV HIGH SPEED NETWORKS 9

Optical links – WDM systems – Optical Cross Connects – Optical paths and Networks – First Generation Optical

Networks: FDDI - SONET/SDH - Computer Interconnects – Metropolitan Area Networks – Layered Architecture.

UNIT V NETWORK MANAGEMENT 9

ICMP the Forerunner – Monitoring and Control – Network Management Systems – Abstract Syntax Notation – CMIP

– SNMP Communication Model – SNMP MIB Group – Functional Model – Major changes in SNMPv2 and SNMPv3

– Remote monitoring – RMON SMI and MIB


4G an 5G Networks-NGN-Content delivery networks-Satellite and Space Communications networks

TOTAL: 45

2. COURSE OUTCOMES

At the end of the course the students should be able

To understand computer networks and their application;

To understand the critical awareness of current problems and/or new insights in the computer networking;

To understand and deal with complex issues in computer networks both systematically and creatively;

To critically evaluate engineering methodologies and where appropriate propose new hypotheses

REFERENCES

1. Larry L Peterson and Bruce S Davie, ‘Computer Networks: A Systems Approach’, Fifth Edition, Morgan

Kaufman Publishers, 2011.

2. Mani Subramaniam, ‘Network Management: Principles and Practices’, Pearson Education, 2010.

3. Mahbub Hassan and Raj Jain, ‘High Performance TCP/IP Networking’, Pearson Education, 2004.

4. Jean Warland and Pravin Vareya, ‘High Performance Networks’, Morgan Kauffman Publishers, 2002

5. William Stallings, ‘High Speed Networks: Performance and Quality of Service’, 2nd Edition, Pearson

Education, 2002.

6. Kasera and Seth, ‘ATM Networks: Concepts and Protocols’, Tata McGraw Hill, 2002.


To practice and implement different ADTs

To employ and execute graph algorithms

To understand the different types of CPU scheduling algorithms and their implementation

To expertise in shell programming

To implement various page replacement algorithms

To implement file locking using semaphore

LIST OF EXPERIMENTS

a) ADT: Singly Linked List - Doubly Linked List – Circular Linked List - Stack - Queue

b) HEAP: Min Heap - Deaps - Leftist heaps

c) TREES: Binary Search Tree - AVL Tree - Splay Trees - Red Black Trees

d) PROBLEMS USING DIVIDE-AND-CONQUER TECHNIQUE - Binary Search - Merge sort - Quick

Sort

e) PROBLEMS USING GREEDY TECHNIQUES - Container Loading - Change making problem -

Knapsack Problem

f) PROBLEMS USING DYNAMIC PROGRAMMING TECHNIQUES - 0/1Knapsack problem -

Travelling Salesperson Problem

g) PROBLEMS USING BACKTRACKING - 8 Queens Problem - Sum of subsets - Graph coloring -

Hamiltonian problem - Knapsack problem

TOTAL: 60

2. COURSE OUTCOMES

At the end of this course student should be able to

understand Basic ability to analyze algorithms and to determine algorithm correctness and time efficiency

class.

Master a variety of advanced abstract data type (ADT) and data structures and their implementations.

Master different algorithm design techniques

Ability to apply and implement learned algorithm design techniques and data structures to solve problems

Master various process management concepts including scheduling, synchronization, page replacement

strategies.


L T P C

15PCS303 DATA STRUCTURES AND ALGORITHMS

LABORATORY 0 0 4 2



To implement Traffic Shaping Algorithms

To understand different scheduling policies and its implementation.

To learn about resource reservation

To gain knowledge about network monitoring and control and its implementation

LIST OF EXPERIMENTS

a) QUALITY AND SERVICE

Traffic shaping algorithms – BE and GS model – Scheduling and dropping polices in BE &GS

b) INTEGRATED AND DIFFERENTIATED SERVICES

Label stacking – RSVP – RTP / RTCP

c) SNMP

Monitoring and control – MIB

TOTAL: 60

2. COURSE OUTCOMES

At the end of the course the student should be able to

Use network programming concepts to develop and implement distributed applications and protocols over

the Internet,

Develop and use software based tools to implement and evaluate the performance of communication network

protocols

Develop and implement next generation protocols required for emerging applications

Model and evaluate performance of networking systems

Carry out research and development tasks in networking

SEMESTER II


To understand the system architecture of distributed and parallel databases

To have a thorough knowledge about object and object relational databases

To gain knowledge about the XML databases and multimedia databases

To have an introductory knowledge about mobile databases and the emerging trends in the area of Data

mining and Data Warehousing.


L T P C

15PCS304 NETWORK PROGRAMMING LABORATORY 0 0 4 2


L T P C

15PCS305 ADVANCED DATABASES 3 1 0 4

UNIT I PARALLEL AND DISTRIBUTED DATABASES 14

Database System Architectures: Centralized and Client-Server Architectures – Server System Architectures – Parallel

Systems- Distributed Systems – Parallel Databases: I/O Parallelism – Inter and Intra Query Parallelism – Inter and

Intra operation Parallelism – Distributed Database Concepts - Distributed Data Storage – Distributed Transactions –

Commit Protocols – Concurrency Control – Distributed Query Processing – Three Tier Client Server Architecture-

Case Studies.

UNIT II OBJECT AND OBJECT RELATIONAL DATABASES 12

Concepts for Object Databases: Object Identity – Object structure – Type Constructors – Encapsulation of Operations

– Methods – Persistence – Type and Class Hierarchies – Inheritance – Complex Objects – Object Database Standards,

Languages and Design: ODMG Model – ODL – OQL – Object Relational and Extended – Relational Systems :

Object Relational features in SQL/Oracle – Case Studies.

UNIT III XML DATABASES 10

XML Databases: XML Data Model – DTD - XML Schema - XML Querying – Web Databases – JDBC – Information

Retrieval – Data Warehousing – Data Mining –Database Tuning

UNIT IV MOBILE DATABASES 12

Mobile Databases: Location and Handoff Management - Effect of Mobility on Data Management -Location

Dependent Data Distribution - Mobile Transaction Models - Concurrency Control – Transaction Commit Protocols-

Mobile Database Recovery Schemes

UNIT V MULTIMEDIA DATABASES 12

Multidimensional Data Structures – Image Databases – Text/Document Databases- Video Databases – Audio

Databases – Multimedia Database Design.

STATE OF ART (NOT FOR EXAMINATION)

JSON - Complex Event Processing - Data Stream Management System -No SQL -In memory database

TOTAL: 60

2. COURSE OUTCOMES


Be familiar with basic concepts and applications of database systems

Develop a XML database

Understand the concepts of mobile databases and its recovery schemes

Differentiate various types of multimedia databases

REFERENCES

1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Sixth Edition, Pearson

Education/Addison Wesley, 2011.

2. Thomas Cannolly and Carolyn Begg, “ Database Systems, A Practical Approach to Design,

Implementation and Management”, 5th Edition, Pearson Education, 2009.

3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”, McGraw Hill, 2010.

4. C.J.Date, A.Kannan and S.Swamynathan, “An Introduction to Database Systems”, Eighth Edition,Pearson

Education, 2006.

5. V.S.Subramanian, “Principles of Multimedia Database Systems”, Second Edition, Harcourt India Pvt Ltd., 2013.

6. Vijay Kumar, “Mobile Database Systems”, John Wiley & Sons, 2006.


To understand the different phases of testing

To gain knowledge about the different types of testing.

To know about test documentation

To understand the significance of quality assurance

UNIT I OVERVIEW OF SOFTWARE TESTING 9

Introduction- Basics of Software Testing- Testing Principles- Goals- Testing Life Cycle- Phases of Testing- Defects-

Defect Life Cycle- Defect Report- Test Plan(IEEE format)- Importance of testing in software production cycle.

UNIT II BLACK BOX TESTING 9

Introduction- Need of black box testing- Black box testing Concept- Requirement Analysis- Test case design criteria-

Testing Methods- requirement based testing- Positive & negative testing- Boundary value analysis- Equivalence

Partitioning class- state based or graph based- cause effect graph based- error guessing- documentation testing &

domain testing- design of test cases. Case studies of Black-Box testing.

UNIT III WHITE BOX TESTING 9

Introduction- Need of white box testing- Testing types- Test adequacy criteria- static testing by humans- Structure -

logic coverage criteria- Basis path testing- Graph metrics- Loop Testing- Data flow testing-Mutation Testing - Design

of test cases. Testing of Object oriented systems - Challenges in White box testing - Case-study of White-Box testing

UNIT IV METRIC TOOLS AND NON FUNCTIONAL TESTING 9

Test organization- Structure of testing- Measurement tools- Testing metrics: Type of metric – Project- Progress-

Productivity. Other Software Testing: GUI testing - Validation testing - Regression testing - Scenario testing -

Specification based testing - Adhoc testing - Sanity testing - Smoke testing - Random Testing. Advances in Software

Testing Methods

UNIT V SOFTWARE QUALITY MANAGEMENT 9

Software quality - Quality attribute - Quality control & assurance - Methods of quality management - Cost of quality

- Quality factor - project management - Software quality metrics - TQM - Six Sigma – ISO - SQA Model.


Test Documentation, Testing roles, Test metrics, Software Quality assurance , Testing certifications

TOTAL: 45


L T P C

15PCS306 SOFTWARE TESTING AND QUALITY ASSURANCE 3 0 0 3

2. COURSE OUTCOMES


Write a test plan

Do different types of white box testing

Do different types of black box testing

Gain insight about the quality standards

REFERENCES

1. Software Testing, Second Edition By: Ron Patton, Pearson Education 2006

2. Software Testing Principles and Tools By M.G. Limaye TMG Hill Publication, 2009

3. Software Testing Principles and Practices By Naresh Chauhan, Oxford University Press, 2010

4. Software testing Principle and Practices By Ramesh Desikan, Pearson Education, 2007

5. Software Testing Concepts and Tools By Nageshwar Rao , Dreamtech , 2006

6. Metric and Model in Software Quality Engineering , By Stephen H Kan, Pearson Education 2014

7. Effective methods for software testing by William Perry , Willey Publication, 2007

8. Foundation of software testing by Dorothy Graham, Erik Van Veenendaal. CENGAGE learning , 2008

9. Software Testing Tools by Dr.K.V.K. Prasad, Dreamtech Press 2004


To identify the different types of systems and understand the basic operating system structure

To understand the concepts of process management, memory management and storage management

To understand the methods of protection and security issues

To understand the concepts and design principles of Linux operating system and windows operating system.

To understand the design issues in distributed operating system.

UNIT I INTRODUCTION 7

Main frame Systems, Desktop Systems – Multiprocessor Systems – Distributed Systems – Clustered Systems – Real

Time systems – Hand held Systems, Operating Systems Structures: System Components – Operating System Services

– System calls – System Programs – System Design and Implementation – CPU scheduling: Basic Concepts –

Scheduling Algorithms.

UNIT II PROCESS MANAGEMENT 11

Process Concepts – Process Scheduling – Operation on Process – Co-Operating process – Inter Process

Communication – Threads: Multithreading Models – Process Synchronization: The Critical Section Problem –

Synchronization Hardware – Semaphores – Classical problem of Synchronization – Monitors – Deadlock: Deadlock

Characterization – Methods for handling Deadlocks – Deadlock Prevention – Deadlock Avoidance – Deadlock

Detection – Recovery from Deadlock.

UNIT III MEMORY MANAGEMENT AND FILE SYSTEMS 11

Background – Swapping – Contiguous Memory Allocation – Paging – Segmentation – Segmentation with paging –

Virtual Memory: Demand paging – Page Replacement– Thrashing. Buddy Systems – Storage Compaction.- File


L T P C

15PCS307 ADVANCED OPERATING SYSTEMS 3 1 0 4

Concepts – Access methods – Directory Structure –File Protection – File System Implementation: File System

Structure and Implementation – Directory Implementation – Allocation methods Free Space Management – Recovery

– Disk Structure – Disk Scheduling.

UNIT IV DISTRIBUTED OPERATING SYSTEM 9

Design issues in distributed operating system-Distributed file systems – Naming and Transparency-Remote File

Access - Stateful versus Stateless service – Distributed Coordination- Event Ordering- Mutual Exclusion - Atomicity-

Concurrency Control- Deadlock Handling-Election Algorithm – Basics of Virtualization Techniques.

UNIT V REAL TIME AND MOBILE OPERATING SYSTEMS 9

Basic Model of Real Time Systems -Characteristics-Applications of Real Time Systems – Real Time Task

Scheduling -Handling Resource Sharing -Mobile Operating Systems –Micro Kernel Design -Client Server Resource

Access –Processes and Threads - Memory Management - File system.


Mobile operating systems, Blackberry 10.3, android 5.0 lollypop, Database operating systems

TOTAL: 60

2. COURSE OUTCOMES


Use the concepts of CPU scheduling, memory management and file management of Operating system.

Evaluate the scheduling requirements of different types of processes and find their solutions

Analyze the performance of multiprocessor operating system and distributed operating system

Implement resource allocation algorithm for a real time example

REFERENCES

10. Avi Silberschatz, P.B.Galvin, G.Gagne “Operating System Concepts” 9th Edition, John Wiley & Sons, 2013

11. Pradeep K.Sinha, “Distributed Operating System: Concepts and Design”, IEEE computer Society Press, PHI,

2011.

12. Andrew S. Tanenbaum, “Modern Operating Systems”, PHI, 3rd Edition, 2008

13. Rajib Mall, “Real-Time Systems: Theory and Practice”, Pearson Education India, 2006.


To understand SQL Fundamentals.

To gain knowledge about Triggers, Functions and procedures

To know about Indexing, Hashing and Xml Queries

To understand the significance of Data base Connectivity

To understand basic concepts of Operating System

To gain knowledge about process Scheduling, Deadlock Detection and Memory Management concepts


L T P C

15PCS308 DATABASE MANAGEMENT AND OPERATING SYSTEM

LABORATORY 0 0 4 2

LIST OF EXPERIMENTS- Database Management

a) ADVANCED SQL

Integrity constraint - Trigger – Embedded SQL – Procedure and Functions -Packages

b) DATA BASE DESIGN

Entity Relational Model – Normalization

c) INDEXING, HASHING, XML

B+ Tree – Static Hashing – XML querying

d) DATABASE CONNECTIVITY

JDBC –ODBC

e) INFORMATION RETRIEVAL

Retrieving information from text database and image database

LIST OF EXPERIMENTS- Operating System

1. Implementation of CPU scheduling algorithms: FCFS, SJF, Round Robin & Priority Scheduling.

2. Implement the Producer – Consumer problem using semaphores.

3. Implementation of Banker‘s algorithm.

4. Implement some memory management schemes (First fit, Best fit & Worst fit) and some page replacement

algorithms (FIFO & LRU).

2. COURSE OUTCOMES


Master the basics of SQL and construct queries using SQL.

develop small applications using data base concepts

develop programs using Scheduling concepts

design a deadlock free system

write programs for Memory management

MANDATORY COURSES


To know fundamental aspects of Intellectual property Rights to students who are going to play a major role

in development and management of innovative projects in industries.

To know all aspects of the IPR Acts. It also includes case studies to demonstrate the application of the legal

concepts in Science, Engineering, Technology and Creative Design.

To create awareness of a multidisciplinary audience.

UNIT I OVERVIEW OF INTELLECTUAL PROPERTY 9

Introduction – Invention and Creativity – Intellectual Property (IP) – Importance – Protection of IPR – Basic types of

property (i. Movable Property ii. Immovable Property and iii. Intellectual Property).

UNIT II COMPONENTS 9

IP – Patents – Copyrights and related rights – Trade Marks and rights arising from Trademark registration –

Definitions – Industrial Designs and Integrated Circuits – Protection of Geographical Indications at national and

International levels – Application Procedures.

UNIT III POLICES AND REGULATIONS 9

International convention relating to Intellectual Property – Establishment of WIPO – Mission and Activities – History

– General Agreement on Trade and Tariff (GATT).

UNIT IV LEGISLATIONS 9

Indian Position Vs WTO and Strategies – Indian IPR legislations – commitments to WTO- Patent Ordinance and the

Bill – Draft of a national Intellectual Property Policy – Present against unfair competition.

UNIT V CASE STUDIES 9

Case Studies on – Patents (Basumati rice, turmeric, Neem, etc.) – Copyright And related rights – Trade Marks –

Industrial design and Integrated circuits – geographic indications – Protection against unfair competition.

STATE OF THE ART/ADVANCES (NOT FOR EXAMINATION)

Intellectual Property Trends and Developments in China - The Securitization of Intellectual Property Assets -

Protecting Intellectual Property Rights in a Global Economy: Current Trends and Future Challenges- IPR and Human

Rights

TOTAL: 45


L T P C

15CS801 INTELLECTUAL PROPERTY RIGHTS 3 0 0 3

2. COURSE OUTCOMES

At the end of this course student should be able

To understand the principles, function and basic legal rules of IP Law.

To recognize the relevant criteria for generating and protecting intellectual works.

To understand the different forms of infringement of intellectual property rights.

To demonstrate appreciation and critical awareness of pertinent IP issues in the academic and professional

lives.

REFERENCES

1. Subbaram N.R. “Handbook of Indian Patent Law and Practice “, S.Viswanathan(Printers and Publishers)

Pvt. Ltd., 1998.

2. Eli Whitney, United States Patent Number: 72X, Cotton Gin.


Building and developing the capacity of students to plan, implement and monitor systematic qualitative

research studies.

Challenge the prevailing notion of a hierarchy of research methods (from stronger experimental designs to

weaker qualitative techniques) and crude dichotomous thinking (hard versus soft, quantitative versus

qualitative, etc).

UNIT I OVERVIEW OF RESEARCH WORK 9

The nature of CS research - what is research? - Project planning, tools and techniques for planning - Literature

searches, information gathering.

UNIT II RESEARCH STRATEGIES 9

Reading and understanding research papers - Project implementation and IT project management -Presentation skills,

written and oral. - Time management, team working.

UNIT III OPTIMIZATION RESEARCH METHODOLOGIES I 9

Optimization Methods I – Linear Programming: Simplex method – Dynamic Programming – Integer Programming -

Hill climbing

UNIT IV OPTIMIZATION RESEARCH METHODOLOGIES II 9

Optimization Methods II - Simulated annealing - Quantum annealing - Genetic algorithms - Ant colony optimization -

Particle swarm optimization - Tabu search - Beam search

UNIT V INDUSTRIAL RESEARCH AND EFFECTIVE RESEARCH PAPER WRITING 9

Commercial and economic considerations in the IT industry - Review of Legal, Ethical, Social and Professional

(LSEP) issues, such as data protection, hacking, etc. - Technical writing, referencing, bibliographies.


L T P C

15CS802 RESEARCH METHODOLOGY 3 0 0 3


Tools for Research Methods – Reasearch Design-Structure and Components of Research Report, Types of Report,

Layout of Research Report,Mechanism of writing a research report, referencing in academic writing

TOTAL: 45

2. COURSE OUTCOMES


To define research and describe the research process and research methods

To know how to apply the basic aspects of the research process in order to plan and execute a research

project

REFERENCES

1. C. W. Dawson, The Essence of Computer Projects: A Student Guide. New Delhi: PHI, 2006.

2. Duane A. Bailey, A Letter to Research Students. Massachusetts.

3. Humdy Taha, Operation Research. New Delhi: PHI, 2007.

4. S. Kirkpatrick and C. D. Gelatt and M. P. Vecchi. Optimization by Simulated Annealing, Science, Vol 220,

1983, 671-680.

5. B. Apolloni, N. Caravalho and D. De Falco. Quantum stochastic optimization, Stochastic Processes and

their Applications, Vol. 33, 1989, 233-244.

6. David E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning, NewDelhi : New

Age, 1989.

PROFESSIONAL ELECTIVES


To understand the fundamental concepts in Artificial Intelligence

To know planning of agent design and handling uncertainty

To apply the design techniques in applications which involve perception, reasoning and learning


Definitions - Foundations - History - Intelligent Agents-Problem Solving-Searching - Heuristics -Constraint

Satisfaction Problems - Game playing.

UNIT II KNOWLEDGE REPRESENTATION AND REASONING 9

Logical Agents-First order logic-First Order Inference-Unification-Chaining- Resolution Strategies-Knowledge

Representation-Objects-Actions-Events

UNIT III PLANNING AGENTS 9

Planning Problem-State Space Search-Partial Order Planning-Graphs-Nondeterministic Domains-

Conditional Planning-Continuous Planning- MultiAgent Planning.


L T P C

15PCS501 AGENT BASED INTELLIGENT SYSTEMS 3 0 0 3

UNIT IV AGENTS AND UNCERTAINITY 9

Acting under uncertainty – Probability Notation-Bayes Rule and use - Bayesian Networks-Other Approaches-Time

and Uncertainty-Temporal Models- Utility Theory - Decision Network – Complex Decisions.

UNIT V HIGHER LEVEL AGENTS 9

Knowledge in Learning-Relevance Information-Statistical Learning Methods-Reinforcement Learning-

Communication-Formal Grammar-Augmented Grammars- Future of AI.


Cognitive Computing, Intelligence Computing, Robotics in Space Mission

TOTAL: 45

2. COURSE OUTCOMES

At the end of this course student will develop the

Ability to perform problem solving in NP hard and NP complete domain

Ability to design Intelligent systems

Ability to design systems with machine learning

REFERENCES

1. Stuart Russell and Peter Norvig, “Artificial Intelligence - A Modern Approach”, 3rd Edition, Prentice Hall,

2010

2. Michael Wooldridge, “An Introduction to Multi Agent System”, John Wiley, 2002.

3. Patrick Henry Winston, Artificial Intelligence, III Edition, AW, 1999.

4. Nils.J.Nilsson, Principles of Artificial Intelligence, Narosa Publishing House, 2000.


To Understand system requirements for mobile applications

To Generate suitable design using specific mobile development frameworks

To Generate mobile application design

To Implement the design using specific mobile development frameworks

To Deploy the mobile applications in marketplace for distribution


Introduction to mobile applications –Embedded systems -Market and business drivers n for mobile applications –

Publishing and delivery of mobile applications –Requirements gathering and validation for mobile applications

UNIT II BASIC DESIGN 9

Introduction –Basics of embedded systems design –Embedded OS -Design constraints for mobile applications, both

hardware and software related –Architecting mobile applications –User interfaces for mobile applications –touch

events and gestures Achieving quality constraints –performance, usability, security, availability and modifiability.


L T P C

15PCS502 MOBILE APPLICATION DEVELOPMENT 3 0 0 3

UNIT III ADVANCED DESIGN 9

Designing applications with multimedia and web access capabilities –Integration with GPS and social media

networking applications –Accessing applications hosted in a cloud computing environment –Design patterns for

mobile applications.

UNIT IV TECHNOLOGY I –ANDROID 9

Introduction –Establishing the development environment –Android architecture –Activities and views –Interacting

with UI –Persisting data using SQLite –Packaging and deployment –Interaction with server side applications –Using

Google Maps, GPS and Wifi –Integration with social media applications.

UNIT V TECHNOLOGY II –IOS 9

Introduction to Objective C –iOS features –UI implementation –Touch frameworks Data persistence using Core Data

and SQLite –Location aware applications using Core Location and Map Kit –


Integrating calendar and address book with social media application –Using Wifi - iPhone marketplace.

TOTAL: 45

2. COURSE OUTCOMES

Upon the Completion of the course students will be able to,

Describe the requirements for mobile applications

Explain the challenges in mobile application design and development

Develop design for mobile applications for specific requirements

Implement the design using Android SDK

Implement the design using Objective C and iOS

Deploy mobile applications in Android and iPone marketplace for distribution

REFERENCES:

1. Jeff McWherter and Scott Gowell, "Professional Mobile Application Development", Wrox, 2012

2. Charlie Collins, Michael Galpin and Matthias Kappler, “Android in Practice”, DreamTech, 2012

3. James Dovey and Ash Furrow, “Beginning Objective C”, Apress, 2012

4. David Mark, Jack Nutting, Jeff LaMarche and Frederic Olsson, “Beginning iOS 6

5. Development: Exploring the iOS SDK”, Apress, 2013.

6. http://developer.android.com/develop/index.html


To understand the basic concepts of Neural Network

To apply neural network and fuzzy techniques to practical problems

To understand the working and applications of hybrid systems


L T P C

15PCS503 SOFT COMPUTING 3 0 0 3

UNIT I NEURAL NETWORK 11

Fundamentals Basic Concepts – Back propagation Network: Architecture, Learning, Illustration, Applications, Effects

of tuning parameter, Selection of parameters Variations of Standard Back propagation Algorithm -Research

Directions – Associative Memory: Autocorrelators - Heterocorrelators: Kosko's Discrete BAM - Wang et al.'s

Multiple Training Encoding Strategy - Exponential BAM - Associative Memory for Real-coded Pattern Pairs -

Applications – Recent Trends – Adaptive Resonance Theory - Introduction – ART 1 and 2- Application –

Sensitiveness of ordering data

UNIT II FUZZY LOGIC 7

Fuzzy Set Theory : Fuzzy versus Crisp - Crisp sets - Fuzzy Sets - Crisp Relations - Fuzzy Relations – Fuzzy Systems -

Crisp Logic - Predicate Logic - Fuzzy Logic - Fuzzy Rule based System - Defuzzification Methods Applications

UNIT III GENETIC ALGORITHMS 9

Genetic Algorithms: History - Basic Concepts - Creation of Offspring - Working Principle - Encoding -Fitness

Function -Reproduction – Genetic Modeling: Inheritance Operators –Cross Over - Inversion and Deletion - Mutation

Operator - Bit-wise Operators - Applications – Multilevel Optimization - Real Life Problem - Differences and

Similarities between GA and Other Traditional Methods Advances in GA

UNIT IV HYBRID SYSTEMS – I 9

Hybrid Systems - Neural Networks, Fuzzy Logic and Genetic Algorithms Hybrids -Preview of the Hybrid Systems –

GA Based BPN - GA Based Weight Determination - Applications – Fuzzy - LR-type Fuzzy Architecture - Learning -

Inference -Applications – Simplified Fuzzy ARTMAP - Introduction -Working – Application- Recent Trends

UNIT V HYBRID SYSTEMS – II 9

FAM: Introduction - Single Association FAM - Fuzzy Hebb FAMs -FAM Involving a Rule Base - FAM Rules with

Multiple Antecedents/Consequents -Applications – Fuzzy Logic Controlled GA - Soft Computing Tools - Problem

Description of Optimum Design –Fuzzy Constraints - Illustrations - GA in Fuzzy Logic Controller Design


ABC Optimization, CUCKOO Model, Honey bee Optimization

TOTAL: 45

2. COURSE OUTCOMES


develop specialized knowledge related to neural networks, supervised training, unsupervised training

methodologies

Analyze a system for the optimization purposes.

develop system using fuzzy, genetic, neural based optimization mechanisms.

REFERENCES

1. S.Rajasekaran and G.A.V.Pai,”Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2004.

2. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, Pearson Education 2004.

3. Timothy J.Ross,”Fuzzy Logic with Engineering Application “, McGraw Hill, 2000.

4. Davis E.Goldberg,”Genetic Algorithms:Search, Optimization and Machine Learning”Addison Wesley, N.Y. 2003


To understand network layering basics

To Study information security policies and procedures

To understand different tools for network security

UNIT I OVERVIEW OF NETWORS SECURITY 9

Basics of networks-LAN,MAN,WAN, Internetworking, Introduction to Computer Security: Definition, Threats to

security, Government requirements, Information Protection and Access Controls, Computer security efforts,

Privacy considerations, International security activity.

UNIT II SECURITY STANDARDS 9

Secure System Planning and administration, Introduction to the orange book, Security policy requirements,

accountability, assurance and documentation requirements, Network Security, The Red book and Government

network evaluations.

UNIT III POLICIES AND PROCEDURES OF INFORMATION SECURITY 9

Information security policies and procedures: Corporate policies- Tier 1, Tier 2 and Tier3 policies - process

management-planning and preparation-developing policies-asset classification policy-developing standards

UNIT IV INFORMATION SECURITY 9

Information security: fundamentals-Employee responsibilities- information classification- Information handling-

Tools of information security- Information processing-secure program administration

UNIT V ORGANIZATIONAL AND HUMAN SECURITY 9

Organizational and Human Security: Adoption of Information Security Management Standards, Human Factors in

Security- Role of information security professionals.


Next generation secure internet,Secure home computing Botnet,antibotnet,Cellphone worms,rootkits,

TOTAL: 45

2. COURSE OUTCOMES


Design of secure aware system applications to avoid threats and malicious worms

Assess the current security landscape, including the nature of the threat, the general status of common vulnerabilities,

and the likely consequences of security failures

Assess the role of strategy and policy in determining the success of information security.


L T P C

15PCS504 CYBER SECURITY 3 0 0 3

REFERENCES

1. Debby Russell and Sr. G.T Gangemi, "Computer Security Basics (Paperback)”, 2nd Edition, O’ Reilly

Media, 2006.

2. Thomas R. Peltier, “Information Security policies and procedures: A Practitioner’s Reference”, 2nd

Edition Prentice Hall, 2004.

3. Kenneth J. Knapp, “Cyber Security and Global Information Assurance: Threat Analysis and Response

Solutions”, IGI Global, 2009.

4. Thomas R Peltier, Justin Peltier and John blackley, ”Information Security Fundamentals”, 2nd Edition,

Prentice Hall, 1996

5. Jonathan Rosenoer, “Cyber law: the Law of the Internet”, Springer-verlag, 1997.



To understand data representation and stages

To understand different views and abstractions

To study multi-dimensional visualization

To understand mapping and workspace in visualization

UNIT I VISUALIZATION 9

Introduction – Issues – Data Representation – Data Presentation - Interaction

UNIT II FOUNDATIONS FOR DATA VISUALIZATION 9

Visualization stages – Experimental Semiotics based on Perception Gibson‘s Affordance theory – A Model of

Perceptual Processing – Types of Data.

UNIT III COMPUTER VISUALIZATION 9

Non-Computer Visualization – Computer Visualization: Exploring Complex Information Spaces – Fisheye Views –

Applications – Comprehensible Fisheye views – Fisheye views for 3D data – Non Linear Magnificaiton – Comparing

Visualization of Information Spaces – Abstraction in computer Graphics – Abstraction in user interfaces.

UNIT IV MULTIDIMENSIONAL VISUALIZATION 9

One Dimension – Two Dimensions – Three Dimensions – Multiple Dimensions – Trees – Web Works – Data

Mapping: Document Visualization – Workspaces.


Small interactive calendars – Selecting one from many – Web browsing through a key hole – Communication analysis

– Archival analysis.


L T P C

15PCS505 VISUALIZATION TECHNIQUES 3 0 0 3


Current Research Trends in Visualization, NIH/NSF Visualization, Visual Analytics

TOTAL: 45

2. COURSE OUTCOMES

At the end of this course student will develop

An ability to apply knowledge of mathematics, science, and engineering

An ability to design and conduct experiments, as well as to analyze and interpret data

An ability to develop programming skills and acquire deep understanding of the basic concepts in Computer

Science domain which will help the student to develop algorithms and computer systems of high standards.

REFERENCES

1. Colin Ware, “Information Visualization Perception for Design” Margon Kaufmann Publishers, 2004, 2nd

edition.

2. Robert Spence “Information visualization – Design for interaction”, Pearson Education, 2nd Edition, 2007

3. Stuart.K.Card, Jock.D.Mackinlay and Ben Shneiderman, “Readings in Information Visualization Using

Vision to think”, Morgan Kaufmann Publishers-1999.


To know different XML technologies


Role of XML – XML and The Web – XML Language Basics – SOAP – Web Services –Revolutions Of

XML – Service Oriented Architecture (SOA).

UNIT II XML TECHNOLOGY 9

XML – Name Spaces – Structuring With Schemas and DTD – Presentation Techniques –Transformation –

XML Infrastructure.

UNIT III SOAP 9

Overview Of SOAP – HTTP – XML-RPC – SOAP: Protocol – Message Structure –Intermediaries – Actors

– Design Patterns And Faults – SOAP With Attachments.

UNIT IV WEB SERVICES 9

Overview – Architecture – Key Technologies - UDDI – WSDL –ebXML – SOAP and Web Services in ECom

– Overview Of .NET And J2EE.


L T P C

15PCS506 XML AND WEB SERVICES 3 0 0 3

UNIT V XML SECURITY 9

Security Overview – Canonicalization – XML Security Framework – XML Encryption –XML Digital Signature –

XKMS Structure – Guidelines For Signing XML Documents –XML in Practice.


Contect aware applications, Light weight implementation of web services, WSDL and UDDI

TOTAL: 45

2. COURSE OUTCOMES


Understand Web Services and its Infrastructure

Build a Web Service

Deploy and Publishing Web Services

REFERENCES

1. Frank. P. Coyle, XML, Web Services And The Data Revolution, Pearson Education, 2002.

2. Ramesh Nagappan , Robert Skoczylas and Rima Patel Sriganesh, “ Developing Java Web Services”, Wiley

Publishing Inc., 2004.

3. Sandeep Chatterjee, James Webber, “Developing Enterprise Web Services”, Pearson Education,2004.

4. McGovern, et al., “Java Web Services Architecture”, Morgan Kaufmann Publishers,2005.


To Apply math and programming skills to make meaning out of large data

To Learn how to analyze and manipulate data

To Learn how to make predictions about data using fundamental modeling techniques that will help to make

better informed business decisions

UNIT INTRODUCTION 9

Introduction to data science - history and context - technology landscape – examples

UNIT II DATA MANIPULATION 9

Databases and the relational algebra - Parallel databases - parallel query processing, in-database analytics –

MapReduce – Hadoop - relationship to databases – algorithms - extensions, languages - Key-value stores and

NoSQL; tradeoffs of SQL and NoSQL

UNIT III ANALYTICS 9

Statistical modeling: basic concepts - experiment design - pitfalls , machine learning: supervised learning (rules,

trees, forests, nearest neighbor, regression) - optimization (gradient descent and variants) - unsupervised learning

UNIT IV COMMUNICATING RESULTS 9

Visualization - data products - visual data analytics - Provenance – privacy – ethics - governance


L T P C

15PCS507 DATA SCIENCES 3 0 0 3

UNIT V GRAPH ANALYTICS AND SEMANTIC WEB 9

Graph Analytics: structure - traversals – analytics – PageRank - community detection - recursive queries - semantic

web


Real time clustering and classification problems

TOTAL: 45

2. COURSE OUTCOMES


Understand applications of open source database

construct a mining techniques in real time problems

Deploy distributed database tools in different scenarios.

REFERENCES

Mining of massive Datasets by Anand Rajaram and JeffUlmann Cambridge University Press, 2011

Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database System Concepts”, Sixth Edition, McGraw

Hill, 2010.


To understand the key methods and tools used in bioinformatics

To build a solid foundation and acquire the vocabulary you need to supervise or to communicate with others

who use these tools

To understand the use of Statistics, Data mining and pattern matching in bioinformatics.

To understand the commonly used bioinformatics techniques actually work;

To get an idea about algorithms and underlying biological principles, and how the available bioinformatics

tools give useful molecular information.

To acquire knowledge required to both become bio informaticians and to develop new biologically relevant

methods.


The Central Dogma – Killer Application – Parallel Universes – Watson’s Definition – Top Down Vs Bottom Up

Approach – Information Flow – Conversance – Communications.

UNIT II DATABASE AND NETWORKS 9

Definition – Data Management – Data Life Cycle – Database Technology – Interfaces – Implementation – Networks:

Communication Models – Transmission Technology – Protocols – Bandwidth – Topology – Contents – Security –

Ownership – Implementation.

UNIT III SEARCH ENGINES AND DATA VISUALIZATION 10

Search Process – Technologies – Searching And Information Theory – Computational Methods – Knowledge

Management – Sequence Visualizations – Structure Visualizations – User Interfaces – Animation Vs Simulation

Course Name

Contact Hours

L T P C

15PCS508 BIO INFORMATICS 3 0 0 3

UNIT IV STATISTICS, DATA MINING AND PATTERN MATCHING 11

Statistical Concepts – Micro Arrays – Imperfect Data – Basics – Quantifying – Randomness – Data Analysis – Tools

Selection – Alignment – Clustering – Classification – Data Mining Methods – Technology – Infrastructure Pattern

Recognition – Discovery – Machine Learning – Text Mining – Pattern Matching Fundamentals – Dot Matrix Analysis

– Substitution Matrix – Dynamic Programming – Word Method – Bayesian Method – Multiple Sequence Alignment

Tools.

UNIT V MODELING SIMULATION AND COLLABORATION 8

Drug Discovery Fundamentals – Protein Structure – System Biology Tools – Collaboration And Communication –

Standards – Issues – Case Study.


Bio molecular and cellular computing, Micro array analysis, Systems biology, BIOMEMBRANE

TOTAL: 45

2. COURSE OUTCOMES


An ability to apply knowledge of mathematics, science, and engineering,

An ability to design a system, component, or process to meet desired needs within realistic constraints

An ability to identify, formulate and solve problems related to bio informatics.

An ability to develop programming skills and acquire deep understanding of the basic concepts in domain

which will help the student to develop algorithms and standards of bio informatics.

REFERENCES

1. Bryan Bergeron, “Bio Informatics Computing”, Prentice Hall, 2003.

2. T.K. Affward, D.J. Parry Smith, “Introduction to Bio Informatics”, Pearson Education, 2001.

3. Pierre Baldi, Soren Brunak, “Bio Informatics – The Machine Learning Approach”, 2nd Edition, First East

West Press, 2003


To understand basics of multimedia

To understand audio and video standards and CODEC’s

To study and understand animation

To know different types of multimedia compression

To study different multimedia tools

UNIT I INTRODUTION 9

Introduction-Multimedia: Media and Data Streams: Medium- main properties –Traditional Data streams

characteristics - Data Stream Characteristics for continuous media- Sound/Audio- Formats - Music – MIDI – Speech –

Generation – Analysis - Transmission


L T P C

15PCS509 MULTIMEDIA SYSTEMS 3 0 0 3

UNIT II MULTIMEDIA REPRESENTATION AND ANIMATION 9

Images and Graphics – Concepts – Representation – Image and Graphics Formats – Computer Image processing –

synthesis – analysis – transmission – Video and Animation – Signal representation – Video format- Television –

Computer based animation – Languages – methods – display – transmission

UNIT III MULTIMEDIA SUBSYSTEM AND COMPRESSION 9

Multimedia communication – Application subsystem – collaborative computing – Session management –Transport

subsystem – Compression Principles – Text Compression – Image Compression –Video Compression

UNIT IV SYNCHRONIZATION 9

Synchronization – basic issues – Intra and Inter object synchronization – Presentation requirements – Lip –Pointer –

Media synchronization – Reference model – Existing classification – Distributed environment –aggregate

characteristics - Specification – methods – Interval – Axes – Control flow - Event based synchronization-scripts

UNIT V MEDIA COMMUNICATION 9

Multimedia applications – programs – structure – media preparation – media composition – media integration – media

communication – media consumption – media entertainment – future directions


Multimedia compression standards,Multimedia tools and support, Compression standards

TOTAL: 45

COURSE OUTCOMES


To make use of multimedia tools and the way in which they are used.

To design multimedia presentation and create visual effects

To provide QoS guarantees in the network and to analyze the performance

REFERENCES

1. Ralf Steinmetz and Klara Nahrstedt ,Multimedia: Computing, Communications and Applications,Pearson

Education Asia, 2001

2. Ranjan Parekh, Principles of multimedia, Tata McGraw Hill,2006.

3. John.F. Koegel Buford, Multimedia Systems , Addison Wesley , 2000.

4. Tay Vaughon , Multimedia making it work, TMC , 1999.

5. Fred Halsall, Multimedia communications, Pearson Education Asia, 2001.


To learn software project management methodologies

To gain knowledge about the different software management disciplines

To understand existing metrics and tools for software project management.


L T P C

15PCS510 SOFTWARE PROJECT MANAGEMENT 3 0 0 3

UNIT I OVERVIEW OF SOFTWARE PROJECT MANAGEMENT 9

Project Definition – Contract Management – Activities Covered By Software Project Management–Overview of

Project Planning – Stepwise Project Planning.

UNIT II PROJECT EVALUATION 9

Strategic Assessment – Technical Assessment – Cost Benefit Analysis –Cash Flow Forecasting – Cost Benefit

Evaluation Techniques – Risk Evaluation.

UNIT III ACTIVITY PLANNING 9

Objectives – Project Schedule – Sequencing and Scheduling Activities –Network Planning Models – Forward Pass –

Backward Pass – Activity Float – Shortening Project Duration – Activity on Arrow Networks – Risk Management –

Nature Of Risk – Types Of Risk – Managing Risk – Hazard Identification – Hazard Analysis – Risk Planning And

Control.

UNIT IV MONITORING AND CONTROL 9

Creating Framework – Collecting The Data – Visualizing Progress – Cost Monitoring – Earned Value – Prioritizing

Monitoring – Getting Project Back To Target – Change Control – Managing Contracts – Introduction – Types Of

Contract – Stages In Contract Placement – Typical Terms Of A Contract – Contract Management – Acceptance.

UNIT V MANAGING PEOPLE AND ORGANIZING TEAMS 9

Introduction – Understanding Behavior – Organizational Behavior Background – Selecting The Right Person For The

Job – Instruction In The Best Methods – Motivation – The Old man – Hackman Job Characteristics Model – Working

In Groups – Becoming A Team –Decision Making – Leadership – Organizational Structures – Stress –Health And

Safety – Case Studies.


Project performance, More Agile, less Waterfall, Big data analytics as a project management tool, More cloud, less

desktop

TOTAL: 45

2. COURSE OUTCOMES

An understanding of professional and ethical responsibility.

REFERENCES

1. Bob Hughes, Mikecotterell, “Software Project Management”, Third Edition, Tata McGraw Hill, 2004.

2. Ramesh, Gopalaswamy, "Managing Global Projects", Tata McGraw Hill, 2001.

3. Royce, “Software Project Management”, Pearson Education, 2010.

4. Jalote, “Software Project Management in Practice”, Pearson Education, 2005.


To understand the concepts of Virtualization.

To gain knowledge about Virtualizing storage

To gain knowledge about Xen Virtual machine

UNIT I OVERVIEW OF VIRTUALIZATION 9

Basics of Virtualization - Virtualization Types – Desktop Virtualization – Network Virtualization – Server and

Machine Virtualization – Storage Virtualization – System-level or Operating Virtualization – Application

Virtualization-Virtualization Advantages - Virtual Machine Basics – Taxonomy of Virtual machines - Process Virtual

Machines - System Virtual Machines – Hypervisor - Key Concepts

UNIT II SERVER CONSOLIDATION 8

Hardware Virtualization – Virtual Hardware Overview - Server Virtualization – Physical and Logical Partitioning -

Types of Server Virtualization – Business cases for Server Virtualization – Uses of Virtual server Consolidation –

Planning for Development – Selecting server Virtualization Platform

UNIT III NETWORK VIRTUALIZATION 10

Design of Scalable Enterprise Networks - Virtualizing the Campus WAN Design - WAN Architecture - WAN

Virtualization - Virtual Enterprise-Transport Virtualization–VLANs and Scalability – Theory- Network Device

Virtualization Layer 2 - VLANs Layer 3 VRF Instances Layer 2 - VFIs Virtual Firewall Contexts Network Device

Virtualization - Data-Path Virtualization Layer 2: 802.1q - Trunking Generic Routing Encapsulation - IPsec L2TPv3

Label Switched Paths - Control-Plane Virtualization–Routing Protocols- VRF - Aware Routing Multi-Topology

Routing.

UNIT IV STORAGE VIRTUALIZATION 10

SCSI- Speaking SCSI- Using SCSI buses – Fiber Channel – Fiber Channel Cables – Fiber Channel Hardware Devices

– iSCSI Architecture – Securing iSCSI – SAN backup and recovery techniques – RAID – SNIA Shared Storage

Model – Classical Storage Model – SNIA Shared Storage Model – Host based Architecture – Storage based

architecture – Network based Architecture – Fault tolerance to SAN – Performing Backups – Virtual tape libraries.

UNIT V VIRTUAL MACHINES PRODUCTS 8

Xen Virtual machine monitors- Xen API – VMware – VMware products - VMware Features – Microsoft Virtual

Server – Features of Microsoft Virtual Server


RV Tools, PowerShell, vControl

TOTAL: 45


L T P C

15PCS511 VIRTUALIZATION TECHNIQUES 3 0 0 3

2. COURSE OUTCOMES


An ability to apply knowledge of mathematics, science, and engineering

A knowledge of contemporary issues

An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice

REFERENCES

1. William von Hagen, “Professional Xen Virtualization”, Wrox Publications, January, 2008.

2. Chris Wolf , Erick M. Halter, “Virtualization: From the Desktop to the Enterprise”, A Press 2005.

3. Kumar Reddy, Victor Moreno, “Network virtualization”, Cisco Press, July, 2006.

4. James E. Smith, Ravi Nair, “Virtual Machines: Versatile Platforms for Systems and Processes”,

Elsevier/Morgan Kaufmann, 2005.

5. David Marshall, Wade A. Reynolds, “Advanced Server Virtualization: VMware and Microsoft Platform in

the Virtual Data Center”, Auerbach Publications, 2006.


To gain an understanding of Semantic Web Languages.

To understand the design principles of semantic web

To know about the applications of semantic web

To know about the Semantic web standards

To learn the constructs of Web Ontology language [OWL]


History – Semantic Web Layers –Semantic Web technologies – Semantics in Semantic Web – XML: Structuring –

Namespaces – Addressing – Querying – Processing

UNIT II RDF 10

RDF and Semantic Web – Basic Ideas - RDF Specification – RDF Syntax: XML and Non- XML - RDF elements –

RDF relationship: Reification, Container, and collaboration – RDF Schema – Editing, Parsing, and Browsing

RDF/XML-RQL-RDQL

UNIT III ONTOLOGY 10

Why Ontology – Ontology movement – OWL – OWL Specification - OWL Elements – OWL constructs: Simple and

Complex – Ontology Engineering : Introduction – Constructing ontologies – Reusing ontologies – On-To-Knowledge

Semantic Web architecture

UNIT IV LOGIC AND INFERENCE 9

Logic – Description Logics - Rules – Monotonic Rules: Syntax, Semantics and examples – Non- Monotonic Rules –

Motivation, Syntax, and Examples – Rule Markup in XML: Monotonic Rules, and Non-Monotonic Rules


L T P C

15PCS512 SEMANTIC WEB 3 0 0 3

http://www.amazon.com/s/ref=si3_rdr_bb_author?index=books&field%2dauthor%2dexact=William%20von%20Hagen

http://www.amazon.com/Professional-Xen-Virtualization-William-Hagen/dp/0470138114/ref=si3_rdr_bb_product

http://www.amazon.com/exec/obidos/search-handle-url/ref=ntt_athr_dp_sr_1?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Chris%20Wolf

http://www.amazon.com/exec/obidos/search-handle-url/ref=ntt_athr_dp_sr_2?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Erick%20M.%20Halter

UNIT V APPLICATIONS OF SEMANTIC WEB TECHNOLOGIES 8

RDF Uses: Commercial and Non-Commercial use – Sample Ontology – e-Learning – Web Services – Web mining –

Horizontal information – Data Integration – Future of Semantic Web


Scalable Reasoning – Linked Data Science- Web intelligence- Live querying

TOTAL: 45

2. COURSE OUTCOMES


Represent and process knowledge

Construct ontologies with OWL

Use ontology engineering approaches in semantic applications

Describe logic semantics and inference

REFERENCES

1. Grigorous Antoniou and Van Hermelen - “A Semantic Web Primer”-The MIT Press –2012

2. “Spinning the Semantic Web: Bringing the world wide web to its full potential” – The MIT Press – 2005

3. Shelley Powers – “Practical RDF” – O’reilly publishers – First Indian Reprint : 2007


To understand the basic concepts of Wireless Sensor Networks (WSNs)

To assess the performance limits, the coverage and the best network configuration

To know about the functionalities of each layer

To gain knowledge about the main challenges of WSNs like power consumption, device sizes and cost

To understand and know about the platforms and tools for the simulation of WSN


Challenges for wireless sensor networks, Comparison of sensor network with ad hoc network, Single node

architecture - Hardware components, energy consumption of sensor nodes, Network architecture - Sensor network

scenarios, types of sources and sinks, single hop versus multi-hop networks, multiple sinks and sources, design

principles, Development of wireless sensor networks - WINS, µAMPS Underwater Acoustic and Deep space

networks.

UNIT II PHYSICAL LAYER 9

Introduction, wireless channel and communication fundamentals - frequency allocation, modulation and

demodulation, wave propagation effects and noise, channels models, spread spectrum communication, packet

transmission and synchronization, quality of wireless channels and measures for improvement, physical layer and

transceiver design consideration in wireless sensor networks, Energy usage profile, choice of modulation, Power

Management.


L T P C

15PCS513 WIRELESS SENSOR NETWORKS 3 0 0 3

UNIT III DATA LINK LAYER 9

MAC protocols - fundamentals of wireless MAC protocols, low duty cycle protocols and wakeup concepts,

contention-based protocols, Schedule-based protocols, Link Layer protocols - fundamentals task and requirements,

error control, framing, link management.

UNIT IV NETWORK LAYER 9

Gossiping and agent-based unicast forwarding, Energy-efficient unicast, Broadcast and multicast, geographic routing,

mobile nodes, Data - centric and content-based networking - Data-centric routing, Data aggregation, Data-centric

storage, Higher layer design issues.

UNIT V CASE STUDY 9

Target detection tracking, Habitat monitoring, Environmental disaster monitoring, Practical implementation issues,

IEEE 802.15.4 low rate WPAN, Sensor Network Platforms and tools - Sensor node hardware, Node-level software

platforms, node-level simulators.


Cross layer design - Distributed processing middleware in WSN - Cross-optimization of protocol elements - Hybrid

technologies.

TOTAL: 45

2. COURSE OUTCOMES


Know the challenges and design principles of WSNs

Design a system, component or process to meet the desired needs within realistic constraints

Design solutions to design issues in each layer

REFERENCES

1. Wireless Sensor Networks: an information processing approach - Feng Zhao, Leonidas Guibas, Elsevier

publication, 2004.

2. Wireless Sensor Networks - C.S. Raghavendra Krishna, M. Sivalingam and Tarib Znati, Springer

publication, 2006.

3. Wireless Sensor Networks: Architecture and protocol –Edgar H .Callaway, First Edition, CRC press 2004.

4. Protocol and Architecture for Wireless Sensor Networks –Holger Karl, Andreas Willig, John wiley

publication, 2007.


To develop a thorough understanding of the principles and formal methods used in the design and analysis of

language processing algorithms.

To have an in-depth presentation of the major algorithms used in NLP, including Lexical, Morphological,

Syntactic, and Semantic analysis, with the primary focus on parsing algorithms and their analysis.

To understand and build probabilistic models of linguistic phenomena.


L T P C

15PCS514 NATURAL LANGUAGE PROCESSING 3 0 0 3


Introduction: Knowledge in speech and language processing - Ambiguity - Models and Algorithms - Language,

Thought and Understanding. Regular Expressions and automata: Regular expressions - Finite-State automata.

Morphology and Finite-State Transducers: Survey of English morphology - Finite-State Morphological parsing -

Combining FST lexicon and rules - Lexicon-Free FSTs: The porter stammer - Human morphological processing

UNIT II SYNTAX 10

Word classes and part-of-speech tagging: English word classes - Tagsets for English - Part-of- speech tagging - Rule-

based part-of-speech tagging - Stochastic part-of-speech tagging - Transformation-based tagging - Other issues.

Context-Free Grammars for English: Constituency - Context-Free rules and trees - Sentence-level constructions - The

noun phrase - Coordination - Agreement - The verb phase and sub categorization - Auxiliaries - Spoken language

syntax - Grammars equivalence and normal form - Finite-State and Context-Free grammars - Grammars and human

processing. Parsing with Context-Free Grammars: Parsing as search - A Basic Top- Down parser - Problems with the

basic Top-Down parser - The early algorithm - Finite-State parsing methods.

UNIT III ADVANCED FEATURES AND SYNTAX 11

Features and Unification: Feature structures - Unification of feature structures - Features structures in the grammar -

Implementing unification - Parsing with unification constraints - Types and Inheritance. Lexicalized and Probabilistic

Parsing: Probabilistic context-free grammar - problems with PCFGs - Probabilistic lexicalized CFGs - Dependency

Grammars - Human parsing.

UNIT IV SEMANTIC 10

Representing Meaning: Computational desiderata for representations - Meaning structure of language - First order

predicate calculus - Some linguistically relevant concepts - Related representational approaches - Alternative

approaches to meaning. Semantic Analysis: Syntax-Driven semantic analysis - Attachments for a fragment of English

- Integrating semantic analysis into the early parser - Idioms and compositionality - Robust semantic analysis. Lexical

semantics: relational among lexemes and their senses - WordNet: A database of lexical relations - The Internal

structure of words - Creativity and the lexicon.

UNIT V APPLICATIONS 8

Word Sense Disambiguation and Information Retrieval: Selectional restriction-based disambiguation - Robust word

sense disambiguation - Information retrieval - other information retrieval tasks. Natural Language Generation:

Introduction to language generation - Architecture for generation - Surface realization - Discourse planning - Other

issues. Machine Translation: Language similarities and differences - The transfer metaphor - The interlingua idea:

Using meaning - Direct translation - Using statistical techniques - Usability and system development.


Supervised and unsupervised learning – Machine Translation – Parts of speech tagging – Spelling Correction

TOTAL: 45

2. COURSE OUTCOMES


Design a morphological analyzer for a language of your choice using finite state automata concepts

Implement a parser by providing suitable grammar and words

Design an application that uses different aspects of language processing.

Analyze the natural language text

apply NLP techniques in information retrieval and machine translation processing.

REFERENCES

1. Daniel Jurafsky & James H.Martin, “Speech and Language Processing”, Pearson Education

(Singapore) Pvt. Ltd., 2002.

2. James Allen, “Natural Language Understanding”, Pearson Education, 2003.

3. 1.Tanveer Siddiqui, U.S. Tiwary, “Natural Language Processing and Information Retrieval”, Oxford

University Press, 2008.


To gain understanding of the basic principles of service orientation

To understand the importance of Service Oriented Architecture.

To learn advanced concepts such as service composition, orchestration and Choreography

To know the implementation of SOA in the Java and .NET frameworks.

To understand the advanced features of SOA.

UNIT I OVERVIEW OF SOA 9

Introduction – Service Oriented Enterprise – Service Oriented Architecture (SOA) –SOA and Web Services – Multi-

Channel Access – Business Process management –Extended Web Services Specifications – Overview of SOA –

Concepts – Key Service Characteristics – Technical Benefits – Business Benefits

UNIT II SOA AND WEB SERVICES 9

SOA and Web Services – Web Services Platform – Service Contracts – Service-Level Data Model – Service

Discovery – Service-Level Security – Service-Level Interaction patterns – Atomic Services and Composite Services –

Proxies and Skeletons –Communication – Integration Overview – XML and Web Services - .NET and J2EE

Interoperability – Service-Enabling Legacy Systems – Enterprise Service Bus Pattern

UNIT III BUSINESS USING SOA 9

Multi-Channel Access – Business Benefits – SOA for Multi Channel Access – Tiers –Business Process Management

– Concepts – BPM, SOA and Web Services – WSBPEL – Web Services Composition

UNIT IV JAVA WEB SERVICES 9

Java Web Services – JAX APIs – JAXP – JAX-RPC – JAXM – JAXR – JAXB


L T P C

15PCS515 SERVICE ORIENTED ARCHITECTURE 3 0 0 3

UNIT V WEB SERVICES SECURITY AND TRANSACTION MANAGEMENT 9

Metadata Management – Web Services Security – Advanced Messaging – Transaction Management


Business Architecture and Business Services, Context Aware Applications, Light weight implementation of SOA

TOTAL: 45

2. COURSE OUTCOMES


Demonstrate an understanding of software oriented architectures.

Demonstrate an understanding of the service composition.

Build applications based on XML

Develop web services using technology elements

Build SOA -based applications for intra-enterprise and inter-enterprise applications.

Demonstrate an ability to manage a modern medium scale software development project using SOA

principles.

REFERENCES

1. Thomas Erl, “Service-Oriented Architecture: Concepts, Technology, and Design”, Pearson Education, 2005.

2. Eric Newcomer, Greg Lomow, “Understanding SOA with Web Services”, Pearson Education, 2005

3. James McGovern, Sameer Tyagi, Michael E Stevens, Sunil Mathew, “Java Web Services Architecture”,

Elsevier, 2003. (Unit 4)

4. Thomas Erl, “Service Oriented Architecture”, Pearson Education, 2005

5. Frank Cohen, “FastSOA”, Elsevier, 2007.

6. Jeff Davies, “The Definitive Guide to SOA”, Apress, 2007.

7. Sandeep Chatterjee, James Webber, “Developing Enterprise Web Services", Pearson Education, 2004.


To learn about the concepts and principles of mobile, and the ubiquity of wireless communication

technologies.

To develop skills of finding solutions and developing software for mobile computing applications

To build knowledge on Pervasive Computing

UNIT I MANET ROUTING AND MAC 9

Introduction - Mobile Ad Hoc Networks - Layered Architecture - Routing Protocols - Mobile Computing

Architecture - Mobile IP Protocols - Applications - Mobile Computing through Telephony - Wireless Media Access

Control - Traffic Integration in Personal, Local, and Geographical Wireless Networks.


L T P C

15PCS516 MOBILE AND PERVASIVE COMPUTING 3 0 0 3

UNIT II WIRELESS TECHNOLOGIES 8

Wireless networks- emerging technologies- Blue tooth, WiFi, WiMAX, 3G- Mobile computing environment -

functions- Solving Fixed-Channel Assignment Problems-Resource Management Tasks -Interference in Cellular

Systems -Frequency Management and Channel Assignment Issues- Channel Assignment.

UNIT III ISSUES IN WIRELESS NETWORKS 8

Handoff in wireless mobile networks-reference model-handoff schemes. Location management in cellular networks –

Power management - Mobility models- location and tracking management schemes- time, movement, profile and

distance based update strategies. ALI technologies

UNIT IV PERVASIVE COMPUTING 10

Pervasive Computing- Principles, Characteristics- interaction transparency, context aware, automated experience

capture. Architecture for pervasive computing- Pervasive devices-embedded controls - smart sensors and actuators -

Context communication and access services

UNIT V SOFTWARE 10

OS for embedded devices: PalmOS - WindowsCE - Mobile JAVA - J2ME - Windows Mobile and .Net Framework -

BREW – JINI- data synchronization- SyncML framework - Context aware mobile services -Context aware sensor

networks, addressing and communications. Context aware security.


Near Field Communication services --Green Mobile Cloud- Small Cell Networks

TOTAL: 45

2. COURSE OUTCOMES


To discover the characteristics of pervasive computing applications including the major system components

and architectures of the systems

To analyze the strengths and limitations of the tools and devices for development of pervasive computing

systems

To explore the characteristics of different types of mobile networks on the performance of a pervasive

computing system

To analyze and compare the performance of different data dissemination techniques and algorithms for

mobile real-time applications

REFERENCES

1 Ivan Stojmenovic, Handbook of Wireless Networks and Mobile Computing, John Wiley & sons Inc, Canada,

2006.

2 Asoke K Taukder, Roopa R Yavagal, Mobile Computing, Tata McGraw Hill Pub Co., New Delhi, 2010.

3 J. Schiller, Mobile Communications, 2nd edition, Pearson Education, 2009.

4 H.M. Deitel, P.J. Deitel, T.R. Nieto, and K. Steinbuhler, Wireless Internet & Mobile Business - How to

Program, Prentice Hall, 2002.

5 JochenBurkhardt, Pervasive Computing: Technology and Architecture of Mobile Internet Applications,

Addison-Wesley Professional; 3rd edition, 2009.


To gain knowledge about the concept of data mining with in detail coverage of basic tasks, metrics, issues,

and implication. Core topics like classification, clustering and association rules are exhaustively dealt with.

To gain knowledge the concept of data warehousing with special emphasis on architecture and design.

To understand the data mining tools and its implementation

To know about spatial data mining and multimedia data mining.

UNIT I INTRODUCTION TO DATAWAREHOUSING AND MINING 9

Data Mining – Knowledge discovery steps -Data Mining Functionalities – Issues in data mining systems Data

warehouse –A Multitiered Architecture – A Multidimensional Data Model – Stars, Snowflakes, and Fact

Constellations: Schemas for Multidimensional Data Models-OLAP operations – Data warehouse Implementations

UNIT II DATA PREPROCESSING AND ASSOCIATION RULE MINING 9

Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Transformation

and Data Discretization - Association Rule Mining – Basic concepts – Frequent Itemset Mining methods – Apriori

Algorithm- Generating Association Rules from Frequent Itemsets - Improving efficiency of Apriori Algorithm –A

Pattern Growth Approach for Mining Frequent Itemsets

UNIT III CLASSIFICATION AND PREDICTION 9

Classification – Basic Concepts- Decision Tree Induction – Bayes Classification Methods – Bayesian Belief

Networks - Classification by Back propagation – Support Vector Machine- Other Classification Methods – Prediction

–Classifier accuracy.

UNIT IV CLUSTER ANALYSIS 9

Requirements for cluster analysis – Overview of Basic clustering Methods - Partitioning Methods – Hierarchical

methods – Density-Based Methods – Grid-Based Methods – Outlier Analysis- Types of outlier

UNIT V MINING COMPLEX TYPES OF DATA AND TOOLS 9

Mining Complex Data Types –Mining Text databases – Mining spatial Database - Mining sequence Data: Time

Series, Symbolic Sequences and Biological Sequences – Mining Graphs and Networks- Mining the World Wide Web

– Data mining Applications. Tools: Text Mining Tools – Spatial Mining Tools - Matlab 10.0


Semantic web Mining, Phenomenal data mining, Information fusion & mining from satellite imaginary GIS data

TOTAL: 45


L T P C

15PCS517 MINING TOOLS AND TECHNIQUES 3 0 0 3

2. COURSE OUTCOMES


Apply different data mining techniques: mining frequent pattern, association, classification, prediction, and

cluster analysis in real world problems.

Evaluate the performance of different data mining algorithms

Gain the knowledge of various data mining tools in implementation

Interpret the contribution of data warehousing and data mining to the decision support level of

Apply to different Organizations.

REFERENCES

1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Third Edition, Morgan

Kauffman, 2012.

2. Margaret H.Dunham, “Data Mining: Introductory and Advanced Topics”, Pearson Education, 2006.

3. Sam Anahory, Dennis Murry, “Data Warehousing in the real world”, Pearson Education 2009.

4. David Hand, Heikki Manila, Padhraic Symth, “Principles of Data Mining”, MIT Press, 2004.

5. W.H.Inmon, “Building the Data Warehouse”, 4th Edition, Wiley, 2005.

6. Paulraj Ponniah, “Data Warehousing Fundamentals For IT Professionals”, 2nd Edition Wiley-Interscience

Publication, 2010.

7. Ian H.Witten ,Eibe Frank ,” Data mining practical machine learning tools and techniques” Second Edition ,

Morgan Kauffman, 2005


To understand various transform techniques.

To gain knowledge in image analysis

To improve the practical skills in image enhancement and restoration

To understand the basic steps in compression and segmentation


Perception Of Light - Eye - Subjective Phenomena - Monochrome Vision Model. Image Representation:2D Systems.

Linearity And Space Invariance - Point Spread Function And Convolution - 2D Fourier Transform And Its Properties

- Discrete Cosine Transform And KL Transform.

UNIT II IMAGE ANALYSIS 9

2D Sampling - Spectrum Of A Samples Image; Image Reconstruction Aliasing -Practical Image Sampling And

Reconstruction Systems- Their Imperfection – Image Quantization - Uniform And Nonuniform Quantization -Max

Lloyd Quantizer.

UNIT III IMAGE ENHANCEMENT 9

Image Enhancement And Restoration- Enhancement- Contrast Enhancement -Histogram Modification-Noise Cleaning -

Edge Crisping. Digital Image Restoration: Sources Of Degradation. Basic Principles Of Inverse Filtering – Super Solution -

System Identification - Noise Modelling -Implementation.


L T P C

15PCS518 IMAGE PROCESSING TECHNIQUES 3 0 0 3

UNIT IV IMAGE COMPRESSION AND CODING 9

Image Compression and Coding Problems. Data Structures for Picture Representation.

Pattern Recognition: Image Segmentation Process - Edge Detection and Linking – Region Growing Binary Image

Processing - Segmental Image Structure.

UNIT V DYNAMIC IMAGE ANALYSIS 9

Region Analysis And Scene Analysis - Statistical And Syntactic Models For Picture Classification -Image

Understanding Systems - Image Processing Case Histories Automatic Visual Application -Process Control - Robotic

Guidance And Control Diagnostic Medical Imaging - Military Guidance And Reconnaissance Remote Sensing- Image

Processing For Remote Sensed Data.


Forensics & security, Multi-variant image processing, Hyper spectral image analysis and its applications, 3D tracking

based on hybrid sensors

TOTAL: 45

2. COURSE OUTCOMES


Analyze and implement image processing algorithms.

Appraise efficacy and drawbacks of several techniques for image segmentation.

Apply various image enhancement techniques in image processing application.

REFERENCES

1. Gonzalez R. and Woods R.E., “Digital Image processing”, Prentice Hall , 3rd Edition , 2007.

2. Kenneth R.Castlemen, “Digital Image processing”, Prentice Hall International editions, 2008.

3. Pratt W.K., “Digital Image Processing”, A Wiley-Interscience Publication, 3rd Edition McGraw Hill, 2001.

4. Jain A .K, “Fundamentals of Digital Image Processing”, Prentice Hall India, 3rd Edition , 1998.

5.G.W. Awcock & R.Thomas ,“Applied Image processing”, McGraw Hill International Editions,1996.


To understand the genesis of grid computing

To know the application of grid computing

To understand the technology and tool kits to facilitate the grid computing

UNIT I GRID COMPUTING 9

Introduction - Definition and Scope of grid computing Data Management in Grid, QoS-Based Grid Network Service

Discovery

UNIT II GRID COMPUTING INITIALIVES 9

Grid Computing Organizations and their roles – Grid Computing analog – Grid Computing road map.


L T P C

15PCS519 GRID COMPUTING 3 0 0 3

UNIT III GRID COMPUTING APPLICATIONS 9

Merging the Grid sources – Architecture with the Web Devices Architecture -Grid Computing Security Mechanisms

UNIT IV TECHNOLOGIES 9

OGSA – Sample use cases – OGSA platform components – OGSI – OGSA Basic Services.

UNIT V GRID COMPUTING TOOL KITS 9

Globus GT 3 Toolkit – Architecture, Programming model, High level services – OGSI .Net middleware Solutions.


Enterprise Optimization Grid, DataSynapse, Platform Computing

TOTAL: 45

2. COURSE OUTCOMES

Upon Completion of the course, the students will be able to

Design and conduct experiments, as well as to analyze, interpret data on experiments relevant to Computer

Science and Engineering practice

Recognize the necessity and an ability to engage in life-long learning.

Acquire the knowledge of contemporary issues

REFERENCES

1. Joshy Joseph & Craig Fellenstein, “Grid Computing”, Pearson/PHI PTR-2003.

2. Ahmar Abbas, “Grid Computing: A Practical Guide to technology and Applications”, Charles River media –

2005.


To understand the different process models for object oriented systems.

To design object oriented systems.

To implement and deploy object oriented software engineering concepts.

UNIT I PROCESS MODELS 9

Life cycle models – Unified Process – Iterative and Incremental – Workflow – Agile Processes

UNIT II MODELING AND REQUIREMENTS ANALYSIS 9

Modeling with UML-Requirements Elicitation Concepts- Requirements Elicitation Activities-Managing

Requirements Elicitation-Analysis concepts- Analysis Activities-Managing Activities.

UNIT III SYSTEM DESIGN 9

System Design Concepts- System Design Activities-Managing System Design-Addressing Design Goals-System

Design Activities- Case Study.


L T P C

15PCS520 OBJECT ORIENTED SYSTEM ENGINEERING 3 0 0 3

UNIT IV OBJECT DESIGN 9

Overview of Object Design – Reuse Concepts- Reuse activities-Interface Specification Concepts-Interface

Specification activities-Managing Object Design- Case Study

UNIT V IMPLEMENTATION AND TESTING 9

Mapping Models to Code –Mapping Conepts-Mapping Activities- Testing – Rationale concepts-Configuration

Management Activities-Concepts.


Reverse Engineering and Re-engineering – wrappers – Case Study of CASE tools- CORBA and COM / DCOM

TOTAL: 45

2. COURSE OUTCOMES


Develop an organized methodology for implementing medium-large software systems.

Learn independently of new concepts, tools, and software frameworks

REFERENCES

1. Bernd Bruegge, Alan H Dutoit, Object-Oriented Software Engineering, 3rd ed, Pearson Education, 2010.

2. Roger S.Pressman, “Software Engineering – a Practitoner’s approach” ,7th edition,TMH,2009

3. Craig Larman, Applying UML and Patterns 3rd ed, Pearson Education, 2005.

4. Ivar Jacobson, Grady Booch, James Rumbaugh, The Unified Software Development Process, Pearson

Education, 1999.

5. Alistair Cockburn, Agile Software Development 2nd ed, Pearson Education, 2007.


To understand basic concepts of Big Data

To gain knowledge about Data Streams and analysis techniques

UNIT I INTRODUCTION TO BIG DATA 9

Introduction to BigData Platform – Traits of Big data -Challenges of Conventional Systems - Web Data – Evolution

Of Analytic Scalability - Analytic Processes and Tools - Analysis vs Reporting - Modern Data Analytic Tools -

Statistical Concepts: Sampling Distributions - Re-Sampling - Statistical Inference - Prediction Error.

UNIT II DATA ANALYSIS 9

Regression Modeling - Multivariate Analysis - Bayesian Modeling - Inference and Bayesian Networks - Support

Vector and Kernel Methods - Analysis of Time Series: Linear Systems Analysis - Nonlinear Dynamics - Rule

Induction - Neural Networks: Learning And Generalization - Competitive Learning - Principal Component Analysis

and Neural Networks - Fuzzy Logic: Extracting Fuzzy Models from Data - Fuzzy Decision Trees - Stochastic Search

Methods.


L T P C

15PCS521 BIG DATA ANALYTICS 3 0 0 3

UNIT III MINING DATA STREAMS 9

Introduction To Streams Concepts – Stream Data Model and Architecture - Stream Computing - Sampling Data in a

Stream – Filtering Streams – Counting Distinct Elements in a Stream – Estimating Moments – Counting Oneness in a

Window – Decaying Window - Real time Analytics Platform(RTAP) Applications - Case Studies - Real Time

Sentiment Analysis, Stock Market Predictions.

UNIT IV FREQUENT ITEMSETS AND CLUSTERING 9

Mining Frequent Itemsets - Market Based Model – Apriori Algorithm – Handling Large Data Sets in Main Memory –

Limited Pass Algorithm – Counting Frequent Itemsets in a Stream – Clustering Techniques – Hierarchical – K-Means

– Clustering High Dimensional Data – CLIQUE And PROCLUS – Frequent Pattern based Clustering Methods –

Clustering in Non-Euclidean Space – Clustering for Streams and Parallelism.

UNIT V FRAMEWORKS AND VISUALIZATION 9

MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed. File Systems –

Visualizations - Visual Data Analysis Techniques - Interaction Techniques; Systems and Analytics Applications -

Analytics using Statistical packages-Approaches to modeling in Analytics – correlation, regression, decision trees,

classification, association-Intelligence from unstructured information-Text analytics-Understanding of emerging

trends and technologies-Industry challenges and application of Analytics


Big Data in Cloud Computing, Big Data Benchmarks and Mobile Networking, Big data lakes

Total: 45

2. COURSE OUTCOMES

At the end of the course the student will be able to

Deploy a structured life cycle approach to data science and big data analytics projects

Select visualization techniques and tools to analyze big data and create statistical models

use tools such as R and RStudio, and MapReduce/Hadoop

REFERENCES

1. Michael Berthold, David J. Hand, “Intelligent Data Analysis”, Springer, 2007.

2. AnandRajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2012.

3. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced

Analytics”, John Wiley & sons, 2012.

4. Glenn J. Myatt, “Making Sense of Data”, John Wiley & Sons, 2007

5. Pete Warden, “Big Data Glossary”, O’Reilly, 2011.

6. Jiawei Han, MichelineKamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier, Reprinted

2008.


To learn the basic issues, policy and challenges in the Internet

To understand the components and the protocols in Internet

To build a small low cost embedded system with the internet

To understand the various modes of communications with internet

To learn to manage the resources in the Internet

To deploy the resources into business

To understand the cloud and internet environment


Definition – phases – Foundations – Policy– Challenges and Issues - identification - security –privacy. Components in

internet of things: Control Units – Sensors – Communication modules –Power Sources – Communication

Technologies – RFID – Bluetooth – Zigbee – Wifi – Rflinks –Mobile Internet – Wired Communication.

UNIT II PROGRAMMING THE MICROCONTROLLER FOR IOT 9

Basics of Sensors and actuators – examples and working principles of sensors and actuators –Cloud computing and

IOT – Arduino/Equivalent Microcontroller platform – Setting up the board -Programming for IOT – Reading from

Sensors -Communication: Connecting microcontroller with mobile devices – communication through bluetooth and

USB – connection with the internet using wifi / Ethernet

UNIT III RESOURCE MANAGEMENT IN THE INTERNET OF THINGS 9

Clustering - Software Agents - Data Synchronization - Clustering Principles in an Internet of Things Architecture -

The Role of Context - Design Guidelines -Software Agents for Object – Data Synchronization- Types of Network

Architectures - Fundamental Concepts of Agility and Autonomy-Enabling Autonomy and Agility by the Internet of

Things-Technical Requirements for Satisfying the New Demands in Production - The Evolution from the RFID-based

EPC Network to an Agent based Internet of Things- Agents for the Behaviour of Objects

UNIT IV BUSINESS MODELS FOR THE INTERNET OF THINGS 9

The Meaning of DiY in the Network Society- Sensor-actuator Technologies and Middleware as a Basis for a DiY

Service Creation Framework - Device Integration - Middleware Technologies Needed for a DiY Internet of Things

Semantic Interoperability as a Requirement for DiY Creation-Ontology- Value Creation in the Internet of Things-

Application of Ontology Engineering in the Internet of Things-Semantic Web-Ontology - The Internet of Things in

Context of EURIDICE -Business Impact

UNIT V FROM THE INTERNET OF THINGS TO THE WEB OF THINGS 9

Resource-oriented Architecture and Best Practices- Designing REST ful Smart Things – Web enabling Constrained

Devices - The Future Web of Things - Set up cloud environment – send data from microcontroller to cloud – Case

studies – Open Source e-Health sensor platform – Be Close Elderly monitoring – Other recent projects.


L T P C

15PCS522 INTERNET OF THINGS 3 0 0 3


IoT in Business Intelligence, Applications and trends of IoT

TOTAL: 45

2. COURSE OUTCOMES


To Identify the components of IOT

To Design a portable IOT using appropriate boards

To Program the sensors and controller as part of IOT

To Develop schemes for the applications of IOT in real time scenarios

To Establish the communication to the cloud through wifi/ Bluetooth

To Manage the internet resources

To Model the Internet of things to business

REFERENCES

1. Charalampos Doukas , Building Internet of Things with the Arduino, Create space, April 2002

2. Dieter Uckelmann et.al, “Architecting the Internet of Things”, Springer, 2011

3. Luigi Atzor et.al, “The Internet of Things: A survey, “, Journal on Networks, Elsevier Publications, October,

2010


To understand the concepts of parallelism and multiprocessing

To analyze and apply techniques of parallelism in high speed computer design

To understand the concepts of advanced memory design and design principles and to evaluate them

UNIT I PIPELINING AND ILP 9

Fundamentals of Computer Design - Measuring and Reporting Performance - Instruction Level Parallelism and Its

Exploitation - Concepts and Challenges - Overcoming Data Hazards with Dynamic Scheduling – Dynamic Branch

Prediction - Speculation - Multiple Issue Processors

UNIT II ADVANCED TECHNIQUES FOR EXPLOITING ILP 9

Compiler Techniques for Exposing ILP - Limitations on ILP for Realizable Processors - Hardware versus Software

Speculation - Multithreading: Using ILP Support to Exploit Thread-level Parallelism - Performance and Efficiency in

Advanced Multiple Issue Processors

UNIT III MULTIPROCESSORS 9

Symmetric and distributed shared memory architectures – Cache coherence issues - Performance Issues –

Synchronization issues – Models of Memory Consistency - Interconnection networks – Buses, crossbar and multi-

stage switches.


L T P C

15PCS523 ADVANCED COMPUTER ARCHITECTURE 3 0 0 3

UNIT IV MULTI-CORE ARCHITECTURES 9

Software and hardware multithreading – SMT and CMP architectures – Design issues – Case studies – Intel Multi-

core architecture – SUN CMP architecture – IBM cell architecture

UNIT V MEMORY HIERARCHY DESIGN 9

Introduction - Optimizations of Cache Performance - Memory Technology and Optimizations - Protection: Virtual

Memory and Virtual Machines - Design of Memory Hierarchies


Speculative Execution, Intelligent Ram, Gpu Programming

TOTAL: 45

COURSE OUTCOMES


An ability to apply knowledge of mathematics, science, and engineering,

An ability to design a system, component, or process to meet desired needs within realistic constraints

An ability to identify, formulate, and solve problems related to computer architecture.

An ability to develop programming skills and acquire deep understanding of the basic concepts in domain

which will help the student to develop algorithms and computer architecture of high standards.

REFERENCES

1. John L. Hennessey and David A. Patterson, “Computer Architecture – A quantitative approach”, Morgan

Kaufmann / Elsevier, 5th. edition, 2011.

2. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture: A hardware/ software approach”,

Morgan Kaufmann / Elsevier, 1999.

3. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Pearson

Education, ninth Edition, 2013.


To understand basic architecture and service models of cloud computing

To understand the concepts of Virtualization and Cloud architecture

To study cloud platforms and applications

UNIT I INTRODUCTION TO CLOUD COMPUTING 9

Vision of cloud computing – Defining a cloud – Cloud computing reference model –Characteristics

and benefits – Challenges – Historical developments: Distributed systems, Virtualization, Web 2.0,

Service – oriented computing, Utility – oriented computing – Building cloud computing environments:

Application development, Infrastructure and system development, Computing platforms and

technologies


L T P C

15PCS524 CLOUD COMPUTING 3 0 0 3

UNIT II VIRTUALIZATION 9

Characteristics of virtualized environments – Taxonomy of virtualization techniques Virtualization and

cloud computing – Pros and cons of virtualization – Technology examples: Xen: Para virtualization,

VMware: full virtualization, Microsoft Hyper – V.

UNIT III CLOUD COMPUTING ARCHITECTURE AND SERVICES 9

Cloud reference model – Architecture – Infrastructure – and hardware as a service – Platform as a

service – Software as a service – Types of clouds: Public, Private, Hybrid, Community Clouds –

Economics of the cloud – Open Challenges

UNIT IV USING CLOUD SERVICES 9

Collaborating on Calendars – Schedules and Task Management – Exploring Online Scheduling

Applications – Exploring Online Planning and Task Management – Collaborating on Event Management

– Collaborating on Contact Management – Collaborating on Project Management – Collaborating on

Word Processing – Collaborating on Databases – Storing and Sharing Files

UNIT V INDUSTRIAL PLATFORMS AND NEW DEVELOPMENTS 9

Cloud Platforms in Industry: Amazon web services – Amazon EC2, Amazon S3 – Google AppEngine,

– Third Party Cloud Services: MetaCDN, SpotCloud– Cloud Applications :Healthcare, Business and

Consumer Applications

UNIT VI STATE OF THE ART/ADVANCES (NOT FOR EXAMINATION)

Energy efficient and green cloud computing architecture – Cloud and Big Data Analytics – Social

Networking-Open Stack-Open Compute.

TOTAL: 45

2. COURSE OUTCOMES

Upon Completion of the course, the students will be able to

Analyze the problems and solutions to cloud application development.

Apply principles of best practice in cloud application design and management.

Design cloud applications and assess their importance

REFERENCES

1. Rajkumar Buyya, Christian Vecchiola, and Thamarai Selvi, ―Mastering Cloud Computing‖, Tata

McGraw Hill, New Delhi, India, 2013.

2. Michael Miller, ― Cloud Computing‖, Pearson Education, New Delhi, 2009

3. . Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing, From Parallel

Processing to the Internet of Things‖, Morgan Kaufmann Publishers, 2012.

4. John W.Rittinghouse and James F.Ransome, ―Cloud Computing: Implementation, Management, and

Security‖, CRC Press, 2010.

5. Toby Velte, Anthony Velte and Robert Elsenpeter ―Cloud Computing – A Practical Approach‖, Tata

McGraw Hill, 2010.

6. Jeorge Reese, ―Cloud Application Architectures: Building Applications and Infrastructure in the

Cloud O‘Reilly Applications, 2009.


To know about the different phases of compilation

To understand the significance of lexical analysis

To gain knowledge about various parsers

To know about code generation and optimization

UNIT I LEXICAL ANALYSIS 9

Compilers – Analysis of the source program – Phases of a compiler – Cousins of the Compiler – Grouping of Phases –

Compiler construction tools – Lexical Analysis – Role of Lexical Analyzer – Input Buffering – Specification of

Tokens- Study of LEX

UNIT II SYNTAX ANALYSIS 9

Role of the parser –Writing Grammars –Context-Free Grammars – Top Down parsing – Recursive Descent Parsing –

Predictive Parsing – Bottom-up parsing – Shift Reduce Parsing – Operator Precedent Parsing – LR Parsers – SLR

Parser – Canonical LR Parser – LALR Parser- Study of YACC

UNIT III INTERMEDIATE CODE GENERATION 9

Intermediate languages – Declarations – Assignment Statements – Boolean Expressions – Case Statements – Back

patching – Procedure calls.

UNIT IV CODE GENERATION 9

Issues in the design of code generator – The target machine – Runtime Storage management – Basic Blocks and Flow

Graphs – Next-use Information – A simple Code generator – DAG representation of Basic Blocks.

UNIT V CODE OPTIMIZATION 9

Importance of Code optimization- Principal Sources of Optimization - Peephole Optimization – Structure of

Optimizing compilers – placement of optimizations in optimizing compilers – ICAN – Introduction and Overview –

Symbol table structure – Local and Global Symbol table management


Just in time compiler -Compiler Optimization -Dynamic Compilation-Compilers for multi core programming

TOTAL: 45

2. COURSE OUTCOMES


Design a lexical analyser

Design a parser

Generate intermediate code

Generate and optimize target code using different schemes.


L T P C

15PCS525 PRINCIPLES OF COMPILER DESIGN 3 0 0 3

REFERENCES

1. Alfred Aho, Ravi Sethi, Monica S.Lam,Jeffrey D Ullman, “Compilers Principles, Techniques and Tools”,

Pearson Education Asia, 2011.

2. Steven S. Muchnick, “Advanced Compiler Design Implementation”, Morgan Koffman – Elsevier Science,

India, Indian Reprint 2008

3. Allen I. Holub “Compiler Design in C”, Prentice Hall of India, 2006

4. C. N. Fischer and R. J. LeBlanc, “Crafting a compiler with C”, Benjamin Cummings, 2007.

5. J.P. Bennet, “Introduction to Compiler Techniques”, Second Edition, Tata McGraw-Hill, 2003.

6. Henk Alblas and Albert Nymeyer, “Practice and Principles of Compiler Building with C”, PHI, 2001.

7. Kenneth C. Louden, “Compiler Construction: Principles and Practice”, Thompson Learning, 2003


To be able to understand and explain concepts relating to protection of information systems against

unauthorized access to or modification of information in its various forms.

To understand how to protect against the denial of service to authorized users, including measures

Necessary to detect, document, and counter such treats.

To develop an understanding of technical terms and knowledge about techniques and Methodologies for data

protection and information assurance in a wide range of information systems.

To develop an understanding in the differences in levels of security in the applications and usage of current

commercial security products available to businesses and how they adhere to an enterprises security policy

and requirements as well as the ethical and privacy issues.


History,-What is Information Security- Critical Characteristics of Information- NSTISSC Security Model-

Components of an Information System-Securing the Components- Balancing Security and Access-The SDLC-The

Security SDLC

UNIT II SECURITY INVESTIGATION 9

Need for Security- Business Needs-Threats- Attacks- Legal- Ethical and Professional Issues

UNIT III SECURITY ANALYSIS 9

Risk Management: Identifying and Assessing Risk- Assessing and Controlling Risk

UNIT IV LOGICAL DESIGN 9

Blueprint for Security-Information Security Policy-Standards and Practices-ISO17799/BS 7799-NIST Models-VISA

International Security Model-Design of Security Architecture- Planning for Continuity

UNIT V PHYSICAL DESIGN 9

Security Technology-IDS-Scanning and Analysis Tools-Cryptography- Access Control Devices- Physical Security-

Security and Personnel


L T P C

15PCS526

INFORMATION SECURITY

3 0 0 3


BYOx, Cybercrime, Internationalization

TOTAL: 45

2. COURSE OUTCOMES


Understand the challenges and scope of information security;

Understand such basic security concepts as confidentiality, integrity, and availability, which are used

frequently in the field of information security;

Conceive the importance of cryptographic algorithms used in information security in the context of the

overall information technology (IT) industry;

Identify and use symmetric algorithms for encryption-based security of information;

Identify and use public-key based asymmetric algorithms for encryption-based security of information;

Apply access control mechanism used for user authentication and authorization;

REFERENCES

1. Micki Krause, Harold F. Tipton, “ Handbook of Information Security Management”,

2. Vol 1-3 CRC Press LLC, 2004.

3. Stuart Mc Clure, Joel Scrambray, George Kurtz, “Hacking Exposed”, Tata McGraw-

4. Hill, 2003

5. Matt Bishop, “ Computer Security Art and Science”, Pearson/PHI, 2002.


To understand the fundamental concepts in machine learning

To know about the significance of dimensionality reduction

To apply the design techniques in applications which involve pattern recognition

UNIT I PATTERN CLASSIFIER 9

Overview of pattern recognition - Discriminant functions - Supervised learning - Parametric estimation -

Maximum likelihood estimation - Bayesian parameter estimation - Perceptron algorithm - LMSE algorithm -

Problems with Bayes approach - Pattern classification by distance functions - Minimum distance pattern

classifier.

UNIT II UNSUPERVISED CLASSIFICATION 9

Clustering for unsupervised learning and classification - Clustering concept - C-means algorithm -

Hierarchical clustering procedures - Graph theoretic approach to pattern clustering - Validity of clustering

solutions.

UNIT III STRUCTURAL PATTERN RECOGNITION 9

Elements of formal grammars - String generation as pattern description - Recognition of syntactic

description –Parsing - Stochastic grammars and applications - Graph based structural representation.


L T P C

15PCS527 PATTERN RECOGNITION 3 0 0 3

UNIT IV FEATURE EXTRACTION AND SELECTION 9

Entropy minimization - Karhunen - Loeve transformation - Feature selection through functions

approximation - Binary feature selection.

UNIT V RECENT ADVANCES 9

Neural network structures for Pattern Recognition - Neural network based Pattern associators –

Unsupervised learning in neural Pattern Recognition - Self organizing networks - Fuzzy logic - Fuzzy

pattern classifiers - Pattern classification using Genetic Algorithms.


Support Vector Machines, Fuzzy Cognitive Maps , Extreme Learning Machines

TOTAL: 45

2. COURSE OUTCOMES


design real time intelligent systems

design automated systems with needs machine learning

REFERENCES

1. Robert J.Schalkoff, Pattern Recognition : Statistical, Structural and Neural Approaches, John Wiley & Sons

Inc., New York, 1992.

2. Tou and Gonzales, Pattern Recognition Principles , Wesley Publication Company, London,1974.

3. Duda R.O., and Hart.P.E., Pattern Classification and Scene Analysis, Wiley, New York,1973.

4. Morton Nadier and Eric Smith P., Pattern Recognition Engineering, John Wiley & Sons, New York, 1993.

OPEN ELECTIVES


To introduce the basic concepts, parts of robots and types of robots.

To make the student familiar with the various drive systems for robot, sensors and their applications in

robots and programming of robots.

To discuss about the various applications of robots, justification and implementation of robot.


pecifications of Robots- Classifications of robots – Work envelope - Flexible automation versus Robotic technology –

Applications of Robots ROBOT KINEMATICS AND DYNAMICS Positions, Orientations and frames, Mappings:

Changing descriptions from frame to frame, Operators: Translations, Rotations and Transformations - Transformation

Arithmetic - D-H Representation - Forward and inverse Kinematics Of Six Degree of Freedom Robot Arm – Robot

Arm dynamics

UNIT II ROBOT DRIVES AND POWER TRANSMISSION SYSTEMS 9

Robot drive mechanisms, hydraulic – electric – servomotor- stepper motor - pneumatic drives, Mechanical

transmission method - Gear transmission, Belt drives, cables, Roller chains, Link - Rod systems - Rotary-to-Rotary

motion conversion, Rotary-to-Linear motion conversion, Rack and Pinion drives, Lead screws, Ball Bearing screws.

UNIT III MANIPULATORS 9

Construction of Manipulators, Manipulator Dynamic and Force Control, Electronic and Pneumatic manipulators.

UNIT IV ROBOT END EFFECTORS 9

Classification of End effectors – Tools as end effectors. Drive system for grippers-Mechanical-adhesive-vacuum-

magnetic-grippers. Hooks&scoops. Gripper force analysis and gripper design. Active and passive grippers.

UNIT V PATHPLANNING & PROGRAMMING 9

Trajectory planning and avoidance of obstacles, path planning, skew motion, joint integrated motion – straight line

motion-Robot languages -.computer control and Robot software.


Cognitive Robotics , Robots for paralysed patients , Robot Scientist

TOTAL: 45

2. COURSE OUTCOMES

The Student must be able to

design automatic manufacturing cells with robotic control.

Apply the principles of robotic drive system, end effectors, sensor,

Understand machine vision robot kinematics and programming.


L T P C

15PCS601 ROBOTICS 3 0 0 3

REFERENCES

1. Deb S. R. and Deb S., “Robotics Technology and Flexible Automation”, Tata McGraw Hill Education Pvt.

Ltd, 2010.

2. John J.Craig , “Introduction to Robotics”, Pearson, 2009.

3. Mikell P. Groover et. al., "Industrial Robots - Technology, Programming and Applications", McGraw Hill,

New York, 2008.


To learn nano computing challenges.

To familiar with the imperfections

To be exposed to reliability evaluation strategies.

To learn nano scale quantum computing.

To understand Molecular computing and Optimal Computing.

UNIT I NANOCOMPUTING-PROSPECTS AND CHALLENGES 9

Introduction - History of Computing - Nanocomputing - Quantum Computers - Nanocomputing Technologies - Nano

Information Processing - Prospects and Challenges - Physics of Nanocomputing : Digital Signals and Gates - Silicon

Nanoelectronics - Carbon Nanotube Electronics - Carbon Nanotube Field-effect Transistors – Nanolithography

UNIT II NANOCOMPUTING WITH IMPERFECTIONS 9

Introduction - Nanocomputing in the Presence of Defects and Faults - Defect Tolerance - Towards Quadrillion

Transistor Logic Systems

UNIT III RELIABILITY OF NANOCOMPUTING 9

Markov Random Fields - Reliability Evaluation Strategies - NANOLAB - ANOPRISM - Reliable Manufacturing and

Behavior from Law of Large Numbers

UNIT IV NANOSCALE QUANTUM COMPUTING 9

Quantum Computers - Hardware Challenges to Large Quantum Computers - Fabrication, Test, and Architectural

Challenges - Quantum-dot Cellular Automata (QCA) - Computing with QCA - QCA Clocking - QCA Design Rules

UNIT V QCADESIGNER SOFTWARE AND QCA IMPLEMENTATION 9

Basic QCA Circuits using QCADesigner - QCA Implementation - Molecular and Optical Computing: Molecular

Computing - Optimal Computing - Ultrafast Pulse Shaping and Tb/sec Data Speeds


Nano Stamping-Nano Sciences- CMOL- Micro to Nano Addressing Block

TOTAL: 45


L T P C

15PCS602 NANO COMPUTING 3 0 0 3

2. COURSE OUTCOMES

At the end of the course, the student should be able to

Discuss nano computing fundamentals and issues

Handle the defects and faults

Apply reliability strategies

Clear about QCA concepts and its implementation

REFERNCES

1. Sahni V. and Goswami D., Nano Computing, McGraw Hill Education Asia Ltd. (2008), ISBN (13):

978007024892.

2. Sandeep K. Shukla and R. Iris Bahar., Nano, Quantum and Molecular Computing, Kluwer Academic

Publishers (2004), ISBN: 1402080670.

3. Sahni V, Quantum Computing, McGraw Hill Education Asia Ltd. (2007).

4. Jean-Baptiste Waldner, Nanocomputers and Swarm Intelligence, John Wiley & Sons, Inc. (2008), ISBN (13):

978-1848210097.


To understand the E-Marketing context: e-business models, performance metrics, and role of strategic

planning.

To know marketing strategies of segmenting, targeting, positioning, and differentiation.

To know how to use marketing functions of product, pricing, distribution, and marketing communication for

a firm's E-Marketing strategy.

To acquire the basic knowledge, concepts, tools, and international terminology necessary to understand

international problems and issues


Theories of E-Marketing – Introduction to E-Marketing- E-Marketing Plan - Strategic E-Marketing and

Performance Metrics - The E-Marketing Plan - Internet Marketing Overview - Website Planning & Development - Let

Companies Search you on Google for Jobs - Internet Marketing Strategy and Planning -

UNIT II E- STRATEGIES AND TACTICS 9

Building E-Presence – Internet Marketing : Online shopping- Internet Marketing Techniques – E-Cycle of Internet

Marketing –How to market Presence – Attracting and tracking customers – Customer relationship and Management -

Business-to-Business Commerce

UNIT III ONLINE SHOPPING 9

Introduction- ATM - Selling Products Through Online-Modes - Making Money via Adsense and Blogging - Search

Engine Optimization – Social Media Marketing - Make E-Commerce website in 20 Minutes – Explore your Talent to

earn money through Internet – Affiliate Marketing- Making Tons of Money Part Time - Making Money as a

FreeLancer.


L T P C

15PCS603 INTERNET MARKETING 3 0 0 3

UNIT IV INTERNATIONAL MARKETING 9

The Globalization Imperative-Entering and managing the global market-Economic and Financial factors affecting the

global market- political and legal factors-cultural factors-Segmenting markets and positioning products/services-

Decisions about global products-Global pricing issues and strategies.

UNIT V MARKETING RESEARCH 9

Integrated marketing communication--International Promotion and advertisement-The dynamic environment of

International Trade- Culture, management style and business system- The international legal environment: playing by

the rules-Developing global vision through marketing research.


Marketing Web Analytics and Intelligence- Business Marketing Management- Law of Unfair Competition and

Intellectual Property

TOTAL : 45

2. COURSE OUTCOMES

At the end of the course, the student should be able to:

Understand the importance of e-marketing

Know the basics of secured internet marketing strategy

Ensure how companies adjust their international strategies based on the global environmental changes

REFERENCES

1. Mary lou Roberts, Debra Zahay ―Internet Marketing: Integrating Online and Offline Strategies‖, Tata

McGraw Hill, 2012

2. Judy Strauss and Raymond Frost (2009). E-Marketing . Upper Saddle NJ: Prentice Hall.

3. Lee, OokInternet Marketing Research: Theory and Practice, Idea Group Inc (IGI), Jul-2000

4. Kotabe and Helsen, Global Marketing Management, 5th Edition(Newyork, Wiley), ISBN: 978-0470381113


To understand the mathematical formulation of the LPP and NLPP.

To understand various linear and Non-linear programming concepts.

To know the Decision making techniques.

To understand the concept of Dynamic Programming.


Statement of an optimization problems – classification of optimization problem – classical optimization techniques;

Single variable optimizations, Multi variable optimization, equality constraints, Inequality constraints, No constraints.


L T P C

15PCS604 OPTIMIZATION TECHNIQUES 3 0 0 3

UNIT II LINEAR PROGRAMMING 9

Graphical method for two dimensional problems – central problems of Linear Programming – Definitions – Simplex –

Algorithm – Phase I and Phase II of Simplex Method – Revised Simplex Method. Simplex Multipliers – Dual and

Primal – Dual Simplex Method – Sensitivity Analysis – Transportation problem and its solution – Assignment

problem and its solution by Hungarian method – Karmakar’s method – statement, Conversion of the Linear

Programming problem into the required form, Algorithm.

UNIT III NON LINEAR PROGRAMMING (ONE DIMENSIONAL MINIMIZATION) 9

Introduction – Unrestricted search – Exhaustive search – Interval halving method – Fibonacci method.

Non Linear Programming: (unconstrained optimization): Introduction – Random search method – Uni variate method

– Pattern search methods – Hooke and Jeeves method, Simplex method– Gradient of a function – steepest descent

method – Conjugate gradient method. Non Linear Programming – (Constrained Optimization): Introduction –

Characteristics of the problem – Random search methods – Complex method.

UNIT IV DYNAMIC PROGRAMMING 9

Introduction – multistage decision processes – Principles of optimality – Computation procedures.

UNIT V DECISION MAKING 9

Decisions under uncertainty, under certainty and under risk – Decision trees – Expected value of perfect information

and imperfect information. \


Integer Programming/Modeling ,Integer and mixed integer models ,Existence of optimal solutions, optimality

conditions ,Branch and bound methods , Cutting plane methods, Decomposition: Lagrangian relaxation and column

generation, Local search and other heuristics

TOTAL: 45

2. COURSE OUTCOMES

At the end of the course the students would be able to

Use numerical optimization algorithms for real world Problems

formulate engineering design problems as mathematical optimization problems.

use scientific computational tools in the emergent areas of nonlinear Programming

REFERENCES

1. Kalyanmoy Deb, “Optimization for Engineering Design, Algorithms and Examples”, Prentice Hall, 2005

2. Hamdy A Taha , “Operations Research – An introduction”, Pearson Education ,8/e 2009

3. Hillier / Lieberman, “Introduction to Operations Research”, Tata McGraw Hill Publishing Company Ltd, 8/e

2005.

4. Singiresu S Rao, “Engineering Optimization Theory and Practice”, New Age International, 4/e 2009 john

wiley & sons

5. Mik Misniewski, “Quantitative Methods for Decision makers”, MacMillian Press Ltd.,5/e , 2009, Prentice

Hall

6. Kambo N S, “Mathematical Programming Techniques”, Affiliated East – West Press, 1991.


To recognize how information technologies (IT) influence businesses and how they provide

Competitive advantages.

To gain knowledge about various electronic payment methods.

To identify desirable properties of secure communication and ways to achieve them.

To know about management‘s role in information security

UNIT I INTRODUCTION TO E-COMMERCE 8

Networks and Commercial Transactions - Internet and Other Novelties - Electronic Transactions Today - Commercial

Transactions - Establishing Trust - Internet Environment - Internet Advantage -World Wide Web.

UNIT II SECURITY TECHNOLOGIES 9

Why Internet Is Unsecured - Internet Security Holes - Cryptography: Objective - Codes and Ciphers -Breaking

Encryption Schemes - Data Encryption Standard - Trusted Key Distribution and Verification - Cryptographic

Applications - Encryption - Digital Signature – Non repudiation and Message Integrity.

UNIT III ELECTRONIC PAYMENT METHODS 9

Traditional Transactions : Updating - Offline and Online Transactions - Secure Web Servers –Required Facilities -

Digital Currencies and Payment Systems - Protocols for the Public Transport – Security Protocols - SET - Credit Card

Business Basics.

UNIT IV ELECTRONIC COMMERCE PROVIDERS 9

Online Commerce Options - Functions and Features - Payment Systems: Electronic, Digital and Virtual Internet

Payment System - Account Setup and Costs - Virtual Transaction Process -InfoHaus – Security Considerations –

Cyber Cash: Model - Security - Customer Protection – Client Application – Selling through Cyber Cash.

UNIT V ONLINE COMMERCE ENVIRONMENTS 10

Servers and Commercial Environments - Payment Methods - Server Market Orientation – Netscape Commerce Server

- Microsoft Internet Servers - Digital Currencies - Dig Cash - Using E-cash – Ecash Client Software and

Implementation - Smart Cards - The Chip - Electronic Data Interchange –Internet Strategies, Techniques and Tools


Internet marketing , Importance of search engine rankings for e-commerce, Ubiquitous commerce, Mobile marketing,

e-tailing– New business models using social medias, e-communities, Comparative analyses of e-marketing strategies,

Innovative e-marketing, e-Marketing strategy & implementation, Multi -and cross- channel marketing

TOTAL: 45


L T P C

15PCS605 E – COMMERCE 3 0 0 3

2. COURSE OUTCOMES


Analyze the impact of E-commerce on business models and strategy

Use critical thinking, problem-solving, and decision-making skills in evaluating e-commerce technologies.

Assess electronic payment systems

Analyze e-commerce concepts and terminology, and the processes and management decisions that are

involved in launching, operating and managing business activity on the World Wide Web

REFERENCES

1. Pete Loshin, ―Electronic Commerce‖, 4th Edition, Firewall media, An imprint of laxmi publications

Pvt. Ltd., New Delhi, 2004.

2. Jeffrey F.Rayport and Bernard J. Jaworski, ―Introduction to E-Commerce‖, 2nd Edition, Tata Mc-

Graw Hill Pvt., Ltd., 2003.

3. Green stein, ―Electronic Commerce‖, Tata Mc-Graw Hill Pvt., Ltd., 2000.

1. COURSE OBJECTIVE

To acquire knowledge to adopt green computing practices to minimize negative impacts on the environment,

skill in energy saving practices in their use of hardware, examine technology tools that can reduce paper

waste and carbon footprint by user, and to understand how to minimize equipment disposal requirements.

UNIT I OVERVIEW OF GREEN COMPUTING 9

Green IT Fundamentals: Business, IT, and the Environment – Green computing: carbon foot print, scoop on power –

Green IT Strategies: Drivers, Dimensions, and Goals – Environmentally Responsible Business: Policies, Practices,

and Metrics.

UNIT II GREEN ASSETS AND MODELING 9

Green Assets: Buildings, Data Centers, Networks, and Devices – Green Business Process Management: Modeling,

Optimization, and Collaboration – Green Enterprise Architecture –Environmental Intelligence – Green Supply Chains

– Green Information Systems: Design and Development Models.

UNIT III GRID FRAMEWORK 9

Virtualizing of IT systems – Role of electric utilities, Telecommuting, teleconferencing and teleporting – Materials

recycling – Best ways for Green PC – Green Data center – Green Grid framework.

UNIT IV GREEN COMPLIANCE 9

Socio-cultural aspects of Green IT – Green Enterprise Transformation Roadmap – Green Compliance: Protocols,

Standards, and Audits – Emergent Carbon Issues: Technologies and Future.


L T P C

15PCS606 GREEN COMPUTING 3 0 0 3


The Environmentally Responsible Business Strategies (ERBS) – Case Study Scenarios for Trial Runs – Case Studies

– Applying Green IT Strategies and Applications to a Home, Hospital, Packaging Industry and Telecom Sector.


Environmentally Sustainable Infrastructure Design - Green Maturity Model for Virtualization - Application Patterns

for Green IT - Profiling Energy Usage for Efficient Consumption - Green IT in Practice: SQL Server Consolidation in

Microsoft IT.

TOTAL: 45

2. COURSE OUTCOMES


Understand the concepts of technologies that conform to low-power computation

Understand green (power-efficient) technologies for components of one single computer, such as CPU,

memory and disk, and appreciate cutting edge designs for these components including memristors

Have a basic understanding of a variety of technologies applied in building a green system (especially green

datacentres), including networks, Virtual Machine (VM) management and storage systems

Be able to use a range of tools to help monitor and design green systems

REFERENCES

1. Bhuvan Unhelkar, “Green IT Strategies and Applications-Using Environmental Intelligence”, CRC Press,

June 2011

2. Woody Leonhard, Katherrine Murray, “Green Home computing for dummies”, August 2009.

3. Alin Gales, Michael Schaefer, Mike Ebbers, “Green Data Center: steps for the Journey”, Shoff/IBM rebook,

2011.

4. John Lamb, “The Greening of IT”, Pearson Education, 2009.

5. Jason Harris, “Green Computing and Green IT- Best Practices on regulations & industry”, Lulu.com, 2008.

6. Carl speshocky, “Empowering Green Initiatives with IT”, John Wiley & Sons, 2010.

7. Wu Chun Feng (editor), “Green computing: Large Scale energy efficiency”, CRC Press, 2012