bharathiar university, coimbatore 641 046 m....

M.Sc.Comp.Science (UD) – 2016-17 Annexure No.77B

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BHARATHIAR UNIVERSITY, COIMBATORE – 641 046

M. Sc COMPUTER SCIENCE (CBCS)

UNIVERSITY DEPARTMENT

(Effective from the academic Year 2016 - 2017)

1. Eligibility for Admission to the Programme

Candidates for admission to the first year programme leading to the Degree of Master of

Science in Computer Science (M.Sc-CS) will be required to possess:

A Pass with 50% of marks in B.Sc. Computer Science / BCA /B.Sc. Computer

Technology / B.Sc. Information Technology / B.Sc. Information Science / B.Sc. Information

Systems / B.Sc. Software Science / B.Sc. Software Engineering / B.Sc. Software Systems. In

case of SC/ST candidates, a mere pass in any of the above Bachelor‟s degree will be sufficient.

2. Duration of the Programme

The programme shall be offered on a full-time basis. The programme will consist of

three semesters of course work and laboratory work and the fourth semester consists of project

work.

3. Regulations

The general Regulations of the Bharathiar University Choice Based Credit System

Programme are applicable to this programme.

4. The Medium of Instruction and Examinations

The medium of instruction and Examinations shall be in English.

5. Submission of Record Notebooks for Practical Examinations & Project Viva-Voce.

Candidates taking the Practical Examinations should submit bonafide Record Note

Books prescribed for the Examinations. Otherwise the candidates will not be permitted to take

the Practical Examinations.

Candidates taking the Project Viva Examination should submit Project Report prescribed

for the Examinations. Otherwise the candidates will not be permitted to take the Project Viva-

voce Examination.


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SCHEME OF EXAMINATION

Core/

Elective/

General/

General

Supportive

Suggested

Code

Sem. Title of the Paper No. of Credits

Hrs.

L P

Marks

Theor

y

Practi

cal

Core 1 16CS1C1 I Compiler Design 4 0 4 0 100

Core 2 16CS1C2 I Advanced Operating System 4 0 4 0 100

Core 3 16CS1C3 I Data Structures and Algorithms 2 2 2 4 100

Core 4 16CS1C4 I Advanced Java Programming 2 2 2 4 100

Core 5 16CS1C5 I Python Programming 2 2 2 4 100

Elective -I 16CS1EXX I Elective – I 4 0 4 0 100

General 16CS1G1 I Industry Literacy 0 1 - - 25

General

Supportive

16CSGSXX I General Supportive - I 2 0 2 0 50

Core 6 16CS2C1 II Linux Programming 2 2 2 4 100

Core 7 16CS2C2 II Data Base Administration and

Management

2 2 2 4 100

Core 8 16CS2C3 II Information Security 4 0 4 0 100

Core 9 16CS2C4 II Internet of Things 4 0 4 0 100

Core 10 16CS2C5 II Data Mining Techniques and

Tools

2 2 2 4 100

Elective -II 16CS2EXX II Elective - II 4 0 4 0 100

General 16CS2G1 II Literature Survey 0 1 - - 25

General

Supportive

16CSGSXX II General Supportive - II 2 0 2 0 50

Core 11 16CS3C1 III Wireless Networks 2 2 2 4 100

Core 12 16CS3C2 III Visual Programming 2 2 2 4 100

Core 13 16CS3C3 III Software Project Management 4 0 4 0 100

Core 14 16CS3C4 III Cloud Computing 4 0 4

0 100

Core 15 16CS3C5 III Big Data Analytics 2 2 2 4 100

Elective-III 16CS3EXX III Elective – III 4 0 4 0 100

General 16CS3G1 III Gap Analysis 0 1 - - 25

General

Supportive

16CSGSXX III General Supportive - III 2 0 2 0 50

Project 16CSPRO IV Project 9 225

Total 90 2250


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Electives for M.Sc. Computer Science (CBCS)

Sem. Suggested

Code

Title of the Paper No. of

Credits

I

16CS1E01 Business Intelligence 4

16CS1E02 Mathematical Foundations of Computer Science 4

16CS1E03 System Programming 4

16CS1E04 Software Metrics 4

16CS1E05 Bio-Informatics 4

16CS1E06 Microprocessor Principles and Design 4

16CS1E07 Principles of Programming Languages 4

16CS1E08 Mainframe Computing 4

16CS1E09 Software Reliability 4

16CS1E10 Parallel Processing 4

II 16CS2E01 Operation Research 4

16CS2E02 Green Computing 4

16CS2E03 Mobile Computing 4

16CS2E04 Image Processing 4

16CS2E05 Web Services 4

16CS2E06 Artificial Intelligence and Expert Systems 4

16CS2E07 TCP/ IP 4

16CS2E08 Embedded Systems 4

16CS2E09 Software Quality Assurance 4

16CS2E10 Natural Language Processing 4

III 16CS3E01 Virtual Reality 4

16CS3E02 Machine Learning Techniques 4

16CS3E03 Human Computer Interaction 4

16CS3E04 Data Compression 4

16CS3E05 Genetic Algorithms 4

16CS3E06 Neural Networks and Fuzzy Systems 4

16CS3E07 Speech Processing 4

16CS3E08 E-Commerce 4

16CS3E09 Distributed Systems 4

16CS3E10 Open Source Technologies 4


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Supportive Subjects

Suggested

Code

Sem. Title of the Paper

Hrs

Cre

dit

s

Mar

ks

14CSS01

I/II/III

Windows and MS Word 2 2 50

14CSS02 Internet and HTML Programming 2 2 50

14CSS03 Relational Database Management System 2 2 50

14CSS04 Object Oriented Programming 2 2 50

14CSS05 Software Engineering 2 2 50

14CSS06 Multimedia Systems 2 2 50


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COMPILER DESIGN

Subject Code: 16CS1C1 Number of Credits: 4

Subject Description: This course presents an Introduction to compilers, different phases of

compilers and compiler construction tools.

Goals: To enable the student to learn the types of translator, phases of compiler, optimization

techniques and code generator.

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

Parsing techniques and different levels of translation.

Apply the various optimization techniques.

Use the different compiler construction tools.

Contents

Unit – I Introduction to Compilers: Translators-Compilation and Interpretation-Language processors –

The Phases of Compiler-Errors Encountered in Different Phases-The Grouping of Phases-

Compiler Construction Tools – Programming Language basics.

Unit – II

Lexical Analysis: Need and Role of Lexical Analyzer-Lexical Errors-Expressing Tokens by

Regular Expressions Converting Regular Expression to DFA- Minimization of DFA-Language

for Specifying Lexical Analyzers-LEX-Design of Lexical Analyzer for a sample Language.

Unit – III

Syntax Analysis: Need and Role of the Parser-Context Free Grammars –Top Down Parsing –

General Strategies Recursive Descent Parser Predictive Parser-LL(1) Parser-Shift Reduce

Parser-LR Parser-LR (0)Item Construction of SLR Parsing Table –Introduction to LALR Parser

– Error Handling and Recovery in Syntax Analyzer-YACC-Design of a syntax Analyzer for a

Sample Language .

Unit – IV

Syntax Directed Translation & Run Time Environment: Syntax directed Definitions-

Construction of Syntax Tree-Bottom-up Evaluation of S-Attribute Definitions- Design of

predictive translator – Type Systems-Specification of a simple type checker Equivalence of Type

Expressions-Type Conversions – Run-Time Environment: Source Language Issues-Storage

Organization-Storage Allocation Parameter Passing-Symbol Tables-Dynamic Storage

Allocation.

Unit – V

Code Optimization and Code Generation: Principal Sources of Optimization-DAG-

Optimization of Basic Blocks-Global Data Flow Analysis Efficient Data Flow Algorithms-Issues

in Design of a Code Generator – A Simple Code Generator Algorithm.

REFERENCES:

1. Alfred V Aho, Monica S. Lam, Ravi Sethi and Jeffrey D Ullman, “Compilers –

Principles, Techniques and Tools”, 2nd

Edition, Pearson Education, 2007.

2. Steven S. Muchnick, “Advanced Compiler Design and Implementation”, Morgan

Kaufmann Publishers – Elsevier Science, India, Indian Reprint 2003.

3. Keith D Cooper and Linda Torczon, “Engineering a Compiler”, Morgan Kaufmann

Publishers Elsevier Science, 2004. 4. Charles N. Fischer, Richard. J. LeBlanc, “Crafting

a Compiler with C”, Pearson Education, 2008.


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ADVANCED OPERATING SYSTEMS

Subject Code: 16CS1C2 Number of credits: 4

Subject Description: This course presents the principles and functions of various types of

operating system and management of computer parts.

Goals: To enable the student to learn the operating system and the functioning.

Objectives: To gain knowledge on Distributed Operating Systems, to gain insight into the

components and management aspects of real time and mobile operating systems.

Contents

Unit – I

Basics of Operating Systems: What is an Operating System? – Main frame Systems – Desktop

Systems – Multiprocessor Systems – Distributed Systems – Clustered Systems – Real-Time

Systems – Handheld Systems – Feature Migration – Computing Environments - Process

Scheduling – Cooperating Processes – Inter Process Communication- Deadlocks – Prevention –

Avoidance – Detection – Recovery.

Unit – II

Distributed Operating Systems: Issues – Communication Primitives – Lamport’s Logical Clocks

– Deadlock handling strategies – Issues in deadlock detection and resolution- distributed file

systems –design issues – Case studies – The Sun Network File System-Coda.

Unit – III

Realtime Operating Systems : Introduction – Applications of Real Time Systems – Basic Model

of Real Time System – Characteristics – Safety and Reliability - Real Time Task Scheduling

Unit – IV

Operating Systems for Handheld Systems: Requirements – Technology Overview – Handheld

Operating Systems – PalmOS-Symbian Operating System- Android –Architecture of android –

Securing handheld systems

Unit – V

Case Studies :Linux System: Introduction – Memory Management – Process Scheduling –

Scheduling Policy - Managing I/O devices – Accessing Files- iOS : Architecture and SDK

Framework - Media Layer - Services Layer - Core OS Layer - File System.

REFERENCES:

1. Abraham Silberschatz; Peter Baer Galvin; Greg Gagne, “Operating System Concepts”,

Seventh Edition, John Wiley & Sons, 2004.

2. Mukesh Singhal and Niranjan G. Shivaratri, “Advanced Concepts in Operating Systems

– Distributed, Database, and Multiprocessor Operating Systems”, Tata McGraw-Hill,

2001

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

4. Pramod Chandra P.Bhatt, An introduction to operating systems, concept and practice,

PHI, Third edition, 2010


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5. Daniel.P.Bovet & Marco Cesati,“Understanding the Linux kernel”,3rd edition,O’Reilly,

2005

6. Neil Smyth, “iPhone iOS 4 Development Essentials – Xcode”, Fourth Edition, Payload

media, 2011

DATA STRUCTURES AND ALGORITHMS


Subject Description: This course presents the principles and functions of various types of

algorithms and management of data structures.

Goals: To enable the student to learn the data structures, algorithms and it’s functioning.

Objectives: To gain knowledge in different data structures, analysis of algorithms, divide and

conquer methods and dynamic programming.

Unit-I

Introduction: Definition, Structure and Properties of algorithms – Development of an algorithm

– Data Structures and algorithms – Data Structure definition and classification. Analysis of

algorithms: Efficiency of algorithms – Apriori analysis – Asymptotic notations – Time

complexity of an algorithm using O notation – Polynomial Vs Exponential algorithms –

Average, Best and Worst case complexities – Analyzing recursive programs. X2

Unit - II

Stacks: Introduction - Stack Operations – Applications – Recursion - Evaluation of Expressions.

Queues: Introduction - Operations on Queues – Circular queues – Application of a linear queue.

Linked Lists: Introduction - Singly linked lists - Circularly linked lists - Doubly linked lists -

Applications – polynomial addition.

Unit – III

Binary Trees: Introduction – Representation of Trees – Binary Tree Traversals. Binary Search

Trees: Introduction – Operations. AVL Trees: Definition - Operations. B-Trees: Introduction –

m-way search trees - B trees definition and operations. Graphs: Introduction – Definitions –

Representation of Graphs – Graph Traversal - Depth-First and Breadth-First Algorithms -

Topological Sorting.

Unit – IV

Divide and Conquer: General Method – Binary Search – Merge Sort – Quick Sort. Greedy

Method: General Method – Knapsack Problem – Minimum Cost Spanning Tree – Single Source

Shortest Path.

Unit –V

Dynamic Programming: General Method – Multistage Graphs – All Pair Shortest Path –

Traveling Salesman Problem. Backtracking: General Method – 8- Queens Problem – Sum of

Subsets – Hamiltonian Cycles. Branch and Bound: The Method – 0/1 Knapsack Problem –

Traveling Salesperson.


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REFERENCES:

1. GAV Pai, Data Structures and Algorithms Concepts, Techniques and Applications, Tata

McGraw Hill.

2. Jean Paul Tremblay, Paul G. Sorenson, An Introduction to Data Structures with

Applications, Tata McGraw Hill, Second Edition.

3. Sahini, “Data Structures, Algorithms and Applications in C++”, Mc GrawHill, 1998.

4. Ellis Horowitz, Sartaj Sahni, Sanguthevar Rajasekaran, “Fundamentals of Computer

Algorithms”, Second Edition, Universities Press, 2008

5. Robert Sedgewick, Phillipe Flajolet, “An Introduction to the Analysis of Algorithms”,

Addison-Wesley Publishing Company, 1996.

6. Alfred V. Aho, John E. Hocroft, Jeffrey D. Ullman, “Data Structures and Algorithms”.

ADVANCED JAVA PROGRAMMING


Subject Description: This course starts with the fundamentals of OOPs with Java and advanced

concepts such as Java swing, RMI, JSP, Hibernate and Struts architectures

Goals: To enable the student to learn the advanced programming concepts in Java.

Objectives: At the end of the course, the student should be able to do advanced programming

using Java swing, RMI, JSP, Hibernate and Struts.

Contents

Unit - I

Introduction to Java - Features – Java programming structure – Tokens - Literals - Data types-

Operators –- Control statements - Arrays. Object Oriented Concepts in JAVA: Classes – Objects

– Method Overloading – Inheritance – Method Overriding – Abstract classes – Interfaces –

Packages- Exception handling -Threads.

Unit –II

Java Swing – Features – Classes and Packages – MVC architecture – Swing basic compontents –

Buttons – Labels – List – Combobox – Menu Simple AWT application using Swing

Components.

Unit – III

RMI: RMI overview - RMI architecture - Example demonstrating RMI. Introduction to servlet -

Developing and Deploying Servlets - Handling Request and Response - Reading Servlet

Parameters - Cookies - Session Tracking- Accessing Database using JDBC.

Unit –IV

Java Server Pages: Basic JSP Architecture - Life Cycle of JSP - JSP Tags and Expressions –

Directives- JSP applications. Java Creating and using JavaBean components –Setting and

retrieving JavaBean components – Java Server Faces Application.


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Unit –V

Introduction to Hibernate – Advantages – Architecture –Spring Framework -Struts Framework:

Introduction to Struts- Struts Architecture.

REFERENCES:

1. Herbert Schildt - JAVA 2 ( The Complete Reference)- Ninth Edition, TMH, 2014

2. Patrick Naughton, “The Java Hand Book, Tata McGraw Hill, 1996.

3. Brian Cole, Robert Eckstein, James Elliott, Marc Loy, David Wood, Java Swing,

O’Reilly Publishers, second edition, 2002

4. Jim Keogh, “The Complete Reference J2EE, Tata McGraw-Hill, 2002.

5. Kogent Solutionss, Java Server Programming Java Ee5 Black Book,Dreamtech Press,

2008

PYTHON PROGRAMMING


Subject Description: This course presents an Introduction to Python Programming concepts and

its various features.

Goals: To enable the student to be familiar with Python Programming.

Objectives: On successful completion of the course the student should have understood the

concepts in Python and its application.

Contents:

Unit - I

Core Python: Introduction-features-Comparative study-Comments-Operators-Variables and

Assignments-Numbers-String-List and Tuple-Dictionary-Statements and Iterative statements-list

comprehensive-Errors and Exception-functions-Classes-Modules-Useful function. Basics:

Syntax and Statements-Variable Assignments-Identifier-Style-Memory Management-

Application Example. Objects: Introduction-Standard Type- Built-in-type-Internal type-Standard

type operator and Built-in functions-Categorizing standard type-Unsupported type.

Unit - II

Numbers: Introduction- Integer-Floating Point-Complex numbers-Operators-Built-in-

functions-Other numeric type-Sequence-Strings-Strings and Operator-String only operator-Built-

in-Functions-Built-in-Methods-String Features-Unicode-Related Modules.

Unit – III

List-Operators-Built-in-Functions-Built-in-Methods-Features of List-Tuple: Introduction-

Operators and Built-in-Functions-Features-Related Modules-Mapping type: Dictionaries-

Operators-Built-in and Factory Functions-Built-in- Methods. Set type: Introduction-Operators-

Built-in Function-Built-in Methods-Related Modules-Conditional and looping statement.


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Unit – IV

File: Objects- Built in Functions-Methods-Attributes-Standard files-Command line Argument-

File System-File Execution-Persistent Storage Modules-Related Module. Class: Introduction-

Class and Instance- Method calls. Exception and Tools: Why use it?-Exception roles-Short

story-Try/finally statement.

Unit – V

Regular Expression: Introduction-Special Symbols and characters-Regexes and Python-

Examples of Regexes. Network Programming: Architecture-Socket. Internet Client

Programming- Transferring files-Email.GUI Programming: Introduction-Tkinter and Python.

DB Programming: Introduction-Python DB-API-Non-Relational DB. Web Services:

Introduction-Microblogging with Twitter.

REFERENCES:

1. Chun, J Wesley, Core Python Programming, 2nd Edition, Pearson, 2007 Reprint 2010.

2. Wesley J Chun Core python Application Programming,3rd Edition,

3. Lutz, Mark, Learning Python, 5th Edition, O Rielly.

LINUX PROGRAMMING


Subject Description: This course presents an introduction to Linux OS, shell programming

concepts and the various inter process communication mechanisms.

Goals: To enable the student to be familiar in advanced Linux OS concepts.

Objectives: On successful completion of the course the student should be able to do

Basics in Linux OS.

Process control in Linux

Inter-process communications in Linux

Socket programming

Contents

Unit - I

Introduction: History - Architecture of UNIX operating system – Features of UNIX - Basic

commands – Working with files and directories – Commands - File types - File access processes

permissions redirection - filters –What is Linux? – Distributions – The GNU Project and the

Free software Foundation.

Unit - II

Shell programming in Linux: – vi editor – Shell syntax - variables – conditions and control

structures- command execution – simple programs – System calls and library: Read – Write –

File and record locking – Adjusting the position of file I/O – Lseek - Close – File creation –

Creation of special files – Changing directory, root, owner, mode – stat and fstat.

Unit- III

Processes and Signals: Processes: Introduction of process – Process structure - Process states -

Process termination – command line arguments - Process control – Process identifiers - Process


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relationships – Zombie process - Signals: Sending signals – Signal sets–Threads:

Synchronization – Thread attributes – Canceling a Threads.

Unit - IV

Inter process Communication: Process – popen() and pclose() – Pipes –Named pipes (FIFO) –

Message queues – Semaphores - Shared Memory – Client-Server application using IPC.

Unit - V

Sockets: Introduction – Socket Connections - TCP sockets -TCP echo client server – UDP

sockets - UDP echo client server - Socket options.

REFERENCES:

1. Neil Matthew, Richard Stones, Beginning Linux Programming, Third Edition, Wrox,

Wiley Publishing Inc., 2004.

2. W. Richard Stevens, Bill Fenner, Andrew Rudoff, "UNIX Network Programming", Vol.

1 , The Sockets Networking API, Third Edition, Pearson education, Nov 2003.

3. W.Richard Stevens, Stephen A. Rago, Advanced programming in the UNIX

environment, second edition, Addison Wesley, 2005.

DATABASE ADMINISTRATION AND MANAGEMENT


Subject Description: This course presents the concepts of designing and management of

relational database system.

Goals: To enable the student to learn the concepts of relational database management system.

Objectives:

To understand the fundamentals of data models and conceptualize and depict a database

system using ER diagram and data storage techniques a query processing.

To make a study of SQL and relational database design

To import knowledge in transaction processing, concurrency control techniques and

recovery procedures

Learnt distributed databases

Understood database administration and security

Contents

Unit - I Introduction: Purpose of Database Systems - View of Data - Database Languages - Data Storage

and Querying -Transaction Management – Storage Management – Data Mining and Information

Retrieval - Specialty Databases - Database Users and Administrators– Relational Databases:

Introduction to the Relational Model - Structure of Relational Databases-Database Schema -

Keys-Schema Diagrams -Relational Query Languages - Relational Operations.

Unit – II

Introduction to SQL: Overview of the SQL -Data Definition – Basic Structure of SQL Queries –

Set operations - Null values-Aggregate Functions - Modification of the Database - Integrity


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Constraints – Views – SQL Data Types and Schemas. Advanced SQL - Accessing SQL From a

Programming Language – Triggers - Advanced Aggregation Features-OLAP.

Unit - III

Transaction Management : Overview of Transaction Management- The ACID Properties-

Transactions and Schedules- Concurrent Execution of Transactions - Lock-Based Concurrency

Control - Performance of Locking - Introduction to Crash Recovery.

Concurrency Control: 2PL, Serializability, and Recoverability - Introduction to Lock

Management - Lock Conversions - Dealing With Deadlocks - Specialized Locking Techniques -

Concurrency Control without Locking.

Unit - IV

Distributed Database Management Systems: The Evolution of Distributed Database

Management Systems - DDBMS Advantages and Disadvantages - Distributed Processing and

Databases - Characteristics of Distributed DBMS - DDBMS Components - Levels of Data and

Process Distribution - Distribution Transparency - Transaction Transparency - Distributed

Database Design - Client/Server vs. DDBMS.

Unit - V

Business Intelligence and Data Warehouses: The Need for Data Analysis - Business Intelligence

and Architecture - Data Warehouse- OLAP - Star Schemas - Implementing a Data Warehouse -

SQL Extensions for OLAP. Database Connectivity and Web Technologies: Database

Connectivity - Internet Databases - Extensible Markup Language (XML). Database

Administration and Security: Security - Database Administration Tools - The DBA at Work:

Using Oracle for Database Administration.

REFERENCES:

1. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database SystemConcepts”, Fifth

Edition, McGraw Hill, 2006.

2. Raghu Ramakrishnan, Johannes Gehrke, “Database Management Systems”, McGraw

Hill, Third Edition 2004.

3. Peter Rob, Carlos Coronel, “Database System Concepts”, Cengage Learning, 2008

INFORMATION SECURITY


Subject Description: This course presents the principles and need for ensuring information

security in organizations

Goals: To enable the student to learn the concepts of information security

Objectives: On successful completion of the course the student should have to:

Understand the basics of Information Security

Know the legal, ethical and professional issues in Information Security

Know the aspects of risk management - Become aware of various standards in this area

Know the technological aspects of Information Security

Contents


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Unit - I INTRODUCTION: History, What is Security, CNSS Security Model, Components of an

Information System, Balancing Information Security and Access, The Systems Development

Life Cycle, The Security Systems Development Life Cycle. Communities of interest-Need for

security: Threats, Attacks

Unit - II

LEGAL, ETHICAL AND PROFESSIONAL ISSUES: Law and Ethics in Information Security,

International Laws and Legal Bodies, Ethics and Information Security, Codes of Ethics and

Professional Organizations Risk Management: An Overview of Risk Management, Risk

Identification, Risk Assessment, Risk Control Strategies, Selecting a Risk Control Strategy

Unit - III

PLANNING FOR SECURITY: Information Security Policy, Standards and Practices, The

Information Security Blueprint, Security Education, Training and Awareness Program,

Continuity Strategies

Unit - IV

SECURITY TECHNOLOGY: Firwalls and VPNs- Intrusion Detection and Prevention Systems,

Honeypots, Honeynets and padded cell systems -Scanning and Analysis Tools- bio metric

access control.

Unit - V

Cryptography: Cipher Methods, Cryptographic Algorithms, Cryptographic Tools, Protocols for

secured communication-Attacks on Cryptosystems.

REFERENCES:

1. Michael E Whitman and Herbert J Mattord, “Principles of Information Security”, 4th

Edition, Course Technology, Cengage Learning.

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

1-3 CRC Press LLC, 2008.

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

Hill, 2003

4. William Stallings, Cryptography and Network Security, Pearson Education, 2000.

5. Nina Godbole, Information Systems Security, Wiley-2009.


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INTERNET OF THINGS


Subject Description: This course presents the communication technologies used in IoT, Web of

Things, Structural models and applications of IoT

Goals: To enable the student to learn the concepts in IoT


Understand the communication technologies in IoT

Know the IoT protocols and web of things Know the various applications of IoT

Unit - I

Introduction : Internet Layers - Protocols - Packets - Services - Performance parameters - Peer-

to-peer networks - Sensor networks - Multimedia - IOT Definitions and Functional

Requirements –Motivation – Architecture - Web 3.0 View of IoT– Ubiquitous IoT Applications

– Four Pillars of IoT – DNA of IoT - The Toolkit Approach for End-user Participation in the

Internet of Things. Middleware for IoT: Overview – Communication middleware for IoT –IoT

Information Security.

Unit - II

IoT protocols : Protocol Standardization for IoT – Efforts – M2M and WSN Protocols –

SCADA and RFID Protocols – Issues with IoT Standardization – Unified Data Standards –

Protocols – IEEE 802.15.4 – BACNet Protocol – point-to-point protocols - Ethernet protocals -

cellular Internet access protocal - Machine-to-machine protocal - Modbus – KNX – Zigbee

Architecture – Network layer – APS layer – Security.

Unit - III

Web of Things: Web of Things versus Internet of Things – Two Pillars of the Web –

Architecture Standardization for WoT– Platform Middleware for WoT – Unified Multitier WoT

Architecture – WoT Portals and Business Intelligence. Cloud of Things: Grid/SOA and Cloud

Computing – Cloud Middleware – Cloud Standards – Cloud Providers and Systems – Mobile

Cloud Computing – The Cloud of Things Architecture.

Unit - IV

Integrating IOT: Integrated Billing Solutions in the Internet of Things Business Models for the

Internet of Things - Network Dynamics: Population Models – Information Cascades - Network

Effects - Network Dynamics: Structural Models - Cascading Behavior in Networks - The Small-

World Phenomenon.

Unit - V

Applications: The Role of the Internet of Things for Increased Autonomy and Agility in

Collaborative Production Environments - Resource Management in the Internet of Things:

Clustering, Synchronisation and Software Agents. Applications - Smart Grid – Electrical

Vehicle Charging - Case studies: Sensor body-area-network and Control of a smart home.

REFERENCES:

1. The Internet of Things in the Cloud:A Middleware Perspective-Honbo Zhou–CRC Press

2012.


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2. Architecting the Internet of Things - Dieter Uckelmann; Mark Harrison; Florian

Michahelles- (Eds.) – Springer – 2011

3. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David

Easley and Jon Kleinberg, Cambridge University Press - 2010.

4. The Internet of Things: Applications to the Smart Grid and Building Automation by -

Olivier Hersent, Omar Elloumi and David Boswarthick - Wiley -2012 5. Olivier Hersent,

David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and

Protocols”, Wiley, 2012.

DATA MINING TECHNIQUES AND TOOLS


Subject Description: This course presents the data mining concepts, techniques in data mining,

web mining and open source tools to manipulate data mining applications.

Goals: To enable the student to learn data mining techniques and tools.


Understand the data mining techniques Know the concept of web mining

Know the usage of WEKA and R Programming to mining knowledge from domain of

interest

Contents:

Unit I

Data mining: Introduction – Definitions - KDD vs. Data mining - DM techniques – Issues and

Challenges in Data Mining – Data mining application areas. Classification Technique:

Introduction – Decision Trees: Tree Construction Principle - Decision Tree construction

Algorithm –CART – ID3 – Rainforest –CLOUDS.

Unit II

Clustering techniques: Clustering paradigms – Partitioning algorithm - K-Means – K-Medeoid

algorithms – CLARA – Hierarchical Clustering - DBSCAN – BIRCH - Categorical clustering

algorithms – STIRR - Other techniques. Introduction to neural network - learning in NN –

Unsupervised Learning - Genetic algorithm.

Unit III

Association Rules: Concepts - Methods to discover association rules - A priori algorithm –

Partition algorithm - Dynamic Item set Counting algorithm - FP-tree growth algorithm -

Incremental algorithm - Generalized association rule.

Unit IV

Web mining: Basic concepts – Web content mining – Web structure mining – Web usage mining

– Text mining: Text clustering - Sequence mining: The GSP algorithm – SPADE.


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Unit V

Tools: Need for data mining tools - Introduction to WEKA – The Explorer – The Experimenter –

Classification-Regression-Clustering- Nearest neighbor - Introduction to R- Data types-

Variables Operators-Decision Making-Loop Control –Function-Strings-Vectors-Lists-Matrices-

Arrays-Factors-Data Frames-Packages- Charts and graphs- Statistics.

REFERENCES

1. Arun K. Pujari, Data Mining Techniques, Third Edition, Universities Press (India)

Limited,

2. Hyderabad, 2009.

3. Margaret H. Dunham, Data Mining Introductory and Advanced Topics, Pearson

Education,

4. 2004.

5. Jaiwei Han and MichelineKamber, Data Mining Concepts and Techniques,

MorganKaufmann Publishers, 2011, 3rd Edition.

6. Pieter Adriaans, DolfZantinge, Data Mining, Addison Wesley, 2008.

7. Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning

Tools and Techniques. Elsevier, 2011.

8. Mark Gardener, Beginning R: The Statistical Programming Language

9. Robert Kabacoff, R in Action: Data Analysis and Graphics R

WIRELESS NETWORKS


Subject Description: This course will cover the fundamental aspects of wireless networks with

emphasis on current and next generation wireless networks.

Goals: To introduce the students to state-of-the-art wireless network protocols and architectures.

Objective: On successful completion of the course the student will be able to:

Explain the fundamental of cellular communication and channel allocation.

Explain the constraints and performance of wireless personal area networks, sensor

and adhoc networks

Unit -I:

Wireless Networks: Evolution of wireless networks – Challenges - Transmission fundamentals:

Analog and digital data transmission - Transmission media - Modulation techniques for wireless

systems - Multiple access for wireless systems - Performance increasing techniques for wireless

networks.

Unit -II:

Wireless LAN: Introduction to Wireless LANs – WLAN Equipment, Topologies, Technologies,

IEEE 802.11 WLAN – Architecture and Services - Physical Layer - MAC Sub Layer –MAC

Management Sub Layer, Other IEEE 802.11 Standards.


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Unit -III:

Wireless Personal Area Networks: Introduction – Bluetooth : Architecture - Protocol Stack -

Physical Connection – Mac mechanism – Frame format – Connection management -Low Rate

and High Rate WPAN , ZigBee Technology IEEE 802.15.4 : Components – Network topologies

– PHY – MAC.

Unit -IV:

Ad-hoc Wireless Networks: Introduction - Characteristics of Adhoc Networks - Classifications

of MAC Protocols - Table driven and Source initiated On Demand routing protocols- OLSR -

Hierarchical routing protocols – CBRP, FSR, TCP over Ad Hoc Wireless Networks.

Unit -V:

Wireless Sensor Networks : Introduction - 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 - Design principles – Operating systems.

REFERENCES:

1. Nicopolitidis P, Obaidat M S, Papadimitriou G S and Pomportsis A S, “Wireless Networks”,

John Wiley and Sons, New York, 2009.

2. Vijay K Garg, Wireless Communication and Networking, Morgan Kaufmann Publishers

2010.

3. Siva Ram Murthy C,. Manoj B S, “Ad Hoc Wireless Networks: Architectures and

Protocols”, Prentice Hall, 2006.

4. Holger Karl and Andreas Willig, “Protocol and Architecture for Wireless Sensor

Networks”, John Willey Publication, 2011.

VISUAL PROGRAMMING


Subject Description: This course presents the introduction to .NET framework, VB.NET,

ASP.NET and Web services.

Goals: To enable the student to be familiar with visual programming concepts.

Objectives: On successful completion of the course the student should have:

Understood the concepts in VB.NET & ASP.NET.

Knowledge in developing web services

Contents:

Unit –I

Introduction to .NET – The .NET Framework – Benefits of .NET - Common Language Runtime

– Features of CLR - Compilation and MSIL – The .NET Framework libraries – The Visual

Studio Integrated Development Environment.


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Unit –II

Introduction to VB.NET – VB.NET fundamentals – Branching and Looping Statements -

Classes and Objects – Constructors – Overloading- Inheritance and Polymorphism – Interfaces –

Arrays – Strings – Exceptions – Delegates and Events.

Unit –III

Building Windows Applications – Creating a Windows Applications using window controls -

Windows Forms, Text Boxes, Rich Text boxes, Labels, and link labels – Buttons, Check boxes,

Radio buttons, Panels and Group Boxes, List Boxes, Checked List boxes, Combo boxes and

Picture boxes, Scroll bars – Calendar control, Timer control – Handling Menus – Dialog boxes –

Deploying an Application – Graphics.

Unit- IV

ASP.NET Basics: Features of ASP.NET – ASP.NET page directives - Building Forms with Web

server Controls – Validation Server Controls - Rich Web Controls - Custom Controls –

Collections and Lists.

Unit –V

Data Management with ADO.NET - Introducing ADO.NET - ADO.NET features - Using SQL

Server with VB.NET – Using SQL Server with ASP.NET – LINQ queries – Building ASP.NET

3.5 Enterprise Applications: Developing ASP.NET Ajax applications – ASP.NET web services.

REFERENCES:

1. Jesse Liberty, Programming Visual Basic.NET 2003, Second Edition, O Reilly, Shroff

Publishers and Distributors Pvt. Ltd.

2. Steven Holzner, Visual Basic.NET Programming Black Book, 2005 Edition, Paraglyph

press USA&Dreamtech Press, India.

3. Bill Evjen, JasonBeres, et al. Visual Basic.NET Programming Bible, 2002 Edition, IDG

books India (p) Ltd.

4. MridulaParihar et al., ASP.NET Bible,2002 Edition,Hungry Minds Inc, New York, USA.

5. Bill Evjen, Hanselman, Muhammad, Sivakumar& Rader, Professional ASP.NET 2.0,

2006 Edition, Wiley India(p) Ltd.

6. KoGENT Solutions Inc., ASP.NET 3.5 (Covers C# and VB 2008 codes) Black Book,

Platinum Edition, Dreamtech press, 2010.


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SOFTWARE PROJECT MANAGEMENT


Subject Description: This course presents a deep insight to software project management

concepts

Goals: Enable the student to be familiar with software project management


Understood the system software project management, project evaluation effort

estimation and risk management.

Contents

Unit - I Introduction: Software Project Management - Software Project Versus Other Project –

Requirement Specification – Information and Control in Organization – Introduction to step

wise Project Planning – Select – Identify Scope and Objectives - Identify Project

Infrastructure – Analyze Project Characteristics – Products and Activities – Estimate Effort for

each Activity – Identify Activity Risks – Allocate Resources - Review / Publicize Plan –

Execute Plan and Lower Levels of Planning.

Unit - II

Project Evaluation : Introduction – Strategic Assessment – Technical Assessment – Cost Benefit

Analysis – Cash Flow Forecasting – Cost Benefit Evaluation Techniques – Risk Evaluation –

Selection of an Appropriate Project App roach – Choosing Technologies – Choice of Process

Models – Structured Methods – Rap id Application Development – Waterfall Model – V-

Process Model – Spiral Model – Software Prototyping – Ways of Categorizing Prototypes –

Tools – Incremental Delivery – Selection Process Model.

Unit - III

Software Effort Estimation : Introduction – Problem s with Over and Under Estimates – Basis

for Software Estimating – Software Effort Estimation Technique – Albrecht Function Point

Analysis – Function Points – Object Points – Procedural Code Oriented Approach – COCOMO

– Activity Planning – Project Schedules - Projects and activities – Sequencing and Scheduling

Activities – Network Planning Models – Formulating a Network Planning – Adding Time

Dimension – Forward Pass – Backward Pas s – Identifying the Critical Path – Activity Float -

Shortening Project Duration – Identifying Critical Activities – Precedence Networks.

Unit - IV

Risk Management : Introduction – Nature of Risk Man aging Identification – Analysis –

Reducing – Evaluating – Z values – Resource Allocation – Nature of Resources – Requirements

– Scheduling – Critical Paths – Counting the Cost – Resource Schedule – Cost Schedule –

Scheduling Sequence – Monitoring and Control – Creating the Frame Work - Collecting the

Data – Visualizing the Progress – Cost Monitoring – Prioritizing Monitoring – Change Control.

Unit - V

Managing Contracts : Introduction – Types of Contract – Stages in Contract Placement – Terms

of Contract – Contract Management – Acceptance – Managing People and Organizing Teams –

Organizational Behavior Background – Selecting the Right Person for the Job – Instruction in

the Best Methods – Motivation – Decision Making – Leadership – Organizational Structures –


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Software Quality – Importance – Practical Measures – Product Versus Process Quality

Management – External Standards – Techniques to Help Enhance Software Quality- Case Study

on Project Management.

REFERENCES:

1. Bob Hughes and Mike Cotterell, “Software Project Management , Mc Graw Hill, Second

Edition.

2. Walker Royce, “Software Project Management , Addition Wesley.

3. DerrelInce, H. Sharp and M. Woodman, “Introduction to Software Project Management

and Quality Assurance , Tata Mc Graw Hill, 1995.

CLOUD COMPUTING

Subject Code: 16CS3C4 Number of Credits : 4

Subject Description: This course presents the Introduction to cloud computing, Cloud

Computing Technology, Virtualization, Migrating into cloud and data security in cloud.

Goals: To enable the student to be familiar with the usage of cloud services, implement the

virtualization, data storage in the cloud and its security techniques.

Objectives: On successful completion of the course the student should have understood the

different concepts of cloud computing and its services, to store and retrieve the data from cloud

and can provide the security to the data in cloud.

Unit - I

Introduction: Cloud Computing Basics: Cloud Computing Overview - Applications of cloud

computing - Intranets and the cloud – First movers in the cloud - Benefits - limitations of cloud

computing – Security Concerns – Cloud Computing Services – Salesforce.com.

Unit - II

Cloud Computing Technology: Hardware and Infrastructure – Clients – Security – Network –

Services - Cloud Storage – Standards – Cloud Computing at work: Software as a Service –

Software Plus Services – Developing Applications.

Unit - III

Virtual Machines and Virtualization: Introduction - Understanding Virtualization - History of

Virtualization – Leveraging Blade Servers – Server Virtualization – Desktop Virtualization –

Virtual Networks – Data Storage Virtualization. Data Storage in Cloud: Evolution of Network

Storage – Cloud based data Storage – Advantages and disadvantages of Cloud based data

storage- Cloud based Backup systems - File Systems – Cloud based Block Storage.

Unit - IV

Migrating into a Cloud: Introduction – Broad approaches of Migrating into cloud – The Seven

Step Models of Migrating into a Cloud. Mobile Cloud Computing: Evolution of Mobile

Computing – Mobile Cloud EcoSystem – Mobile Players.


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Unit - V

Data security in cloud: Introduction – Current state of data security – Homo sapiens and Digital

Information – Cloud Computing and Data security Risk – Cloud Computing and Identity – The

Cloud, Digital Identity and Data Security- Content Level Security- Pros and Cons.

REFERENCES:

1. Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, “ Cloud Computing: A Practical

Approach”, McGraw Hill.

2. Kris Jamsa, “ Cloud Computing” Jones and Barlett Student Edition 2014.

3. Rajkumar Byya, James Broberg, Andrzej Goscinski, “ Cloud Computing Prnciples and

Paradigms”, Wiley & sons

BIG DATA ANALYTICS


Subject Description: The course provides grounding in basic and advanced methods to big data

technology and tools, including MapReduce and Hadoop and its ecosystem.

Goals: To enable the student to learn big data technologies such as Hadoop, Hbase. NoSQL and

visual analytics.

Objectives: On successful completion of the course the student should

able to apply Hadoop ecosystem components.

able to participate data science and big data analytics projects.

Contents

Unit - I What is big data – why big data – convergence of key trends – unstructured data – industry

examples of big data – web analytics – big data and marketing – fraud and big data – risk and

big data – credit risk management – big data and algorithmic trading – big data and healthcare –

big data in medicine – advertising and big data – big data technologies - open source

technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics –

inter and trans firewall analytics

Unit – II

History of Hadoop- The Hadoop Distributed File System – Components of Hadoop- Analyzing

the Data with Hadoop- Scaling Out- Hadoop Streaming- Design of HDFS-How Map Reduce

Works-Anatomy of a Map Reduce Job run-Failures-Job Scheduling-Shuffle and Sort – Task

execution - Map Reduce Types and Formats- Map Reduce Features

Unit - III

Hbase – data model and implementations – Hbase clients – Hbase examples – praxis. Cassandra

– cassandra data model – cassandra examples – cassandra clients – Hadoop integration. Pig –

Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types

and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.

Unit – IV

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data

models – relationships– schemaless databases – materialized views – distribution models -peer-


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peer replication –consistency – relaxing consistency – version stamps – partitioning and

combining – composing map-reduce calculations -Document based Database – MongoDB-

Introduction- Data Model- Working with data- Replication & Sharding- Development

Unit – V

Graph databases Neo4J- Key concept and characteristics-Modelling data for neo4j-Importing

data into neo4j-Visualizations neo4j-Cypher Query Language-Data visualization- Creating

Visual analytics with Tableau-Connecting your data-Creating Calculation-Using maps-

Dashboard-Stories

REFERENCES:

1. Tom White, “Hadoop: The Definitive Guide”

2. The Defininetive Guide to Mongodb

3. Rik Van Bruggen, “Learning Neo4j”

4. Daniel G. Murray, “Tableau Your Data!: Fast and Easy Visual Analysis with Tableau

Software”

5. Dirk deRoos, Paul Zikopoulos, Bruce Brown, Roman B. Melnyk,Rafael Coss, “Hadoop

For Dummies”

6. Gaurav Vaish, “Getting Started with NoSQL”

7. Pramod J. Sadalage, Martin Fowler, “NoSQL Distilled: A Brief Guide to the Emerging

World of Polyglot Persistence”

8. Joshua N. Milligan, “Learning Tableau”


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BUSINESS INTELLIGENCE

Subject Code: 16CS1E01 Number of credits: 4

Subject Description: This course presents the business intelligence architecture and design, business process and information flow and business trends and knowledge delivery. Goals: To enable the student to learn business intelligence concepts Objectives: On successful completion of the course the student should have to

Understood the industry and overall business environment. Know the business processes and information flow.

now the business intelligence techniques.

UNIT – 1

Introduction to Business Intelligence: Introduction – Data Information and Knowledge – What is

Business intelligence? – Business Intelligence and related technologies – Obstacles to Business

Intelligence – Factors driving Business Intelligence – Improving the Decision making Process –

Why a Business intelligence Program?.

UNIT – II

Business Intelligence Capabilities : Introduction – Four Synergistic capabilities – Organizational

Memory – Technologies Enabling Organizational Memory Capability - Information Integration

Capability – Insight Creation – Technologies Enabling Insight Creation Capability.

UNIT – III

The Business Intelligence Program : Business Intelligence Architecture and Design – Data

Preparation – Data Integration – Business Intelligence Platforms – Analysis – Delivery and

Presentation - The Organizational Business Framework – Metadata management – Data

Modelling – Data Profiling – Data Quality – Data Integration – Text Analysis – Predictive

Analysis – Data Security - Data Governance.

UNIT – IV

Business Processes and Information Flow : Information Processing and Information flow –

Transaction Processing – operational Processing – Batch Processing – Analytical Processing -

The Information Flow Process : Information flow model : Processing Stages - Directed

Channels - Business Process Model and Notation (BPMN) – Data Recruitment Analysis :

Business Use of Information – Metrics : Facts , Qualifiers and Models – What is Data

Requirements Analysis ?

UNIT – V

Emerging Business Intelligence Trends and Knowledge Delivery : Introduction in Searching a

Business Intelligence Technique – Text Analysis – Entity Extraction and Entity Recognition –

Sentiment Analysis – Mobile Business Intelligence – Event Stream Processing – Big Data

Analytics - Knowledge Delivery : Standard Reports –Dimensional Analysis – Visualization :

Charts , Graphs , Widgets – Score cards and Dashboards – Geographic visualization – Integrated

Analytics.

REFERENCES: Business Intelligence, Practice, Technologies and Management, Rajiv Sabherwal, Irma Becerra-

Fernandez.

Business Intelligence: The Savvy Manager's Guide, David Loshin , 2013 Edition


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MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE

4 Subject Code: 16CS1E02 Number of credits: 4

Subject Description: This course presents the properties of matrices and concepts of probability. Goals: To enable the student to learn the mathematical foundations of computer science. Objectives: On successful completion of the course the student should have:

Understood the mathematical logic grammars and languages. Learned probability concepts.

Contents: Unit - I Matrices: Types of Matrices - Matrix Operations - Inverse of a Matrix - Properties of Determinants - Eigen Values - Cayley-Hamilton Theorem. Set Theory: Basic Set Operations - Relations and Functions – Relation Matrices - Principle of Mathematical Induction. Unit - II Introduction to Probability: Sample Space and Events - Axioms of Probability - Conditional Probability – Independence of Events - Bayes Theorem. Regression and Correlation : Introduction – Linear Regression – Method of Least Squares – Normal Regression Analysis – Normal Correlation Analysis. Unit - III Grammars and Languages: Context Free Grammars – Introduction – Context Free Grammars – Derivation Trees. Finite Automata: Finite State Systems – Basic Definitions – Non Deterministic Finite Automata. Unit - IV Mathematical Logic: Statements and Notations – Connectives – Consistency of Premises and Indirect Method of Proof – Automatic Theorem Proving. Unit - V Numerical Methods Finding Roots : Bisection Method - Regula–Falsi Method - Newton–RaphsonMethod. Solution of Simultaneous Linear Equations: Gaussian Elimination - Gauss-Seidal Method. Numerical Integration: Trapezoidal Rule - Simpson s Rule. REFERENCES:

1. M. K. Venkataraman, “Engineering Mathematics, Volume II, National Publishing Company.

2. John E. Freunds, Irwin Miller, Marylees Miller, “Mathematical Statistics, Pearson Education, Sixth Edition.

3. Hopcroft and Ullman, “Introduction to Automata Theory, Languages and Computation , Pearson Education, Second Edition.

4. Tremblay and Manohar, “Discrete Mathematical Structures with Applications to Computer Science , Tata McGraw-Hill.

5. Rama B. Bhat, Snehashish Chakraverty, “Numerical Analysis in Engineering , Narosa Publishing House, 2004.

6. Radha Muthu, T. Santha, “Discrete Mathematics for Computer Science and Applications, Kalaikathir Achchagam, Coimbatore, 2003.


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SYSTEM PROGRAMMING


Subject Description: This course presents the different system programming software such as language processors, assemblers, macro processors, loaders, linkers, compilers and interpreters Goals: To enable the student to learn system programming. Objectives: On successful completion of the course the student should have:

Understood the functions of various system programming soft wares.

Able to simulate and design a simple system programming soft wares.

Contents:

UNIT I

Language Processors: Introduction - language processing activities - Fundamentals of language

processing - Fundamentals of language – Specification - language Processor development tools.

UNIT II

Assemblers and Macros and Macro Processors : Elements of assembly language programming -

A simple assembly scheme - Pass structure of assemblers - design of a two pass assembler - A

single pass assembler for IBM PC. Macro definition and call - Macro Expansion - Nested macro

calls.

UNIT III

Compilers and Interpreters, Debuggers: Aspects of compilation - compilation of expressions -

code optimization. Linkers: Relocation and linking concepts - design of a linker - Self-relocating

programs - Debuggers: Types of Errors - Debugging Procedures - Classification of

Debuggers - Dynamic/Interactive Debugger

UNIT IV

Loaders : Function of loader - general loader scheme - Absolute loader - Relocating loader -

Direct linking loader - Dynamic loading - Design of direct linking loader.

UNIT V

Scanning and Parsing : Programming Language Grammars - Classification of Grammar -

Ambiguity in Grammatic Specification – Scanning – Parsing - Top Down Parsing - Bottom up

Parsing - Language Processor Development Tools – LEX - YACC.

REFERENCES: 1. D.M. Dhamdhere, System Programming and operating systems, 2nd Edition (TMGH)

2. J. J. Donovan, System Programming, Mc-Graw Hill

3. System Software- An Introduction to Systems Programming- 3rd Edition- Leland L. Beck (Pearson Education)

4. Srimanta Pal, System Programming,OXFORD Publication

5. R.K. Maurya & A. Godbole, System Programming and Compiler Construction


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SOFTWARE METRICS


Subject Description: This course presents the fundamentals of software metrics, role of metrics and measurements of software engineering. Goals: To enable the student to learn software metrics. Objectives: On successful completion of the course the student should have:

Understood the goals of software metrics.

Know the role of software metrics in software development. Know the quality control and how to ensure good quality software.

Contents:

UNIT - I

Software metrics - software metrics used in industry, and how? - Limitations on applying

software metrics - Types of software metrics – Goal of Software Metrices- Software Entities-

Errors, Faults and Failures - Reliability as a Quality Attribute - Software Metrics for Reliability-

The Dark Side of Software Metrics.

UNIT - II

The role of Metrics in software and software development- Scope of Software Metrics- Cost and

Effort Estimation- Data Collection- Quality Models and Measures- Reliability Models –Security

metrics –Structural and Complexity metrics – Capability maturity Assessment- Management by

metrices – Evaluation of Methods and Tools.

UNIT - III

Static Analysis of Code - McCabe’s Cyclomatic Complexity - Static Analysis of Code - Bug

Counting using Dynamic Measurement - Estimating Failure Rates.

UNIT - IV

Measurements in Software Engineering – Scope of Software metrics – Measurements theory –

Goal based Framework – Software Measurement Validation - Reliability Growth Models -

Software Uncertainty - Software Requirements Metrics - Software Design Metrics -

Management Metrics - Algorithmic Cost Modeling - Evaluation of Management Metrics

UNIT - V

Measurement of Internet Product Attributes – Size and Structure – External Product Attributes –

Measurement of Quality –Reliability Growth Model – Model Evaluation

REFERENCES:

1. Software Metrics: A Rigorous and Practical Approach, Third Edition (Chapman & Hall/CRC

Innovations in Software Engineering and Software Development Series) 3rd Edition

2. Software Metrics and Software Metrology ,1st Edition, Alain Abran

3. Software Metrics: A Rigorous and Practical Approach, Third Edition, Norman Fenton, James

Bieman.


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BIOINFORMATICS


Subject Description: This course presents the importance of biological concepts and biological databases. Goals: To enable the student to know about sequence analysis, all biological databases, perl programming. Objectives:

On successful completion of the course the student should have:

Understood different structure and functions. Learnt the different modeling techniques & sequence analysis.

Contents: Unit-I Introduction – importance of bioinformatics – biological concepts – DNA & protein (Structure and functions)

Unit - II Model organisms and genome projects, Biological Databases, Sequence databases, Primary, secondary, composite databases, Nucleotide sequence databases (NCBI, EBI, DDBJ), Protein sequence databases (SwissPROT, TrEMBL, PIR, Expasy), Structural databases, DNA structure databases, Protein structure database (PDB, SCOP, CATH), Genome databases, NCBI genome, Pathway database, KEGG.

Unit - III Sequence analysis – gene identification methods (Prokaryotic and eukaryotic), Needleman and Wunsch algorithm, Smith and Waterman algorithm, pair wise sequence alignment (local and global alignment), scoring a matrix (Pam and Blosum), Multiple sequence alignment, sequence motif analysis

Unit - IV Elements of PERL Programming – Data types, syntax, loops, input and outputs.

Unit - V Structural biology and molecular modeling - Molecular visualization, RasMol, ViewerPro, Swiss PDB Viewer, Protein conformational analysis, Ramachandran plot, Secondary structure prediction, 3DPSSM, Protein Domains, Blocks and Motifs, CD Search, PDB Search, PDB Format, Comparative Modeling.

REFERENCES: 1. T.K. Attwood, D.J. Parry-Smith, “Introduction to Bioinformatics”,

Pearson Education, Asia, 2003 2. Dan E.Krane, Michael L.Raymer, “Fundamental concept s of Bioinformatics”,

Pearson Education, Asia, 2003. 3. Dr. K, Mani and N. Vijayaraj, “Bioinformatics for beginners”, Kalaikathir Achchagam.


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MICROPROCESSOR PRINCIPLES AND DESIGN


Subject Description: This course presents the architecture memory, I/Devices, interrupts, signals DMA controller and chips. Goals: To enable the student learn the programming concepts in microprocessor. Objectives:


Understood assembly languages. Contents: Unit - I Introduction - Microprocessor instruction set and Computer Languages – Microcomputer and Large computers –8085 Pin configuration – 8085 Architecture– Memory – Input and Output devices – Example of a microcomputer system - Review: Logic devices for interfacing – Memory Interfacing – Interfacing I/O devices.

Unit - II Instructions – Instruction format – Addressing modes –Types of Instructions – Intel 8085 instruction set –Development of Assembly Language Programs – Programming Techniques: Looping, counting and indexing – Additional data transfer and 16 bit Arithmetic instructions – Arithmetic operations related to memory - Logical Operations: Rotate and Compare – Counters and Time delays – Stack and Subroutines – BCD to Binary conversion – Binary to BCD Conversion – BCD to seven segment LED code conversion – Binary to ASCII and ASCII to Binary code conversion – BCD Arithmetic.

Unit - III 8085 interrupts – Hardware and Software interrupts – Multiple interrupts – 8259A programmable Interrupt controller- DMA controller - 8255A Programmable peripheral interface – 8254 programmable interval timer.

Unit - IV Basic concepts in serial I/O – Software controlled asynchronous serial I/O –8085 serial I/O lines: SOD and SID- Hardware controlled serial I/O using programmable chips. Microprocessor Applications: Designing a Scanned display – Interfacing a Matrix Keyboard – Memory design.

Unit - V ADC/DAC interface – Keyboard interfacing – Printer Interfacing - Contemporary 8 bit microprocessors – Single chip micro controllers – 16 bit microprocessors – 32 bit microprocessors.

REFERENCES: 1. Ramesh S.Gaonkar, “Microprocessor Architecture, Programming and Applications

with the 8085”, Penram International Publications, Fourth Edition. 2. Mohammad Rafiguzzaman, “Microprocessor and microcomputer based system Design”,

Universal Bookstall, 1990.


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PRINCIPLES OF PROGRAMMING LANGUAGES


Subject Description: This course presents the various principles of programming languages. Goals: To enable the student to learn the Language Design Issues, data types, inheritance, control structure, and storage management. Objectives:


Understood the structure and operation of a computer with various programming Languages.

Contents: Unit - I Language Design Issue: The structure and operation of a computer – Virtual Computers and Binding Times – Language Paradigms. Language Translation Issues: Programming Language Syntax – Stages in Translation – Formal Translation Models.

Unit - II Data Types: Properties of Types and Objects – Elementary Data Types – Structured Data Types. Abstraction: Abstract Data Types – Encapsulation by Subprograms – Type Definitions – Storage Management.

Unit - III Sequence Control: Implicit and Explicit Sequence Control – Sequencing with Arithmetic and Non arithmetic Expressions – Sequence Control between Statements. Subprogram Control: Subprogram Sequence Control – Attributes of Data Control – Shared Data in Subprograms

Unit - IV Inheritance: Inheritance – Polymorphism, Advances i n Language Design: Variation on Subprogram Control – Parallel Programming - Language Semantics – Software Architecture.

Unit - V Logic Programming Language: PROLOG – Overview – Data Objects – Sequence Control - Subprograms and Storage Management –Abstraction and Encapsulation – Sample Program. Functional Language: LISP –Overview-Data Objects – Sequence Control- Subprograms and Storage Management –Abstraction and Encapsulation – Sample Program.

REFERENCES: 1. Terrance W. Pratt, Marvin V. Zelkowitz, “ Programming Languages, Design and

Implementation”, PHI, 3 rd

Edition.

2. A.B.Tucker, “Programming Languages”, McGraw Hill. 3. D. Appleby, J. J. Vandekopple, “Programming Languages – Paradigm and Practices,

McGrawHill, Second Edition.


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MAINFRAME COMPUTING

Subject Code: 16CS1E0 8 Number of credits: 4

Subject Description: This course presents the introduction to COBOL, DB2, CICS Goals: To enable the student to familiar with to be familiar with the concepts in databases Objectives: On successful completion of the course the student should have:

Understood concepts of COBOL, JCL, and DB2. Contents:

Unit-I History of MVS- Basic concepts of JCL- Introduction to ISPF-JOB statement-EXEC statement-DD statement- Procedures-GDG-Utility program-VSAM-JES2 and JES3-ALTER, DELETE, EXPORT, IMPORT command in VSAM-SMS. Unit-II Introduction to COBOL- INDENTIFICATION DIVISIION-ENVIRONMENT DIVISION-DATA DIVISION-PROCEDURE DIVISION-SYNCHRONIZED clause-JUSTIFIED clause-REDEFINES clause- RENAMES clause- SIGN clause- VERBS-CONDITIONAL and SEQUENCE CONTROL VERBS. Unit-III Table Handling- Sequential files-sorting and merging of files-EXAMINE verbs-INSPECT verb-STRING and UNSTRING verb- Direct access files- Report. Unit-IV Introduction to DB2- Data types-Literals-Scalar operators and functions- assignment and comparison - DDL statement-DML statement: Simple queries, sub queries correlated queries; join queries, quantified comparison-Catalog: Introduction, Quantifying catalog, Updating catalog, Aliases and Synonyms, labels- Views- Security and Authorization –Integrity – Embedded SQL-Transaction processing –Lock and Dead Lock – Dynamic SQL. Unit–V Introduction to CICS-House Keeping: HANDLE CONDITION, IGNORE CONDITION, PUSH & POP, Alternates to HANDLE CONDITION, SERVICE RECORD, ADDRESS, ASSIGN, EXEC Interface Block-Program control- File control-Terminal control-BMS-Transient Data Control- Systems security –Recovery and Restart - Test and Debugging – Inter communication.

REFERENCES:

1. Kip R. Irvine, “COBOL for the IBM Personal Computers, Prentice Hall, 1988. 2. Craig S. Mullins, “Developers Guide DB2 , Tech Media Publications, Third Edition, 1997. 3. Yukihisa Kageyama, “CICI Hand Book , Tata McGraw H ill, 1997. 4. C. J. Date, Colin J. White, “A Guide to DB2 , Addis ion Wesley Publication, Fourth Edition,

1993. 5. Alexis Leon, Gibu Thomas, “IBM Mainframe and Solutions, Comdex

Publishing Company, 1997. 6. Stren, Stren, “Structured COBOL Programming . 7. Mainframe Handbook 8. M. K. Roy, “COBOL Programming .


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SOFTWARE RELIABILITY


Subject Description: This course provides the insight in to the reliability factors of the Software. Goals: To enable the students to learn about the principle and concepts of Software reliability. Objectives: On successful completion of the course the students must have

understood the concepts of Software reliability to analyze the quality standards

Contents:

Unit - I Software Reliability Definitions - software disasters - Errors - faults - failures - different views of software reliability – software requirements specification - Causes of unreliability in software - Dependable systems: reliable, safe, secure, maintainable, and available - Software maintenance.

Unit - II The phases of a Software Project - Monitoring the development process – The software life cycle models - software engineering - Structured Analysis and structured Design - Fault tolerance - Inspection - Software cost and schedule. Unit - III Software quality modeling - Diverse approaches and sources of information - Fault avoidance, removal and tolerance - Process maturity levels (CMM) - Software quality assurance (SQA) - Monitoring the quality of software - Total quality management (TQA) - Measuring Software Reliability - The statistical approach - Software reliability metrics.

Unit - IV Data Trends - Complete prediction Systems - overview of some software reliability models - The recalibration of the models - Analysis of model accuracy - Reliability growth models and trend analysis - Software Costs Models - Super models. Unit -V Testing and maintaining more reliable software –logical testing – functional testing – algorithm testing – regression testing - fault tree analysis – failure mode effects and critical analysis – reusability - case studies.

REFERENCES: 1. J.D. Musa, A. Iannino and K.Okumoto, Software Reliability, Measurement, Prediction,

Application, McGraw Hill, 1990. 2. J.D. Musa, Software Reliability Engineering, McGraw Hill, 1998. 3. Michael R. Lyer, Handbook of Software Reliability Engineering, McGraw Hill, 1995.

4. Xie, M., Software Reliability Modelling, World Scientific, London, 1991.


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PARALLEL PROCESSING Subject Code: 16CS1E10 Number of credits: 4 Subject Description: This course presents an Introduction to parallel processing, Memory and input/output system, Pipeline computers, Array Processors, Multiprocessor architecture. Goals: To enable the student to familiar definition and functions of parallel processing, Interrupt Mechanism and special hardware, principles of linear pipelining. Objectives: On successful completion of the course the student should have:

Understood concepts and principles of parallel processing, Multiprocessor architecture.

Contents: Unit - I Introduction to parallel processing – definition an d functions of parallel processing – uni-processor and parallel processing systems – parallel computers – pipeline computers – array processor – multiprocessor systems – performance of parallel computers – application of parallel processor. Unit - II Memory and input/output system – memory system for parallel processor computers – hierarchical memory structures – virtual memory system – paged system – segmented system with paged segments – memory management policies – fixed partitioning and variable partitioning – cache memories and management – characteristics of cache memories – cache memory organization – input/output subsystem – characteristics of I/O subsystem – Interrupt Mechanism and special hardware – I/O processor and channel architecture. Unit - III Pipeline computers – principles of linear pipelining – pipelined structures of a typical central processing unit – classification of pipeline processors – interleaved memory organization – S access memory organization – C access memory organization – C & S access memory organization – Static & dynamic pipelining – principles of designing static pipeline processors – Instruction prefetch and branch handling – data buffering and busing structures – Internal forwarding and register tagging – vector processing – requirements and characteristics of pipelined vector processing methods. Unit - IV Array Processors – Single Instruction stream – Multiple data stream – SIMD processors – Types of SIMD computer organization – Array process or organization and associative processors – Array processor computer organization – SIMD interconnection networks – Static and Dynamic networks – Linear array, mesh, ring, star, tree, systolic, completely connected, chordalring and cube networks – Parallel algorithms for array processors – SIMD matrix multiplication – Parallel sorting on array processors. Unit - V Multiprocessor architecture – Functional structures of a multiprocessor system loosely and tightly coupled multiprocessor – Processor characteristics of multiprocessing – Inter processor communication mechanism – Instruction set – Interconnection networks – Time shared or common bus – cross bar switch and multi p ort memories and multistage networks for multiprocessor – Parallel memory organization – Interleaved memory configurations – classification of multiprocessor operating system. REFERENCES:

1. Kai Hwang, Faye A.Briggs, “Computer Architecture and Parallel Processing , Prentice Hall of India, 1985.


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OPERATION RESEARCH


Subject Description: This course provides a formal quantitative approach to problem solving Goals: To enable the students to learn about the techniques applied to business decision-making Objectives: On successful completion of the course the students must have understood

understood the concepts of quantitative tools and techniques applied to business decision-making

a formal quantitative approach to problem solving Contents:

Unit - I Introduction to Operations Research: Basics definition, scope, objectives, phases, models and limitations of Operations Research. Linear Programming Problem – Formulation of LPP, Graphical solution of LPP. Simplex Method, artificial variables, simplex Gauss-Jordan reduction process in simplex methods, Big-M method, two-phase method, degeneracy and unbound solutions. Unit - II Transportation Problem. Formulation, solution, unbalanced Transportation problem. Finding basic feasible solutions – Northwest corner rule, least cost method and Vogel‟s approximation method. Optimality test: the stepping stone method and MODI method, Minimization and Maximization problem Unit - III Dual Problem : Relation between primal and dual problems, Dual simplex method, Sensitivity analysis Transportation algorithms –Assignment problem –Hungarian Method (Minimization and Maximization), Branch & Bound technique. Unit - IV Shortest route – minimal spanning tree - maximum flow models – project network- CPM and PERT network-critical path scheduling. Unit - V Games Theory. Competitive games, rectangular game, saddle point, minimum (maximum) method of optimal strategies, value of the game. Solution of games with saddle points, dominance principle. Rectangular games without saddle point – mixed strategy for 2 X 2 games. REFERENCES:

1. Handy A Taha, Operations Research – An Introduction, Pearson Education 2. P. Sankara Iyer, ”Operations Research”, Tata McGraw-Hill, 2008. 3. A.M. Natarajan, P. Balasubramani, A. Tamilarasi, “Operations Research”, Pearson

Education 4. J K Sharma., “Operations Research Theory & Applications , 3e”, Macmillan India Ltd, 2007.

5. P. K. Gupta and D. S. Hira, “Operations Research”, S. Chand & co., 2007. 6. J K Sharma., “Operations Research, Problems and Solutions, 3e”, Macmillan India Ltd. 7. N.V.S. Raju, “Operations Research”, HI-TECH, 2002.


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GREEN COMPUTING


Objective To provide graduate students with an understanding of the role of ICTs and impact on the global carbon footprint.

To estimate the carbon footprint of the ICT operations of an organization and access ways to reduce the carbon footprint by

changes to policies for procurement of ICT, changes.

To make ICT operations and revising business processes.

UNIT I

Fundamentals of Green IT : 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 - Approaches to green

computing - Middleware Support - Compiler Optimization - Product longevity - Software

induced energy consumption - its measurement and rating.

UNIT II

Green Assets and Modeling : 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 : 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 and Green Mobile : Socio-cultural aspects of Green IT – Green Enterprise

Transformation Roadmap – Green Compliance: Protocols, Standards, and Audits – Emergent

Carbon Issues: Technologies and Future - Green mobile - optimizing for minimizing battery

consumption - Web, Temporal and Spatial Data Mining Materials recycling.

UNIT V

Case Studies : 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.

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. REFERENCES: 1. Alin Gales, Michael Schaefer, Mike Ebbers, “Green Data Center:

steps for the Journey”, Shoff/IBM rebook, 2011. 2. John Lamb, “The Greening of IT”, Pearson

Education, 2009.

3. Green Computing and Green IT Best Practices on Regulations and Industry Initiatives,

Virtualization, Power Management, Materials Recycling and Telecommuting by Jason Harris,

Emereo Publishing

4. Jason Harris, “Green Computing and Green IT- Best Practices on regulations & industry”,

Lulu.com, 2008.

5. Wu Chun Feng (editor), “Green computing: Large Scale energy efficiency”, CRC Press,

2012.


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MOBILE COMPUTING

Subject Code: 16CS2E03 Number of Credits: 4

Subject Description: This course presents an introduction to mobile communications, Digital cellular system, Mobile switching systems, and Network management systems Goals: To enable the student learn Digital cellular system. Objectives:


Understood the generation of mobile communication.

Contents: Unit - I Introduction: Introduction to mobile communications – generation of mobile communication FM, TDMA, CDMA – basic cellular architecture.

Unit - II Digital cellular system infrastructure: global system for mobile communication (GSM) – GSM architecture – principles of synchronous digital hierarchy – principles of Pleisosynchronous digital hierarchy – principles of fiber optics communications.

Unit - III Mobile switching systems: Mobile service switching centre (MSC) – inter working functions (IWF) – home location register (HLR) and Vister Location register (VLR) – Gateway MSC – Signaling transfer point (STP)

Unit - IV Base station sub systems: Base station controller (BSC) – base transceiver station (BTS) – transcoder rate adaptation unit (TRAU) – open system interconnection – frequency management.

Unit - V Network management systems: Operating sub systems – network operation, maintenance and administration – subscription management and charging – mobile equipment management.

REFERENCES: 1. J. Schiller, Mobile Communications, Addison Wesley, 2000.

2. William C.Y.Lee, Mobile Cellular telecommunication, Mc Graw Hill, Int. Edition.

3. William C.Y.Lee, Mobile Communication Engineering, Mc Graw Hill, Inter. Edition.

4. Rajan Kurupillai and others, Wireless PCS, Mc Graw Hill, Inter. Edition

5. Johan Powers, Fiber optics systems, Mc Graw Hill Inter. Edition

6. William Stallings, Wireless Communications and networks, Pearson education

7. Joachim Tisal, GSM radio telephony, John Wiley.


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IMAGE PROCESSING


Subject Description: This course presents the introduction image processing fundamentals,

segmentation, image enhancements and image perception. Goals: To enable the student to be familiar with image data compression, image recognition, image

filtering and segmentation Objectives: On successful completion of the course the student should have understood image

processing fundamentals, segmentation, image enhancements and image perception, filtering and

compression.

Contents:

UNIT- I

Introduction: Digital image processing - Fundamental steps in digital image processing -

components of image processing system. Digital Image Fundamentals: A simple image

formation model - image sampling and quantization - basic relationships between pixels.

UNIT- II

Image enhancement in the spatial domain : Basic gray-level transformation - histogram

processing, enhancement using arithmetic and logic operators - basic spatial filtering -

smoothing and sharpening spatial filters - combining the spatial enhancement.

UNIT- III

Image restoration: A model of the image degradation/restoration process - noise models -

restoration in the presence of noise–only spatial filtering - Weiner filtering - constrained least

squares filtering - geometric transforms; Introduction to the Fourier transform and the

frequency domain - estimating the degradation function.

UNIT- IV

Color Image Processing: Color fundamentals - color models - pseudo color image processing -

basics of full – color image processing - color transforms - smoothing and sharpening - color

segmentation. Image Compression: Fundamentals - image compression models - error-free

compression - lossy predictive coding - image compression standards.

UNIT- V

Morphological Image Processing: Preliminaries - dilation, erosion, open and closing, hit or

miss transformation, basic morphologic algorithms. Image Segmentation: Detection of

discontinuous - edge linking and boundary detection – thresholding - region–based

segmentation. Object Recognition : Patterns and patterns classes - recognition based on

decision – theoretic methods – matching - optimum statistical classifiers - neural networks -

structural methods – matching shape numbers - string matching.

REFERENCES: 1.

Rafeal C.Gonzalez, Richard E.Woods, Digital Image Processing, Second Edition, Pearson

Education/PHI.

2.

Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis, and Machine Vision,

Second Edition, Thomson Learning.

3.

Alasdair McAndrew, Introduction to Digital Image Processing with Matlab, Thomson Course

Technology


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4.

Adrian Low, Computer Vision and Image Processing, Second Edition, B.S.Publications

5.

Rafeal C.Gonzalez, Richard E.Woods, Steven L. Eddins, Digital Image Processing using Matlab,

Pearson Education.

WEB SERVICES


Subject Description: This course presents an Overview of Distributed Computing, XML, web

services Goals: To enable the student to be familiar with distributed services, XML and web services Objectives: On successful completion of the course the student should have:

Understood the concepts of web services Contents:

Unit - I Overview of Distributed Computing. Introduction to web services – Industry standards, Technologies and concepts underlying web services – their support to web services. Applications that consume web services.

Unit - II XML – its choice for web services – network protocols to back end databases- technologies – SOAP, WSDL – exchange of information between applications in distributed environment – locating remote web services – its access and usage. UDDI specification – an introduction.

Unit - III A brief outline of web services – conversation – static and interactive aspects of system interface and its implementation, work flow – orchestration and refinement, transactions, security issues – the common attacks – security attacks facilitated within web services quality of services – Architecting of systems to meet users requirement with respect to latency, performance, reliability, QOS metrics, Mobile and wireless services – energy consumption, network bandwidth utilization, portals and services management.

Unit - IV Building real world enterprise applications using web services – sample source codes to develop web services – steps necessary to build and deploy web services and client applications to meet customer s requirement – Easier development, customization, maintenance, transactional requirements, seamless porting to multiple devices and platforms. Unit - V Deployment of Web services and applications onto Tomcat application server and axis SOAP server (both are free wares) – Web services platform as a set of enabling technologies for XML based distributed computing. REFERENCES:

1. Sandeep Chatterjee, James Webber, “Developing Enterprise Web Services : An Architects Guide , Prentice Hall, Nov 2003.


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ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS


Subject Description: This course presents the problem solving and AI, search methods and expert

systems. Goals: Enable the student to be familiar with theorems and algorithms. Objectives: On successful completion of the course the student should have:

Understood the problem states and AI, state space methods, problem reduction search methods, predicate calculus, and knowledge engineering in expert systems.

Contents: Unit- I Problem solving and AI – Puzzles and Games – Problem States and operators – Heuristic programming – state space representations – state descriptions – graph notations – non-deterministic programs

Unit - II State space search methods – breadth first and dept h first search – heuristic – admissibility – optimality of algorithms – performance measures – problem reduction representations – AND/OR graphs and higher level state space

Unit - III Problem reduction search methods – cost of solution trees – ordered search – alpha beta and minimum procedure – theorem proving in predicate calculus – syntax, semantics, Herbrand universe: variables, qualifiers, unification, resolvents

Unit - IV Predicate calculus in problem solving – answer extraction process – resolution – Automatic program writing – predicate calculus – proof finding methods

Unit - V Expert systems: Expert systems and conventional programs – expert system organization – Knowledge engineering: knowledge representation techniques – knowledge acquisition – acquiring knowledge from experts – automating knowledge acquisition –Building an expert system – difficulties in developing an expert system

REFERENCES: 1. E Charnail, CK Reiesbeck and D V Medermett, “Artificial Intelligence

Programming”, Lawrence Erlbaum Associates, N J, 198 0 2. N J Nilson, “Principles of Artificial Intelligence” , Tiega Press, Polo Alto, 1980 3. Elain Rich and Kevin Knight, “Artificial Intelligence”, McGraw Hill, 1991 4. Donald A Waterman, “A Guide to Expert Systems”, Tech knowledge series in

knowledge engineering, 1986


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TCP/IP


Subject Description: This course presents the introduction of TCP/IP architecture. Goals: To enable the student to learn routing mechanisms and applications. Objectives:


Understood OSI layers, features and its applications.

Contents: Unit - I Introduction: TCP /IP Internet – Internet Services – Internet Protocols and Standardization – Application level interconnection-Network Level Interconnection –Internet Architecture – Interconnection through IP Routers – TCP /IP Intern et Address Concepts.

Unit - II Technical Features: Mapping Internet addresses to Physical addresses – RARP-Connectionless Datagram Delivery – Routing IP Datagrams – Error and Control Messages (ICMP)

Unit - III Subnet and Supernet address Extensions-User Datagram Protocol (UDP) – Internet Multicasting – The Domain Name System (DNS)

Unit - IV Routing: Introduction to Routing and the Origin of Routing Tables – Core Routers – Peer Backbones – Gateway-to-Gateway Protocol – GGP Messages Formats – Link State Routing and Protocols-Exterior gateway protocol.

Unit - V Applications: Remote Login (Telnet) – File Transfer and Access (FTP, NFS) –Electronic Mail (SMTP) – Internet Management (SNMP).

REFERENCES:

1. Douglas E. Comer, “Internetworking with TCP /IP”, P rentice-Hall. 2. W. Richard Stevens, “TCP/IP Illustrated”, Addison W esley. 3. Pete Loshin, “TCP/IP Clearly Explained”, Morgan Kau fmann.


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EMBEDDED SYSTEMS


Subject Description: This course presents the Hardware Fundamentals, Software Architecture Interrupts, RTOS Operating System Services, Embedded software life cycle and tools. Goals: To enable the student to learn fundamentals, and concepts of operating system. Objectives:


Understood: Hardware fundamentals, Software Architecture, Interrupts, Embedded software lifecycle and tools.

Contents: Unit - I Hardware Fundamentals Hardware Fundamentals: Terminology-Gates-Timing Diagrams-Memory Advanced Hardware Fundamentals: Microprocessors-Microprocessor architecture-Direct Memory Access-Conventions and Schematics-Introduction to embedded systems: An embedded system-Processor in the system-Exemplary embedded systems.

Unit - II Interrupts and Software Architecture Interrupts: Interrupt basics-Interrupt service routines Survey of Software Architectures: Round Robin with interrupts-Function-Queue-Scheduling Architecture-Real Time Operating Systems Architecture Introduction to Real Time Operating Systems: Selecting in RTOS-Tasks and Task States-Tasks and Data-Semaphores and shared data

Unit - III Concepts of RTOS More Operating System Services: Interrupt process communication-Message queues-Mailboxes and pipes-Timer functions-Events-Memory management-interrupt routines in an RTOS environment Basic design using a Real Time Operating System: Principles-encapsulating semaphores and queues-hard real time scheduling considerations-saving memory space and power-introduction to RTL & QNX

Unit - IV Embedded software life cycle and tools Embedded software Lifecycle : Software Algorithm complexity-Software development process life cycle and its models Software development tools: development tools-hosts and target machine-linker/locators for embedded software-getting embedded software into the target machine Debugging techniques: testing on your host machine-instruction set simulators-the asset macro-using laboratory tools

Unit – V Case Study

REFERENCES: 1.

David.E.Simon, “An embedded system primer”, Addison Wesley-2001

2. Raj Kamal, “Embedded Systems architecture, programming and design”, Tata McGraw Hill

Publishing Company Ltd., New Delhi, 2003.


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SOFTWARE QUALITY ASSURANCE


Subject Description: This Course presents the essentials of Software Quality, Plan for SQA, Standards, Tools for SQA. Goals: To enable the students to learn the Concepts and Principles of SQA. Objectives: On successful completion of the course the students should have:

Understood the principles of SQA

Must be able to judge the quality of Softwares.

Unit-I Introduction to Software quality – Software modeling – Scope of the software quality program – Establishing quality goals –

purpose, quality, goals – SQA Planning software of productivity and documentation.

Unit -II

Software quality assurance plan – Purpose and Scope, Software quality assurance management

- Organization – Quality tasks – Responsibilities – Documentation.

Unit –III

Standards, Practices, Conventions and Metrics, Reviews and Audits – Management, Technical

review – Software inspection process – Wa lk through process – Audit process – Test

processes – ISO, cmm compatibility – Problem r eporting and corrective action.

Unit -IV

Tools, Techniques and methodologies, Code control, Media control, Supplier control, Records

collection, Maintenance and retention, Training and risk management.

Unit -V

ISO 9000 model, cmm model, Comparisons, ISO 9000 weaknesses, cmm weaknesses, SPICE

– Software process improvement and capability determination.

REFERENCES: 1.

Mordechai Ben – Meachem and Garry S.Marliss, “Software Quality – Producing Practical,

Consistent Software”, International Thom pson Computer Press, 1997

2.

Watt. S. Humphrey, “Managing Software Process”, Addison – Wesley, 1998.

3.

Philip.B.Crosby, “Quality is Free : The Art of making quality certain”, Mass Market,1992.


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NATURAL LANGUAGE PROCESSING


Subject Description: This course presents the linguistic background, grammars, syntax and semantic analysis of natural language.

Goals: To enable the student to learn concepts in Natural language processing.


Understood the natural language processing.

Contents: Unit - I Introduction to Natural Language Understanding – Linguistic Background – Grammars and Parsing – Features and Augmented Grammars.

Unit - II Grammars for Natural Languages – Towards Efficient Parsing – Ambiguity Resolution Statistical Methods – Semantics and Logical Forms.

Unit - III Linking Syntax and Semantics – Resolution – Strategies for Semantic Interpretation – Scoping and Interpretation of Noun Phrases.

Unit - IV Knowledge Representation and Reasoning – Local Discourse Context and Reference – World Knowledge – Discourse Structure.

Unit – V Conversational Agent – Logic and Natural Language – Model – Theoretic Semantics – Semantics of Set Theoretic Models.

REFERENCES:

1.

James Allen, “Natural Language Understanding , Pear son Education, Second

Edition.


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VIRTUAL REALITY


Subject Description: This course presents an idea on Computer graphics, Generic VR

Systems, Physical Simulation, VR Hardware.

Goals: To enable the student to familiar with to computer graphics, 3D Computer graphics,

and simulation.

Objectives: On successful completion of the course the student should have: Understood

virtual reality in detail.

Contents:

Unit – I

Virtual Reality and Virtual Environments: Introduction – Computer Graphics – Real-time

computer Graphics – Flight Simulation – Virtual Environment – Benefits of Virtual Reality –

Historical Development of VR: Scientific Landmarks.

Unit - II

3D Computer Graphics: Virtual world Space – Positioning the Virtual Observer – The

Perspective Projection – Human Vision – Stereo Perspective Projection – 3D Clipping – Color

Theory – Simple 3D Modeling – illumination, reflection Models- Shading Algorithms –

Radiosity – Hidden surface removal – realism- stereographic Images Geometric Modeling: 3D

Space Curves – 3D boundary representation – other modeling strategies – Geometrical

Transformations: Frames of reference – Modeling – Instances – Picking, Flying, scaling –

Collision detection.

Unit - III

Generic VR System: Virtual Environment – computer Environment – VR technology – Models

of Interaction – VR Systems – Animating the Virtual Environment: The Dynamics of numbers

– animation of objects – Shape and object i n between – Free-form deformation – Particle

Systems

Unit - IV

Physical Simulation: Objects Falling in a gravitational field – Rotating wheels – Elastic

Collisions – Projectiles – Simple Pendulums – Springs – Flight dynamics of an aircraft. Human

Factors: The eye – the ear – The Somatic senses – Equilibrium.

Unit - V

VR Hardware: Sensor Hardware – Head-Coupled display s – Acoustic Hardware – Integrated

VR Systems – VR Software: Modeling Virtual World – Physical Simulation – VR Tool Kids –

VR Applications: Engineering – Entertainment – Science – training – The Future: Virtual

Environments – Modes of Interaction.

REFERENCES: 1.

John Vince, “Virtual Reality Systems”, Pearson Education Asia, 2001


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MACHINE LEARNING TECHNIQUES


Subject Description: This course presents the foundations of learning, linear models, distance based models, tree and rule based model and reinforcement learning.

Goals: To enable the student to learn techniques in machine learning.


Understood the techniques in machine learning and apply machine learning techniques to any domain of interest.

Contents:

Unit - I

FOUNDATIONS OF LEARNING : Components of learning – learning models – geometric

models – probabilistic models – logic models – grouping and grading – learning versus design

– types of learning – supervised – unsupervised – reinforcement – theory of learning –

feasibility of learning – error and noise – training versus testing – theory of generalization –

generalization bound –bias and variance – learning curve

Unit - II

LINEAR MODELS : Linear classification – univariate linear regression – multivariate linear

regression – regularized regression – Logistic regression – perceptrons – multilayer neural

networks – learning neural networks structures – support vector machines – soft margin SVM

– generalization and over fitting – regularization – validation

Unit - III

DISTANCE-BASED MODELS : Nearest neighbor models – K-means – clustering around

medoids – silhouttes – hierarchical clustering – k- d trees – locality sensitive hashing – non -

parametric regression – ensemble learning – bagging and random forests – boosting – meta

learning

Unit -IV

TREE AND RULE MODELS : Decision trees – learning decision trees – ranking and

probability estimation trees – Regression trees – clustering trees – learning ordered rule lists –

learning unordered rule lists – descriptive rule learning – association rule mining – first -order

rule learning

Unit - V

REINFORCEMENT LEARNING : Passive reinforcement learning – direct utility estimation –

adaptive dynamic programming – temporal - difference learning – active reinforcement

learning – exploration – learning an action utility function – Generalization in reinforcement

learning – policy search – applications in game playing – applications in robot control

REFERENCES:

1. Y. S. Abu - Mostafa, M. Magdon-Ismail, and H.-T. Lin, “Learning from Data”, AMLBook

Publishers, 2012.


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2. P. Flach, “Machine Learning: The art and science of algorithms that make sense of data”,

Cambridge University Press, 2012.

3. K. P. Murphy, “Machine Learning: A probabilistic perspective”, MIT Press, 2012.

4. C. M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2007.

5. D. Barber, “Bayesian Reasoning and Machine Learning”, Cambridge University Press,2012.

HUMAN COMPUTER INTERACTION


Subject Description: This course presents the design process of HCI, Cognitive models,

interaction styles and design issues in HCI. Goals: To enable the student to learn concepts in Human computer interaction.


Understood the design processes and issues in HCI

Able to discover models which can be used for designing systems.

Contents:

Unit - I

Introduction, The human, The computer, The interaction, Paradigms, Usability of Interactive

Systems, Guidelines, Principles, and Theories.

Unit - II

Design Process - Interaction design basics, HCI in the software process, Design rules,

Implementation support, Evaluation techniques, Universal design, User support.

Unit - III

Cognitive models, Socio-organizational issues and stakeholder requirements, Communication

and collaboration models, Task analysis, Dialogue notations and design, Models of the system,

Modelling rich interaction

Unit - IV

Interaction Styles- Direct Manipulation and Virtual Environments, Menu Selection, Form

Filling and Dialog Boxes, Command and Natural Languages, Interaction Devices,

Collaboration and Social Media Participation

Unit - V

Design Issues- Quality of Service, Balancing Function and Fashion, User Documentation and

Online Help, Information Search, Information Visualization , Information Search and

visualization - Introduction, Search in Textual Documents and Database Querying, Multimedia

Document Searches. Hypertext, Multimedia and the world wide web, Introduction,

Understanding hypertext, Web technology and issues, Static web content, dynamic web

content.


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REFERENCES: 1.

Human Computer Interaction, Alan Dix, Janet Finlay, Gregory Abowd and Russel Beale, Prentice

Hall Publication

2.

Designing the User Interface, Ben Shneiderman, 4th Edition, Pearson Education, 2008, ISBN 81-

7808-262-4

3.

Human Computer Interaction, Dan R. Olsen, Cengage Learning, India Edition, ISBN No.978-81-

315-1137-4

4.

The Essential Guide to User Interface Design, Second Edition, An Introduction to GUI Design

Principles and Techniques, Wilbert O. Galitz, Wiley India (P) Ltd., ISBN : 81- 265-0280-0

5.

The Essential of Interaction Design, Alan Copper, Robert Reimann, David Cronin, Wiley India (P)

Ltd., ISBN : 978-81-265-1305-

DATA COMPRESSION


Subject Description: This course presents a brief introduction to compression schemes and

modulation Goals: To enable the student to familiar with Information and Coding and Compression of Still Images.


Understood data compression concepts and principles

Contents:

Unit - I Information and Coding: Information and Entropy – Noiseless and Memoryless Coding Shannon – Fano Coding: Shannon Coding – Shannon-Fano Coding.

Unit - II Huffman Coding – Arithmetic Coding - Dictionary Techniques - Sampling and Quantization

Unit - III Predictive Coding: Delta Modulation – Differential Pulse Code Modulation. Transform Coding: Defining a Transform – Interpretation of Transforms – Karhenun-Loeve Transform – Hadamard Transform – Discrete Wavelet Transform. Subband Coding: Down sampling and Up sampling – Bit Allocation

Unit - IV JPEG – The Baseline System – Progressive DCT-base d Mode of Operation – Hierarchical Mode


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of Operation – Sequential Losses Mode of Operation. Video Image Compression: MPEG – MPEG-1, MPEG-2, MPEG-4 and MPEG-7

Unit - V Fourier analysis: Fourier series – The Fourier Transform – The Discrete Fourier Transform - The Sampling Theorem. Wavelets: Wavelet Transforms – Multiresolution Analysis

REFERENCES:

1. Adam Drozdek, “Elements of Data Compression , Vikas Publishing House, 2002. 2. Mark Nelson, Jean-Loup Gailly, “The Data Compression Book , BPB Publication,

Second Edition, 1996.

GENETIC ALGORITHMS


Subject Description: This course presents an introduction to genetic algorithms and its applications

Goals: To enable the student to familiar with the concepts of genetic algorithms Objectives:

On successful completion of the course the student should have: Understood the concepts and applications of genetic algorithms

Contents: Unit - I Introduction: Genetic algorithms (GA) – Traditional optimization and search methods – GA Vs Traditional methods – Simple GA- schemata – learning the Lingo- GA mathematical foundation: Schema processing – Two armed and K – armed bandit problem – building block hypothesis – minimal deceptive problem. Data structure – GA operations – mapping objective functions to fitness values. Fitness scaling – coding – multi parameter representation Discretization – constrains.

Unit - II Applications of GA: The Rise of GA – Bagley and Adaptive Game playing program, Tosenberg and Biological cell simulation – pattern recognition – metalevel GAs – Hollstien and Function optimization – Real genes – Box and Evolutionary operations – Evolutionary optimization techniques, programming. Function optimization – improvements in basic techniques – Current applications – Pipeline system s – Structural optimization – medical registration

Unit - III Dominance – Diploidy and Abeyance and reordering operators- other micro operators: Segregation, Translocation and multiple chromosome structure – Duplication and Deletion. Sexual determination and Differentiation – Niche an d speciation. Multi objective optimization – Knowledge based techniques – GA and Parallel Processors.

Unit - IV Genetic based Machine: Classifier system – Rule and Message system – The Bucket Brigade GA – Implementation issues.


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Unit - V Genetic Based Machine Learning (GBML) – Development of CS-1-Smith s Poker Player – LS – 1 Performance – GBML efforts – ANIMAT classifier system pipeline operation classifier system.

REFERENCES:

1. D.E. Goldberg, “Genetic Algorithms, Optimization, a nd Machine Learning”, Addison Wesley 2000.

NEURAL NETWORKS AND FUZZY SYSTEMS


Subject Description: This course presents the fundamentals of neural networks and fuzzy systems Goals: To enable the student to familiar with to Fuzzy Set Theory, Fuzzy Systems Adaptive Resonance Theory and Back Propagation Networks Objectives:


Understood concepts and principles of fuzzy and neural networks.

Contents: Unit - I Fundamentals of Neural Networks : basic Concepts of Neural Networks – Human Brain – Model of an Artificial Neuron – Neural Network Architectures – Characteristics of Neural Networks – Learning Methods – Taxonomy of Neural Network Architectures – History of Neural Network Research – Easy Neural Network Architectures – Some Application Domains.

Unit - II Back Propagation Networks : Architecture of a Back Propagation Network – Back Propagation Learning – Illustration – Applications – Effects of Tuning Parameters of the Back Propagation Neural Network – Selection of Various Parameters in BPN – Variations of Standard Back Propagation Algorithm.

Unit - III Adaptive Resonance Theory: Introduction – ART1 – AR T2 – Applications.

Unit - IV Fuzzy Set Theory: Fuzzy versus Crisp – Crisp Sets – Fuzzy Sets – Crisp Relations – Fuzzy Relations.

Unit - V Fuzzy Systems: Crisp Logic – Predicate Logic – Fuzz y Logic – Fuzzy Rule Based System – Defuzzification Methods.


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REFERENCES:

1. S. Rajasekaran, G. A. Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic

Algorithms Synthesis and Applications, Prentice Hall of India, 2003. 2. James A. Freeman, David M. Skapura, “Neural Networks – Algorithms, Applications and

Programming Techniques, Pearson Education. 3. Fredric M. Ham, Ivica Kostunica, “Principles of Neuro Computing for Science of

Engineering , Tata McGraw Hill. 4. Simon Haykin, “Neural Networks – A Comprehensive Foundation , Prentice Hall of

India.

M.Sc. Computer Science (UD)- 2016-17 onwards Annexure No.76B

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SPEECH PROCESSING


Subject Description: This course presents an idea on fundamentals of speech recognition, pattern comparison techniques and processing Goals: To enable the student to familiar with to overview of signals and systems, Fundamentals of speech recognition, Pattern-comparison techniques. Objectives:


Understood concepts and principles in speech recognition.

Contents: Unit - I Overview of signals and systems – Review of One dimensional and two dimensional signal processing and discrete Fourier transforms and digital filters- domain models for speech processing.

Unit - II Fundamentals of speech recognition. The speech signal – production, perception and Acoustic-Phonetic characterization. Signal processing and analysis methods of speech recognition. Bank-of-filters-front-end processor-linear predictive coding model for speech recognition-vector quantization-auditory based spectral analysis models.

Unit - III Pattern-comparison techniques. Speech recognition system analysis and implementation issues: Application of source-coding techniques-template training methods-performance analysis and recognition enhancements.

Unit - IV Homomorphic speech processing-Speech Recognition algorithm: Pattern Recognition based and knowledge based – Discrete utterance and continuous speech recognition systems-Principles of speaker recognition-projects.

Unit - V Speech recognition based on connected word models-Large vocabulary continuous speech recognition – Task oriented applications of automatic speech recognition.

REFERENCES:

1. Rabiner & Schaffer, “Digital processing of speech signals”, Prentice Hall. 1980. 2. Lawrence Rabiner, ”Fundamentals of speech recognition”, Prentice Hall. 3. Samuel D.Stearns and Ruth A.David “Signal Processing algorithms”, Prentice Hall, 1988. 4. D.Shanghessuy, “Speech Communication”, Prentice Hal l,1987


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E-COMMERCE


Subject Description: This course presents an idea on fundamentals of E-Commerce. Goals: To enable the student to familiar with network infrastructure, Information Publishing Technology, Search Engines and Directory Services. Objectives:

On successful completion of the course the student should have: Understood concepts and principles in E-Commerce

Contents: Unit - I Introduction to E-Commerce: Benefits-Impacts-Classification and Application of E-Commerce-Business Model-Architectural Frame Work

Unit - II Network Infrastructure: Local Area Network-Ethernet-Wide Area Network-Internet-TCP/IP Reference Model-Domain Name System-Internet Industry structure-Information Distribution and Messaging: FTP Application-Electronic Mail-World Wide Web Server-HTTP-Web Server Implementations

Unit - III Information Publishing Technology: Information publishing-Web Browsers-HTML-CGI-Multimedia Content - Other Multimedia Objects-VRML- Securing the Business on Internet-Why Information on Internet is vulnerable?-Security Policy-Procedures and Practices-Site Security-Protecting the Network-Firewalls-Securing the Web Service

Unit - IV Securing Network Transaction-Electronic Payment Systems: Introduction –Online Payment Systems-Pre-paid Electronic Payment System- Post-paid Electronic Payment System-Requirement Metrics of a Payment System

Unit - V Search Engines and Directory Services: Information Directories –Search Engines –Internet Adverting- Agents in Electronic Commerce: Needs and Types of Agents-Agent Technologies-Agents Standards and Protocols-Agents Applications-Case Study.

REFERENCES: 1. Bharat Bhasker, “Electronic Commerce Framework, Technologies and Applications”,

Tata McGraw Hill Publication, 2003.


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DISTRIBUTED SYSTEMS


Subject Description: This course presents an Introduction to Distributed Systems, Client/Server Network Model and Distributed Databases Goals: To enable the student to familiar with distributed systems and client server computing Objectives:


Understood Distributed Systems in detail

Contents: Unit - I Distributed Systems: Fully distributed processing systems – Networks and Interconnection structures – Designing a Distributed Processing System.

Unit - II Distributed Systems: Pros and Cons of Distributed processing – Distributed databases – the challenge of distributed data – loading factors – managing the distributed resources – division of responsibilities.

Unit - III Design Considerations: Communications line loading – Line loading Calculations – Partitioning and allocation – Data flow systems – dimension analysis – network database design considerations – ration analysis – database decision trees – synchronization of network databases.

Unit - IV Client/Server Network Model: Concept – file server – printer server – an e-mail server. Unit - V Distributed Databases: An overview – Distributed Databases – Principles of Distributed Databases – levels of transparency – Distributed Database Design – The R* Project Technique Problems of Heterogeneous Distributed Databases.

REFERENCES: 1. John A. Sharp, “An Introduction to Distributed and Parallel Processing , Blackwell

Scientific Publications, 1987. 2. Uyless D.Black, “Data Communications & Distributed Networks . 3. Joel M.Crichlow, “Introduction to Distributed & Parallel Computing . 4. Stefans Ceri, Ginseppe Pelagatti, “Distributed Databases Principles and Systems,

McGraw Hill Book Co., New York, 1985.


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Open Source Technologies

Subject Code: 16CS3E10 Number of credits: 4 Subject Description: This course presents the principles of Open Source, platform and various

technologies and their integration. Goals: To enable the student to learn and use the available Open Source Software Technologies. Objectives: On successful completion of the course, the student should have

Understood the principles of open source technologies and the usage of Linux, Apache, PHP and MySQL

Unit I Introduction: Open Source, Free Software, Free Software vs. Open Source software, Public Domain Software, History : BSD, The Free Software Foundation and the GNU Project, Philosophy: Software Freedom, Open Source Development Model, Licenses and Patents, Economics of FOSS - Zero Marginal Cost, Income-generation opportunities, Problems with traditional commercial software, Internationalization. Unit II Open Source Platform and Technologies: The Open Source Platform – Operating Systems, Windowing Systems and Desktops, GIMP, Technologies Underlying Open source Development. Unit III Linux Application: Accessing and Running Applications-Multimedia in Linux : Listening to Audio, Playing video, Using Digital Camera, Recording music / video CDs. Publishing: Open office, Working with Graphics, Printing Documents, Displaying documents with Ghost script and Acrobat, Using Scanners driven by SANE. Unit IV PHP: Installing and Configuring PHP, Building Blocks of PHP, Flow control functions in PHP, Working with functions, arrays, objects and forms. Unit V PHP and MySQL Integration: Understanding the Database Design Process, Learning Basic SQL commands, Using Transactions and Stored Procedures in MySQL, Interacting with MySQL using PHP. REFERENCES:

1. Christopher Negus, Red Hat Linux Bible, Wiley Publishing, ISBN: 0-7645-4333-4. 2. Fadi P. Deek, James A. M. McHugh, Open Source Technology and Policy, Cambridge

University Press, 2008. 3. Julie C Melonie, PHP, MySQL and Apache, Pearson Education, ISBN: 81-297-0443-9.

4. http://en.wikibooks.org/wiki/Open_Source.