+Programme Name : M.Sc. Computer Science Programme …

+Programme Name : M.Sc. Computer Science

Programme Code : PCS

Programme Specific Outcomes

At the completion of the M.Sc. programme in Computer Science the students will be able to

1. Have ability to communicate Computer Science concepts

2. Use software development tools and Modern computing platforms

3. Formulate solution for computing problems and provide arguments for the solutions.

4. Design and implement software systems to meet the desired needs.

5. Apply algorithms and mathematical concepts to design and Analysis of software.

6. get employed in the field of software industry.

7. pursue research in the field of Computer Science.

ST. XAVIER’S COLLEGE(AUTONOMOUS)

Palayamkottai - 627 002

(Recognized as “College with Potential for Excellence” by UGC) (Re-accredited with “A” Grade with a CGPA of 3.50)

SYLLABUS

M.Sc. COMPUTER SCIENCE

(w.e.f from June 2018)

M. Sc. Computer ScienceProgramme Structure

Sem Course Code Title of the Course Hours Credits

I

18PCS11 Advanced Data Structures and Algorithms 5 5

18PCS12 J2EE 5 5

18PCS13 Object Oriented Analysis and Design 5 5

18PCSE14

(Elective 1)

Theory of Computation / Adv. System Programming / Logic

Programming / Graph Theory 5 5

18PCS15 Lab 1: Advanced Data Structures and Algorithms 4 2

18PCS16 Lab 2: J2EE 4 2

Library/Seminar 2

Sub Total 30 24

II

18PCS21 Machine Learning with Python 5 5

18PCS22 Internet Technologies 5 5

18PCS23 Compiler Design 5 5

18PCSE24

(Elective 2)

Mobile Communication / Big Data Analytics / Decision Support

System / Embedded System 5 5

18PCS25 Lab 3: Machine Learning with Python 4 2

18PCS26 Lab 4: Internet Technologies 4 2

Library/Seminar 2

Sub Total 30 25

III

18PCS31 Mobile Apps with Android 5 5

18PCS32 Data Mining and MatLab 5 5

18PCS33 Image Processing 5 5

18PCSE34

(Elective 3)

Internet Of Things/ Pervasive Computing/Virtual Reality/Green

Computing 5 5

18PCSE35 Lab 5: Android Programming 4 2

18PCSE36 Lab 6: Data Mining and MatLab 4 2

18PCSE37 Mini Project 2

Library/Seminar 2

Sub Total 30 26

IV

18PCS41 Soft Computing 5 5

18PCS42 Dot Net Programming 5 5

18PCS43 Lab 7:Dot Net Programming 6 3

18PCS44 Project work and viva-voce 10 3

Library/Seminar 4

Sub Total 30 16

STAND

1

Total 120 91

ADVANCED DATA STRUCTURES AND ALGORITHMS

(Course code 18PCS11)

SEMESTER– I HOURS – 5 CREDITS – 5

Course Outcomes: At the end of the course the students must be able to

1. estimate time complexity in big-O notation.

2. analyze algorithms

3. get an understanding of the advanced data structures.

4. get an understanding of the appropriate use of a particular data structure and algorithm to solve a problem.

5. Know the different techniques of algorithm

UNIT I FUNDAMENTALS

Mathematical Proof Techniques: Induction, proof by contradiction, direct proofs – Asymptotic Notations – Properties

of Big-oh Notation –Conditional Asymptotic Notation – Algorithm Analysis – Amortized Analysis –– Recurrence

Equations – Solving Recurrence Equations – Time-Space Tradeoff- NP Hard And NP-Complete Problems

UNIT II HEAP STRUCTURES

Min/Max heaps – Deaps – Leftist Heaps – Binomial Heaps – Fibonacci Heaps – Skew Heaps – Lazy-Binomial

Heaps

UNIT III SEARCH STRUCTURES

Binary Search Trees – AVL Trees – Red-Black trees – Multi-way Search Trees –B-Trees – Splay Trees – Tries.

UNIT IV GREEDY METHOD

Greedy Method: General Method Knapsack Problem Job Sequencing with deadlines Minimum Spanning Trees -

Single Source Shortest Path

UNIT V DYNAMIC PROGRAMMING, BACK TRACKING

Dynamic Programming: General Method, Multistage Graphs, All pair Shortest Paths -Optimal Binary Search Tree,

0/1 Knapsack, Reliability Design, Travelling Sales Person Problem.Back Tracking: General method- 8 Queen

Problem -Sum of Subsets- Graph Coloring -Hamiltonian cycles.

TEXTBOOKS

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

Press, 2007

2. E. Horowitz, S. Sahni and S Rajasekaran, Fundamentals of computer Algorithms, Second Edition, University

Press.

REFERENCES

1) Mark Allen Weiss, Data Structures and Algorithm Analysis in C++ , Pearson Publication, 2002

J2EE

(Code : 18PCS12)

SEMESTER – I HOURS – 5 CREDITS – 5


1. Impart the theoretical and practical knowledge about servlets and JSP

2. Learn the JDBC connectivity with servlets and JSP

3. Study RMI and improve the knowledge about EJBs

4. Give practical exposure to create web sites by using JSP

UNIT I

Understanding Java and the J2EE platform: Brief History of Java – Examining the origin of J2EE –

Application components – roles – J2EE API’s . Studying Servlet Programming: Introduction –creating html login

screen- servlet structure and life cycle methods – writing servlet – servlet context – url redirection – session tracking

with servlets – cookies – url rewriting – hidden fields – session tracking object with HttpSession Object –Login Servlet

example – Listeners – Filters- deploying servlets- web application archive – web.xml deployment descriptor – applet

servlet communication.

UNIT II

Java Database Connectivity: Introduction – JDBC Driver types – creating first JDBC program – retrieving

data – database error processing – processing result sets – resultset metadata class – scrollable result set –

preparedstatement class – callablestatement class – performing batch updates – using savepoints – configuring jdbc-

odbc bridge – database connection pools and data sources – using row set interface.

UNIT III

JSP Basics – JSP scripting elements and Directives –Declarations – Expressions – Directives – Scriplets –

Comments – Actions – Implicit JSP Objects – error Pages using JavaBean in JSP – Creating a Login using

JavaBean - embedded control flow statements .

UNIT IV

The JSP Engine - Multithreading and persistence - the implicit objects - the JSP cycle – Sending

Information-setting Cookies-Handling errors. Tracking Sessions : Tracking data between request.

UNIT V

Remote Method Invocation: Overview of RMI – developing application with RMI – Flight Server Example.

Enterprise Java Beans : EJB Component Model – The Enterprise Java Bean – life of an entity bean – life of a

session bean – transaction and EJB – container managed transactions –bean managed transactions –working with

Entity Beans- working with EJB QL- bean managed persistence-Exceptions.

TEXT BOOKS :

1. James McGovern, Rahim Adatia et al, “J2EE 1.4 Bible”,Wiley Publishing, Inc, 2009,ISBN 10 : 8126504536

2. Damon, Hougland, Aaron Tavistock, “Core JSP”, PHI, Second edition. Prentice Hall

REFERENCE BOOKS

1. Jim Keogh, “ The Complete Reference J2EE” , Tata McGraw Hill, Edition, 2002.

2. Justin Couch and Daniel H. Steinberg, “J2EE Bible”, Dreamtect India, First edition,

2002.

OBJECT ORIENTED ANALYSIS AND DESIGN

(Course Code:18PCS13)



1. Learn the basics of OO analysis and design and map design to code

2. Design models for software

3. Understand the basic concepts to identify state & behavior of real world objects 4. learn the various object oriented methodologies and choose the appropriate one for solving the problem with

the help of various case studies 5. implement analysis, design & testing phases in developing a software project

UNIT I INTRODUCTION

Objects Oriented Analysis – Object Oriented Themes, Modeling as a Design Technique: Modeling – The Object

Modeling Technique. Object Modeling: Object and Classes – Links and Associations – Advanced Link and

Association Concepts – Generalization and Inheritance – Grouping Constructs, Advanced objectModeling:

Aggregation – Abstract Classes – Generalization as extension and Restriction – Multiple Inheritance – Metadata –

Candidate Key – Constraints.

UNIT II DYNAMIC MODELING

Events and States – Operations – Nested State Diagrams – Concurrency – Advanced Dynamic Modeling Concepts –

Relation of Object And Dynamic Models, Functional Modeling: Functional Models – Data Flow Diagrams – Specifying

Operations – Constraints – Relation of Functional to Object and Dynamic Models, Methodology Preview: OMT as a

Software Engineering Methodology – The OMT Methodology – Impact of an Object Oriented Approach

UNIT III SYSTEM DESIGN

Overview of System Design – Breaking a System into sub Systems – Identifying Concurrency – Allocating

Subsystems to Processors and Tasks – Management of Data Stores – Handling Global Resources – Choosing

Software Control Implementation – Handling Boundary Conditions – Setting Trade off priorities – Common

Architectural Frameworks – Architecture of the ATM machine, OBJECT Design: Overview of Object Design –

Combining the Three Models – Designing Algorithms – Design Optimization – Implementation of Control –

Adjustment of Inheritance – Design of Association – Object Representation – Physical Packaging – Documenting

Design Decisions.

UNIT IV AN OVERVIEW of UML

Views- use case view-logical view-implementation view-process view-deployment view-diagrams-use case diagram-

object diagram-state machine-Activity diagram-interaction diagram-component diagram-deployment diagram-

composite structure diagram-model event general mechanism-adornments-comments-specifications.

UNIT V USE CASE MODELING

Basics of use cases-use case diagram-system-action-finding actors-actors in UML-relationship between actors-

finding use cases-use cases in UML-relationship between use cases-generalization relationship-extend relationship-

include relationship-organizing use cases-describing use cases-assessing use cases -testing use cases-use cases

and requirement management

TEXT BOOK

1) James Rumbaugh Michael Blaha , William Premerlani, Frederick eddy and William Lorensen,”Object

Oriented Modeling and Design” , Prentice Hall of India Pvt Ltd., Second edition,2006

2) Hans Erik Erikson,Magnus Penker,Brian Lyons,”UML 2 Tool kit”Wiley India Pvt Ltd,OMG Press,2008

REFERENCE BOOKS :

1) H.Srimathi,H.Sriram,A.Krishnamoorthy,”Object oriented analysis and design using UML” ,Scitech

publications Pvt Ltd,2006

2) Simon Bennett,Steve Mcrobb,Ray farmer,”Object oriented system analysis and design using

UML”,Tata-MCGraw Hill Publishing company Lmt,2010

Lab 1 : ADVACED DATA STRUCTURES

(Course Code : 18PCS15)


Course Outcomes: At the end of the course the students must be able to 1. Develop programming skill in handling Queues 2. Understand the Sort algorithms 3. Write program for Trees 4. Effectively handle linked list 5. Understand various problems

List of Practical

1. Stack & Queue

2. Binary Search

3. MergeSort

4. Quicksort

5. Selectionsort

6. Greedy Knapsack Problem

7. Job Sequencing with deadlines

8. Minimum Spanning Tree

9. Single Source Shortest Path

10. All Pair Shortest Path

11. 0/1 Knapsack

12. Travelling Sales Person Problem

13. 8-Queen Problem

Lab 2 : J2EE



Course Outcomes: At the end of the course the students must be able to 1. Write simple programs with Servlet

2. Write programs using JDBC with database

3. Develop Cookies using JSP

4. Develop programs using JDBC in JSP

Servlet

1. Simple Servlet Program using GenericServlet

2. Program for Login Page using HttpServlet

3. Program to implement Hidden fielddirection

4. Program to implement Session tracking in Servlet

5. Program to implement Cookies in Servlet

6. Program for JDBC to insert , update and delete records to and from database using Servlet

7. Program to display employee details in tabular format using Servlet

JSP

8. Program for voting eligibility using JSP

9. Program using Scriplets of JSP

10. Program using embedded control flow statement

11. Program to implement Session tracking

12. Program to implement Cookie in JSP

13. Program to implement implicit objects of JSP.

14. JSP interact with Java Bean

15. Program for JDBC to insert , update and delete records to and from database using JSP

16. Program to display student details in tabular format using JSP.

THEORY OF COMPUTATION

(Sub. Code:18PCSE14)

Semester:I Hours:5 Credits:5 Course Outcomes: At the end of the course the students will be able to

1. Understand the basic concepts and application of Theory of Computation.

2. Students will apply this basic knowledge of Theory of Computation in the computer field to solve computational problems and in the field of compiler also.

3. Design Finite State Machine, Pushdown Automata and Turing Machine. 4. Explain decidability and undecidability of various problems.

UNIT I: FINITE AUTOMATA Introduction - Basic Definitions – Finite Automaton – DFA & NDFA – Finite Automaton with €- transitions – Regular Languages- Regular Expression – Applications of Regular Expressions – Algebraic laws - Closure and Decision properties of Regular Languages - Equivalence of DFA –Equivalence of finite Automaton and regular expressions –Minimization of DFA. (Chap: 2,3,4) UNIT II: GRAMMARS Introduction– Types of Grammar - Context Free Grammars and Languages– Derivations and Languages - Parse trees – Construction and the Yield - Applications of CFG – Ambiguity – Removing ambiguity – inherent ambiguity.(Chap: 5) UNIT III: PUSHDOWN AUTOMATA Pushdown Automata- Definitions – the Language of a PDA – Deterministic pushdown automata – Equivalence of Pushdown automata and CFG.(Chap: 6) UNIT IV: TURING MACHINES Definitions of Turing machines – Combining Turing Machines – Variations of TM – Nondeterministic TMs –Multi tape Turing Machines - Models of Computation –Recursively enumerable languages. (Chap: 9, 10) UNIT V: UNSOLVABLE PROBLEMS AND COMPUTABLE FUNCTIONS Unsolvable Problems and Computable Functions –Measuring and Classifying Complexity: Time and space complexity of a Turing Machine. Tractable and Intractable problems: Tractable and possibly intractable problems: P and NP - NP completeness - Polynomial time reductions. (Chap: 11, 12, 13, 14) TEXT BOOKS:

1. Hopcroft J. E., Motwani R and Ullman J. D, “ Introduction to Automata Theory, languages and

Computations”, Second Edition, Pearson education, 2008(Unit 1,2, 3)

2. John C. Martin, “Introduction to the Languages and the Theory of Computation”, third edition, Tata

McGrawHill Publishing Company, New Delhi 2007(Unit 4,5)

Reference Books: 1. Adesh K. Pandey, “An introduction to automata theory and formal languages”, Publisher: S.K. Kataria&

Sons

2. Deniel I. Cohen , “Introduction to computer theory “,Joh Wiley & Sons, Inc.

3. Marvin L. Minsky ,”Computation: Finite and Infinite” , Prentice-Hall India.

4. Alfred V Aho,,” Compiler Design”, Addison Weslley.

5. Michael Sipser ,”Introduction to the Theory of Computation”.

ELECTIVE I

ADVANCED SYSTEM PROGRAMMING

(Course Code:18PCSE14)



1. Be familiar with evolution of programming system components

2. Be exposed to General machine structure.

3. Understand the concepts of Machine Language.

4. Learn to identify the Loader Schemes and gain the overall knowledge for Advanced System

programming

UNIT I – BACKGROUND

Machine Structure-Evolution of the Components of Programming System-Evolution of Operating Systems-Operating

System User Viewpoint: Functions, Batch Control Language, Facilities – General Machine Structure – General

approach to a new machine-Machine Structure -360 and 370.Machine Language - Assembly language.

UNIT II – Assemblers & Loaders

General Design Procedure-Design of Assembler-Statement of problem-Data Structure-Format of Data Bases-

Algorithm-Look for modularity-Loader Schemes- Compile and Go Loaders-General Loader Scheme-Absolute

Loaders-Subroutine Linkages-Relocating Loaders-Direct-linking Loaders-Design of Absolute Loader.

UNIT III -PROGRAMMING LANGUAGES

Importance of High Level Languages-Features of High Level Languages-Data types and Data Structures –Character

String – Bit String- Data Structures-Storage Allocation and Scope of Names-Storage Classes-Block Structure-

Accessing Flexibility-Pointers-Label Variables and Label Arrays-Functional Modularity-Procedures-Recursion-

Asynchronous Operation

UNIT IV – FORMAL SYSTEMS

Uses of Formal Systems in programming Languages-Language Specification-Syntax-directed Compliers-Complexity

Structure Studies-Structure Analysis-Formal Specification-Approaching a formalism-Development of Formal

Specification-Formal Grammars-Example of Formal Grammars-Derivation of Sentences-Sentential Forms and

Sentences.

UNIT V – SYSTEM ADMINISTRATION

Duties of the Administrator, Administration tools, Overview of permissions - Processes: Process status, Killing

processes, process priority. Starting up and Shut down: Peripherals, Kernel loading, Console, The scheduler,

Managing User Accounts: Principles, password file, Password security, Shadow file, Groups and the group file,

Shells.

TEXT BOOK

1. John J.Donovan, “Systems Programming”, 43rd reprint,TATA McGRAW-HILL Edition,2008.

2. Kirch – “ Linux network Administrator’s guide (2nd Ed.)” – O’Rielly

3. Maxwell – “Unix system administration” - TMH 5. Limoncelli –“The Practice of System & Network

Administration”-Pearson 6. Wells, LINUX Installation & Administration, Vikas

REFERENCES:

1. W. R. Stevens – “Unix network programming, vol. 1(2nd Ed.)” – Pearson Education/PHI

2. W. R. Stevens – “TCP/IP illustrated, vol. 1” – PHI/Pearson Education

3. Comer – “Internetworking with TCP/IP, vol. 1(4th Ed.)” – Pearson Education/PHI Computer Science &

Engineering Syllabus 27

4. E. Nemeth, G. Snyder, S. Seebass, T. R. Hein – “ Unix system administration handbook” – Pearson Education

Object Technology & UML Code: CS 605

ELECTIVE I

LOGIC PROGRAMMING



Course Outcomes : At the completion of this course the students should be able to:

1. know the fundamental elements of the Logic programming.

2. to write a program using a variety of language elements.

3. develop structured flowcharts for report programs of varying levels of complexity and code the program.

4. learn techniques for developing logic, designing and writing a well structured program.

Unit I: Programming Paradigms

Programming Paradigms-Logic Programming-Prolog Syntax-Unification-Meaning of Prolog Programs-List

Processing: Operators.

Unit II: Arithmetic

Structures-controlling backtracking-Negation as Failures-Built-in Procedures.

Unit III: Grammars

Definite Clause Grammars-Meta Programming- Interpreters-constraint logic programming-Constrait satisfaction

Problem-Practical Applications.

Unit IV: Logic and Models

Logic and Models-First Order Logic Model Theory for Knowledge Representers-Semantic of Prolog Programs.

Unit V: Inductive Logic Programming

Inductive Logic Programming-Query Evaluation Strategies-Efficiency-Semantic web and Logic Programming.

TEXT BOOK

Ivan Bratko, “Prolog Programming for Artificial Intelligence”, Third Edition, Addison-Wesley Publ. Co., 2000.

REFERENCE BOOKS

1. W. F. Clocksin and C.S.Mellish, “Programming in Prolog”, Fourth Edition, Springer-Verlag, 2000.

2. Ulf Nilsson and Jan Maluszynki, “Logic Programming and Prolog” ,Second Edition, 2000.

http://www.pearsoned.co.uk/highereducation/resources/bratkoprologprogrammingforartificialintelligence3e/

http://www.ida.liu.se/~ulfni/lpp/

ELECTIVE I

GRAPH THEORY



Course Outcomes: After completing the course, the students will be able to 1. Apply concepts and fundamentals theorems of Graphs to model problems of real world.

2. Implement Graph algorithms.

3. Find the research directions in the field of Graphs.

4. Learn about the various graphs and its applications

5. Gain the overall knowledge in Graph Theory Unit-1 Graph definitions - Definitions of terms such as graph - sub-graph – vertex – edge - directed/undirected graph - weighted/un-weighted edges – degree - cut vertex/articulation point - clique - complete graph - Finite and Infinite Graphs - bipartite graphs - Isolated Vertex - Pendant Vertex - Null Graph.Path and Circuits - Walks, paths and Circuits - Connected Graphs - Disconnected Graph - Components - Euler Graph - Hamiltonian Paths – Circuits – The Travelling Salesman problem.

Unit -2

Trees – Definitions – some properties of trees – pendant vertices in a tree – distance and centers - rooted trees - binary trees – counting trees - spanning trees – fundamental circuits – finding all spanning trees.

Cut-Set and its Properties- Different Cut Sets in a graph – fundamental circuits and cut-sets – connectivity and separability – network flows – 1-Isomorphism – 2-Isomorphism.

Unit -3

Combinatorial vs Geometric graphs – planar graphs – Kuratowski’s two graphs – detection of planarity – geometric dual – combinatorial dual – thickness and crossings.

Matrix representation of graphs – incidence matrix – submatrices – cirtuit matrix – fundamental circuit matrix and rank – cut-set matrix – Path matrix – Adjacency matrix.

Unit - 4

Chromatic Number – Chromatic Partitioning – Chromatic Polynomial – Matchings – Coverings – Four Color problem. Directed graphs – types of digraphs – digraphs and binary relations – directed paths and connections – Euler digraphs – trees with directed edges – fundamental circuits in digraphs - matrices of digraphs – adjacency matrix of digraphs – paired comparisons and tournaments – acyclic digraphs.

Unit – 5

Graph theoretic algorithms and computer programs – computer representation of graphs – basic algorithms – connectedness and components algorithm – spanning tree algorithms – shortest-path algorithms – depth-first search on a graph – breadth first search – isomorphism algorithm.

TEXT BOOK

1. Narsingh Deo, “Graph Theory with Applications to Engineering and Computer Science”, Prentice -Hall India (PHI), 2003.

REFERENCE BOOKS

1. Clark J. and Holton D.A, “A First Look at Graph Theory”, Allied Publishers, 1995.

2. R.J. Wilson, "Introduction to Graph Theory", Fourth Edition, Pearson Education, 2003

3. R. Diestel, "Graph Theory", Springer-Verlag, 2nd edition, 2000

MACHINE LEARNING WITH PYTHON

(Code:18PCS21)

SEMESTER –II HOURS – 5 CREDITS – 5


1. Understand the concepts of machine Learning

2. Understand the fundamentals of Classification and probability theory

3. Understand the concepts of Python

4. Learn various Data storage using Hadoop.

5. Gain overall knowledge about the subject UNIT - I Classification - Machine learning basics - Key terminology - Key tasks of machine learning - How to choose the right algorithm - Steps in developing a machine learning application - Getting started with the NumPy library - Classifying with k-Nearest Neighbors - Classifying with distance measurements - Example: a handwriting recognition system - Splitting datasets one feature at a time: decision trees - Tree construction - Plotting trees in Python with Matplotlib annotations - Testing and storing the classifier. UNIT – II Classifying with probability theory: naïve Bayes - Classifying with Bayesian decision theory - Classifying with conditional probabilities - Document classification with naïve Bayes - Classifying text with Python - Logistic regression - Classification with logistic regression - Using optimization to find the best regression - Support vector machines - Separating data with the maximum margin - Finding the maximum margin - Efficient optimization with the SMO algorithm - Speeding up optimization with the full Platt SMO - Using kernels for more complex data - Improving classification with the AdaBoost meta-algorithm - Classifiers using multiple samples of the dataset - Train: improving the classifier by focusing on errors - Creating a weak learner with a decision stump - Implementing the full AdaBoost algorithm - Test: classifying with AdaBoost. UNIT – III Forecasting numeric values with regression - Finding best-fit lines with linear regression - Locally weighted linear regression - Shrinking coefficients to understand our data - The bias/variance tradeoff - Tree-based regression - Locally modeling complex data - Building trees with continuous and discrete features - Using CART for regression - Tree pruning - Model trees - Example: comparing tree methods to standard regression - Using Tkinter to create a GUI in Python. UNIT – IV Unsupervised learning - Grouping unlabeled items using k-means clustering - The k-means clustering algorithm - Improving cluster performance with postprocessing - Bisecting k-means - Association analysis with the Apriori algorithm - Association analysis - The Apriori principle - Finding frequent itemsets with the Apriori algorithm - Mining association rules from frequent item sets - Efficiently finding frequent itemsets with FP-growth - FP-trees: an efficient way to encode a dataset - Build an FP-tree - Mining frequent items from an FP-tree. UNIT – V Using principal component analysis to simplify data - Dimensionality reduction techniques - Principal component analysis - Simplifying data with the singular value decomposition - Applications of the SVD - Matrix factorization - SVD in Python - Collaborative filtering–based recommendation - Big data and MapReduce - MapReduce: a framework for distributed computing - Hadoop Streaming - Running Hadoop jobs on Amazon Web Services - Machine learning in MapReduce - Using mrjob to automate MapReduce in Python - Example: the Pegasos algorithm for distributed SVMs. TEXT BOOK 1. Peter Harrington,” Machine Learning in Action”, Manning Publications Co., 2012 REFERENCE BOOKS

1. Willi Richert, Luis Pedro Coelho, “Building Machine Learning Systems with Python”, Packt Publishing, 2013. 2. Andreas C. Müller, Sarah Guido, “Introduction to Machine Learning with Python: A Guide for Data

Scientists”, O'Reilly Media, 2016.

https://www.amazon.com/s/ref=dp_byline_sr_book_1?ie=UTF8&text=Andreas+C.+M%C3%BCller&search-alias=books&field-author=Andreas+C.+M%C3%BCller&sort=relevancerank

https://www.amazon.com/s/ref=dp_byline_sr_book_2?ie=UTF8&text=Sarah+Guido&search-alias=books&field-author=Sarah+Guido&sort=relevancerank

INTERNET TECHNOLOGIES

(Code:18PCS22)


Course Outcomes : At the end of the course the students will be able to

1. familiarize the different Internet Technologies.

2. understand the concepts of Internet Programming and its related programming and scripting languages.

3. learn concepts related to XML & PHP

4. understand the java specific web services architecture.

5. Gain overall knowledge about the subject

UNIT I BASICS

WEBSITES BASICS, HTML 5, CSS 3, WEB 2.0 8 Web 2.0: Basics-RIA Rich Internet Applications - Collaborations

tools - Understanding websites and web servers: Understanding Internet – Difference between websites and web

server- Internet technologies Overview –Understanding the difference between internet and intranet; HTML and CSS:

HTML 5.0 , XHTML, CSS 3.

UNIT II: XHTML

Introduction to XHTML – Structure of XHTML Document – Headings – Links – Images – Lists – Tables – Forms –

Frames – Internal Linking – Web Page Design – Introduction to CSS – Inline Styles – Embedded Style Sheets –

Conflicting Styles – Liking External Style Sheets – Positioning Elements – Backgrounds – Element Dimensions – Box

Model and Text Flow – Media Types – Drop Down Menu - User Style Sheets – Sample Web Applications.

UNIT III PHP and XML

PHP and XML An introduction to PHP: PHP- Using PHP- Variables- Program control- Built-in functions-Connecting to

Database – Using Cookies-Regular Expressions; XML: Basic XML- Document Type Definition- XML Schema DOM

and Presenting XML, XML Parsers and Validation, XSL and XSLT Transformation.

UNIT IV INTRODUCTION TO JAVA SCRIPT

Java Script: An introduction to JavaScript Structure of JavaScript – Sample Programs – Operators – I/O Structures -

Control Structures: Selection and Multiple Selection Structures – Functions: Programmer Defined Functions –

Function Definition – Arrays – Objects: Object Technology Concepts – Various JavaScript Objects – DOM Nodes and

Trees - JavaScript DOM Model - Exception Handling-Validation--Event Handling

UNIT V INTRODUCTION TO AJAX and WEB SERVICES:

Ajax Client Server Architecture-XML Http Request Object-Call Back Methods; Web Services: Introduction- Java web

services Basics – Creating, Publishing, Testing and Describing a Web services (WSDL)-Consuming a web service,

Database Driven web service from an application – SOAP– Session Tracking in Web Services

TEXT BOOKS:

1. Deitel and Deitel and Nieto, “Internet and World Wide Web - How to Program”, Prentice Hall, 5 th Edition, 2011.

2. Herbert Schildt, “Java-The Complete Reference”, Eighth Edition, Mc Graw Hill Professional, 2011.

REFERENCES:

1. Stephen Wynkoop and John Burke “Running a Perfect Website”, QUE, 2nd Edition,1999.

2. Chris Bates, Web Programming – Building Intranet Applications, 3 rd Edition, Wiley Publications, 2009.

3. Jeffrey C and Jackson, “Web Technologies A Computer Science Perspective”, Pearson Education, 2011.

4. Gopalan N.P. and Akilandeswari J., “Web Technology”, Prentice Hall of India, 2011.

COMPILER DESIGN




1. Understand the concepts in Compilers

2. understand the fundamental concepts Lexical analysis and Syntax analysis

3. understand the concepts of runtime environment

4. learn various code generation and code optimization techniques

UNIT I

Introduction to Compiling: Compilers – Analysis of the source program – The phase of a compiler –

Cousins of the compiler – The grouping of phases – Compiler constructions tools. A Simple One-Pass Compiler:

Overview – Syntax definition – Syntax-directed translation – Parsing – A translator for simple expressions – Lexical

analysis – Incorporating a symbol table – Abstract stack machines – Putting the techniques together.

UNIT II

Lexical Analysis : The role of the lexical analyzer – Input buffering – Specification of tokens – Recognition

of tokens – A language of specifying lexical analyzers – Finite automata – From a regular expression to an NFA –

Design of a lexical analyzer generator – Optimization of DFA-based pattern matchers.

UNIT III

Syntax Analysis : The role of the parser – Context-free grammar – Writing a grammar – Top-down parsing

– Bottom-up parsing - Operator-precedence parsing – LR parsers – Using ambiguous grammars – Parser generator.

UNIT IV

Run-Time Environment: Source language issues – Storage organization – Storage-allocation strategies –

Access to nonlocal names – Parameter passing – Symbol tables – Language facilities for dynamic storage allocation

– Dynamic storage allocation techniques – Storage allocation in FORTRAN. Intermediate Code Generation:

Intermediate languages – Declarations – Assignment statements -Boolean expressions – Case statements – Back

patching – Procedure calls.

UNIT V

Code Generation : Issues in the design of a code generator – Target machine – Run-time storage

management – Basic blocks and flow graphs – Next-use Information – A simple code generator – Register allocation

and assignment – the DAG representation of basic blocks – Peephole optimization – Generatingcode from DAGs

Code Optimization: Introduction – the principal sources of optimization – optimization of basic blocks – loops in flow

graphs – global data flow analysis

TEXT BOOK :

Alfred V. Aho, Ravi Sethi, Jeffrey D.Ullman. “Compilers Principles, Techniques and Tools”, Addison Wesley

Publishing Company1998.

REFERENCE BOOKS :

1. A.V. Aho and J.D. Ullman, “The Principles of Compiler Design”, Narosa Publishing House, 1999.

2. D.M..Dhamdhere, “Compiler Construction – Principles and Practices”, Macmillon India Limited, 2005.

Lab 3 : MACHINE LEARNING WITH PYTHON



Course Outcomes: At the end of the course the students must be able to 1. Write programs to recognize and analyze handwriting

2. Develop programs in Python using various objectives

3. Develop programs for various algorithms using Python

4. Update knowledge to learn any future version of the language

List of Practical

1. Implementing a handwriting recognition system 2. Program using decision trees to predict contact lens type 3. Program for classifying spam email with naïve Bayes 4. Implementing handwriting classification using SVM 5. Program for predicting the age of an abalone shellfish 6. Program to create a forecast with tree-based regression 7. Program to cluster points on a map 8. Program for finding co-occurring words in a Twitter feed 9. Program for image compression with the SVD 10. Implement the Pegasos algorithm for distributed SVMs

Lab 4: INTERNET TECHNOLOGIES LAB

(Course Code 18PCS26)



1. Design Web pages using HTML/DHTML and style sheets

2. Create dynamic web pages using server side scripting.

3. Write web service applications.

4. Design and Implement AJAX

LIST OF EXPERIMENTS:

1. Design a web site using HTML and DHTML. Use Basic text Formatting, Images.

2. Design web page with forms and links using HTML.

3. Design web page with frames and tables using HTML.

4. Create web sites in HTML with CSS.

5. Create an XML document to store employee details using DTDS.

6. Create an XML schema for book’s information with validation.

7. Write an XSLT program to extract the details of a student.

8. Write a DOM to extract the details from product document.

9. Create a java script that to validate the user login on the page

10. Create a java script that collects numbers from a page and then adds them up and prints them to next page.

11. Create a script that will check the field for data and alert the user if it is blank. This script should run from a

button.

12. Create a simple DOM using java script.

13. Write a web service for arithmetic operation application.

14. Write a web service to do temperature conversion.

15. Create a simple application using AJAX.

ELECTIVE – II

MOBILE COMMUNICATIONS

(Course Code : 18PCSE24)


Course Outcomes: At the end of the course the students must be able to 1. Understand the concepts of Mobile Communications 2. Gain knowledge in the field of Wireless Networks 3. Understand the concepts of Mobile Communication systems 4. Identify the functions of Mobile Network and Layers 5. Gain overall knowledge about the subject

UNIT I - WIRELESS COMMUNICATION

Cellular systems- Frequency Management and Channel Assignment- types of handoff and their

characteristics, dropped call rates & their evaluation -MAC – SDMA – FDMA – TDMA – CDMA – Cellular Wireless

Networks

UNIT II - WIRELESS NETWORKS

Wireless LAN – IEEE 802.11 Standards – Architecture – Services – Mobile Ad hoc Networks- WiFi and

WiMAX - Wireless Local Loop

UNIT III - MOBILE COMMUNICATION SYSTEMS

GSM-architecture-Location tracking and call setup- Mobility management- Handover- Security-GSM SMS –

International roaming for GSM- call recording functions-subscriber and service data mgt –-Mobile Number portability -

VoIP service for Mobile Networks – GPRS –Architecture-GPRS procedures-attach and detach procedures-PDP

context procedure-combined RA/LA update procedures-Billing

UNIT IV - MOBILE NETWORK AND TRANSPORT LAYERS

Mobile IP – Dynamic Host Configuration Protocol-Mobile Ad Hoc Routing Protocols– Multicast routing-TCP

over Wireless Networks – Indirect TCP – Snooping TCP – Mobile TCP – Fast Retransmit / Fast Recovery –

Transmission/Timeout Freezing-Selective Retransmission – Transaction Oriented TCP- TCP over 2.5 / 3G wireless

Networks

UNIT V - APPLICATION LAYER

WAP Model- Mobile Location based services -WAP Gateway –WAP protocols – WAP user agent profile-

caching model-wireless bearers for WAP - WML – WMLScripts – WTA - iMode- SyncML.

TEXT BOOKS :

1. Jochen Schiller, “Mobile Communications”, Second Edition, Pearson Education, 2003.

2. William Stallings, “Wireless Communications and Networks”, Pearson Education, 2002.

REFERENCES :

1. Kaveh Pahlavan, Prasanth Krishnamoorthy, “Principles of Wireless Networks”, First

Edition, Pearson Education, 2003.

2. Uwe Hansmann, Lothar Merk, Martin S. Nicklons and Thomas Stober, “Principles of

Mobile Computing”, Springer, 2003.

3. C.K.Toh, “AdHoc Mobile Wireless Networks”, First Edition, Pearson Education, 2002.

ELECTIVE – II

BIG DATA ANALYTICS



Outcomes: At the end of the course the students must be able 1. understand big data analytics as the next wave for businesses looking for competitive

2. understand the financial value of big data analytics

3. explore tools and practices for working with big data

4. understand how big data analytics can leverage into a key component

5. know about the research that requires the integration of large amounts of data

UNIT I – INTRODUCTION TO BIG DATA

Introduction – 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.

UNIT II – INTRODUCTION HADOOP

Big Data – Apache Hadoop&HadoopEcoSystem – Moving Data in and out of Hadoop – Understanding inputs and

outputs of MapReduce - Data Serialization.

UNIT- III HADOOP ARCHITECTURE

Hadoop Architecture, Hadoop Storage: HDFS, Common Hadoop Shell commands , Anatomy of File Write and Read.,

NameNode, Secondary NameNode, and DataNode, HadoopMapReduce paradigm, Map and Reduce tasks, Job,

Task trackers - Cluster Setup – SSH &Hadoop Configuration – HDFS Administering –Monitoring & Maintenance.

UNIT-IV HADOOP ECOSYSTEM AND YARN

Hadoop ecosystem components - Schedulers - Fair and Capacity, Hadoop 2.0 New FeaturesNameNode High

Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN.

UNIT-V HIVE AND HIVEQL, HBASE

Hive Architecture and Installation, Comparison with Traditional Database, HiveQL - Querying Data - Sorting And

Aggregating, Map Reduce Scripts, Joins &Subqueries, HBase concepts-Advanced Usage, Schema Design, Advance

Indexing - PIG, Zookeeper - how it helps in monitoring a cluster, HBase uses Zookeeper and how to Build

Applications with Zookeeper.

REFERENCES

1. Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 2015.

2. Chris Eaton, Dirk deroos et al. , “Understanding Big data ”, McGraw Hill, 2012.

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

ELECTIVE – II

DECISION SUPPORT SYSTEMS




1. Understand the concepts of Decision support Systems

2. Gain knowledge in Components Hardware and Software Technologies

3. Understand the concepts of Client Server Network

4. Identify the functions of Electronic Data Interchange(EDI)


UNIT I - Concepts in General Management

Introduction – Definition of Management – Brief History of Scientific Management – Basic Responsibility of a

manager. Information Systems: Introduction – Human Information processing – Newell and Simon model –

Information and Information value – classification of decisions – Types of Information Systems – TPS – OAS –DSS.

UNIT II - Decision Support System

Introduction – subsystems in a DSS – computer Hardware for DSS – GDSS. Database Management

Systems Introduction – Information Characteristics – Pre-database systems – Data Base models – CODD’s rule –

Architecture of a data base – normalization – Data base language – Data base Dictionary – Data Base Administrator.

UNIT III - Model –Base management systems

Introduction – Representation of models – Benefits of modeling a problem – computer software for DSS

model. Dialogue Management subsystem: Introduction – classification of users – major factors to be considered in

designing user Interfaces – Interface styles.

UNIT IV - H/W and S/W technologies for DSS

Introduction -micro processor technology – storage technologies – output devices – technologies of today –

new directions in computing. Artificial Intelligence and expert systems : Introduction – what is intelligence? – AI –

knowledge – knowledge representation – application areas – integration of AI and DSS.

UNIT V - Internet

Introduction – Brief history of internet – internet – communication in internet – internet browsers – search

engines – web authoring tools – firewalls – intranets – extranets. Electronic Data Interchange(EDI) : What is EDI –

components of EDI – Hardware and communication devices for EDI – EDI standards – EDI software – Advantages of

EDI – security – legal Issues Associated with EDI.

Text book :

V.S. Janakiraman, K. Sarukesi, “Decision Support Systems”, PHI, 2002.

Reference books :

1. Turban E., “Decision Support and Expert Systems Management support systems”,

IVth edition, Maxwell Macmillan , 1995

2. Sitausa and mitra, “Decision support systems, Tools and Techniques” John wiley, 1986.

ELECTIVE – II

EMBEDDED SYSTEMS



Course Outcomes: At the end of the course the students must be able to 1. Understand the concepts of Embedded Systems

2. Gain knowledge in Devices and drivers for Embedded systems

3. Understand the concepts of Real Time Operating Systems

4. Identify the functions of RTOS


UNIT I

Introduction to Embedded Systems – Processor and memory organization: Structured units in processor –

Processor selection for an embedded system – memory devices – memory selection for an embedded system –

Allocation of memory to program segments and blocks and memory map of a system – DMA – Interfacing processor,

memories and I/O Devices.

UNIT II

Devices and buses for device networks – Device Drivers and Interrupts servicing mechanism –

Programming concepts and Embedded programming in C and C++ - Program modeling concepts in single and

multiprocessor systems software – Development process.

UNIT III

Software Engineering practices in the embedded software development process – Inter process

communication and synchronization of processor, tasks and threads.

UNIT IV

Real time operating systems : Operating system services – I/O sub systems – Network operating systems –

Real time and embedded system operating systems– Interrupt routines in RTOS environments – RTOS task

scheduling models, Interrupts, Latency and Response times of the tasks as periodic task – IEEE standard POSIX

1003.1s. Functions and intertask communication functions – List of basic actions in a preemptive scheduler and

expected times taken at a processor – Fifteen point strategies – Embedded Linux internals – OS security issues –

Mobil OS.

UNIT V

Real time OS programming tools : Micro c/OS-II and Vx works – case studies of programming with RTOS –

H/w – S/w Co-design in an Embedded system.

TEXT BOOK

Raj Kamal, “Embedded Systems Architecture, Programming and Design”, Tata McGraw Hill Publishing

Company Ltd., New Delhi, 2003.

REFERENCE BOOK

David E. Simon, “An Embedded Software Primer”, Addison Wesley Publication Company

USA, 2001.

MOBILE APPS WITH ANDROID


SEMESTER –III HOURS – 5 CREDITS – 5

Course Outcome: At the end of the course students will be able to

1. Impact the theoretical knowledge of Android programming

2. Develop the Programming Skills in Android language

3. Gain experience about Android Development Environment

4. Create own application by using the Android Components

5. Be familiar with database handling

Unit I

Android: An Open Platform for Mobile Development -Native Android Applications -Android SDK Features - Background Services- Introducing the Open Handset Alliance - Introducing the Development Framework- What Comes in the Box -Understanding the Android Software Stack -The Dalvik Virtual Machine -Android Application Architecture -Android Libraries- Developing for Android- Developing for Mobile and Embedded Devices- Android Development Tools. Unit II

Creating Applications And Activities: What Makes an Android Application?- Introducing the Application Manifest File- Externalizing Resources- The Android Application Lifecycle- Understanding an Application’s Priority and Its Process’ States- Introducing the Android Application Class- A Closer Look at Android Activities- Android User Interface Fundamentals- Introducing Layouts- The Android Widget Toolbox- Introducing Adapters.

Unit III

Intents And Broadcast Receivers: Introducing Intents- Creating Intent Filters and Broadcast Receivers- Using Intents to Broadcast Events- Monitoring Device State Changes Using Broadcast Intents- Using the Download Manager- Using Internet Services -Connecting to Google App Engine. Unit IV

Introducing Android Databases- Introducing SQLite- Content Values and Cursors- Working with SQLite Databases- Creating Content Providers- Using Content Providers- Adding Search to Your Application- Native Android Content Providers. Unit V

Introducing the Action Bar- Creating and Using Menus and Action Bar Action Items- Introducing Dialogs- Introducing Notifications- Designing for Every Screen Size and Density- Working with Animations- Using Sensors and the Sensor Manager- Monitoring a Device’s Movement and Orientation- Introducing the Environmental Sensors. Text Book

1) Reto meier,”Professional Android 4 Application Development”,John wiley & Sons,Inc,2012 Reference Books:

1) W.Frank,Robi,Chris and Enrique, “Android in Action” ,dreamtech Publications; Third edition,2014 2) Android Community Experts, “Android Cookbook”, O’REILLY Publications First Edition 2011, O’REILLY Publications 3) Marko Gargenda, “Learning ANDROID”, O’REILLY Publications,First Edition 2011,

DATA MINING



Course Outcome: At the end of the course students will be able to

1. Know the fundamentals of data mining

2. Preprocess the data

3. Understand frequent pattern mining

4. Classify the dataset using supervised learning methods

5. Cluster the dataset using unsupervised learning methods

UNIT I

Introduction – Data mining : Motivation – On what kind of data – Data Mining Functionalities – Classification

of Data Mining systems – Major Issues in Data Mining systems – Introduction to Weka Tool

UNIT II

Data Preprocessing – Data cleaning – Data Integration and Transformation – Data Reduction –

Discretization and concept Hierarchy Generation.

UNIT III

Mining Frequent Patterns, Associations, and Correlations – Efficient and Scalable Frequent Itemset Mining

Methods – Mining Various Kinds of Association Rules – From Association Mining to Correlation Analysis – Constraint

– Based Association Mining.

UNIT IV

Classification and Prediction – What is Classification and Prediction – Issues regarding Classification and

Prediction – Classification by Decision Tree Induction – Bayesian Classification – Prediction – Classifier Accuracy.

UNIT V

Cluster Analysis – What is Cluster Analysis? Types of Data in Cluster Analysis – A Categorization of Major

Clustering Methods – Partitioning Methods – Hierarchical Methods

Text Books :

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

Kaufmann Publishers, Third Edition, 2012.

2. Weka online tutorial

Reference Book :

1. M. Barry and G. Linoff “Mastering Data Mining”, John Wiley.

IMAGE PROCESSING

(Course Code:18PCS33)


CourseOutcome: At the end of the course students will be able to

1. Be familiar with image compression and segmentation techniques.

2. Be exposed to simple image processing techniques.

3. Learn digital image fundamentals.

4. Learn to represent image in form of features.

UNIT I - DIGITAL IMAGE FUNDAMENTALS

Introduction – Origin – Steps in Digital Image Processing – Components – Elements of Visual Perception – Image

Sensing and Acquisition : Image Acquisition using Single Sensor, Sensor Strips, Sensor Arrays – Image Sampling

and Quantization : Basic concepts –Representing Digital Images - Image Interpolation – Relationships between

pixels .

UNIT II - IMAGE ENHANCEMENT

Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering– Smoothing and

Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform – Smoothing and Sharpening

frequency domain filters – Ideal, Butterworth and Gaussian filters.

UNIT III - IMAGE RESTORATION AND SEGMENTATION

Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch

Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering. Segmentation: Detection of Discontinuities–

Edge Linking and Boundary detection – Region based segmentation- Morphological processing.

UNIT IV - IMAGE COMPRESSION & MORPHOLOGICAL IMAGE PROCESSING

Compression: Fundamentals – Image Compression models – Error Free Compression – Variable Length Coding –

Bit-Plane Coding – Lossless Predictive Coding – Lossy Compression – Lossy Predictive Coding – Transform Coding

- Wavelet coding –Morphological : Gray Scale Morphology Erosion & Dilation – Opening & Closing – Gray Scale

Morphological Reconstruction .

UNIT V - IMAGE REPRESENTATION AND RECOGNITION

Boundary representation – Chain Code – Polygonal approximation, signature, boundary segments – Boundary

description – Shape number – Fourier Descriptor, Regional Descriptors – Topological feature, Texture - Patterns and

Pattern classes - Recognition based on matching-Structural Methods.

TEXT BOOK

1. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson Education, 2010.

REFERENCES:

1. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using MATLAB”, Third

Edition Tata McGraw Hill Pvt. Ltd., 2011. 93

2. Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011.

3. Willliam K Pratt, “Digital Image Processing”, John Willey, 2002.

4. Malay K. Pakhira, “Digital Image Processing and Pattern Recognition”, First Edition, PHI Learning Pvt. Ltd., 2011.

LAB 5: ANDROID PROGRAMMING



Course Outcomes: At the end of the course the students must be able to 1. Create debugging and running own Android applications

2. Apply Android Components to create applications

3. Implement various features to create innovative applications

4. Create application using various UI components

5. Create applications that deal with database concept

List of Practical

1. Android application to display toast message on button click

2. Android applications using basic user interface controls

3. Android applications to use android specific user inteface controls

4. Simple Android applications to implement control structure

5. Android application to use intents and activities

6. Android application for login operation

7. Android applications to make use of different layouts

8. Android application to make use of database

9. Android application to implement various Event listeners

10. Android application to display dialog box and alert messages

11. Android application to create animation

12. Android application to deal with basic sensor activities

Lab 6: DATA MINING USING WEKA




1. Understand the different file formats used to store data set

2. Preprocess the data

3. Find out frequent pattern using association rule mining

4. Classify the dataset using supervised learning methods

5. Cluster the dataset using unsupervised learning methods

List of Practical

1. File formats

2. Data Preprocessing

3. Association rule mining

4. Classification

5. Prediction

6. Cluster analysis

ELECTIVE III

INTERNET OF THINGS

(Course Code 18PCSE34)


Course Outcomes: At the end of the course the students must be able to 1. understand the application areas of IOT ·

2. realize the revolution of Internet in Mobile Devices, Cloud & Sensor Networks ·

3. understand building blocks of Internet of Things and characteristics.

4. understand the physical and cloud services

5. apply IoT

UNIT I INTRODUCTION Introduction- Physical Design of IoT -Logical Design of IoT-IoT Enabling Technologies-IoT Levels & Deployment Templates- Domain Specific IoTs UNIT II IoT and M2M Introduction - M2M - Difference between IoT and M2M -SDN and NFV for IoT - IoT System Management with NETCONF-YANG-Need for IoT Systems Management - Simple Network Management Protocol (SNMP) - Limitations of SNMP - Network Operator Requirements - NETCONF - YANG - IoT Systems Management with NETCONF-YANG - NETOPEER UNIT III IoT Platforms Design Methodology Introduction - IoT Design Methodology- IoT Physical Devices & Endpoints - What is an IoT Device Exemplary Device: Raspberry Pi - About the Board - Linux on Raspberry Pi - Raspberry Pi Interfaces -Other IoT Devices UNIT IV IoT Physical Servers & Cloud Offerings Introduction to Cloud Storage Models & Communication APIs - WAMP - AutoBahn for IoT - Xively Cloud for IoT - Python Web Application Framework - Django - Designing a RESTful Web API - Amazon Web Services for IoT - SkyNet IoT Messaging Platform UNIT V Applications of IoT Introduction- Home Automation - Smart Lighting - Home Intrusion Detection – Cities- Smart Parking - Environment - Weather Monitoring System - Weather Reporting Bot - Air Pollution Monitoring - Forest Fire Detection - Agriculture - Smart Irrigation TEXT BOOK 1. Internet of Things: A Hands-On Approach - Arsheep Bahga , Vijay Madisetti – Universities Press –2015 REFERENCES

1) Olivier Hersent, David Boswarthick, Omar Elloumi, The Internet of Things: Key Applications and Protocols,

2nd Edition, Wiley Publication , 2012

ELECTIVE III

PERVASIVE COMPUTING

(18PCSE34)



1. attain the knowledge and skills about a new trend in computing.

2. learn about creating a ubiquitous environment.

3. learn WAP technology

UNIT I - INTRODUCTION Pervasive Computing: Past, Present and Future - Pervasive Computing Market – m-Business – Application examples: Retail, Airline check-in and booking – Health care – Car information system – E-mail access via WAP and voice. UNIT II - DEVICE TECHNOLOGY Hardware – Human Machine Interfaces – Biometrics – Operating Systems – Java for Pervasive devices. UNIT III - DEVICE CONNECTIVITY & WEB APPLICATION CONCEPTS Protocols – Security – Device Management - Web Application Concepts: WWW architecture – Protocols – Transcoding - Client Authentication via Internet. UNIT IV - WAP & VOICE TECHNOLOGY WAP and Beyond: Components of the WAP architecture – WAP infrastructure – WAP security issues – WML – WAP push – Products – i-Mode - Voice Technology: Basics of Speech recognition- Voice Standards – Speech applications – Speech and Pervasive Computing. UNIT V - PDA & PERVASIVE WEB APPLICATION ARCHITECTURE Device Categories – PDA operation Systems – Device Characteristics – Software Components - Standards – Mobile Applications - PDA Browsers - Pervasive Web Application architecture: Background – Development of Pervasive Computing web applications - Pervasive application architecture. TEXT BOOK 1. Jochen Burkhardt, Horst Henn, Stefan Hepper, Thomas Schaech & Klaus Rindtorff, “Pervasive Computing, Technology and Architecture of Mobile Internet Applications”, Pearson Education, 2012.

REFERENCES 1) Steve Mann , The Wireless Application Protocol (WAP) , Wiley Publication , 2002

ELECTIVE III

VIRTUAL REALITY

(18PCSE34)



1. Understand geometric modeling.

2. Learn about virtual environment.

3. Understand virtual hardware and software.

4. Develop virtual reality applications

UNIT I

Introduction : The three I’s of virtual reality, commercial VR technology and the five classic components of a VR

system. Input Devices : (Trackers, Navigation, and Gesture Interfaces): Three-dimensional position trackers,

navigation and manipulation, interfaces and gesture interfaces. (Chap: 1, 2).

UNIT II

Output Devices: Graphics displays, sound displays & haptic feedback. Modeling: Geometric modeling, kinematics

modeling, physical modeling, behaviour modeling, model management. Chap: 3, 5 21of Text Book (1))

UNIT III

Human Factors: Methodology and terminology, user performance studies, VR health and safety issues. Applications:

Medical applications, military applications, robotics applications. (Chap: 7, 8, 9 21of Text Book (1))

UNIT IV

VR Programming-I : Introducing Java 3D, loading and manipulating external models, using a lathe to make shapes.

(Chap: 14, 16 and 17 21of Text Book (2))

UNIT V

VR Programming-II : 3D Sprites, animated 3D sprites, particle systems. (Chap:18, 19 and 21of Text Book (2))

TEXT BOOKS:

1. Virtual Reality Technology, Second Edition, Gregory C. Burdea & Philippe Coiffet, John Wiley & Sons, Inc.,

2. Killer Game Programming in Java, Andrew Davison, Oreilly-SPD, 2005.

REFERENCE BOOKS:

1. Understanding Virtual Reality, interface, Application and Design, William R.Sherman, Alan Craig, Elsevier(Morgan

Kaufmann).

2. 3D Modeling and surfacing, Bill Fleming, Elsevier(Morgan Kauffman).

3. 3D Game Engine Design, David H.Eberly, Elsevier.

ELECTIVE III

GREEN COMPUTING

(Course Code 18PCSE34)

SEMESTER –III HOURS – 5 CREDITS –5

Course Outcome: At the end of this course the student will learn to

1. reduce the use of hazardous materials

2. go green to overcome climate change

3. maximize energy efficiency during the product’s lifetime

4. reduce Greenhouse Gas Emissions

5. understand Deep Green Computing

UNIT I

Green Computing and Saving Money: Key Concepts – Why Saving Money Is Green – Getting Focused on Money-

Saving Efforts – Implementing Energy Efficiency – Changing How Current Devices Are Used – Moving to Cloud

Services – Digitizing Non-IT Functions – Greening Your Energy-Saving Moves – Some Big Thinking About Money-

Saving Efforts

Green Computing and the Environment: Key Concepts – Environmental Drivers for Green Computing – What

Drives the Green Agenda? – Key Roots of Environmentalism – Environmentalism and IT – The New Imperative of

Climate Change – A Brief History of the Climate and Climate Change – The 2°C Warming "Limit" – Climate Change

and IT – What’s Next with Climate Change? – What It Means to "Go Green" – Why IT Is a Climate Change Solution –

Career Development and "Going Green"

UNIT II

A New Vision of Computing: Key Concepts – Cloud Computing Emerges – The End of the PC Era – Some New-

Model IT – Challenges – A Few Examples from a Multinational – How a Company Adopted the iPhone – A Mental

Model for IT Simplicity – Why Green Computing Fits the New Model – Is Cloud Computing the Whole Answer? –

Disadvantages of Cloud Computing – Managing Disadvantages of Cloud Computing – What to Do Besides Cloud

Computing – Efficiency and Cloud Computing – Greenability and Cloud Computing – Responsibility, Usability, and

Cloud Computing – The Philosophical Implications of Green Computing – The Zen of Green Computing

Building a Green Device Portfolio : Key Concepts – Introduction – Why Green Works for Device Purchases –

Pushing Computing Down the Device Pyramid – Another Dimension of Device Pyramid Greenness – Green

Computing and Embodied Energy – Green Computing and Running Costs – Planned Obsolescence Isn’t Green –

Green Computing and Device Disposal – The Greenpeace Guide to Greener Electronics – Support Employees’

Device Choices – Publicizing Your Process

UNIT III

Green Servers and Data Centers: Key Concepts – Choosing and Creating Green Data Centers – Green Data

Centers as a Model – The Last Shall Be First – What Makes a Data Center Green? – Building and Power Supply

Considerations – Servers, Storage, and Networking – Data Center Suppliers

Saving Energy: Key Concepts – Saving Energy Serves Many Masters – Cost Savings through Energy Savings –

Risk Reduction through Energy Savings – Carbon Footprint Reduction through Energy Savings – Improving Your

Reputation and Brand – Why Energy Prices Will Stay High –Embodied Energy – Analyzing Your Energy Usage – A

Recipe for Energy Savings – Understanding the Unique Energy Needs of IT – Focusing on Solar Power – Saving

Energy and the Supply Chain – Energy-Saving Pilot Projects – Selling Energy Savings

UNIT IV

Reducing Greenhouse Gas Emissions: Key Concepts – Why Greenhouse Gas Emissions Are Important –

Sources and Sinks of Greenhouse Gases and Warming – Is There Still Doubt About Climate Change? – Why Are

There Still Doubters and Deniers? – What If I Work for Doubters and Deniers? – What’s Next with Climate

Change? – Reducing Emissions I: Embodied Energy – Reducing Emissions II: Daily Energy Use – Reducing

Emissions III: Taking Steps to Use Different Sources – Reducing Emissions IV: Supply Chain Success

Reducing Resource Use: Key Concepts – Why Resource Use Is Important – A Resource Use Checklist – Planned

Obsolescence and Resource Use – The Story of Apple and EPEAT – Case Study: Computer Hardware and RSI

UNIT V

Green Computing by Industry Segment: Key Concepts – Evaluating Greenness – The Newsweek – Green 500

Approach – Looking at Industry Segments – Analyzing Your Own Initiatives, Company, and Sector

The Future: Deep Green Computing: Key Concepts – Green Computing and the Future – Megatrends for Green

Computing – An Increasing Need for Sustainability – The Continually Decreasing Cost of Core Computing

Capabilities – The Ability of Computing to Do More and More Telepresence Instead of Travel – Telecommuting

Instead of Commuting – Toward Deep Green Computing – Platforms for Deep Green Computing – Selling Deep

Green Computing

Text Book:

1. Bud E. Smith, Green Computing Tools and Techniques for Saving Energy, Money and Resources, CRC

Press, 2014.

Reference Books:

1. Toby Velte, Anthony Velte, Robert Elsenpeter, Green IT, McGraw Hill, 2008.

2. Alvin Galea, Michael Schaefer, Mike Ebbers, Green Data Center: Steps for the Journey, Shroff Publishers

and Distributers, 2011.

SOFT COMPUTING


SEMESTER –IV HOURS – 5 CREDITS – 5

Course Outcome: At the end of the course the student will be able to

1. conceptualize the working of human brain using ANN.

2. introduce the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience.

3. Analyze the applications which can use fuzzy logic and neural networks.

4. Understand genetic algorithms and classification.

5. Understand the various applications of soft computing.

UNIT I – INTRODUCTION

Introduction to Neural Networks – Fuzzy Logic – Genetic Algorithm - Hybrid Systems – Soft Computing. Artificial

Neural Network: An Introduction – Fundamental Concepts –Evolution of Neural Networks Basic Models- Important

Terminologies. Supervised Learning Network : Introduction – Perceptron Networks- Adaptive Linear Neuron- Multiple

Adaptive Linear Neuron – Back Propagation Network.

UNIT II – ASSOCIATIVE MEMORY NETWORKS AND UNSUPERVISED LEARNING NETWORKS

Introduction –Training algorithms – Autoassociative Memory Network – Bidirectional Associative Memory- Hopfield

Networks. Unsupervised Learning Networks: Introduction –Fixed Weight Competitive Nets – Kohonen Self-Organized

– Learning Vector Quantization.

UNIT III – FUZZY LOGIC

Classical sets and Fuzzy sets: Introduction – Classical Sets – Fuzzy Sets – Classical Relations – Fuzzy Relations.

Membership Functions: Introduction – Features – Fuzzification – Methods – Defuzzification Lambda Cuts- Methods.

Fuzzy Arithmetic

UNIT IV - GENETIC ALGORITHMS

Introduction – Basic Operators and Terminologies General Genetic Algorithm – Classification – Holland Classifier

Systems – Genetic Programming.

UNIT V - APPLICATIONS OF SOFT COMPUTING

ANFIS – Adaptive Neuro Fuzzy Inference Systems- Architecture – Coactive Neuro Fuzzy Modeling. Applications: A

fusion approach of Multispectral Images with SAR image for flood area analysis Optimization of TSP using Genetic

Algorithm- Soft Computing Based Hybrid Fuzzy Controllers.

TEXT BOOK:

1. S.N.Sivanandam ,S.N.Deepa” Principles of Soft Computing”, Wiley-India,2007.

REFERENCE BOOKS:

1. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.

2. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.

3. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004, Eastern Economy Edition

2007. 4. Davis E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley,

N.Y., 2003.

DOT NET PROGRAMMING (Course Code : 18PCS42)

SEMESTER –IV HOURS – 5 CREDITS – 5 Course Outcomes: At the end of the course the students must be able to

1. Understand the development and deployment cycles of enterprise applications.

2. utilize the .NET framework to build distributed enterprise applications.

3. develop ASP.NET Web Services, secure web services, and .NET remoting applications.

4. develop web applications using a combination of client-side and server-side technologies.

5. understand and experiment with the deployment of enterprise applications.

UNIT I

Introduction to Visual Basic .NET : Window forms – working with controls – working with dialog boxes –MDI-

Drag and drop operation – variables – Controlling Program flow.-Procedures in VB.Net-Accessing a Database.

UNIT II

Introducing ASP.Net – Getting started with ASP.Net applications: Web forms – creating ASP.Net Webform

applications – Using ASP.Net Webforms for server controls : Beginning with server controls – Taking a closer look at

web controls – Illustrating Basic web controls – Working with Validation Controls : The compare Validator – The

Range Validator – Regular Expression Validator – Custom validator –Validation Summery control – Multiple

validation control.

UNIT III

Developing ASP.Net Server controls : Developing ASP.Net server controls – Creating and using Web User

Control – Creating ASP.Net Pages to web user control – Using Rich Web controls: Calendar web server control.

UNIT IV

Debugging ASP.Net Web Applications: Tracing ASP.Net Applications – Handling Errors in ASP.Net

applications – Using ADO.Net with ASP.Net: ADO.Net – ADO.Net Object model .

UNIT V

Welcome to C# - Working with variables, Operators and Expressions – Writing methods applying scope –

using decision statements-Using Iteration Statements – Managing Errors and Exceptions – Creating and Managing

Classes – Using Arrays and Collections

Text Book : 1. Mridula Parihar, Yesh Sinhal and Nitin Pandey, “Visual Studio .Net Programming”, PHI, 2002. 2. John Sharp, Jon Jagger, “Microsoft Visual C# .Net Step by Step”, PHI, 2005. Reference Books : 1. Nitin Pandey, “Microsoft Asp.NET”, PHI, 2002. 2. “ASP.NET Made Simple” BPB Publictaions, First Edition, 2001. 3. Kiric Allen Evans, Ashwin Kamanna, Joel and Muller, “XML and ASP.NET”, Pearson

Education, First Indian Reprint, 2002. 4. Andrew Trolsen, “C# and the .NET Platform”, APress, Second Print, 2006.

Lab 7 : DOT NET PROGRAMMING (Course Code : 18PCS43)

SEMESTER –IV HOURS – 6 CREDITS – 3

Course Outcomes: At the end of the course the students must be able to 1. utilize the .NET framework to build distributed enterprise applications.

2. develop web applications using a combination of client-side and server-side technologies.

3. understand and experiment with the deployment of enterprise applications.

VB.NET 1. Designing a Simple Winform application. 2. Designing a MDI application with multiple winforms. 3. Designing an application for procedure handling. 4. Designing an application for Accessing database ASP.NET 1. Designing a simple Web form application. 2. Designing an application to implement validation controls. 3. Designing an application for creating and using webuser controls. 4. Designing an application form working with Rich web controls 5. Designing an application to work with Databases. C#.NET 1. Simple Application with Class and Objects 2. Simple application with Control Statements 3. Handling single dimension and multidimensional Arrays

Documents

+Programme Name : M.Sc. Computer Science Programme …