SCHEME OF STUDIES BCA, MCA (Integrated) Degree Programme

3rd

Year

Semester-V

THEORY

SI. No.

Course No Course Name Periods

Cr L-T-P

1 CA-1302 Software Engineering Principles 3-1-0 4

2 Elective – I 3-0-0 3

3 CA-1303 Programming Using C# 3-1-0 4

4 CA-1304 Artificial Intelligence 3-1-0 4

5 CA-1305 Introduction to E-commerce 3-0-0 3

PRACTICAL / DRAWING / DESIGN

SI. No.

Course No. Course Name Periods

Cr L-T-P

2 CA-1353 Programming Using C# Lab 0-0-2 1

CA-1354 Artificial Intelligence Lab 0-0-2 1

3 CA-1381 Minor project -1 0-0-10 5

3 PD-392 Problem Solving Skills 0-0-2 1

4 PD-391 Co-Curricular Activities 1*

TOTAL CONTACT HOURS TOTAL CREDITS

15-3-14(32) 26

FINAL EVALUATION IN GRADES (L-T-P-Cr) – Lectures-Tutorials-Practicals-Credits, CW – Class Work MSE – Mid-Semester Exam, ESE – End-Semester Exam *One credit to be earned in Semester-VI through Co-curricular Activities outside contact hours..

Note: Elective 1

st is be chosen from Department Elective List No.1,

SCHEME OF STUDIES Integrated BCA-MCA Degree Programme

3rd

Year ( who opt to leave with BCA degree)

Semester-VI ( for BCA degree)

THEORY

SI. No.

Course No Course Name Periods

Cr L-T-P

1 Elective - 2 3-0-0 3

2 CA-1306 Software Project Management 3-1-0 4

3 CA-1382 Project Training (Major Project)** 0-0-30 15

4 PD-391 Co-Curricular Activities 1*

TOTAL CONTACT HOURS TOTAL CREDITS

6-1-30(37) 22+1*

FINAL EVALUATION IN GRADES (L-T-P-Cr) – Lectures-Tutorials-Practicals-Credits CW – Class Work MSE – Mid-Semester Exam ESE – End-Semester Exam * One credit to be earned in Semester-VI through Co-curricular Activities outside

contact hours. However, a student is to register for this course in both the semesters of third year. ** Contact hours for courses CA-1381 and CA-1382 are nominal.

3rd Yr Year ( who opt to continue for MCA Degree) Semester-VI (for MCA Degree)

THEORY

SI. No.

Course No Course Name Periods

Cr L-T-P

1 Elective -2 3-0-0 3

2 CA-1306 Software Project Management 3-1-0 4

3 CA-1307 Neural Network 3-1-0 4

4 CA-1308 Data Mining & Warehousing 3-0-0 3

5 CA-1309 Network security & management 3-1-0 4

6 CA-1310 3 D multimedia & Animation 3-0-0 3

PRACTICAL / DRAWING / DESIGN

SI. No.

Course No.

Course Name Periods

Cr L-T-P

1 CA-1357 Neural network lab 0-0-2 1

CA-1358 Data Mining & warehousing Lab 0-0-2 1

2 CA-1360 3 D Multimedia & Animation Lab 0-0-2 1

3 CA-1371 Research work /Seminar I 0-0-2 1

4 PD-391 Co-Curricular Activities 1*

TOTAL CONTACT HOURS TOTAL CREDITS

18-3-8(29) 25+1*

FINAL EVALUATION IN GRADES (L-T-P-Cr) – Lectures-Tutorials-Practicals-Credits CW – Class Work MSE – Mid-Semester Exam ESE – End-Semester Exam *One credit to be earned in Semester-VIIIthrough Co-curricular Activities outside contact hours

However, a student is to register for this course in both the semesters of forth year.

LIST OF DEPT. ELECTIVES BCA-MCA INTEGRATED

Dept. Electives List 1

1 CA-1323 Advanced Computer Architecture

2 CA-1324 Advanced Database Management System

3 CA-1325 Cryptography & Data Compression

Dept. Electives List 2

1 CA-1326 Expert System

2 CA-1327 Natural language processing

3 CA-1328 Digital image processing

Dept. Electives List 3

1 CA-1421 Compiler Design

2 CA-1422 Soft Computing

3 CA-1423 Bluetooth Technology

Dept. Electives List4

1 CA-1424 Distributed computing

2 CA-1425 Information Storage & Management

3 CA-1426 Human computer Interaction

CA-1427 Android Application development

BCA- MCA INTEGRATED 3RD

YR Syllabus

CA-1302 SOFTWARE ENGINEERING PRINCIPLES L T P Cr 3 1 0 4

OBJECTIVE To provide basic knowledge of properties of software and its development processes, software quality, CASE tools, etc. PRE-REQUISITES Knowledge of computer programming, principles of management

1- Introduction: Introduction to Software Engineering, Definition of Software Engineering, Software

Components, Software Characteristics, Software Crisis, Software Engineering Processes, Similarity and

Differences from Conventional Engineering Processes, Applications, Software Myths. Software

Development Life Cycle Model: Water Fall Model, Prototype Model, Spiral Model, Evolutionary

Development Models, Iterative Enhancement Models.

2- Software Requirement Specifications: Requirement Engineering Process: Elicitation, Analysis,

Documentation, Review and Management of User Needs, Feasibility Study, Information Modeling, Data

Flow Diagrams, Control Flow Model, SRS Document, IEEE Standards for SRS, Data Dictionary.

3- Software Design: Basic Concept of Software Design, Architectural Design, Low Level Design:

Modularization, Design Structure Charts, Flow Charts, Coupling and Cohesion Measures, Design

Strategies: Function Oriented Design, Top-Down and Bottom-Up Design.

4- Coding & Software Testing: Top-Down and Bottom –Up programming, structured programming, Code

Inspection, Compliance with Design and Coding Standards. Testing Objectives, Unit Testing, Integration

Testing, Acceptance Testing, Regression Testing, Top-Down and Bottom-Up Testing Strategies: Test

Drivers and Test Stubs, Structural Testing (White Box Testing), Functional Testing (Black Box Testing),

Alpha and Beta Testing of Products.

5- Software Measurement & Matrices: Halestead’s Software Science, Function Point (FP) Based

Measures, Cyclomatic Complexity Measures: Control Flow Graphs. Estimation of Various Parameters

such as Cost, Efforts, Schedule/Duration, Constructive Cost Models (COCOMO), Resource Allocation

Models, Software Risk Analysis and Management.

6- Quality Assurance: Introduction of Quality, Quality Assurance, Quality Control, Software Quality

Attributes, Software Quality Assurance (SQA): Verification and Validation, SQA Plans, Software Quality

Frameworks, ISO 9000 Models, SEI-CMM Model.

7- Software Maintenance & Project Management: Need for Maintenance, Categories of Maintenance:

Preventive, Corrective and Perfective Maintenance, Cost of Maintenance, Software Re-Engineering,

Reverse Engineering. Software Configuration Management Activities, Change Control Process,

Software Version Control, An Overview of CASE Tools.

TEXT BOOK 1. Pressman Roger S., “Software Engineering – A Practitioner’s Approach”, 6th Edition, McGraw Hill, 2004.

REFERENCE BOOKS 2. Aggarwal KK, Singh, Yogesh, “Software Engineering”, New Age International, 2000. 3. Jalote Pankaj,”An Integrated Approach to Software Engineering”, 3rd edition, Narosa, 2005. 4. Sommerville Ian, Pearson Education, “Software Engineering”, 5th edition, Addison Wesley, 1999. 4. Mall Rajib, “Fundamentals of Software Engineering”, Prentice Hall of India

5. Gustafson David, “Software Engineering”, Tata McGraw Hill, 2002.

CA-1303 PROGRAMMING USING C# L-T-P Cr 3-1-0 4

OBJECTIVE To equip students with C# programming Concepts 1. PHILOSOPHY OF .NET: overview of distributed computing; origin of .NET technology; understanding; .NET

platform; do’s and don’ts of .NET; benefits and limitations of .NET; building blocks of .NET framework; .NET programming languages; role of MSIL and Metadata; .NET types and .NET namespaces.

2. VISUAL STUDIO .NET AND ITS. MAJOR COMPONENTS: understanding CLR; CTS and CLS; developing C# Applications using Visual Studio .Net

3. EVOLUTION OF C#: comparison among C++; Java and C#; benefits of C#; object-oriented programming using C#

4. C# PROGRAMMING: introduction to C#; creating a C# program; types in C#; classes; inheritance and polymorphism; methods; statements and control; arrays and strings; interfaces; abstract and base classes.

5. STATEMENTS AND CONTROL: properties and indexers; delegates and their usefulness; attributes; I/O in C#; exception and error handling in C#; C# and windows application.

6. ADO .NET: comparison of ADO and ADO. NET; introduction to data access with ADO.NET components of ADO.NET; overview of XML; XML and ADO.NET.

7. WEB DEVELOPMENT AND ASP .NET: comparison of ASP and ASP .NET; features of ASP .NET; benefits of ASP .NET; features provided by ASP .NET; web forma and their components; overview of web services.

TEXT BOOK 1. Balaguruswammy, E, “Programming in C#”, Tata McGraw Hill REFERENCE BOOKS 2. Jain, V K, “The Complete Guide to C# Programming”, IDG Books India. 3. Pappas & Murray, “C# Essentials”, Prentice Hall of India 4. Gunnerson Eric, “A programmer’s Introduction to C#”, IDG Books 5. Wakefield, “C# and .NET Web Developers Guide”, IDG Books India.

CA-1304 ARTIFICIAL INTELLIGENCE L T P Cr 3-1 0 4

OBJECTIVE To introduce about artificial intelligence approaches to problem solving, various issues involved and application areas PRE-REQUISITES Knowledge of neural networks, data structures

1. Introduction: Definition of Artificial Intelligence (AI), Evolution of Computing History of AI, data, information

and knowledge; AI problems and techniques – AI programming languages; problem space representation with examples, Applications of Artificial Intelligence.

2. Search strategies: Breadth first search; Depth first search; heuristic search techniques: Hill climbing: Best

first search; A* algorithm; AO* algorithm; Means-ends analysis 3. Production System & knowledge based representation: Production rules, the working memory,

Recognize-act cycle, conflict resolution by Meta rules, Architecture of production system. Semantic net, Frames. 4. Propositional Logic: Proposition, tautologies, Theorem proving, Semantic method of theorem proving,

forward chaining, backward chaining standard theorems, method of substitution, Theorem proving using Wang’s algorithm. 5. Predicate Logic: Alphabet of first order logic(FOL), predicate, well formed formula, clause form, algorithm for

writing sentence into clause form, Unification of predicates, unification algorithm, resolution Robinson’s interface rule, Scene interpretation using predicate logic 6. Reasoning Under Uncertainty: reasoning under uncertainty; non monotonic reasoning; review of

probability; Baye’s probabilistic interferences and Dempster Shafer theory; Heuristic methods; Fuzzy reasoning. 7. Planning & Game Playing: Minimax search procedure; Goal stack planning; non linear planning; hierarchical

planning; representation for planning. TEXT BOOK

Elaine Rich and Kevin Knight, “Artificial Intelligence”, 3rd Edition, Tata McGraw Hill, 1991 REFERENCE BOOKS

1. Nils J Nilson, “Artificial Intelligence”, Harcourt Asia Pvt. Ltd. 2. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall of India, 1998 3. O. W. Patterson, “Introduction to Artificial Intelligence & Expert Systems”, Prentice Hall of India 4. Patrick Henry Winston, “Artificial Intelligence”, 3rd Edition, Addition Wesley, 1992 5. Programming PROLOG, Clockson & Mellish, Narosa Publications

CA-1305 INTRODUCTION TO E-COMMERCE L T P Cr 3 0 0 3

Pre-requites

Knowledge of internet and web development, data mining, computer networks, software

engineering.

1. INTRODUCTION TO E-COMMERCE: Benefits; impact of e-commerce; classification of

e-commerce; application of e-commerce technology; business models; framework of e-

commerce.; business to business; business to customer; customer to customer; advantages

and disadvantages of e-commerce; electronic commerce environment and opportunities: back

ground – the electronic commerce environment – electronic market place technologies.

2. NETWORK INFRASTRUCTURE OF E-COMMERCE: Network infrastructure to e-

commerce & internet; lan; ethernet ( ieee 802.3); wan; internet; tcp/ip reference model;

domain names; internet industry structure; ftp applications; protocols required for

ecommerce; HTTP; CGI 3; firewalls; securing web service; secure payment system

transaction security (SET); cryptology; digital signatures

3. ELECTRONIC PAYMENT SYSTEM: Introduction to electronic cash and electronic

payment schemes – internet monitory payment; different models; framework; prepaid and

post-paid payment model and security requirements – payment and purchase order process –

online electronic cash. search tools: directories; search engines; meta search engines.

4. EDI & E-content : Business Trade Cycle; EDI; EDI Fact, Electronic content.

5. E-BUSSINESS: Business requirements – concepts; payment processing. launching your e

business- marketing an e-business; public relations; consumer communication; news groups

& forums; exchanging links; web rings; e-business back end systems; business record

maintenance; back up procedures and disaster recovery plans.

6. M-COMMERCE: Introduction to mobile commerce; framework; applications; design

methodology and advantages; future trends in m-commerce. Supply chain management in e-

commerce.

7. ADVERTISING & CRM: Internet Advertising; Models of Internet advertising;

sponsoring content; Corporate Website; Weaknesses in Internet advertising; web auctions. E-

retailing; Role of retailing in E-commerce; E-marketing and advertising. CRM in e-

commerce. Case Study: discussion on a corporate web site. e-commerce legal issues and

cyber laws.

TEXT BOOK 1- Chaffey, Dave, “E-business and E-commerce Management”, Pearson Education

REFERENCE BOOKS

1. Kalakota, Ravi, Whinston Andrew B . , “E-Commerce-A Manager’s guide”, Addison

Wesley.

2. David Whetley; E-commerce concepts.

3. M- commerce; Norman Sadeh; Wiley

CA-1306 SOFTWARE PROJECT MANAGEMENT L T P Cr

3 1 0 4

OBJECTIVE To provide the foundation required for becoming a good software project manager by means of planning, evaluation and estimation, risk management, allocation and monitoring of resources, controlling software quality PRE-REQUISITES Knowledge of software engineering and the basic principles of management. 1. INTRODUCTION: Definition of a Software Project (SP), SP vs. other types of projects activities covered by SPM;

categorizing SPs; project as a system; management control, requirement specification; information and control in organization

2. STEPWISE PROJECT PLANNING: Introduction, selecting a project; identifying project scope and objectives; identifying project infrastructure, analyzing project characteristics; identifying project products and activities; estimate efforts each activity; identifying activity risk; allocate resources; review/ publicize plan

3. PROJECT EVALUATION AND ESTIMATION: Cost benefit analysis; cash flow forecasting; cost benefit evaluation techniques; risk evaluation; Selection of an appropriate project report; Choosing technologies, choice of process model, structured methods: rapid application development, water fall, V-process-, spiral- models; Prototyping; delivery; Albrecht function point analysis

4. ACTIVITY PLANNING AND RISK MANAGEMENT: Objectives of activity planning; project schedule; projects and activities; sequencing and scheduling activities, network planning model; representation of lagged activities; adding the time dimension, backward and forward pass; identifying critical path; activity throat, shortening project; precedence networks; Risk Management: Introduction, the nature of risk, managing risk, risk identification, risk analysis, reducing the risks, evaluating risks to the schedule, calculating the z values

5. RESOURCE ALLOCATION AND MONITORING THE CONTROL: Introduction, the nature of resources, identifying resource requirements; scheduling resources creating critical paths; counting the cost; being specific; publishing the resource schedule; cost schedules, the scheduling sequence; Monitoring the control: Introduction, creating the frame work, collecting the data, visualizing progress, cost monitoring, earned value, prioritizing monitoring, getting the project back to target, change control

6. MANAGING CONTRACTS AND PEOPLE: Introduction, types of contract, stages in contract, placement, typical semesters of a contract, contract management, acceptance, Managing people and organizing semesters: Introduction, understanding behavior, organizational behavior: a back ground, selecting the right person for the job, instruction in the best methods, motivation, working in groups, becoming a team, decision making, leadership, organizational structures, conclusion, further exercises

7. SOFTWARE QUALITY: Introduction; the place of software quality in project planning; the importance of software quality; defining software quality, ISO 9126; Practical software quality measures; product versus process quality management; external standards; techniques to help enhance software quality; Study of any software project management software: viz Project 2005 or equivalent

REFERENCE BOOKS 1. Bob Hughes and Mike Cotterell, “Software Project Management”, 2nd Edition, Tata McGraw Hill, 1999 2. Futrell, “Software Quality & Project Management”, Pearson Education, 2002. 3. Jalote Pankaj, Software Project Management, Pearson Education, 2002. 4. Gopalaswamy Ramesh, “Managing Global Software Projects”, Tata McGraw Hill, 2001 5. Pressman Roger S., “Software Engineering – A Practitioner’s Approach”, 5th Edition, McGraw Hill, 2001 6. Walker Royce, “Software Project Management”, Addison Wesley, 1998 7. Maylor, “Project Management”, Third Edition, 2003. 8. Demarco Tom, “Controlling Software Project Management and Measurement”, Prentice Hall, 1982 9. Glib Tom and Susannah Finzi, “Principles of Software Engineering Management”, Addison Wesley, 1998.

CA-1307 NEURAL NETWORKS L T P Cr

3 1 0 4

OBJECTIVE The goal is to relay the theoretical and practical fundamental knowledge of neural networks and studying its analogy to human brain. PRE-REQUISITES Knowledge of mathematics, computer architecture and organization

1. OVERVIEW OF BIOLOGICAL NEURONS: Structure of biological neurons relevant to ANNs. 2. FUNDAMENTAL CONCEPTS OF ARTIFICIAL NEURAL NETWORKS: Models of ANNs; Feed forward and

feedback networks; learning rules: Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff learning rule, correction learning rule, Winner –lake all learning rule, etc.

3. SINGLE LAYER PERCEPTION CLASSIFIER: Classification model, features and decision regions; training and

classification using discrete perceptron, algorithm, single layer continuous perceptron networks for linearly separable classifications.

4. MULTI-LAYER FEED FORWARD NETWORKS: linearly non-separable pattern classification; delta learning rule for multi-perceptron layer; generalized delta learning rule, error back-propagation training; learning factors; examples.

5. SINGLE LAYER FEED BACK NETWORKS: Basic concepts; Hopfield networks; training and examples. 6. ASSOCIATIVE MEMORIES: Linear association, basic concepts of recurrent auto associative memory: retrieval

algorithm, storage algorithm; bi-directional associative memory, architecture, association encoding and decoding, stability.

7. SELF ORGANIZING NETWORKS: Unsupervised learning of clusters, winner-take-all learning, recall mode, Initialization of weights, separability limitations

TEXT BOOK Zurada Jacek M., “Introduction to Artificial Neural Systems”, 5th Edition, India Reprint 2003

REFERENCE BOOKS

. 1. Haykin Simon, “Neural Networks: A Comprehensive Formulation”, Addison Wesley, 1998 2. Kosko, “Neural Networks”, Prentice Hall of India, 1992 3. Bose N. K. and Liang P., “Neural Network Fundamentals”, Tata McGraw Hill, 2002. 4. Sivanandan, “Introduction to Neural Networks with MATLAB 6.0”, Tata McGraw Hill, 2005

CA-1308 DATA MINING AND DATA WAREHOUSING L T P Cr 3 0 0 3

OBJECTIVE This course introduces basic concepts, tasks, methods, and techniques in data mining. The emphasis is on various data mining problems and their solutions. Students will develop an understanding of the data mining process and issues, learn various techniques for data mining, and apply the techniques in solving data mining problems using data mining tools and systems. Students will also be exposed to a sample of data mining applications. PRE-REQUISITES Basic knowledge of data base management system 1. DATA WAREHOUSING: Definition, usage and trends. DBMS vs data warehouse; data marts; metadata;

multidimensional data mode; data cubes; schemas for multidimensional database: stars, snowflakes and fact constellations.

2. DATA WAREHOUSE PROCESS AND ARCHITECTURE: OLTP vs OLAP, ROLAP vs MOLAP; types of OLAP, servers, 3-Tier data warehouse architecture; distributed and virtual data warehouses; data warehouse manager.

3. DATA WAREHOUSE IMPLEMENTATION: Computation of data cubes; modelling OLAP data, OLAP queries manager; data warehouse back end tools; complex aggregation at multiple granularities; tuning and testing of data warehouse.

4. DATA MINING: Definition and task; KDD versus data mining; data mining techniques, tools and applications. 5. DATA MINING QUERY LANGUAGES: Data specification, specifying knowledge; hierarchy specification; pattern

presentation and visualization specification; data mining languages and standardization of data mining. 6. DATA MINING TECHNIQUES: Association rules; clustering techniques; decision tree knowledge discovery

through neural networks and genetic algorithm; rough sets; support vector machines and fuzzy techniques. 7. MINING COMPLEX DATA OBJECTS: Spatial databases, multimedia databases, time series and sequence data;

mining text databases and mining Word Wide Web. REFERENCE BOOKS 1. Anahory Sam and Murray Dennis, “Data Warehousing In the Real World”, Pearson Education, 1997 2. Han Jiawei and Kamber Micheline, “Data Mining - Concepts & Techniques”, Morgan Kaufmann, 2001 3. Berson Alex, “Data Warehousing, Data Mining and OLTP”, Tata McGraw Hill, 1997 4. Pujari Arun K., “Data Mining Techniques”, University Press, Hyderabad, 2001 5. Adriaans Pieter and Zantinge Dolf, “Data Mining”, Pearson Education, 1997 6. Mallach, “Data Warehousing System”, McGraw Hill, 2000 7. W. H. Inman, “Building the Data Warehouse”, John Wiley & Sons, 1996 8. Inman W. H. and Gassey C. L., “Managing the Data Warehouses”, John Wiley & Sons. 9. Mitchell T. M., “Data Mining”, McGraw Hill, 1997

CA-1309 NETWORK SECURITY & MANAGEMENT L T P Cr 3 1 0 4

OBJECTIVE The main objective behind this course is to learn about the various network attacks and preventing attacks. This course is designed to cover Application security, Operating system security, Network security, Web security etc. PRE-REQUISITES Knowledge of data communications and computer networks, computer programming, data structures, mathematics, telecom network. Knowledge of digital signal processing is desirable 1. INTRODUCTION: Codes and ciphers; some classical systems; statistical theory of cipher systems: complexity

theory of crypto systems; stream ciphers, block ciphers. 2. STREAM CIPHERS: Rotor based system; shift register based systems; design considerations for stream ciphers,

crypt-analysis of stream ciphers; combined encryption and encoding; block ciphers: DES and variant, modes of use of DES; public key systems: knapsack systems, RSK, Diffie Hellman exchange; authentication and digital signatures; elliptic curve based systems.

3. SYSTEM IDENTIFICATION AND CLUSTERING: Cryptology of speech signals: narrow band and wide band systems; analogue and digital Systems of speech encryption.

4. SECURITY: HASH FUNCTION – AUTHENTICATION: Protocols; digital signature standards; electronic mail security: PGP (Pretty Good Privacy), MIME; data compression technique; IP security: architecture, authentication leader, encapsulating security; payload: key management; web security: secure socket layer & transport layer security, secure electronics transactions; firewalls design principle; established systems.

5. TELECOMMUNICATION NETWORK ARCHITECTURE: TMN management layers, management information model; management servicing and functions; structure of management information and TMN information model; SNMP v1, SNMP2 & SNMP3, RMON1 & 2; Broadband Network Management (ATM, HFC, DSL); ASN

6. SECURITY IN NETWORKS: Threats in networks, Network security control, Firewalls, Intrusion detection systems, Secure e-mail, Networks and cryptography, Example protocols: PEM, SSL, IPsec, Administrating Security: Security planning, Risk analysis, Organizational security policies, Physical security.

7. LEGAL, PRIVACY, AND ETHICAL ISSUES IN COMPUTER SECURITY: Protecting program and data; information and law; rights of employees and employers; software failures; computer crime, privacy; ethical issues in computer society; case studies of ethics

REFERENCE BOOKS 1. Stallings William, “Cryptography and Network Security”, 4th Edition, Prentice-Hall, Englewood Cliffs, 2006 2. “Cryptography and Network Security: Principal & Practices”, 3rd Edition, Prentice Hall of India, 2002 3. Mani Subramanian, “Network Management Principles & Practices”, Addison Wesley, 1999 4. Burke J. Richerd, “Network Management Concepts and Practice A Hand-on Approach, Pearson Education,

Reprint 2004 5. Kauffman C., Perlman R. and Spenser M., “Network Security”, 2nd Edition, Prentice Hall, 2002. 6. Stallings William, “SNMP”, Addison Wesley, 1999 7. “SNMP: A Guide to Network Management”, McGraw Hill, 2005 8. Wang H.H., “Telecom Network Management”, 3rd Ed., McGraw Hill, 1997 9. Dlack U., “Network Management”, 3rd Edition, McGraw Hill, 1997 10. Menezes Alfred, van Oorschot Paul, and Vanstone Scott, “Handbook of Applied Cryptography”, CRC Press, NY,

2004. 11. Bellovin S. and Chesvick W., “Internet Security and Firewalls”, 2nd Edition, Addison Wesley, 1998. Schneier Bruce, “Applied Cryptography”, Wiley Student Edition, 2nd Edition

CA-1310 3 D MULTIMEDIA AND ANIMATION L T P Cr 3 1 0 4

Unit 1 – Basics of Animation

Introduction to animation; 12 basic principles of animation; Inbetweening; Using Key

Points; Superimposition; Arcs; Head Turns; Eye Movement; Walking; Running; Timing;

Anticipation; Realistic Touches; Exaggerated Action; Special Effects.

Unit 2 – Basic Operations & 3D Modelling

Moving, Rotating and scaling the objects; Edit a Mesh; Sculpting Modelling.

Unit 3 – UV Mapping, Curves and NURBS

Creating a UV Map; Texture Painting; Projection Painting; Metaballs; Curves; Spin and

NURBS.

Unit 4 - Rigging and Animation

Key-framing with the Timeline; Pivot Point: The Center of Rotation; Basic Tracking: Eyes

That Follow; Rigging with Bones; Rigging a Simple Character

Unit 5 - Making Movies

The Compositing Node Editor; Lighting Adjustments; A Practical Example of Compositing;

The Video Sequence Editor; Making Particles; Making Hair; Fluid Dynamics; Smoke; Soft

Body Physics.

Unit 6 - Gaming

Creation of a simple game world and a fully rigged character for a game; controlling

characters and scenes with logic blocks Game Engine Physics; Creating Your Own Droid; Silly Soccer

Game; A Change of Scene.

Unit 7 – Game scripting with Python

Introduction to python; installation and configuration of python; writing programs with python;

Python to create more streamlined, organized, and powerful game logic.

REFERENCE Books

1. Blender for Dummies, Janson Van Gumster. Wiley Pub.

2. Mastering Blender, second edition; Tony mullen.

3. Animating with blender, D. Orland Hess; Focal press.

4. Beginning Blender, Lance Flavell; Apress.

Web Site REFERENCE

1. www.Blenderguru.com

2. www.Blender.org

3. www.python.org

CA-1353 PROGRAMMING USING C# LAB L-T-P Cr 0-1-2 2

LIST OF EXPERIMENTS 1. Write a program in C# illustrating the use of sequence, conditional and iteration construct. 2. Write a program in C# illustrating various operators like logical, arithmetical, relational, etc. 3. Write a program in C# illustrating overloading of various operators. 4. Write a program in C# illustrating use of friend, inline and Static Member functions, default arguments. 5. Write a program in C# illustrating use of destructor and various types of constructor. 6. Write a program in C# illustrating various forms of inheritance. 7. Write a program in C# illustrating use of virtual functions, Virtual base class, delegates. 8. Write a program in C# illustrating file operations. 9. Write a program in C# illustrating simple web applications using ASP.net 10. Write a program in C# illustrating use of Active X Controls.

CA-1354 ARTIFICIAL INTELLIGENCE LAB L T P Cr

0-0-2 1

LIST OF EXPERIMENTS 1. Study of Prolog programming language 2. Write programs to use iterative structures using Prolog (at least 3 programs) 3. Write programs to demonstrate inferencing/ deductive logic using Prolog (at least 3 programs) 4. Write a program to solve 8 queens problem using Prolog. 5. Solve any problem using depth first search using Prolog. 6. Solve any problem using best first search using Prolog. 7. Solve 8-puzzle problem using best first search using Prolog 8. Solve Robot (traversal) problem using means End Analysis using Prolog. 9. Solve traveling salesman problem using Prolog. 10. Write program to exhibit the ability of building an Expert System using Prolog 11. Study the properties and issues of Natural Language Processing 12. Study the grammar mapping issues in language translation from English to Hindi and vice versa REFERENCE BOOKS 1. Clockson & Mellish, “Programming PROLOG”, Narosa Publications, 3rd Edition, 2002. 2. Winston Patrick Henry, “Artificial Intelligence”, 3rd Edition, Addition Wesley, 1992

CA-1381 MINOR PROJECT -1 L-T-P Cr 0-0-8 4

The project involves in-depth study on the topic, design, development, analysis fabrication and/or experimental work – Hardware and/or Software. It is intended to give an opportunity to a student to apply his knowledge to solve real-life problem. The student has to select a project work based on a topic of interest.

CA-1357 NEURAL NETWORK LAB L T P Cr

0 0 2 1

LIST OF EXPERIMENTS

1 Demonstrate functioning of a neuron

2 DEMONSTRATION OF various learning rules

3. Implement a Hopfield neural network

4 Implement back propagation network (BPN)

5. Implement multi-layer perceptron (MLP)

6. Implement k-means clustering

7. Demonstrate unsupervised clustering capability using Self Organizing Maps (SOM)

8. Implement object recognition/image processing

9. Demonstrate prediction ability using neural networks REFERENCE BOOKS

1. Haykin Simon, ―Neural Networks: A Comprehensive Formulation‖, Addison Wesley

CA-1358 DATA MINING & WAREHOUSING LAB L T P Cr

0 0 2 1

LIST OF EXPERIMENTS

1. Schematic implementation of a University Data Warehouse (Virtual Data Warehouse)

2. Experiment to include elements of an ETL tool like data scrubbing and loading

3. Implementation of a popular algorithm like Apriori to find association from any market basket dataset

4. Implementation of an Outlier detection mechanism based on any of the standard methods (distance/density, etc.) and demonstration of outliers detected from a standard dataset

5. Use of Regression techniques in making effective prediction

6. How to design effective classifiers using training and testing data

7. Implementation of a popular clustering algorithm like K-Mean, K Medoid or DBSCAN and determination of resultant clusters of a standard dataset like Iris.

8. Methodology to find Principal Components in a dataset

9. Implementation of Kohonen Self Organising Map and how it categorises data 10. Computation of Decision Trees and Splitting points for a suitable dataset

11. Implementation of a popular fuzzy clustering algorithm like FCM and determination of resultant clusters of a standard dataset like Iris.

12. A simple experiment to highlight the usefulness of sampling in large scale data mining

13. An experiment to highlight the use of Genetic Algorithms in rule mining or clustering

14. An experiment to highlight the use of Rough Sets in Data Mining REFERENCE BOOKS

1. Anahory Sam and Murray Dennis, ―Data Warehousing In the Real World‖, Pearson Education, 1997

2. Han Jiawei and Kamber Micheline, ―Data Mining - Concepts & Techniques‖, Morgan Kaufmann, 2001

3. Berson Alex, ―Data Warehousing, Data Mining and

CA-1360 3 D MULTIMEDIA AND ANIMATION LAB L T P Cr

0 0 2 1

LIST OF EXPERIMENTS

1. Installation of 3D multimedia toll (Blender).

2. Study of IDE of the Blender (Any other tool).

3. Installation & configuration of Python.

4. Basic operation with python.

5. Basic transformation operation of animation like (Rotating, scaling, Moving).

6. Creating a real object by joining the existing 3D objects (like Dog, house, helicopter etc.).

7. Editing in a existing object.

8. Design a 3D logo.

9. Wrapping and painting a Cube.

10. Create material.

11. Modelling 3D object.

12. Texturing 3D object.

13. Sculpting modelling.

14. Rending.

15. Use of game engine.

16. Boning and designing a real character.

17. Animation a object.

18. Writing python script.

Reference Book

1. Blender for Dummies, Janson Van Gumster. Wiley ub.

2. Mastering Blender, second edition; Tony mullen.

3. Animating with blender, D. Orland Hess; Focal press.

PD-391 CO-CURRICULAR ACTIVITIES L T P Cr

0 0 2 1 OBJECTIVE To help the students in their all round growth and acquire attributes like team spirit, organizational ability, leadership qualities, etc. OPERATION

The students are to take part in Co-curricular activities outside contact hours through clubs/ societies spread over all the three terms of the year. They are required to register for this course in each term and their performance will be evaluated in last term of the year.

PD-392 PROBLEM SOLVING SKILLS L T P Cr

0 0 2 1

OBJECTIVE

To train and enhance the students’ problem solving skills, reasoning ability, quantitative ability, and reading comprehension skills. 1. LOGICAL REASONING: Logical deductions (Syllogism & Venn Diagrams); logical connectives. 2. ANALYTICAL REASONING: Seating arrangements; combinations; selections; comparisons; blood relations;

directions, etc. 3. NON-VERBAL REASONING (ALPHA-NUMERIC & VISUAL PUZZLES): To solve problems on numbers,

alphabet, symbols and visuals; problem types are series, analogies, odd man out, coding decoding, and symbols & notations.

4. BUSINESS MATHS: Number system; ratios; averages; time & work; time & distance; percentages; profit &

loss; simple & compound interest. 5. HIGHER MATHS: Algebra; Mensuration. 6. DATA INTERPRETATION & SUFFICIENCY: Tables, Bar chart, line graph, pie charts; to enable student

assess whether the given data is sufficient to solve a question; for both reasoning based and quant based problems.

7. READING COMPREHENSION: To enable a student comprehend short and long passages from the

perspective of solving questions based on the passage.

TEXT BOOK

Aggarwal R. S., “Verbal & Non-Verbal Reasoning”, 2008, S.Chand, 1994 REFERENCE BOOKS

1. Aggarwal R. S., “Quantitative Aptitude for Competitive Examinations”, S. Chand, 2008 2. Gulati, SL, “Quantitative Ability”, Bookhive India, 2006 3. “GRE Barron’s”, 13

th Edition, Barron’s Educational Series, 2009

4. Devi Shakuntla, “Book of Numbers”, 1984 5. Summers George J., “The Great Book of Puzzles & Teasers”, Jaico Publishing House, 1989

CA-1323 ADVANCED COMPUTER ARCHITECTURE L T P Cr 3 0 0 3

OBJECTIVE To introduce various technological aspects about parallelism in super computing, microprocessors supporting such high scale computing, other hardware architectures, ultimately leading to high performance computing through grid computing. 1. PARALLEL COMPUTER MODELS: The state of computing; multiprocessors and multicomputers; multivector and

SIMD computers; architectural development tracks. 2. PROGRAM AND NETWORK PROPERTIES: Conditions of parallelism; data and resource dependences; hardware

and software parallelism; program partitioning and scheduling; grain size and latency; program flow mechanisms; control flow versus data flow; data flow architecture; demand driven mechanisms; comparisons of flow mechanisms

3. SYSTEM INTERCONNECT ARCHITECTURES: Network properties and routing; static interconnection networks; dynamic interconnection networks; multiprocessor system interconnects; hierarchical bus systems; crossbar switch and multiport memory; multistage and combining network.

4. PROCESSORS AND MEMORY HIERARCHY: Advanced processor technology; instruction-set architectures; CISC scalar processors; RISC scalar processors; superscalar processors; VLIW architectures; vector and symbolic processors; memory technology: hierarchical memory technology, inclusion, coherence and locality, memory capacity planning, virtual memory technology

5. BACKPLANE BUS SYSTEM: Backplane bus specification; addressing and timing protocols; arbitration transaction and interrupt; cache addressing models; direct mapping and associative caches.

6. PIPELINING: Linear pipeline processor; nonlinear pipeline processor; instruction pipeline design; mechanisms for instruction pipelining; dynamic instruction scheduling; branch handling techniques; arithmetic pipeline design; computer arithmetic principles; static arithmetic pipeline; multifunctional arithmetic pipelines.

7. VECTOR PROCESSING PRINCIPLES: Vector instruction types; vector-access memory schemes; synchronous parallel processing: SIMD architecture and programming principles, SIMD parallel algorithms, SIMD computers and performance enhancement

REFERENCE BOOKS 1. Hwang Kai and Briggs A., “Advance Computer Architecture”, Tata McGraw Hill, 1993 2. Hwang Kai and Briggs A., “Computer Architecture and Parallel Processing”, International Edition, McGraw-Hill,

1984 3. Hennessy John L. and Patterson David A., “Computer Architecture: A Quantitative Approach”, 4th Edition, Morgan

Kaufmann (An Imprint of Elsevier), 2006 4. Flynn Michael J., “Pipelined and Parallel Processor Design”, 1st Edition, Narosa Publications, 1995 5. Sima Dezso, Fountain Terence and Kacsuk Peter, “Advanced Computer Architectures”, 1st Edition, Pearson

Education/Addison Wesley, 1997

CA-1324 ADVANCED DATABASE MANAGEMENT SYSTEMS L T P Cr 3 0 0 3

OBJECTIVE To bring out various issues related to advanced computing with respect to database management systems such as parallelism in implementation, data backup and recovery management, intelligent data mining techniques, standards, etc. PRE-REQUISITES: Knowledge of database management systems 1. DATA MODELS: EER model and relationship to the OO model; object oriented data model and ODMG standard;

other data models - NIAM, GOOD, ORM 2. QUERY OPTIMISATION: Query execution algorithms; heuristics in query execution; cost estimation in query

execution; semantic query optimisation; database transactions and recovery procedures: transaction processing concepts, transaction and system concepts, desirable properties of a transaction, schedules and recoverability, serializability of schedules; transaction support in SQL; recovery techniques; database backup; concurrency control, locking techniques for concurrency control, concurrency control techniques; granularity of data items

3. CLIENT/SERVER COMPUTING: Client/Server concepts; 2-tier and 3-tier client/server systems; client/server architecture and the internet; client /database server models; technology components of client/server systems; application development in client/server systems

4. DISTRIBUTED DATABASES: Reliability and commit protocols; fragmentation and distribution; view integration; distributed database design; distributed algorithms for data management; heterogeneous and federated database systems

5. DEDUCTIVE DATABASES: Recursive queries; Prolog/Datalog notation; basic inference mechanism for logic programs; deductive database systems; deductive object oriented database systems

6. DATA WAREHOUSING: Basic concepts; data warehouse architecture; data characteristics; reconciled data layer data transformations; derived data layer user interface.

7. COMMERCIAL AND RESEARCH PROTOTYPES: Parallel database; multimedia database, mobile database; digital libraries; temporal database

REFERENCE BOOKS 1. Ramakrishnan Raghu, “Database Management System”, McGraw Hill, 3rd Ed., 2003 2. Elmasri R. and Navathe S. B., “Fundamentals of Database Systems”, 3rd Edition, Addison Wesley, Low Priced

Edition, 2000. 3. Tamer M. and Valduricz, “Principles of Distributed Database Systems”, 2nd Edition, LPE Pearson Edition. 4. Silbershatz A., Korth H. F. and Sudarshan S., “Database System Concepts”, 3rd Edition, McGraw-Hill, International

Edition, 1997. 5. Desai Bipin C., “An Introduction to Database Systems”, Galgotia Pub. 6. lioffer Feffray A., Prescotl Mary B.and McFadden Fred R., “Modern Database Management”, 6th Edition, Pearson

Education.

CA-1325 CRYPTOGRAPHY AND DATA COMPRESSION L T P Cr

3 0 0 3

OBJECTIVE The course will provide a down-to-earth overview of cryptographic techniques applicable in an IT environment, and outline the constraints and limitations of realistic secure systems. A running theme is the tradeoff between usability and security of a system. Also covered are a number of compression techniques - data compression and data encryption are, in some respects, closely related. A working knowledge of C is assumed and essential. 1. COMPRESSION: Packing; Huffman coding; run length encoding; Lempel-Ziv-Welch; Phil Katz’s PKZIP; Delta

modulation; JPEG. 2. ERROR DETECTION AND CORRECTION: Parity; 1, 2, n-dimensions, Hamming codes; p-out-of-q codes 3. CRYPTOGRAPHY: Vocabulary; history, steganography – visual, textual; cipher hiding; false errors; public key

cryptography - authentication, signatures, deniability 4. MATHEMATICS: Information; confusion; diffusion; modular arithmetic; inverses; Fermat’s little theorem, Chinese

remainder theorem; factoring; prime numbers; discrete logarithms 5. ALGORITHMS: DES; AES (Rijndael); IDEA; one time pad; secret sharing and splitting; RSA; elliptic curves;

modes; random numbers 6. ATTACKING SYSTEMS: Recognition; destroying data; cryptanalysis - differential cryptanalysis; cracking DES 7. ENCRYPTION: Advanced Encryption Standard; Evaluation Criteria for Advanced Encryption Standard; The

Advanced Encryption Standard Cipher; Substitute Byte Transformation; Contemporary Symmetric Ciphers; Triple Data Encryption Standard; Blowfish; RC5; Characteristics of Advanced Symmetric Block Ciphers; Confidentiality using Symmetric Encryption; Key Distribution.

REFERENCE BOOKS 1. IEEE, “Integration of Data Compression and Cryptography: Another Way to Increase the Information Security”,

IEEE Computer Society 2. Schneier B., “Applied Cryptography: Protocols, Algorithms and Source Code in C”, 2nd edition, Wiley, 1996. 3. Desai Suhag, “Security in Computing”, Pearson Education 4. Trappe W. and Washington L., “Introduction to Cryptography”, 2nd edition, Pearson Education, 2006

CA-1326 EXPERT SYSTEMS L T P Cr 3 0 0 3

OBJECTIVE To highlight the issues in knowledge representation, learning and understanding by a computer obtained from real world, and to take decisions based on them 1. ARTIFICIAL INTELLIGENCE: History and applications; AI problems and techniques; concept of AI; approaches:

acting and thinking like humans and rationally; brief history of AI; foundations of AI; underlying assumptions; application areas.

2. PRODUCTION SYSTEMS, STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH: Data driven and goal driven search; depth first and breadth first search; DFS with iterative deepening.

3. HEURISTIC SEARCH: Best first search; A* algorithm; AO* algorithm; constraint satisfaction; using heuristics in games: Minimax search, alpha beta procedure; state space theory/representation.

4. KNOWLEDGE REPRESENTATION: Simple relational knowledge; inheritable knowledge; inferential knowledge; procedural knowledge propositional calculus; predicate calculus; theorem proving by resolution; answer extraction; AI representational schemes: semantic nets, conceptual dependency, scripts, frames; introduction to agent based problem solving.

5. MACHINE LEARNING: Symbol based and connectionist; social and emergent models of learning; the genetic algorithm: genetic programming; overview of expert system technology: rule based expert systems; introduction to natural language processing; neural networks.

6. LANGUAGES AND PROGRAMMING TECHNIQUES FOR AI: Introduction to PROLOG and LISP; search strategies and logic programming in LISP; production system examples in PROLOG.

7. KNOWLEDGE BASED SYSTEMS: Expert systems; components; characteristic features of expert systems; applications; rule based system architecture; representing and using domain knowledge; expert system shell; explaining the reasoning and knowledge acquisition; applications.

REFERENCE BOOKS 1. Mauss Rex and Keyes Jessica, “Handbook of Expert Systems in Manufacturing”, McGraw Hill, 1991 2. Gonzalez, Fu and Lee, “Robotics: Control, Sensing, Vision and Intelligence”, McGraw Hill,1987 3. Nilsson N. J., “Principles of Artificial Intelligence”, Narosa Publishing House, 1990. 4. Patterson Dan W., “Introduction to Artificial Intelligence & Expert Systems”, Seventh Indian Reprint, Eastern Economy

Edition, Prentice Hall of India, 1999 5. Winston P. H., “Artificial Intelligence”, 3rd Ed., Pearson Education, 2000 6. Schalkoff R. J., “Artificial Intelligence – An Engineering Approach”, McGraw Hill Int. Ed. Singapore, 1992. 7. Sasikumar M. and Ramani S., “Rule Based Expert Systems”, Narosa Publishing House, 1994.

CA-1327 NATURAL LANGUAGE PROCESSING L T P Cr 3 0 0 3

OBJECTIVE To motivate understanding of issues related to natural language understanding, generation and translation, which ultimately linked to machine learning, computer vision and expert systems. This course provides an introduction to the field of computational linguistics, also called natural language processing (NLP) - the creation of computer programs that can understand and generate natural languages (such as English). Natural language understanding as a vehicle will be used to introduce the three major subfields of NLP: syntax (which concerns itself with de termining the structure of an utterance), semantics (which concerns itself with determining the explicit truth-functional meaning of a single utterance), and pragmatics (which concerns itself with deriving the context-dependent meaning of an utterance when it is used in a specific discourse context). The course will introduce both knowledge-based and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems. PRE-REQUISITES Knowledge of theory of computations 1. INTRODUCTION TO NATURAL LANGUAGE UNDERSTANDING: The study of language; applications of NLP;

evaluating language understanding systems; different levels of language analysis; representations and understanding; organization of natural language understanding systems; linguistic background: an outline of English syntax.

2. GRAMMARS AND PARSING: Grammars and sentence structure; top-down and bottom-up parsers; transition network grammars; top-down chart parsing; feature systems and augmented grammars: basic feature system for English

3. MORPHOLOGICAL ANALYSIS AND THE LEXICON: Brief review of regular expressions and automata; finite state transducers; parsing with features; augmented transition networks

4. GRAMMARS FOR NATURAL LANGUAGE: Auxiliary verbs and verb phrases; movement phenomenon in language; handling questions in context-free grammars; hold mechanisms in ATNs.

5. HUMAN PREFERENCES IN PARSING: Encoding uncertainty; deterministic parser; word level morphology and computational phonology; basic text to speech; introduction to HMMs and speech recognition, parsing with CFGs; probabilistic parsing; representation of meaning.

6. AMBIGUITY RESOLUTION: Statistical methods; estimating probabilities; part-of- speech tagging; obtaining lexical probabilities; probabilistic context-free grammars; best first parsing.

7. SEMANTICS AND LOGICAL FORM: Word senses and ambiguity, encoding ambiguity in logical form, semantic analysis; lexical semantics; word sense; disambiguation; discourse understanding; natural language generation, Indian language case studies.

REFERENCE BOOKS 1. Allen James, “Natural Language Understanding”, 2nd edition, Pearson Education, 2003. 2. Winograd Terry, “Language as a Cognitive Process”, Addison Wesley, 1983 3. Gazder G., “Natural Language Processing in Prolog”, Addison Wesley, 1989 4. Arbib Mdlj and Kfaury, “Introduction of Formal Language Theory”, Springer Verlag, 1988 5. Jurafsky D. and Martin J. H., “Speech and Language Processing”, Pearson Education, 2002. 6. Manning Christopher D. and Schütze Hinrich, “Foundations of Statistical Natural Language Processing”, The MIT

Press, Cambridge, Massachusetts, 1999.

CA-1328 DIGITAL IMAGE PROCESSING L T P Cr 3 1 0 3

OBJECTIVE To introduce the students about the basic concepts and analytical methods of processing digital signals, especially, the images and imaging part; to understand the properties of static and streaming images/video. PRE-REQUISITES Knowledge of data compression, discrete structures, digital signal processing, computer graphics 1. INTRODUCTION AND DIGITAL IMAGE FUNDAMENTALS: Origins of digital image processing; examples of

fields that use digital image processing; fundamentals steps in image processing; elements of digital image processing systems; image sampling and quantization; some basic relationships like neighbours; connectivity, distance measures between pixels; linear and non linear operations.

2. IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN: Some basic gray level transformations; histogram processing; enhancement using arithmetic and logic operations; basics of spatial filters, smoothening and sharpening spatial filters, combining spatial enhancement

3. IMAGE ENHANCEMENT IN THE FREQUENCY DOMAIN: Introduction to Fourier transform and the frequency domain, smoothing and sharpening frequency domain filters; homomorphic filtering; image restoration: a model of the image degradation / restoration process, noise models, restoration in the presence of noise only spatial filtering, periodic noise reduction by frequency domain filtering; linear position-invariant degradations; estimation of degradation function; inverse filtering; Wiener filtering, constrained least square filtering, geometric mean filter; geometric transformations.

4. IMAGE COMPRESSION: Coding; inter-pixel and psycho visual redundancy; image compression models; elements of information theory; error free compression; lossy compression; image compression standards.

5. IMAGE SEGMENTATION: Detection of discontinuities; edge linking and boundary detection; thresholding; region oriented segmentation; motion based segmentation

6. REPRESENTATION AND DESCRIPTION: Representation, Boundary Descriptors, Regional Descriptors, Use of Principal Components for Description, Introduction to Morphology, Some basic Morphological Algorithms.

7. OBJECT RECOGNITION: Patterns and Pattern Classes, Decision-Theoretic Methods, Structural Methods.

REFERENCE BOOKS 1. Jain A. K., “Digital Image Processing”, Prentice Hall of India, 1995 2. Gonzalez Rafael C. and Woods Richard E., “Digital Image Processing”, 2nd edition, Pearson Education, 2002 3. Jahne Bernd, “Digital Image Processing”, 5th Ed., Springer, 2000 4. Pratt William K., “Digital Image Processing: Piks Inside”, John Wiley & Sons, 2001. 5. Forsyth D. A. and Ponce J., “Computer Vision: A Modern Approach”, Prentice Hall, 2003 6. Horn Berthold, “Robot Vision”, MIT Press, McGraw Hill, 1986 7. Jain R., Kasturi R. and Schunck B. G. , “Machine Vision”, McGraw Hill, 1995