Syllabus for
Master of Science (Computer Science)
Academic Year  (2018)

To introduce the concepts and principles of Java Programming language and to design, implement object oriented solutions to simple and complex problems. To give students experience in Java Programming and program development within an integrated development environment.

Data Types, Expressions, Keywords, Operators and Control Flow Statements, Structure of Java Program, Creating and Running Java Programs, Arrays.

Creating class and objects, methods, this keyword, Constructors, Garbage Collection, the finalize() method, Access Control, Static Blocks, Finals, Nested and Inner Classes, String Class and String Buffer Class, Command Line Arguments. 

History of Java, Characteristics of Java, The Java Environment – JVM, JDK & JRE, Different versions of Java, OOP Principles, Comparison of Java with C and C++.

Inheritance in classes, using super, Method overriding, Dynamic Method Dispatch.Abstract Classes, Using final with inheritance, the Object Class.

try-catch-finally mechanism, throw statement, throws statement, Packages and Classes for Exception Handling

Inheritance in java with Interfaces – Defining Interfaces, Implementing Interfaces, Extending Interfaces, Creating Packages, CLASSPATH variable, Access protection, Importing Packages, Interfaces in a Package.  

Life cycle of Applet, Applet Architecture, Applet restrictions, Creation and Execution of Java Applets, Animation in Applets, Advantages of Applets, Applets vs. Applications. 

java.io package, I/O Streams, Readers and Writers, Using various I/O classes: Reader, Writer, InputStream and Output Stream, Serialization of objects.

Life cycle of a thread, Java Thread priorities, Runnable interface and Thread Class, sharing limited resources, shared Object with synchronization. 

GUI concepts in Java, Basic GUI Components in AWT, Container Classes, Layout Managers, Flow Layout, Border Layout, Card Layout, Box Layout, Difference between AWT and SWING.

Self-Learning

SWING Java foundation Classes – javax.swing and Model View Controller, Creating a Frame in Swing, Displaying Image in Swing, JComponent class methods – Creating components in Swing, Writing GUI programs in java (with AWT or SWING), Event Handling – Handling Keyboard Events and Mouse Events. 

Creating a server that sends data, Creating a client that receives data, two way communication between server and client, Stages in a JDBC program, Registering the driver, Connecting to a database, Preparing SQL statements, Improving the performance of a JDBC program.

[2] Rao Nageswara ,Core Java-An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

To help the students to understand the concept of HTML, CSS, Java script and PHP.

Cascading Style Sheet

Introduction, Levels of Style Sheet and specification formats, embedded style sheet, External Style Sheet, inline Style Sheet, Class and ID method, DIV and SPAN tags, Inheritance with CSS, Introduction to CSS 3, HTML 5 and CSS3.

Internet

WWW, Web Browsers, and Web Servers, URLs, MIME, HTTP, Security. HTML and CSS 

HTML - XHTML – HTML 5

Creating simple web page, basic text formatting, presentation elements, phrase elements, lists, font, grouping elements, basic links, internal document links, email link, Image, Audio and Video, image maps, image formats, Adding flash content and video, Tables – attributes, nested tables, Forms – Attributes, form controls, Frames – Frame set, nested frames, attributes. Introduction to HTML 5 - New tags of HTML 5 – embedding Media content, building input forms, painting on canvas.

Javascript

JavaScript Implementation, JavaScript in HTML, language basics – variables, operators, statements, functions, data type conversions, reference types, Document Object Model - Browser Object Model - window object, location object, navigator object, screen object, history object, Events and Event handling, Button elements, Navigator object, validations with regular expressions. Introduction to dynamic documents, positioning elements, moving elements, elements visibility, changing colors and fonts, dynamic content, locating mouse cursor, reacting to a mouse click, dragging and dropping of elements, basic animation with image using JavaScript.

PHP

Introduction to Server Side Programming, Introduction to PHP, PHP and HTML, essentials of PHP, Why Use PHP, Installation of Web Server, WAMP Configurations, Writing simple PHP program, embedding with HTML, comments in PHP, variables, naming conventions, strings, string concatenation, string functions, float functions, Arrays, Array – key pair value, array functions, isset(), unset(), gettype(), settype(), control statements (if, switch), loops, user defined functions (with argument and return values), global variable, default value, GET & POST method, URL encoding, HTML Encoding, Cookies, Sessions, Include statement, File – read and write from the file.

MySql

Introduction to MySQL, CRUD - select statements, creating database/tables, inserting values, updating and deleting, PHP with MySQL, creating connection, selecting database, perform database (query), use returned data, close connections, file handling in PHP – reading and writing from and to FILE, Using MySQL from PHP (Building a Guestbook).

Object Oriented Programming with PHP

Introduction to OOPS, creating classes, creating objects, setting access to properties and methods, constructors, destructors, overloading and overriding of methods. Accessing PHP and HTTP Data, Reading POST and GET variables, Form validation.

[2] Steve suehring, JavaScript Step by Step, Microsoft Press, 2nd Edition, PHI, 2012.

[3] Matt Doyle, Beginning PHP 5.3, Willey Publishing, 2010.  

[2] David  Sawyer McFarland,  CSS – The Missing Manual, Pogue Press, O’Reilley  Willey Publishing, 2nd Edition, 2009.

The objectives of this course are : Understanding the elements of digital system abstractions such as digital representations of information, digital logic, Boolan algebra, state elements and finite state machine (FSMs). Learning of this subject explains about combinational circuits like multiplexers, demultiplexers, decoders, encodes and sequential circuits. The course also focuses on architecture of 8085 microprocessor and its operations;

Upon successful completion of the course, students should be able to:

·         Understand the number systems and conversions form one to other.

·         Be able to use Boolean algebra, and methods to simplify the expression using K- maps and design logic circuits.

·         Understand the concepts and working of sequential circuits and combinational circuits.

·         Identify the basic element, functions and architecture of 8085 microprocessor. 

·         Finally learn the instruction set of 8085 and code in the assembly language program to develop the microprocessor based application.

Number System (Binary, Hexadecimal, Octal, Decimal, Binary Arithmetic, Hexadecimal addition & subtraction, BCD addition & subtraction – 1's and 2's Compliment addition and subtraction, Basic gates, Universal gates, Positive and negative logic, Boolean Algebra, Simplification using Boolean Laws.

DeMorgan’s Theorem, Karnaugh map, sum of products, Pairs-Quads-Octets, Karnaugh map simplifications, Don't care conditions. Multiplexers, Demultiplexers, Decoders.

RS Flip-Flops, Edge-Triggered RS, D, JK Flip-Flops, Flip-Flop Timing, JK Master-Slave  Flip-Flops. Types of Registers, Serial in-Serial out, Serial in-Parallel out, Parallel in-Serial out, Parallel in-Parallel out. Asynchronous Counters, Synchronous Counters, Decade Counters.

Microprocessor Architecture and its operations – Address Bus, Data Bus, Control Bus, Internal data operations and Registers, The 8085 MPU – Architecture, Communication and Bus Timings, Demultiplexing the Bus, Generating Control Signals.   

Self learning

Memory, I/O Devices.

The 8085 programming model, Instruction classification, Instruction and Data Format, Data Transfer Operations, Arithmetic Operations, Logic Operations, Branch Operations, Writing ALP Programs.

Looping, Counting, Indexing, Additional Data Transfer and 16-Bit Arithmetic Instructions, Arithmetic operations related to memory, Rotate and Compare of Logic operations. Assembly Language Programming – Addition of two 8 bit Hexadecimal numbers, Addition of N Hexadecimal numbers, Interchange N one byte numbers, etc.

Counters and Time Delays, Stack, Subroutines, Restart –Simulate a decade counter to count up to 99. The 8085 Interrupt – RST, SIM and RIM Instructions, Multiple Interrupts and Priorities, 8085 Vectored Interrupts – TRAP, RST 7.5, 6.5, 5.5.

[1] Mano, Morris M and Kime, Charles R, Logic and Computer Design Fundamentals, Pearson Education, 2nd Edition, 2010.

[2] Tokheim, Digital Electronics Principles and Applications, Tata McGraw-Hill, 6th Edition, 2010.

[3] Charles M Gilmore, Pal Ajit, Microprocessor Principles and Applications, Tata McGraw-Hill, 2nd Edition, 1998.

[4] Hall.D.V, Microprocessor and Digital System, McGraw Hill Publishing Company, 2nd Edition, 1990.

[1] Leach P, Donald and Malvino, Albert Paul, Digital Principles and Applications, Tata McGraw-Hill, 5th Edition, 2010.

[2] Ramesh.S.GoankarMicroprocessor Architecture, Programming & Applications With 8085, PenramInternational , 5th Edition ,  2011.

Evaluation Pattern (Theory)

To provide strong foundation for database design and application development, and understand the underlying core database concepts and emerging database technologies.

·         Consolidate theoretical database understanding

·         Get insights into recent developments in database technologies

Using High-Level Conceptual Data Models for Database Design - Entity Types, Entity Sets, Attributes, and Keys - Relationship Types, Relationship Sets, Roles, and Structural Constraints - Weak Entity Types - ER Diagrams, Naming Conventions, and Design Issues - Relationship Types of Degree Higher than Two - Subclasses, Superclasses, and Inheritance – Enhanced Entity Relationship Model - Relational Database Design by ER- and EER-to-Relational Mapping - Role of Information Systems in Organizations - Database Design and Implementation Process

Design Guidelines for Relation Schemas - Functional Dependencies - Normal Forms Based on Primary Keys - Second and Third Normal Forms - Boyce-Codd Normal Form - Multivalued Dependency and Fourth Normal Form - Join Dependencies and Fifth Normal Form - Inference Rules, Equivalence and Minimal Cover - Properties of Relational Decompositions - Nulls and Dangling Tuples - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices.

PL/SQL Block Structure – Identifiers – Literals – Comments - Conditional and Sequential Control - Iterative Processing with Loops - Exception Handlers – Data Retrieval with Cursors - Procedures, Functions, and Parameters – Packages

Introduction - Documents and Collections - Data Types - Create, Read, Update and Delete Operations - Querying using Find - Query Criteria – Type-Specific Queries – Where Queries

Mobile databases – Multimedia Databases – Geographic Information Systems – Genome Database

[2]Korth, Sudershan, Database System Concepts, McGraw Hill, Sixth Edition, 2013.

[3]Kristina Chodorow, MongoDB: The Definitive Guide, O’Reilly, Second Edition, 2013.

[4]Steven Feuerstei, Oracle PL/SQL Programming, O’Reilly, Sixth Edition, 2014

[2] ShashankTiwari, Professional NoSQL, Wrox, Second Edition, 2013.

CIA II 25%

CIA III 10%

Attendance 05%

ESE 50%

To prepare the students for a background in abstraction, notation and critical thinking in the Discrete Mathematics closely related  to computer science.

• Construct mathematical arguments using logical connectives and quantifiers.
• Verify the correctness of an argument using propositional and predicate logic and truth tables.
• Understand how Graphs  are used as tools and Mathematical Models in  the study of networks
• Construct proofs using direct proof, proof by contraposition, proof by contradiction, proof by cases and mathematical induction. 
• Apply algorithms and use definitions to solve problems to prove statements in elementary number theory. 
• Perform operations on discrete structures such as sets, relations and functions and be familiar with concepts like Groups and Rings.

How to do Mathematics – Compound statements – Existential and Universal statements – Proof techniques – Logical operations – Logical equivalence- Conditional statements – Universal and Existential quantifiers – Concept of a function – Types of functions – Composition of functions.

How to do Mathematics – Compound statements – Existential and Universal statements – Proof techniques – Logical operations – Logical equivalence- Conditional statements – Universal and Existential quantifiers – Concept of a function – Types of functions – Composition of functions.

Introduction to numbers – Divisibility – Greatest common divisor – Existence and uniqueness of prime factorization – Partition of a set – Partition of a positive integer – Even and odd permutations –  modular arithmetic – Latin squares.

Introduction to numbers – Divisibility – Greatest common divisor – Existence and uniqueness of prime factorization – Partition of a set – Partition of a positive integer – Even and odd permutations –  modular arithmetic – Latin squares.

Types of relations – Graphs as network – Types of graphs-Representation of graphs – Representation of relations through graphs – Paths and Cycles- Eulerian and Hamiltonian properties of paths – Equality of graphs  – Trees – Coloring of graphs – Max-Flow  –Min- Cut theorem.

Types of relations – Graphs as network – Types of graphs-Representation of graphs – Representation of relations through graphs – Paths and Cycles- Eulerian and Hamiltonian properties of paths – Equality of graphs  – Trees – Coloring of graphs – Max-Flow  –Min- Cut theorem.

Groups  –  Axiom of a group –  Examples and basic algebra in groups – Order of an element of a group – Isomorphism of groups – Cyclic groups – Subgroups – Cosets and Lagrange’s theorem – Rings-Fields.

Groups  –  Axiom of a group –  Examples and basic algebra in groups – Order of an element of a group – Isomorphism of groups – Cyclic groups – Subgroups – Cosets and Lagrange’s theorem – Rings-Fields.

Pattern of the Question Paper

Course Description

The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. It continues with finding out the literature using computer technology, basic statistics required for research and ends with linear regression.

•      Define research and describe the research  process and research methods
•      Understand and apply basic research methods including research design, data analysis, and interpretation.

Defining research problem - selecting the problem - necessity of defining the problem - techniques involved in defining a problem- Ethics in Research . 

Principles of experimental design Working with Literature Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review On-line Searching: Database – SciFinder – Scopus - Science Direct - Searching research articles - Citation Index - Impact Factor - H-index etc,

Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation

Descriptive Statistics Measures of Central Tendency, Measures of Dispersion, Measure of Skewness, Kurtosis, Measure of Relationship Linear Regression Analysis: Dependent and Independent variables, Simple Linear Regression model 

Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates(IEEE style), paper writing for international journals, Writing scientific report.

Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.

1.   C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint  2014.
2.   Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.

    Recommended Reading

[1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th. ed. SAGE Publications, 2014.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.

 Exercises:

·         Review 1 Pre-Selected Papers  (U: Unique for each student)

·         Review 1 papers in areas of scholars choice (U)

·         Using an IEEE MS Word Template and convert literature review done in previous paper reviews. Follow Reference Styles

·         Gather data from Wikipedia and populate a spreadsheet. (U)

·         Using the data collected, analyze the data using 7 different spreadsheet statistics functions.

·         Script a paper in IEEE LATEX template(U)

Evaluation Pattern

CIA for this course is similar to other regular courses in the curriculum as per university guidelines. Additionally, students have to maintain a record of the following:

·         Papers reviewed (minimum 5 papers)

·         Data collected (7 different graphs)

·         Analysis of data

·         Statistical functions

ESE is conducted by the department during which each student should come up with a research proposal of any domain of his/her interest. Students should incorporate the research objectives and methodologies along with justification for why those methodologies were suggested to achieve the objectives. Each student is evaluated based on a presentation that highlights the application of various research methodology concepts learnt in the semester.

• Guidelines

·       The output of the programs should be neatly formatted

·       The source code should be indented

·       The programs need to be interactive

·       Data validations can be done wherever applicable

·       Include comments to improve the readability of the program

·       Use meaningful variable names

·       Program should be prepared by their own

Programs should follow coding standards, no duplication of programs

1. Demonstrate various data types and operators.

2. Demonstrate method overloading and constructor overloading.

3. Demonstrate the usage of static keyword in Java – use static data and static block.

4. Demonstrate final keyword with respect to variable, method and class.

5. Demonstrate inner classes in Java.

               6. Demonstrate multilevel inheritance and usage of the keywords this & super.

7. Demonstrate abstract class.

8. Demonstrate the usage of interface for multiple inheritance.

               9. Differentiate the usage of throw, throws and try-catch-finally by writing a Java program.

10. Demonstrate various I/O streams in Java.

11. Demonstrate the Reader/Writer classes in Java.

12. Demonstrate the multithreading concept by implementing Runnable interface.

13. Demonstrate the multithreading concept by extending Thread class.

14. Make some graphics in an applet program using paint function.

15. Demonstrate the usage of different Layouts in Java.

16. Demonstrate various GUI components in Java (AWT / SWING) with appropriate Event Handling.

17. Implement two way communication between server and client.

              18. Retrieve data from the table of the database.

To help the students to understand the concept of HTML, CSS, Java script and PHP.

Guidelines:

* The output of the programs should be neatly formatted.

* The source code should be indented.

* The programs need to be interactive.

* Data validations can be done wherever applicable. 

* Include comments to improve the readability of the program .

* Use meaningful variable names.

* Program should be prepared by their own.

* Follow the ethics of Programming, Web Design and Development.

Programs:

• Create a Web page by making use of the following tags: Headers, Linking and Images. 

• Create a Web page that will have the following:  Frames, Unordered Lists, Nested and ordered Lists.

• Create a Web page Layout with Tables and all its attributes.

• Create a Web page that will have Application form (Forms),  make use of  Image Maps and  <meta> Tags.

• Create an External Style Sheet that defines the style for the following tag : H1, H2, Body , P, Li. 

• Create an Internal Style Sheet that defines a style for Positioning elements & setting the background (color / image). 

• Create a Style Sheets that defines the style with class method , Id method , make use of DIV and Span TAG. 

• Create a Style Sheet that demonstrate Box Model. 

• Write a JavaScript program to Demonstrate the use of Variable , message box , and loops. 

• Write a JavaScript program to demonstrate Functions (predefined / user defined). 

• Write a JavaScript program to demonstrate Event Handling. 

• Object Creation and modification in JavaScript. 

• Write a PHP program to demonstrate GET and POST method of passing the data between pages. 

•  Write a PHP program to demonstrate Array , Key-pair values.  

•  Write a PHP program to read and write the Data from the Database.

• Create a PHP page that uses Session and cookies. 

• File Handling in PHP .

• Implementing the OOP concept in PHP.

[2] Steve suehring, JavaScript Step by Step, Microsoft Press, 2nd Edition, PHI, 2012 [3] Matt Doyle,

Beginning PHP 5.3, Willey Publishing, 2010.

[2] David Sawyer McFarland, CSS – The Missing Manual, Pogue Press, O’Reilley Willey Publishing, 2nd Edition, 2009.

Data Structure is considered as one of the fundamental paper towards a more comprehensive understanding of programming and application development. Student is expected to work towards a sound theoretical understanding of Data Structures and also compliment the same with hands on implementing experience

·         Understand the need for Data Structures when building application

·         Appreciate the need for optimized algorithm

·         Able to walk through insert and delete for different data structures

·         Ability to calculate and measure efficiency of code 

·         Appreciate some interesting algorithms like Huffman, Quick Sort, Shortest Path etc

·         Able to walkthrough algorithm

·          Improve programming skills

Introduction and overview        

Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time & Space Complexity, Algorithmic Notation, Abstract Data Types. Programming standards and ethics.

Stacks and Queues         

Stacks, Array Representation, Arithmetic Expressions, Polish Notation, Application of Stacks, Recursion, Towers of Hanoi, Implementation of Recursive procedures by Stack, Queues, Queue Array Representation.

Introduction, Linked lists and Memory Representation, Traversing, Searching, Memory Allocation, Garbage Collection, Insertion, Deletion, Circular Linked list, Two-way Lists(Doubly). Linked List Implementation of Stack and Queue

Self Learning

Infix to Prefix

Sorting          

Introduction, Sorting, Insertion Sort, Selection Sort, Shell Sort, Merging, Merge-Sort, Quick Sort, Radix Sort, External Sorting.

Searching 

Hashed List Searches: Hashing Methods - Direct method, Subtraction Method, Modulo-division Method, Digit-extraction Method, Midsquare Method, Folding Method, Rotation Method, Pseudorandom Hashing.

Collision Resolution – Open addressing, Linear Probe, Quadratic Probe, Pseudorandom Collison Resolution, Linked List Collision Resolution, Bucket Hashing, Combination Approaches.  Text Searching using Knuth-Morris-Pratt algorithm.

Trees 

Introduction, Binary Trees, Representing Binary Trees in memory, Traversing Binary Trees, Traversal Algorithms, Binary Search Trees, Searching, Inserting and deleting in Binary Search Trees, Heap, Heap sort, Huffman’s Algorithm.

Balanced Tree 

AVL Trees: AVL Balance Factor, Balancing Trees, AVL node structure, AVL Tree Rotate Algorithms.

Self learning

Heap, Heap Sort, splay and Red Black tree.

Multiway Search Trees, B-Trees          

B-Trees: B-Tree insertion, Deletion, Traversal and Search algorithm, Simplified B Trees, 2-3 Tree, 2-3-4 Tree, Variations of B Tree - B+ Tree, B* Tree.

Graphs          

Graph Theory Terminology, Sequential representation of Graphs, Adjacency matrix, Path matrix, Linked representation of a Graph, Operations on Graphs, Depth First and Breadth First Traversing a Graph, Minimum Spanning Tree Algorithm.

[2] Richard Johnsonbaugh, Algorithims,Pearson Education, 2nd Edition, 2008

[3] Robert Sedgwick, Algorithim in C++, Addison-Wesley Publishing Company. [4] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, Addison-Wesley, 2005.

The course provides comprehensive understanding of the layered architecture of UNIX operating system, system calls, and file system structure. It also focuses on acquiring skills needed to develop UNIX shell programs, making effective use of wide range of UNIX programming standard and tools.

History, System structure, Users Perspective, OS Services.Architecture, System Concepts. The Buffer Cache: Headers, Structure of the Buffer Pool, Scenarios, Reading and writing Disk Blocks, Advantages and disadvantages of buffer cache. Algorithms: getblk, brelse, bread, breada, bwrite
INODES, Structure of a regular file, Directories, Conversion of a path name to an INODE, Super Block, INODE assignment, Allocation of Disk Blocks, System calls for the file system: Open, Read, Write, Close, Pipes, Mounting and Unmounting Files. Algorithms: iget, iput, ialloc, ifree, open, read, write, creat.

General purpose utilities, The File system, Handling Ordinary files, Basic File attributes, The Shell, The process, Hard links, Symbolic links, Umask, Modification and access time , Simple Filters: pr, head, tail, cut, paste, sort, uniq, tr, Filters using regular expressions: grep and sed ,Advanced Filters-awk, Essential System Administration

read, using command line arguments, exit and exit status command, logical and conditional operators, if condition, using test and [] ,case, expr, Loooping – while, for ,set and shift, trap, debugging, functions.
Advanced Shell Programming
Shells, sub shells, export, running a script in current shell, eval, exec.

Process States and Transitions, Layout of System Memory, Context of a Process, Manipulation of the process address space, Process Control: Creation, Signals, Process termination, Awaiting process termination, invoking other programs, The Shell, System Boot and Init Process, Process Scheduling and Time: Process scheduling, System calls for time, Clock.
Algorithms: fork, exit, wait, exec

Swapping, Demand Paging, the I/O sub system: Driver Interfaces, Disk Drivers, Terminal Drivers, and Streams.
Process Tracing, System V IPC: Messages, Shared memory, Semaphore, Network Communications: Sockets. Algorithms: msgsnd, msgrcv, shmat, semop.

This course aims at providing a comprehensive overview of the important issues related to Wireless communication and Mobile Computing. They are grouped into four main areas: basic issues and problems, mobile communications, cellular networks and protocols. Different mobile communications methods and latest communication technologies are discussed. Protocols such as Mobile IP, medium access control and mobility management strategies that are needed to support mobile devices are covered under the specified topic. 

•Describe the basic issues and problems in mobile computing.

•Describe the transmission mechanisms and characteristics of different mobile/wireless communication.

•Describe the strengths and limitations of different types of mobile/wireless networks.

•Explain the mechanisms for supporting mobility. 

Different Generations of Wireless Cellular Networks, 1G Cellular Systems, 2G Cellular Systems, 2.5G Cellular Systems, 3G Cellular Systems, 4G Cellular Systems and Beyond, Wireless Standards Organizations.

History and Evolution of Wireless Radio Systems, Development of Modern Telecommunications Infrastructure, Overview of Existing Network Infrastructure, Wireless Network Applications: Wireless Markets.

The Cellular Concept, Cell Fundamentals, Capacity Expansion Techniques, Mobility Management, Wireless Network Security.

Common Cellular Network Components, Hardware and Software Views of the Cellular Network, 3G Cellular System Components, Cellular Component Identification, Cell establishment.

Introduction to GSM and TDMA, GSM Network and System Architecture, GSM Channel Concept, GSM Identities, GSM System Operations, GSM Infrastructure Communications.

Introduction to CDMA, CDMA Network and System Architecture, CDMA Channel Concept, CDMA System Operations.

CDPD, GPRS, GPRS Networks, GPRS Network Details, GPRS Network Layout and Operation, GPRS Packet Data Transfer, GPRS Protocol Reference Model, GPRS Logical Channels, GPRS Physical Channels, GSM/GPRS/Edge Technology.

Introduction to wireless LAN 802.11X technologies, Evolution of Wireless LAN, Introduction to IEEE 802.15x Technologies, Wireless PAN Applications and Architecture, Bluetooth,  Introduction to Broadband wireless MAN,802.16 technologies.

[2] Stallings William, Wireless Communications and Networks, Pearson Education Asia, 2nd Edition, 2009.

[3] Theodore S Rappaport, Wireless Communications: Principles and Practice, Pearson Education Asia, 2nd Edition, 2009. [4] Jochen Schiller, Mobile Communication, Addison-Wesley, 2nd Edition, 2011.

Course Objectives:

To understand the need for Software Quality, Tools Used and Metrics involved. To appreciate software testing principles and methods to detect in the ever changing software technological changes.

Fundamental concepts of Software Quality and Testing

·         Ability to test code, artifacts better

·         Learn to apply different Quality Tools

·         Understand the advantages of Extreme Testing and High Order Testing

·         Create effective test plan

·         Create detailed test cases

·         Appreciate the need for Software Quality Metrics and Assessments

Quality: popular view, Quality: professional view, software quality, total quality management, The defect prevention process, process maturity framework and quality standards (CMM , SPR Assessment, Malcolm Bridge, ISO9000)

Levels of measurement some basic measures, reliability and validity

Product Quality Metrics, in-process quality process, example of Metrics Program – Motorola, HP

Literature review, a close look at DRE, defect removal effectiveness and quality planning

Ishikawas’ seven basic tools, checklist, pareto diagram, histogram, runchart, scatter diagram control chart cause and effect diagram.

Software Testing Principles, Economics of Testing Inspection and walkthrough, code inspection, an error checklist for Inspection, Walkthroughs, Desk Checking, Peer Rating Module Testing

Two testing tools

High Order Testing - Debugging by Brute Force, Induction, Deduction, Backtracking Extreme Programming basics, Extreme Testing, Extreme Testing Applied

Java Unit Testing, Automatic testing.

[2] GlenfordJ.Myers ,The Art of Software Testing” John Wiley and Sons publications, 2004

[1] Stepen H Kan, Metrics and Models in Software Quality Engineering, 2nd  Edition ,reprint 2006

[2] GlenfordJ.Myers ,The Art of Software Testing” John Wiley and Sons publications, 2004

Recommended Reading

[1] S A Kelkar,Software Quality and Testing, PHI, 1st Edition, 2012.

The World Wide Web has become a major delivery platform for information resources. Many applications continue to be developed in an ad-hoc way, contributing to problems of usability, maintainability, quality and reliability. This Course Description examines systematic, disciplined and quantifiable approaches to developing of high-quality, reliable and usable web applications.

RE Fundamentals and Specifics - Principles for RE - Adapting RE Methods - Modeling Fundamentals and Specifics - Modeling Requirements - Content Modeling - Hypertext Modeling - Presentation Modeling - Customization Modeling 

Fundamentals and Specifics – Components - Layered Architectures - Data-aspect Architectures - Evolutionary Perspective - Presentation Design - Interaction Design - Functional Design – Outlook 

Fundamentals and Specifics of Testing - Test Approaches and Schemes - Test Methods and Techniques - Test Automation – Challenges in Launch of a Web Application - Promoting a Web Application - Content Management - Usage Analysis 

Characteristics of Performance - Definition and Indicators – Workload - Analytical Techniques - Representing and Interpreting Results - Performance Optimization Methods - Aspects of Security - Encryption, Digital Signatures and Certificates - Secure Client/Server-Interaction - Client Security Issues - Service Provider Security Issues

Fundamentals - Client/Server Communication - Client-side Technologies - Ajax - Documentspecific Technologies - Server-side Technologies - Fundamentals and Specifics of Semantic Web - Technological Concepts and Tools

CIA2 25%

CIA3 10%

Attendance 5%

ESE 50%

To make the students learn the principles and practices of Cryptography, Network Security and to enable the students understand the various methods of encryption and authentication and help them identify the application of these techniques for providing Network and System Security.

Introduction, The need for security, Security Approaches, Security Attacks, Security Services, Security Mechanisms, A Model for Network Security. Symmetric Cipher Models – Substitution techniques, Transposition techniques, Steganography, Block Cipher Operation, Electronic Code Book, Cipher Block Chaining, Block Cipher Principles, The Data Encryption Standard, A DES Example, The Strength of DES, Evaluation criteria for AES, AES Cipher.

Introduction To Number Theory, Modular Arithmetic, Prime Numbers, Euler’s Totient Function, Principles of Public Key Cryptosystems, The RSA Algorithm, Other Public key cryptosystems, Diffie Hellman Key Exchange. Applications of Cryptographic Hash Functions, Two Simple Hash Functions, Hash Functions Based on Cipher Block Chaining, MD5 Message Digest Algorithm, Secure Hash Algorithm SHA 512.

Message Authentication Requirements – Message Authentication Functions –Requirements for Security of MACs, MACs Based on Hash Functions, HMAC, MACs Based on Block Ciphers, Data Authentication Algorithm. Digital Signatures, Elgamal Digital Signature Scheme, Schnorr Digital Signature Scheme, Digital Signature Standard.

Symmetric Key Distribution Using Symmetric Encryption, Symmetric Key Distribution Using Asymmetric Encryption, Distribution of Public Keys, X.509 Certificates, Public Key Infrastructure.

Remote user Authentication Principles, Remote User-Authentication Using Symmetric Encryption, Kerberos, Motivation, Kerberos Version 4, Remote User-Authentication Using Asymmetric Encryption, Federated Identity Management.

Network & Internet Security

Transport-Level Security

Web security Considerations, Secure Socket Layer and Transport layer Security; E-Mail Security – Pretty Good Privacy, S/MIME.

IP Security

IP Security Overview, IP Security Policy, Encapsulating Security Payload, Combining Security Associations, Internet Key Exchange.

Self Learning

Legal and ethical aspects

Cyber Crime and computer crime, Intellectual property, privacy, Ethical issues.

[2] Brijendra Singh, Network Security and Management, PHI ,3rd Edition, 2013.

[3] Eric Maiwald, Information Security Series, Fundamental of Network security, Dreamtech press,2010.

[4] Charlie Kaufman, Radia Perlman, Mike Speciner, Network Security: Private communication in public world, Prentice Hall, 2009.

CIA2 25%

CIA3 10%

Attendance 5%

ESE 50%

Data Science is the latest buzz word in the modern era of cloud and big data in academic research and corporate world. Data Science experts must acquire analytical skill set for pursuing research and generating new knowledge in the business process. Data Analytics course delivers various techniques to discover new and hidden knowledge from the data set. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, MATLAB and Excel are the suggestive tools for implementation.

Upon successful completion of the course students will be able to

·         collect data from various sources

·         explore data using tools

·         build analytical models

·         interpret results based on the choice of domain

Introduction, Data and Relations-Matrix representation, variable measures, sequential relation, sampling and quantization.

Data Pre-processing-Cleaning, Transformation, Basic Visualization-PCA, multidimensional scaling, Histograms, Correlation.

Linear and non-linear regression, Feature Selection.Forecasting-Recurrent Models, Classification-Rules, Trees, Naïve Bayes, SVM, Vector Quantization.Evaluation Metrics-Validation and Interpretation.

Optimization Methods – With derivatives, Gradient Descent. Clustering-Cluster Partition, Sequential, Prototype-Based, Relational, Cluster Validity and Self Organizing Map.

Introduction to Multi-criteria Decision Making, Using Numerical Methods in Data Science, Mathematical Modeling with Markov Chains.Modeling Spatial Data with Statistics- Getting predictive surfaces from special point data, Using trend surface analysis on spatial data.

Principles of Visualization-Understanding the type, Design Style, Data Graphic Type, Web-based Applications for Visualization Design, Best practices in dashboards, Making maps for Spatial Data.

Self Learning: Additional Exploration and Modeling Algorithms
Service based learning: Building models for social relevance issues

[1] Runkler, Thomas. A, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer, 2012.

[2] Lillean Pearson, Data Science For Dummies, John Wiley and Sons, 2015.

[2] John W Foreman, Data Smart: Using Data Science to Transform Information into Insight, Wiley, 2013.

Understand programming paradigms brought in by Python with a focus on Regular Expressions, List and Dictionaries. Work on Python case studies on Data Mining, Image and Data Processing to appreciate the fast changing landscape of programming. 

 Ability to program in Python

 Hands on Regular Expression

 Ability to Text Processing scripts

 Write to file handling scripts

 Learn to use Python for Data and Image processing

 Get hands on experience of Cluster Analysis using Python 

Introduction, Python Objects, Built-in Functions, Numbers and Strings, Conditionals and Loops, Functions, Passing Arguments, String Functions

Operators, Built-in Functions, List Type Built-in Methods, Special Features of Lists, Tuples, Tuple Operators and Built-in Functions, Special Features of Tuples File Objects, File Built-in Function, File Built-in Methods, File Built-in Attributes, Standard Files, Command-line Arguments, File System, File Execution, Persistent Storage Modules

Introduction/Motivation, Special Symbols and Characters for REs, REs and Python Introduction to Dictionaries, Operators, Built-in Functions, Built-in Methods, Dictionary Keys

Storing in List and Strings, Dispersion, Central Tendency, Mean Median Mode,FrequencyDistribution, Standard Deviation Using Files for large dataset, statistics with real data, reading data from internet, Accessing Stock Market Data, Correlating Stock data

Introduction, RGB Color Model, Object for Image Processing, Image Processing (Negative Images, Gray Scale, Resizing, Stretching, Flipping, Edge Detection) What is Data Mining? Implementing Cluster Analysis on Simple Data, Distance between two points, Clusters and Centroids, K-Means cluster Analysis, File Processing, Visualization

[2] Bradley N Miller, David L Ranum, Python Programming in Context, Second Edition, 2014

[2]Lutz, Mark, Learning Python, 4th Edition, O Rielly, 2009

CIA 2 - 50 marks

CIA 3 - 20 marks

Attendance - 10 marks

Weightage:

CIA 50%

ESE 50%

The subject is intended to give the knowledge of Big Data evolving in every real-time applications and how they are manipulated using the emerging technologies. This course breaks down the walls of complexity in processing Big Data by providing a practical approach to developing Java applications on top of the Hadoop platform. It describes the Hadoop architecture and how to work with the Hadoop Distributed File System (HDFS) and HBase in Ubuntu platform.

Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce - Data Serialization. Problems with traditional large-scale systems-Requirements for a new approach- Hadoop – Scaling - Distributed Framework - Hadoop v/s RDBMS - Brief history of Hadoop.

Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications. Algorithms using map reduce, Matrix-Vector Multiplication by Map Reduce.

Using languages other than Java with Hadoop, Analysing a large dataset.

Hadoop Processes (NN, SNN, JT, DN, TT) - Temporary directory-UI-Common errors when running Hadoop cluster, solutions.

Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudo-distributed mode, HDFS directory, NameNode, Examples of MapReduce, Using Elastic MapReduce, Comparison of local versus EMR Hadoop.

Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output.

How  Streaming  Works  -   Streaming  Command  Options - Specifying  a  Java  Class  as  the  Mapper/Reducer - Packaging Files With Job Submissions - Specifying Other Plug-ins for Jobs.

 Simple, advanced, and in-between Joins, Graph algorithms, using language-independent data structures.

Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling.

Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Why Pig, Use case of Pig, Pig Components, Data Model, Pig Latin.

RDBMS Vs NoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service.

Single Node Hadoop Cluster Set up in Amazon Cloud - How to create instance on Amazon EC2. How to connect that Instance Using putty.Installing Hadoop framework on this instance. Run sample programs which come with Hadoop framework.

[2]   Tom White, “Hadoop: The Definitive Guide”, Publisher: O’Reilly Media, Inc., 2015 Edn.

[3]   Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013 edn.

[2]   Jonathan R. Owens, Jon Lentz, Brian Femiano, “Hadoop Real-World Solutions Cookbook”, Packt Publishing, 2013 edn.

[3]   Tom White, “HADOOP: The definitive Guide”, O Reilly 2012.

CIA 2 - 50 marks

CIA 3 - 20 marks

Attendance - 10 marks

End Semester Exam - 100 marks

CIA (Weight) - 50%

ESE (Weight) - 50%

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1. Implement sequential search and binary search techniques.

2. Implement Selection sort.

3. Implement Insertion sort.

4. Implement Stacks.

5. Implement Queues.

6. Implement linked lists and some operations on linked lists.

7. Write a program to convert an infix expression to the postfix form.

8. Write a program to evaluate a postfix expression.

9. Implement Quick sort.

10. Implement Merge sort for array.

11. Merge Sort a file contents (without loading the content into an internal data structure).

12. Implement Two-Way linked lists.

13. Implement Circular linked lists.

14. Implement Binary Search Tree.

15. Implement Shell sort.

16. Implement Heap sort.

17. Implement Radix sort.

18. Implement Depth First Search for Graphs.

19. Implement Breadth First Search for Graphs.

The course focuses on acquiring skills needed to develop UNIX shell programs, making effective use of wide range of UNIX programming standard and tools.

• Able to write shell scripts for basic and advanced level shell programming. 
• Able to create programs with awk.
• Able to create programs for system calls, threads etc.

1. Write a shell script to print prime numbers up to a given range using arguments.

2. Write a shell script which  a. Converts  a decimal number to binary b. Converts an octal number to hexadecimal. 

3. Write a shell script which merge the contents of file1, file2, file3, sort them and display the sorted output on the screen page by page. 

4. Write a shell script to locate users who have logged in today or earlier but have not logged out and mail the list to root. Users who have logged more than once should appear in the list only once. 

5. Write a shell script to order the file /etc/passwd on GID (primary) and UID (secondary) which would place all users with same GID together. Users with a lower UID should be placed higher in the list 

6. Write a script to find the number of days between two given dates using functions. 

7. Write a script to compute the factorial value with and without using recursive functions. 

8. Write a shell script to search given number using binary search using function. 

9. Write a awk program that reads a file and prints a  report that groups employees of the same department The following are the contents of the report 

a. The department name in the top 

b. All detail of the employees 

c. Total salary for the department 

10. Write an awk program which accepts input from the standard input and prints the total of any column specified as an argument.  

11. Demonstrate fork(), kill(), sleep() system calls 

12. Demonstrate explicit locking and unlocking on a file using lockf()

13. Demonstrate process synchronization 

14. Create a file and read, write operations using different child process 

15. Demonstrate data sharing between process using Files 

16. Implement sorting using pipes 

17. Demonstrate FIFO’s 

18. Implement Message Queues 

19. Demonstrate Semaphores 

20. Demonstrate Threads

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1. DBMS Lab includes an application project. The backend of the project may be any one of the following:

•         MS-SQL Server

•         Oracle 

•         DB2

•         MySql

2. User interface could be made with any one of the front end tools available.

3. Students should have in-depth knowledge of the front and backend tool, which they are using.

4. Database tables are required to be normalized, at least to the second level.

5. There need to be independent forms for data entry operations.

6. All the forms in the project need to have similar look and feel in terms of background/foreground color, arrangement of controls, spacing and sizing of the controls, size of forms, etc.

7. There could be separate forms for searching purposes.

8. Master table data entry forms may include navigational buttons along with Add, Save, Delete etc.   

9. Reports should be generated dynamically. 

The Objective of this course is to cover the basic theory and algorithms that are widely used in Digital image processing. Develop hands-on experience in using computers to process images with Matlab image processing toolbox.

Upon successful completion of the course the student would: 

-   Understand the theoretical background of Image processing.

-   Apply image enhancement, restoration, compression and segmentation in both frequencyand spatial domain.

-  Represent and recognize objects through patterns in application.

The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System

Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.

Gray Level Transformations, Histogram Processing, Histogram equalization, Histogram specification, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters.

Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters.

Self Learning topic : Homomorphic Filtering

A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering.

Image Compression models: Huffman coding, Run length coding, LZW coding.

Point, Line and Edge detection.Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.

Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes

Self Learning topic:  Polygonal approximations using minimum perimeter polygons.

Boundary descriptors – Fourier descriptors.Regional descriptors –Topological descriptors and Moment invariants.

Introduction to Patterns and Pattern Classes. Decision-Theoretic Methods – Minimum distance classifier, K-NN classifier and Bayes.

Self Learning topic : Classification

[1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, 3rd Edition.PearsonEducation, 2009.

[2] A.K. Jain, Fundamental of Digital Image Processing, 4th Edition.PHI, 2011.

[3] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, 2nd Edition. PHI, 2009.

[1] M. A. Joshi, Digital Image Processing: An algorithmic approach, 2nd Edition.  PHI 2009.

[2] B.Chanda, D. DuttaMajumdar, Digital Image Processing and analysis, 1st Edition, PHI,2011.

This Course Description aimed at helping learners create applications using Google's Android™ open- source platform. The Course Description explains what Android™ is and how it compares to  other mobile environments, the setup of the Android™ Eclipse-based development tools, the Android™ SDK, all essential features, as well as the advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS

·       Build your own Android apps.

·       Explain the differences between Android and other mobile development environments.

·       Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.

·       Secure, tune, package and deploy Android Applications.

Brief History of Embedded Device Programming, Introduction to Android, Get to know the required tools,Creating your first Android application, Anatomy of android Application. Understanding Activities, linking Activities using intents, fragments, calling Built-in Applications using Intents, Displaying Notifications.

Understanding the components of a screen, adapting to display orientation, managing changes to screen orientation, Utilizing the Action Bar, Creating the user Interface programmatically,

Listening for UI Notifications. Using Basic Views, Using Picker views, Using List views to display lists, Understanding specialized fragments.

Using Image Views to display pictures, using menus with views, some additional views. Saving and loading user preferences, persisting Data Files, Creating and using Databases. Sharing Data in Android, using content provider, creating your own content providers, using content providers.

SMS Messaging, Sending E-mail, Displaying Maps, Getting Location Data, Monitoring a Location. Hands on project: Building a Location Tracker.

Creating your own services, Establishing Communications between a service and an activity, binding activities to services 

Self Learning

understanding Threads, Preparing for Publishing, Deploying APK Files.

[2] Jerome (J.F) DiMarzio , "Android - A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591.

To introduce the classic algorithms in various domains and study the different techniques for designing efficient algorithms.

• Design efficient algorithms using the various approaches for real world problems.

• Analyze the running time of algorithms for problems in various domains.

• Apply the algorithms and design techniques to solve problems.

The role of Algorithms in Computing – Algorithms, Algorithms as a technology. Getting Started – Insertion sort, Analyzing algorithms, Designing Algorithms. Growth of Functions – Asymptotic Notations. Recurrences – The Substitution method, Recursion Tree method and Master method.

Knap Sack Problem, Minimum Spanning Trees , Prims algorithm and Kruskal’s algorithm.

General Method, Binary Search, Finding the Maximum and Minimum, Merge Sort, Quick Sort, Selection sort, Strassens Matrix Multiplication.

Optimal Binary Search Trees, Traveling Salesman Problem, Longest Common Subsequence

General Method- Traveling Salesman Problem 

Introduction - The 8-queens problem, Sum of Subsets

Representation of Graph, Depth First Search, Breadth first search.Single Source shortest path – Dijkstra’s Algorithm and Bellman Ford Algorithm. All Pair Shortest Path – Floyd-Warshall Algorithm.

Comparison trees for sorting and searching.

Self Learning

Representation of graphs (from discrete mathematics)

Basic Concepts, NP_Hard graph problems, NP-Hard Scheduling problems, NP- Hard code generation problems, some simplified  NP-Hard problems

[2] Horowitz  E and Sahni S. Fundamentals of Computer Algorithms, Computer SciencePress,2008.

[2] Aho A V, Hopcroft J E and Ullman J D., The Design and Analysis of Computer  Algorithms, Addison Wesley Publishing House, 1983.

[3] Dromey, R.G., How to solve it by Computer, Prentice-Hall International, 2006.

To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics.

• Understand the basic principles of machine learning techniques.
• Understand the supervised and unsupervised machine learning algorithms.
• Choose appropriate techniques for real time problems. 

Machine Learning, types of machine learning, examples. Supervised Learning: Learning class from examples, VC dimension, PAC learning, noise, learning multiple classes, regression, model selection and generalization, dimensions of a supervised learning algorithm. Parametric Methods: Introduction, maximum likelihood estimation, evaluating estimator, Bayes’ estimator, parametric classification.

Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis.

Clustering: Introduction, mixture densities, k-means clustering, expectation-maximization algorithm, hierarchical clustering, choosing the number of clusters. Non-parametric: Introduction, non-parametric density estimation, non-parametric classification.

Introduction, univariate trees, pruning, rule extraction from trees, learning rules from data.

Multilayer perceptron: Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropogation algorithm, training procedures.

Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class kernel machines.

Bayesian Estimation: Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes.

Hidden Markov Models: Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM.

Introduction, canonical cases for conditional independence, d-separation, Belief propagation, undirected graph: Markov random field.

Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partiIntroduction, canonical cases for conditional independence, d-separation, Belief propagation, undirected graph: Markov random field.

Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.ally observed state.

Self Learning            

Clustering , Decision tree 

ESE - 50%

Evaluation scheme for Theory

To introduce basic theory and practical techniques in Artificial Intelligence. The course would provide emphasis to the principles and applications of Artificial Intelligence.

·         Understand what AI mean and the foundations of it.

·         Understand those elements constituting problems and learn to solve it by various uninformed and informed (heuristics based) searching techniques  

·         Understand the formal method for representing the knowledge and the process of inference to derive new representations of the knowledge to deduce what to do

·         Understand  the  notion of  Planning, Game playing and NLP  in AI and basic techniques in the classical systems

Introduction to AI, The Foundations of AI, AI Technique -Tic-Tac-Toe.Problem characteristics, Production system characteristics, Production systems: 8-puzzle problem. Searching: Uniformed search strategies – Breadth first search, depth first search.

Generate and Test, Hill climbing, simulated annealing search, Constraint satisfaction problems, Greedy best first search, A* search, AO* search.

Propositional logic - syntax & semantics

First order logic. Inference in first order logic, propositional Vs. first order inference, unification & lifts, Clausal form conversion, Forward chaining, Backward chaining, Resolution.

Overview, Minimax algorithm, Alpha-Beta pruning, Additional Refinements.

Classical planning problem, STRIPS- basic process and working of system.

Introduction, Syntax processing, Semantic Analysis, Pragmatic and Discourse Analysis

[2] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach, 2nd Edition. Pearson Education, 2007.

[2] George F Luger, Artificial Intelligence Structures and Strategies for Complex ProblemSolving, 4th Edition. Singapore: Pearson Education, 2008, ISBN-13  9780321545893

[3] N.L. Nilsson, Artificial Intelligence: A New Synthesis, 1st Edition. USA: Morgan        Kaufmann, 2000.

To help student to understand image enhancement techniques using spatial and Frequency domain of the images. This course also focuses on Image Restoration and classification using Matlab image processing toolbox.

- Enhancement of images using Spatial and Frequency domain Filtering

- Edge Detection 

- Processing of color images

- Classifiers

- Otsu Threshold algorithm

List of Programs

1.      Display of Grayscale Images  and Rotation

2.      Scaling  (Image Resizing)

3.      Quantization and Histogram Equalization.

4.      Linear Spatial Filtering (Average and Laplacian)

5.      Non-linear Spatial Filtering (Median)

6.      Edge detection using Operators- Part I (Sobel, Prewitt)

7.      Edge detection using Operators- Part II (Roberts)

8.      Display of colour images (Extracting the three components in the images)

9.      Unsharp Masking and high boost filtering

10.  Fourier descriptor

11.  Minimum distance classifiers

12.  Segmentation using an algorithm (Thresholding)

13.  Bayes Classifier

Aimed at helping learners create applications using Google's Android™ open- source platform. Learn, Understand, create and deploy  advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

·       Explain the differences between Android and other mobile development environments.

·       Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.

Secure, tune, package and deploy Android Applications

1. Installation – Android Studio

2.Develop an application that uses GUI components, Font and Colors.

3.Develop an application that uses Layout Managers and event listeners.

4.Develop a native calculator application

5. Develop an Application that used the camera in phone and allows to click the photo.

6.Develop an application that uses that demonstrates the use of Fragments

7.Develop a Music player – Basic controls to play, pause and stop the MP3 file

8.Implement an application that writes data to the SD card.

9.Write a mobile application that creates alarm clock.

10.Develop an application that makes use of database.

11.  Creating application using App Inventor

12.  Packaging and Deploying the APK file in Google Play store

[2] Jerome (J.F) DiMarzio , "Android - A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591

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Project will be based on the specialization papers which students are opted for.

The course is designed to train and equip the students to carryout research.

There is only CIA for this paper. Research work carried out in this semester is divided in two parts.

Part A constitutes data collection and pre-processing in which students should carry out the following tasks and submit the document for the same before the MSE.

·         Literature survey of existing data sets or any primary data sets in the respective area (5 marks)

·         Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.) (5 marks)

·         Steps in pre-processing (5 marks)

Part B constitutes modelling and implementation of their research work. Students should perform the following tasks:

·         Methodology (10 marks)

·         Evaluation and Discussion of Results (5 marks)

·         Limitations, Conclusions and Scope for future enhancements (5 marks)

·         Plagiarism report (5 marks)

Students should give a comprehensive presentation based on the results and interpretations of their research work.

Students will be giving presentations on any advanced concepts and technologies in computer science.