master of computer applications scheme of v vi semester …...nie, mysuru – 570 008 department of...

NIE, Mysuru – 570 008 Department of MCA

THE NATIONAL INSTITUTE OF ENGINEERING

MYSURU – 570 008

(Autonomous Institution under VTU)

Master of Computer Applications

Scheme of

V – VI Semester MCA

( 2016 – 2017 )

Department of MCA


Master of Computer Applications

Vision of the Institution

“NIE will be a globally acknowledged institution providing value based

technological and educational services through best-in-class people and

infrastructure”

Mission of the Institution

To impart state-of-the-art engineering education through strong theoretical

foundations and practical training to students in their choice of specialization.

To create new knowledge through innovation and cutting-edge research in science

and engineering.

To provide a platform for inclusiveness and collaboration by following ethical and

responsible engineering practices for long-term interaction with academia and

industry.

To encourage entrepreneurship and to develop sustainable technologies for the benefit

of global society.

Vision of the Department

“MCA will be an outstanding department contributing significantly to teaching,

research and consultancy, through well equipped laboratories and well trained staff

to meet global challenges in the field of computer engineering & applications”

Mission of the Program

To impart quality technical education and provide skills in Computer Application

through best of practices.

To produce graduates who can contribute professionally to the society and widely as

IT professionals or entrepreneurs.


Graduate Attributes

1. Computational Knowledge: Apply knowledge of computing fundamentals,

computing specialization, mathematics and domain knowledge appropriate for the

computing specialization to the abstraction and conceptualization of computing

models from defined problems and requirements.

2. Problem Analysis: Identify, formulate, research literature and solve complex

computing problems reaching substantiated conclusions using fundamental

principles of Mathematics, Computing Sciences and relevant domain disciplines.

3. Design / Development of solutions: Design and evaluate solutions for complex

computing problems and evaluate systems, components or processes that meet

specified needs with appropriate considerations for public health and safety,

cultural societal and environmental considerations.

4. Conduct Investigations of complex computing problems: Use research based

knowledge and research methods including design of experiments, analysis and

interpretation of data and synthesis of the information to provide valid

conclusions.

5. Modern Tool Usage: Create, select, adopt and apply appropriate techniques,

resources and modern computing tools to complex computing activities with an

understanding of the limitations.

6. Professional Ethics: Understand and commit to professional ethics and cyber

regulations, responsibilities and norms of professional computing practice.

7. Life Long Learning: Recognize the need and have the ability to engage in

independent learning for continual development as a Computing Professional.

8. Project Management and Finance: Demonstrate knowledge and understanding

of the computing and management principles and apply these to once own work

as a member and leader in a team to manage projects and in multidisciplinary

environments.

9. Communication Efficacy: Communicate effectively with the computing

community and society at large about complex computing activities by being able

to comprehend and write effective reports and design documentation, make

effective presentations and give and understand clear instructions.

10. Societal and environmental concern: Understand and assess societal,

environmental, health safety, legal and cultural issues within local and global

contexts and consequential responsibilities relevant to professional computing

practice.

11. Individual and Team work: Function effectively as an individual and as a

member or leader in diverse teams in multi-disciplinary environments.


12. Innovation and entrepreneurship: Identify a timely opportunity and using

innovation to pursue that opportunity to create value and wealth for the betterment

of the individual and society at large.

Program Educational Objectives

The Department will produce graduates who

PEO1: Work productively as IT professional both at supportive and leadership roles.

PEO2: Advance Successfully in their chosen career path utilizing technical abilities,

leadership qualities, communication and interpersonal skills with high regard to

legal and ethical responsibilities.

PEO3: Build their profession adopting to the changes in the technology with lifelong

learning.

Program Specific Outcomes

PSO1: MCA graduates will be able to understand and analyze computer systems, focused

with hardware, software and application needs.

PSO2: Develop software and hardware systems/solutions, with a knowledge of software

design life cycle process and skills with a broad range of programming tools and

platforms.


Program Outcomes Graduates will have an

PO1: Ability to apply knowledge of mathematics, computer science and domain

knowledge to solve problems in the computational world.

PO2: Ability to analyze real world/scientific problems and convert them to

computable algorithm.

PO3: Ability to evaluate, analyze and use available technological solutions to design

and implement the same.

PO4: Ability to work with complex computing problem environment, use knowledge

both technical and research to provide valid conclusions of experiments based

on analysis and interpretation of data.

PO5: Ability to use/evaluate the various software tools and networking requirements

for solutions.

PO6: Ability to adhere to the professional ethics, follow cyber rules and regulations

and be a responsible citizen.

PO7: Ability to be a lifelong learner in the field of computer science.

PO8: Ability to demonstrate the knowledge and understanding hardware, software,

networking and Finance requirements for the Society

PO9: Ability to communicate effectively with the fellow members and with other uses

of the computing community and society.

PO10: Ability to experience the industrial environment for understanding the impact of

computational solutions in a global and societal context.

PO11: Ability to function effectively as an individual and work collaboratively in a

team.

PO12: Ability to become leaders, entrepreneurs, and provide solutions to complex

problems in life.


SCHEME OF TEACHING AND EXAMINATION

FIFTH SEMESTER MCA (AUTONOMOUS SCHEME)

Sl.

No Sub Code Subject Title

Teaching

Dept.

Contact

Hrs./Week Credits

Marks

Exam

Duration

L T P CIE SEE Total (Hrs)

1 MCA0419

System

Simulation

and Modeling

MCA 4 0 0 4 50 50 100 3

2 MCA0420

Object

Oriented

Modeling and

Design

Patterns

MCA 4 0 0 4 50 50 100 3

3 MCA0421 .Net and C# MCA 4 0 0 4 50 50 100 3

4 MCA04-- Elective – IV MCA 4 0 0 4 50 50 100 3

5 MCA04-- Elective – V MCA 4 0 0 4 50 50 100 3

6 MCA0112 Software

Design Lab MCA 0 0 3 1.5 50 – 50 --

7 MCA0113 .Net Lab MCA 0 0 3 1.5 50 – 50 --

8 MCA0114 Mini Project MCA - - 2 1.0 50 -- 50 --

9 MCA0115 Seminar – I MCA - - 2 1.0 50 -- 50 --

TOTAL 20 0 10 25 450 250 700

SCHEME OF TEACHING AND EXAMINATION

SIXTH SEMESTER MCA (AUTONOMOUS SCHEME)

Sl.

No

Subject

Code Subject Title

Teaching

Dept.

Contact

Hrs./Week Credits

Marks

Exam

Duration

L T P CIE SEE Total (Hrs)

1 MCA2801 Major Project MCA - - - 28 150 75 225 3

2 MCA0201 Seminar - II MCA - - - 2 - 50 50 -

TOTAL - - - 30 150 125 275

Pattern of course evaluation for both CIE and SEE will mentioned in the abridged lesson

plan and the Course Instructor (CI) will discuss the same with the students during the

first/second session of the semester


ELECTIVES

Sub. Code Subjects L T P Credits

MCA0422 Computer Graphics and Visualization 4 0 0 4

MCA0423 UNIX system Programming 4 0 0 4

MCA0424 Multimedia Systems 4 0 0 4

MCA0426 Artificial Intelligence 4 0 0 4

MCA0428 Information Retrieval& Search

Engines

4 0 0 4

MCA0429 Data Mining 4 0 0 4

MCA0431 Network Management 4 0 0 4

MCA0433 Software Architectures 4 0 0 4

MCA0434 Information and Network Security 4 0 0 4

MCA0435 Software Testing and Practices 4 0 0 4

MCA0436 Services Oriented Architectures 4 0 0 4

MCA0437 Mobile Computing and Wireless

Communication

4 0 0 4

MCA0438 Principles of User Interface Design 4 0 0 4

MCA0439 Web 2.0 and rich internet applications 4 0 0 4

MCA0441 Digital Image Processing 4 0 0 4

MCA0442 Advanced Computer Networks 4 0 0 4

MCA0446 Cloud Computing 4 0 0 4

MCA0407 Introduction to Python Programming 4 0 0 4

MCA0408 Big Data Analytics 4 0 0 4

MCA0410 Introduction to Android Programming 4 0 0 4


System Simulation and Modeling (4:0:0)

Sub Code : MCA0419 CIE: 50%

Hrs/Week : 04 SEE: 50%

SEE Hours : 3 Hrs Max Marks: 100

Pre-requisite: NA

Course outcomes

On successful completion of the course, the students will be able to

1. Understand when simulation is important tool and when not.

2. Apply Discrete-Even Simulations to solve various problems.

3. Summarize the various Techniques for generating random numbers.

4. Demonstrate the ability to apply the techniques of modeling and simulation to

optimize a system via Simulation. Verify and validate various simulation models.

5. Verify and validate various simulation models.

UNIT 1 10 Hours

Introduction When simulation is the appropriate tool and when it is not

appropriate; Advantages and disadvantages of Simulation; Areas of application;

Systems and system environment; Components of a system; Discrete and continuous

systems; Model of a system; Types of Models; Discrete-Event System Simulation; Steps

in a Simulation Study. Simulation examples: Simulation of queuing systems;

Simulation of inventory systems.

Self-Learning Exercise: Other examples of simulation

UNIT II 8 Hours

General Principles, Simulation Software Concepts in Discrete-Event Simulation: The

Event-Scheduling / Time-Advance Algorithm, World Views, Manual simulation

Using Event Scheduling, List processing.

Self-Learning Exercise: Simulation in GPSS.

UNIT III 8 Hours

Statistical Models in Simulation and Queuing Models Review of terminology and

concepts; Useful statistical models; Discrete distributions; Continuous

distributions; Poisson process; Empirical distributions. Characteristics of queuing

systems; Queuing notation, Long-run measures of performance of queuing systems.

Self-Learning Exercise: Steady-state behavior of M/G/1 queue.


UNIT IV 8 Hours

Random-Number Generation, Random-Variate Generation Properties of random

numbers; Generation of pseudo-random numbers; Techniques for generating random

numbers; Tests for Random Numbers. Random-Variate Generation: Inverse transform

technique; Acceptance-Rejection technique.

Self-Learning Exercise: Special properties.

UNIT V 10 Hours

Input Modeling and Output Analysis for a Single Model Data Collection; Identifying

the distribution with data; Parameter estimation; Goodness of Fit Tests; Fitting a

non-stationary Poisson process; Selecting input models without data; Multivariate and

Time-Series input models. Types of simulations with respect to output analysis;

stochastic nature of output data; Measures of performance and their estimation.

Self-Learning Exercise: Output analysis for steady-state simulations.

UNIT VI 8 Hours

Verification and Validation of Simulation Models, Optimization Model building,

verification and validation; Verification of simulation models; Calibration and validation

of models.

Self-Learning Exercise: Optimization via Simulation.

Text Books: 1. Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol: Discrete-Event

System Simulation, 4th Edition, Pearson Education / PHI, 2007.

Reference Books:

1. Lawrence M. Leemis, Stephen K. Park: Discrete – Event Simulation: A First Course,

Pearson / Prentice-Hall, 2006.

2. Averill M. Law: Simulation Modeling and Analysis, 4th Edition, Tata McGraw-Hill,

2007.


Object-Oriented Modeling and Design Patterns (4:0:0)

Subj. Code : MCA0420 CIE : 50%

Hrs/Week : 04 SEE : 50%

SEE Hours : 3 Hrs Max Marks: 100

Pre-requisite: OOP with C++

Course Outcomes:


1. Recall the concept of object-orientation methodologies and use them for

designing classes and objects.

2. Create use case documents that capture requirements for a software design.

3. Create class diagrams that model both the domain model and design model of a

software system.

4. Design the interface between the classes and objects.

5. Create interaction diagrams that model the dynamic aspects of a software system.

6. Explain communication and design patterns.

UNIT I 8 Hours

Introduction, Modeling as a Design Technique, Class Modeling What is Object

Orientation? What is OO development? OO themes; Evidence for usefulness of OO

development; OO modeling history. Modeling as Design Technique: Modeling;

abstraction; the three models. Class Modeling: Object and class concepts; Link and

associations concepts; Generalization and inheritance.

Self-Learning Exercise: A sample class model; Navigation of class models; Practical

tips.

UNIT II 10 Hours

Advanced Class Modeling, State Modeling Advanced object and class concepts;

Association ends; N-array associations; Aggregation; Abstract classes; Multiple

inheritance; Metadata; Reification; Constraints; Derived data; Packages; Practical tips.

State Modeling: Events, States, Transitions and Conditions; State diagrams;

Practical tips. Advanced State Modeling, Interaction Modeling: Advanced State

Modeling: Nested state diagrams; Nested states; Signal generalization; Concurrency;

Practical tips. Interaction Modeling: Use case models; Sequence models; Activity

models. Use case relationships; Procedural sequence models; Special constructs for

activity models.

Self-Learning Exercise: State diagram behavior, A Sample state model, Relation of

class and state Models.


UNIT III 8 Hours

Process Overview, System Conception, Domain Analysis Process Overview:

Development stages; Development life cycle. System Conception: Devising a system

concept; Elaborating a concept; Domain Analysis: Overview of analysis; Domain class

model; Domain state model; Domain interaction model; Iterating the analysis.

Self-Learning Exercise: Preparing a problem statement.

UNIT IV 8 Hours

Application Analysis, System Design Application Analysis: Application interaction

model; Application class model; Application state model; adding operations. Overview of

system design; Estimating performance; Making a reuse plan; Breaking a system in to

sub-systems; Identifying concurrency; Allocation of sub-systems; Management of data

storage; Handling global resources; Choosing a software control strategy; Handling

boundary conditions; Setting the trade-off priorities; Common architectural styles.

Self-Learning Exercise: Architecture of the ATM system as the example.

UNIT V 10 Hours

Class Design, Implementation Modeling, Legacy Systems Class Design: Overview of

class design; Bridging the gap; Realizing use cases; Designing algorithms; Recursing

downwards, Refactoring; Design optimization; Reification of behavior; Adjustment of

inheritance; Organizing a class design. Implementation Modeling: Overview of

implementation; Fine-tuning classes; Fine-tuning generalizations; realizing associations;

Testing. Legacy Systems: Reverse engineering; Building the class models; Reverse

engineering tips; Wrapping; Maintenance.

Self-Learning Exercise: ATM example, Building the class, the interaction model and

state model.

UNIT VI 8 Hours

Design Patterns, Idioms What is a pattern and what makes a pattern? Pattern

categories; Relationships between patterns; Pattern description; Communication Patterns:

Forwarder-Receiver; Client-Dispatcher-Server; Publisher-Subscriber; Command

processor; View Handler; Idioms: Introduction; what can idioms provide?

Self-Learning Exercise: Management Patterns, Idioms and style; Where to find idioms;

Counted Pointer example.


Text Books: 1. Michael Blaha, James Rumbaugh: Object-Oriented Modeling and Design with

UML, 2nd Edition, Pearson Education / PHI, 2007. (Chapters 1 to 17, 23)

2. Frank Buschmann, RegineMeunier, Hans Rohnert, Peter Sommerlad, Michael Stal: Pattern-Oriented Software Architecture, A System of Patterns, Volume 1, John Wiley and Sons, 2008. (Chapters 1, 3.5, 3.6, 4)

Reference Books:

1. Grady Booch et al: Object-Oriented Analysis and Design with Applications, 3rd Edition, Pearson, 2007.

2. Mark Priestley: Practical Object-Oriented Design with UML, 2nd Edition, Tata McGraw-Hill, 2003.


.NET and C# (4:0:0)

Sub Code : MCA0421 CIE : 50%


SEE Hours : 3 Hrs Max Marks : 100


Course Outcomes:


1. Identify a solution in .NET environment after proper analysis of previous state of

affairs and designing simple applications in C#.

2. Discuss C# language fundamentals

3. Demonstrate the Object Oriented Programming concepts in C#

4. Experiment the applications with different exceptions scenarios and determining

the Object lifetime

5. Devise applications using interfaces

6. Describe delegates and few advanced techniques in C#

UNIT 1 10 Hours

The philosophy of .NET Understanding the previous State of Affairs, The .NET

Solution, The Building Block of the .NET Platform (CLR,CTS, and CLS), The Role of

the .NET Base Class Libraries, What C# Brings to the Table, An Overview of .NET

Binaries ( aka Assemblies ), the Role of the Common Intermediate Language , The Role

of .NET Type Metadata, The Role of the Assembly Manifest, Compiling CIL to Platform

–Specific Instructions, Understanding the Common Type System, Intrinsic CTS Data

Types, Understanding the Common Languages Specification, Understanding the

Common Language Runtime A tour of the .NET Namespaces, Increasing Your

Namespace Nomenclature, Deploying the .NET Runtime Building C# Applications The

Role of the Command Line Complier (csc.exe), Building C # Application using csc.exe

Working with csc.exe Response Files, Generating Bug Reports, C# Compiler

Options, Command Line Debugger (cordbg.exe) , C# “Preprocessor:” directives, An

Interesting Aside: The System. Environment Class. Self-Learning Exercise: Execution of simple C# application programs

UNIT 2 8 Hours

C# Language Fundamentals The Anatomy of a Basic C# Class, Creating objects:

Constructor Basics, The Composition of a C# Application, Default Assignment and

Variable Scope, The C# Member Initialization Syntax, Understanding Value Types and

Reference Types, The Master Node: System, Object, The System Data Types (and C#

Aliases), Converting Between Value Types and Reference Types: Boxing and Unboxing,

Defining Program Constants, C# Iteration Constructs, C# Controls Flow Constructs, The


Complete Set of C# Operators, Defining Custom Class Methods, Understating Static

Methods, Methods Parameter Modifiers, Array Manipulation in C#,String

Manipulation in C#,C# Enumerations, Defining Structures in C#, Defining Custom

Namespaces.

Self-Learning Exercise: Basic Input and Output with the Console Class UNIT-3 8 Hours

Object- Oriented Programming with C# Defining of the C# Class, Recapping the

Pillars of OOP, The First Pillars: C#’s Encapsulation Services, Pseudo- Encapsulation:

Creating Read-Only Fields, The Second Pillar, keeping Family Secrets: The “Protected”

Keyword, Nested Type Definitions, The Third Pillar: C#’s Polymorphic Support,

Understanding Base Class/Derived Class Casting Rules, The Master Parent Class:

System.Object.

Self-Learning Exercise: C#’s Inheritance Supports UNIT 4 10 Hours

Exceptions and Object Lifetime Ode to Errors, Bugs and Exceptions, The Role of .NET

Exception Handing, the System. Exception Base Class, Throwing a Generic Exception,

Catching Exception, CLR System–Level Exception, Custom Application-Level

Exception Handling, Multiple Exceptions, The Finally Block; Dynamically Identifying

Application and System Level Exceptions, Understanding Object Lifetime, the CIL of

‘new’, The Basics of Garbage Collection, Finalization of a Type, The Finalization

Process, The System. GC Type.

Self-Learning Exercise: Garbage Collection Optimizations.

UNIT 5 8 Hours

Interfaces Defining Interfaces Using C# Invoking Interface Members at the object Level,

Exercising the Shapes Hierarchy, Understanding Explicit Interface Implementation,

Interfaces as Polymorphic agents, Building Interface Hierarchies.

Self-Learning Exercise: Exploring the System.Collections Namespace

UNIT 6 8 Hours

Delegates &Advanced Techniques Understanding the .NET Delegate Type, Members

of System, Multicast Delegate, The Simplest Possible Delegate Example, Building a

more elaborate Delegate Example, Catalog of C# Keywords, Building a Custom Indexer,

differentiating of indexer from properties, Creating Custom Conversion Routines,

Overloading of operators.

Self-Learning Exercise: Advanced Keywords of C#


Text Books:

1. Andrew Troelsen: Pro C# with .NET 3.0, Special Edition, Dream tech Press, India, 2007.

2. E. Balagurusamy: Programming in C#,, 5th Reprint, Tata McGraw Hill, 2004.

Reference Books: 1. Tom Archer: Inside C#, WP Publishers, 2001.

2. Herbert Schildt: The Complete Reference C#, Tata McGraw Hill, 2004.


Software Design Laboratory (0:0:3)

Sub Code : MCA0112 Hrs/Week : 03

Max Marks : 50 Credit : 1.5


Course Outcomes:


1. Describe the concept of object-orientation methodologies

2. Create use case documents that capture requirements for a software system.

3. Create class diagrams that model both the domain model and design model of a

software system.

4. Design the interface between the classes and objects.

5. Create interaction diagrams that model the dynamic aspects of a software system.

6. Design communication and design patterns.

Objective:

To design and develop various applications using IBM Rational Rose

Experiments

The student has to draw the necessary UML diagrams using any suitable UML

Drawing Tool and implement in Java OR C++ OR C# program to demonstrate the

working of object oriented methodologies and some design patterns listed below:

Basic introductory programs, Data hiding, inheritance, polymorphism, operator

overloading, function overloading, exception handling and File I/O, Publisher-

Subscriber, Forward-Receiver, Client-Dispatcher, command.


.Net Laboratory (0:0:3)


Max Marks: 50 Credits : 1.5

Pre-requisite: OOP concepts

Course Outcomes:


1. Display proficiency in C# by building stand-alone applications in the .NET

framework using C# that is completely event driven.

2. Create simple web applications and window application features of C#

3. Create distributed data-driven applications using the .NET Framework, C#

4. Create web-based distributed applications using C#

5. Debug an application using breakpoints and Try/Catch/Finally blocks

6. Debug an application using delegates and demonstration of inheritance support.

IDE: Microsoft Visual Studio8 Initially work with simple programs in runtime

environment .NET framework i.e., SDK command prompt. Simple programs in C#,

Programs on Array processing in C#, interfaces, use of Virtual and override key words,

collections, abstract classes and methods, exception handling etc.


Mini Project

Sub Code : MCA0114 CIE : 50

Max Marks : 50 Credits : 1.0

Pre-requisite: NA

Course Outcomes:


1. Analyze real world problems

2. Implement different design methodologies

3. Select appropriate tools for solving problems

4. Implementing advanced programming techniques/languages

5. Perceive the art of verification and validation

6. Write technical reports

Groups of two student, one project per group, must carry out mini project. Student must

submit a Detailed Project Report in a format as specified by the department.


Seminar – I (0:0:2)

Sub Code: MCA0115 Hrs/Week : 02

Max Marks: 50

Pre-requisite: NA

Course Outcomes:


1. Understand current trends in specific area of interest

2. Perform literature survey related to the specific topics of interest

3. Appreciate the results of technical work


5. Summarize and present the technical contents

A Seminar should be given by an individual student based on topics chosen from the

emerging areas and technologies of Computer science & Computer Applications.

References from journals such as IEEE, ACM etc., shall be used. A report on this

seminar with 15-20 pages shall also be prepared.


VI MCA


Seminar – II (0:0:2)


Max Marks: 50

Pre-requisite: NA

Course Outcomes:


1. Understand current trends in specific area of interest

2. Perform literature survey related to the specific topics of interest

3. Appreciate the results of technical work


5. Summarize and present the technical contents

A Seminar should be given by an individual student based on topics chosen from the

emerging areas and technologies of Computer science & Computer Applications.

References from journals such as IEEE, ACM etc., shall be used. A report on this

seminar with 15-20 pages shall also be prepared.


Major Project

Sub Code : MCA2801

Credits : 28

Pre-requisite: NA

Course Outcomes:


1. Analyze real world problems

2. Implement the different design methodologies

3. Select appropriate tools for solving problems

4. Implementing advanced programming techniques/languages

5. Perceive the art of verification and validation


Individual student, one project per student, must carry out major project. Student must

submit a Detailed Project Report (60 to 80 Pages) in a format as specified by the

department. Internal guides will evaluate the performance (Continuous Internal

Evaluation) for 50 Marks. The Report will be evaluated for 125 marks by both internal

and external evaluators. Internal and external examiners for 75 marks will evaluate final

viva-voce, which includes demonstration and presentation of project work, jointly.


ELECTIVES


Computer Graphics and Visualization (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Recall the different applications of computer graphics

2. Explain the potential and benefits of using openGL in field of computer graphics

3. Show the working of various animated interactive programs by using suitable input

and output devices.

4. Explain the different aspects of modeling and transforming using homogeneous

coordinates.

5. Design and implement various three dimensional Line clipping algorithms

6. Demonstrate the use of modeling and transformation in order to visualize various

models

UNIT – 1

Introduction Applications of computer graphics; A graphics system; Images: Physical

and synthetic; The human visual system; The pinhole camera; The synthetic camera

model; The programmer’s interface; Graphics architectures. Graphics Programming:

Self-Learning Exercise: The Sierpinski gasket.

UNIT – 2

The OpenGL The OpenGL API; Primitives and attributes; Color; Viewing; Control

functions; The Gasket program; Polygons and recursion.

Self-Learning Exercise: The three dimensional gasket.

UNIT – 3

Input and Interaction Interaction, Input devices; Clients and servers; Display lists;

Programming event-driven input; Menus; Picking; A simple paint program; Animating

interactive programs.

Self-Learning Exercise: Design of interactive programs.

UNIT – 4


Geometric Objects and Transformations Scalars, points, and vectors; Three-

dimensional primitives; Coordinate systems and frames; Modeling a colored cube; Affine

transformations; Rotation, translation and scaling; Transformation in homogeneous

coordinates. Viewing Classical and computer viewing; Positioning of the camera; Simple

projections; Projections in OpenGL; Hidden-surface removal; Walking through a scene;

Parallel-projection matrices.

Self-Learning Exercise: OpenGL transformation matrices

UNIT – 5

Shading Light and matter; Light sources; The Phong reflection model; Computation of

vectors; Polygonal shading; Approximation of a sphere by recursive subdivisions; Light

surfaces in OpenGL; Specification of materials in OpenGL. Implementation - The

major tasks; Implementation of transformations; Line-segment clipping; Polygon

clipping; Clipping of other primitives; Clipping in three dimensions; Hidden-surface

removal; Scan conversion; Bresenham’s algorithm.

Self-Learning Exercise: Antialiasing

UNIT – 6

Visualization Data + Geometry; Height field and contours; Visualizing surfaces and

scalar fields; Isosurfaces and marching cubes; Direct volume rendering; Vector-field

visualization.

Self-Learning Exercise: Tensor-visualization.

Text Books:

1. Edward Angel: Interactive Computer Graphics A Top-Down Approach with OpenGL,

2nd Edition, Addison-Wesley, 2000. (Chapters 1, 2, 3, 4, 5, 6, 7, 12)

Reference Books:

1. F.S. Hill,Jr.: Computer Graphics Using OpenGL, 2nd Edition, Pearson education /

PHI, 2001.

2. James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes: Computer

Graphics, Addison Wesley, 1997.

3. Donald Hearn and Pauline Baker: Computer Graphics- C Version, 2nd Edition, Pearson

Education / PHI, 2003.


UNIX Systems Programming (4:0:0)




Pre-requisite: Introduction to UNIX

Course Outcomes:

On successful completion of the course, the students will be able to:

1. Identify the role of various UNIX programming standards and file types.

2. Demonstrate the usage of file types with respect to different types of operations

3. Discuss UNIX OS concepts such as: process, context switching, kernel support

for processes, running of programs

4. Illustrate process controlling procedures, relationship between processes, process

groups, controlling of terminals

5. Describe signal handling mechanisms and daemon processes

6. Explain the inter-process communication

UNIT – 1

Introduction UNIX and ANSI Standards: The ANSI C Standard, the ANSI/ISO C++

Standards, Difference between ANSI C and C++, The POSIX Standards, The POSIX.1

FIPS Standard, The X/Open Standards. UNIX and POSIX APIs: The POSIX APIs, The

UNIX and POSIX Development Environment, API Common Characteristics. UNIX

Files File Types, The UNIX and POSIX File System, The UNIX and POSIX File

Attributes, inodes in UNIX System V, Application Program Interface to Files, UNIX

Kernel Support for Files, Directory Files, Hard and Symbolic Links.

Self-Learning Exercise: Relationship of C Stream Pointers and File Descriptors

UNIT – 2

UNIX File APIs General File APIs, File and Record Locking, Directory File APIs,

Device File APIs, FIFO File APIs, Symbolic Link File APIs.

Self-Learning Exercise: File Listing Program

UNIT – 3

UNIX Processes The Environment of a UNIX Process: Introduction, main function,

Process Termination, Command-Line Arguments, Environment List, Memory Layout of

a C Program, , Environment Variables, setjmp and longjmp Functions, getrlimit, setrlimit

Functions, UNIX Kernel Support for Processes.


Self-Learning Exercise: Shared Libraries, Memory Allocation

UNIT – 4

Process Control Introduction, Process Identifiers, fork, vfork, exit, wait, waitpid, waited,

wait3, wait4 Functions, Race Conditions, exec Functions, Changing User IDs and Group

IDs, Interpreter Files, system Function, Process Accounting, User Identification, Process

Times. Process Relationships: Introduction, Terminal Logins, Network Logins,

Controlling Terminal, Job Control, Shell execution of programs.

Self-Learning Exercise: Process Groups, Sessions

UNIT – 5

Signals and Daemon Processes Signals: The UNIX Kernel Support for Signals, signal,

Signal Mask, sigaction, The SIGCHLD Signal and the waitpid Function, The sigsetjmp

and siglongjmp Functions, Kill, Alarm, Interval Timers, POSIX.lb Timers. Daemon

Processes: Introduction, Daemon Characteristics, Coding Rules, Error Logging, Single

instance daemons; Daemon conventions.

Self-Learning Exercise: Client-Server Model.

UNIT – 6

Interprocess Communication Introduction; Pipes, popen, pcloseFunctions;

Coprocesses; FIFOs,Message Queues; Semaphores.Network IPC: Sockets Introduction;

Socket Descriptors; Addressing; Connection establishment; Data transfer; Socket options;

Out-of-band data.

Self-Learning Exercise: XSI IPC and Non-blocking and asynchronous I/O.

Text Books:

1. Terrence Chan: Unix System Programming Using C++, Prentice-Hall of India /

Pearson Education, 1999.. (Chapters 1, 5, 6, 7, 8, 9)

2. W.Richard Stevens, Stephen A. Rago: Advanced Programming in the UNIX

Environment, 2nd Edition, Pearson Education / Prentice-Hall of India, 2005.

(Chapters 7, 8, 9, 13, 15, 16)

Reference Books: 1. Marc J. Rochkind: Advanced UNIX Programming, 2nd Edition, Pearson

Education, 2005.

2. Maurice.J.Bach: The Design of the UNIX Operating System, Pearson Education /

PHI, 1987.

3. UreshVahalia: UNIX Internals, Pearson Education, 2001.


Multimedia Systems (4:0:0) Sub Code : MCA0424 CIE : 50%



Pre-requisite : NA

Course Outcomes:


1. Understand various elements of multimedia and its applications.

2. Analyze the different data compression techniques for both audio and video.

3. Explain the need for different type of storage media and its applications.

4. Illustrate design and implementation of different data file formats.

5. Apprise different applications of multimedia.

UNIT – 1

Introduction, Media and Data Streams, Audio Technology Multimedia Elements;

Multimedia Applications; Multimedia Systems Architecture; Evolving Technologies for

Multimedia Systems; Defining Objects for Multimedia Systems; Multimedia Data

Interface Standards; The need for Data Compression; Multimedia Databases. Media:

Perception Media, Representation Media, Presentation Media, Storage Media;

Characterizing Continuous Media Data Streams. Sound: Frequency, Amplitude, Sound

Perception and Psychoacoustics; Audio Representation on Computers.

Self-Learning Exercise: Three Dimensional Sound Projection; Music and MIDI

Standards; Speech Signals; Speech Output; Speech Input; Speech Transmission.

UNIT – 2

Graphics and Images, Video Technology, Computer-Based Animation Capturing

Graphics and Images Computer Assisted Graphics and Image Processing; Reconstructing

Images; Graphics and Image Output Options. Basics; Television Systems; Digitalization

of Video Signals; Digital Television; Basic Concepts; Specification of Animations;

Methods of Controlling Animation; Display of Animation.

Self-Learning Exercise: Transmission of Animation; Virtual Reality Modeling

Language.

UNIT – 3

Data Compression - Storage Space; Coding Requirements; Source, Entropy, and Hybrid

Coding; Basic Compression Techniques; JPEG: Image Preparation, Lossy Sequential

DCT-based Mode.H.261 (Px64) and H.263: Image Preparation, Coding Algorithms,


DataStream, H.263+ and H.263L; MPEG: Video Encoding, Audio Coding, Data Stream,

MPEG-2.

Self-LearningExercise:Expanded Lossy DCT-based Mode, Lossless Mode, Hierarchical

Mode, MPEG-4, MPEG-7; Fractal Compression.

UNIT – 4

Optical Storage Media History of Optical Storage; Basic Technology; Video Discs and

Other Worms; Compact Disc Digital Audio; Compact Disc Read Only Memory; CD-

ROM Extended Architecture; Further CD-ROM-Based Developments; Compact Disc

Recordable. Content Analysis Simple Vs. Complex Features; Analysis of Individual

Images; Analysis of Image Sequences.

Self-Learning Exercise: Compact Disc Magneto- Optical; Compact Disc Read/Write;

Digital Versatile Disc, Audio Analysis; Applications.

UNIT – 5

Data and File Format Standards Rich-Text Format; TIFF File Format; Resource

Interchange File Format (RIFF); MIDI File Format; JPEG DIB File Format for Still and

Motion Images; AVI Indeo File Format.

Self-Learning Exercise:MPEG Standards; TWAIN.

UNIT – 6

Multimedia Application Design Multimedia Application Classes; Types of Multimedia

Systems; Virtual Reality Design; Components of Multimedia Systems; Organizing

Multimedia Databases.

Self Learning Component: Application Work flow Design Issues; Distributed

Application Design Issues.

Text Books: 1. Ralf Steinmetz, KlaraNarstedt: Multimedia Fundamentals: Vol 1-Media Coding

and Content Processing, 2nd Edition, Pearson Education / PHI, 2003. (Chapters 2,

3, 4, 5, 6, 7, 8, 9)

2. Prabhat K. Andleigh, KiranThakrar: Multimedia Systems Design, PHI, 2003.

(Chapters 1,3,7)

Reference Books: 1. K.R Rao, Zoran S. Bojkovic and Dragorad A. Milovanovic: Multimedia

Communication Systems: Techniques, Standards, and Networks, Pearson

Education, 2002.

2. Nalin K Sharad: Multimedia information Networking, PHI, 2002.


Artificial Intelligence (4:0:0)




Pre-requisites: NA

Course Outcomes:


1 Explain the fundamentals of Artificial Intelligence – Understanding Level

2 Explain the search techniques and knowledge representation – Understanding

Level

3 Describe the Rules for knowledge representation – Understanding Level

4 Define the reasoning and planning – Understanding Level

5 Explore the concept of Machine Learning and Common Sense – Understanding

Level

UNIT-1

Introduction , Problems, Problem Spaces & Search What is Artificial Intelligence?

What is an AI Technique, The Level of the Model; Problems, Problem Spaces &

Search Defining the problem as a state space search, Production Systems: Control

strategies, Heuristic Search, Problem Characteristics: Is the problem decomposable? Can

solution steps be ignored or undone? Is the Universe Predicate? Is a good solution

Absolute or Relative? Is the solution a State or a Path? What is the role of knowledge?

Does the task Require Interaction with a Person? Problem Classification, Production

System Characteristics.

Self-Learning Exercise: Issues in the design of search programs

UNIT- 2

Search Techniques and Knowledge Representation Heuristic Search Techniques:

Generate-and-Test, Hill-Climbing: Simple Hill climbing, Steepest Ascent Hill Climbing,

Best-First search: OR Graphs, The A* Algorithm, Problem reduction: AND-OR Graphs,

The AO* Algorithm. Constraint satisfaction, Means-ends analysis; Knowledge

Representation: Representations and Mappings, Approaches to knowledge

representation, Issues in Knowledge Representation: attributes, relationships among

attributes, choosing granularity of Representation, Representing set of objects.

Self-Learning Exercise: Finding right structures as needed


UNIT-3

Representing knowledge using Rules Representing knowledge using Rules: Procedural

Vs Declarative Knowledge, Logic Programming, Backward Reasoning: Backward

chaining rule systems, Forward chaining Rule systems, Combining Forward and

Backward Reasoning, Matching: Indexing, Matching with variables, Complex and

Appropriate Matching.

Self-Learning Exercise: conflict resolution

UNIT-4

Statistical Reasoning and Planning Statistical Reasoning: Probability and Bayes’

Theorem, Certainty and Rule-Based System, Bayesian Networks, Dempster-Shafer

Theory, Fuzzy Logic; Planning: Components of Planning system, Hierarchical Planning.

Self-Learning Exercise: Reactive Systems

UNIT-5

Learning and Common Sense What is Learning? Rote Learning, Learning by taking

advice, Learning in Problem-solving, Learning from Examples: Wintson Learning

Program; Common Sense: Common Sense Onto logies: Time, Space, Materials,

Memory Organization, Case Based Reasoning.

Self-Learning Exercise: Explanation based learning

UNIT-6

Artificial Immune Systems The phenomenon of Immunity, Immunity and Infection, The

Innate Immune system, Adaptive Immune system, Clonal Selection, Learning, Immune

Network Theory, Mapping Immune systems to practical Applications: Modeling

affinities, Case studies: Case-1 Network Security, Case-2 AIS for Robot control and

Navigation.

Self-Learning Exercise: Other applications

Text Book:

1. Elaine Rich, Kelvin Knight, Shiva Shankar B Nair: Artificial Intelligence, 3rd

Edition, Tata McGraw Hill, 2009.

Reference Books:

1. Stuart Russel, Peter Norvig: Artificial Intelligence: A Modern Approach, 2nd

Edition, Pearson Education, 2003.

2. Nils J. Nilsson: Principles of Artificial Intelligence, Elsevier, 1980.


Data Mining (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Understand the necessity of data mining.

2. Identify different tasks of data mining.

3. Identify the different methods in association analysis

4. Apply algorithms for association analysis in identifying associations

5. Understand the importance of cluster analysis.

6. Understand the importance of advanced data mining topics like spatial data

mining, text mining, mining the web.

UNIT - 1

Introduction, Data – 1 What is Data Mining? Motivating Challenges; The origins of

data mining; Data Mining Tasks.Types of Data.

Self-Learning Exercise: Data Quality.

UNIT – 2

Data – 2 Data Preprocessing; Measures of Similarity and Dissimilarity Classification

Preliminaries; General approach to solving a classification problem; Decision

tree induction; Rule-based classifier;

Self-Learning Exercise: Nearest-neighbor classifier.

UNIT - 3

Association Analysis – 1 Problem Definition; Frequent Itemset generation; Rule

Generation; Compact representation of frequent itemsets;

Self-Learning Exercise: Alternative methods for generating frequent itemsets.

UNIT - 4

Association Analysis – 2 FP-Growth algorithm, Evaluation of association patterns;

Effect of skewed support distribution.


Self-Learning Exercise: Sequential patterns.

UNIT - 5

Cluster Analysis Overview, K-means, Agglomerative hierarchical clustering,

DBSCAN.

Self-Learning Exercise: Overview of Cluster Evaluation.

UNIT - 6

Further Topics in Data Mining Multidimensional analysis and descriptive mining of

complex data objects; Spatial data mining; Multimedia data mining; Text mining;

Mining the WWW, Outlier analysis. Applications Data mining system products and

research prototypes; Additional themes on Data mining; Social impact of Data

mining; Trends in Data mining.

Self-Learning Exercise: Data mining applications

Text Books:

1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data

Mining, Pearson Education, 2007. (Chapter 1, 2, 4.1 to 4.3, 5.1, 5.2, 6, 8.1 to

8.4, 8.5.1)

2. Jiawei Han and MichelineKamber: Data Mining – Concepts and Techniques, 2nd

Edition, Morgan Kaufmann, 2006. (Chapters 7.11, 10, 11)

Reference Books:

1. K.P.Soman, ShyamDiwakar, V.Ajay: Insight into Data Mining – Theory and

Practice, PHI


Network Management (4:0:0)




Pre-requisite : Computer Networks

Course Outcomes:


1. Describe the importance of Network topology.

2. Differentiate between organization model and information model.

3. Analyze the management information base with managed objects.

4. Compare RMON1 groups and functions

5. Explain the broadband network management

6. Formulate performance metrics with network management

UNIT – 1

Introduction Analogy of Telephone Network Management, Data and

Telecommunication Network Distributed computing Environments, TCP/IP-Based

Networks: The Internet and Intra-nets, Communications Protocols and Standards-

Communication Architectures, Protocol Layers and Services; Case Histories of

Networking and Management – The Importance of topology , Filtering Does Not Reduce

Load on Node, Some Common Network Problems; Challenges of Information

Technology Managers, Network Management: Goals, Organization, and Functions- Goal

of Network Management, Network Provisioning, Network Operations and the NOC,

Network Installation and Maintenance.

Self-Learning Exercise: Network and System Management, Network Management

System platform, Current Status and Future of Network Management.

UNIT – 2

Basic Foundations: Standards, Models, and Language Network Management

Standards, Network Management Model, Organization Model, Information Model –

Management Information Trees, Managed Object Perspectives, Communication Model;

ASN.1- Terminology, Symbols, and Conventions, Objects and Data Types, Object

Names.SNMPv1 Network Management – 1Managed Network: The History of SNMP

Management, Internet Organizations and standards, Internet Documents, The SNMP

Model.

Self-Learning Exercise: An Example of ASN.1 from ISO 8824; Encoding Structure;

Macros, Functional Model,The Organization Model, System Overview.


UNIT – 3

SNMPv1 Network Management – 2 The Information Model – Introduction, The

Structure of Management Information, Managed Objects, Management Information Base.

The SNMP Communication Model – The SNMP Architecture, Administrative Model.

Self-Learning Exercise:SNMP Specifications, SNMP Operations, SNMP MIB Group,

Functional Model.

UNIT – 4

SNMP Management – RMONRemote Monitoring, RMON SMI and MIB, RMONI1-

RMON1 Textual Conventions, RMON1 Groups and Functions, Relationship Between

Control and Data Tables, RMON1 Common and Ethernet Groups, RMON Token Ring

Extension Groups, RMON2 – The RMON2 Management Information Base, RMON2

Conformance Specifications.

Self-Learning Exercise:ATM Remote Monitoring, A Case Study of Internet Traffic

Using RMON.

UNIT – 5

Broadband Network Management Broadband Access Networks and Technologies –

Broadband Access Networks, Broadband Access Technology; HFCT Technology – The

Broadband LAN, The Cable Modem, The Cable Modem Termination System, The HFC

Plant, The RF Spectrum for Cable Modem; Data Over Cable Reference Architecture;

HFC Management – Cable Modem and CMTS Management, HFC Link Management,

RF Spectrum Management, DSL Technology; Asymmetric Digital Subscriber Line

Technology – Role of the ADSL Access Network in an Overall Network, ADSL

Architecture, ADSL Channeling Schemes, ADSL Encoding Schemes; ADSL

Management – ADSL Network Management Elements, ADSL Configuration.

Self-Learning Exercise: ADSL Configuration

UNIT-6

Network Management ApplicationsConfiguration Management- Network

Provisioning, Inventory Management, Network Topology, Fault Management –

Fault Detection, Fault Location and Isolation Techniques, Performance Management –

Performance Metrics, Data Monitoring, Problem Isolation, Performance Statistics; Event

Correlation Techniques – Rule-Based Reasoning, Model- Based Reasoning, Case-Based

Reasoning, Codebook correlation Model, State Transition Graph Model, Finite State

Machine Model, Security Management – Policies and Procedures, Security Breaches and

the Resources Needed to Prevent Them, Firewalls, Cryptography, Authentication and

Authorization, Client/Server Authentication Systems, Messages Transfer Security,


Protection of Networks from Virus Attacks.

Self-Learning Exercise: Report Management, Policy-Based Management, Service Level

Management.

Text Books: 1. Mani Subramanian: Network Management- Principles and Practice,

2ndedition,Pearson Education Publication, 2009.

Reference Books:

1. J. Richard Burke: Network management Concepts and Practices: a Hands- On

Approach, PHI, 2008.


Software Architectures (4:0:0)




Pre-requisite : Software Engineering

Course outcomes:

On successful completion of this course the students should be able to

1. Understand the architecture business cycle.

2. Differentiate architectural styles and patterns

3. Understand the quality attributes in the architecture.

4. Illustrate the different design patterns.

5. Understanding the design and documentation of the architecture.

UNIT – 1

1. Introduction The Architecture Business Cycle: Where do architectures come from?

Software processes and the architecture business cycle; What makes a “good”

architecture? What software architecture is and what it is not; Other points of view;

Architectural patterns, reference models and reference architectures; Importance of

software architecture.

Self-Learning Exercise: Architectural structures and views.

UNIT – 2

Architectural Styles and Case Studies Architectural styles; Pipes and filters; Data

abstraction and object-oriented organization; Event-based, implicit invocation;

Layered systems; Repositories; Interpreters; Process control; Other familiar

architectures; Heterogeneous architectures. Case Studies: Keyword in Context;

Instrumentation software.

Self-Learning Exercise: Mobile robotics; Cruise control; Three vignettes in mixed

style.

UNIT – 3

Quality Functionality and architecture; Architecture and quality attributes; System

quality attributes; Business qualities; Architecture qualities. Achieving Quality:

Introducing tactics; Availability tactics; modifiability tactics; Performance tactics;

Security tactics; Testability tactics; Usability tactics; Relationship of tactics to

architectural patterns; Architectural patterns and styles.


Self-Learning Exercise: Quality attribute scenarios in practice; Other system quality

attributes.

UNIT – 4

Architectural Patterns Introduction; From mud to structure: Layers, Pipes and Filters,

Blackboard. Distributed Systems: Broker; Interactive Systems: MVC, Presentation-

Abstraction-Control: Microkernel; Reflection.

Self-Learning Exercise: Adaptable Systems

UNIT – 5

Some Design Patterns Structural decomposition: Whole – Part: Master –Slave; Access

Control: Proxy.

Self-Learning Exercise: Organization of work.

UNIT – 6

Designing and Documenting Software Architecture Architecture in the life cycle;

Designing the architecture; Forming the team structure; Creating a skeletal system. Uses

of architectural documentation; documenting a view; Documentation across views.

Self-Learning Exercise: Views; Choosing the relevant views

Text Books: 1. Len Bass, Paul Clements, Rick Kazman: Software Architecture in Practice, 2nd

Edition, Pearson Education, 2003. (Chapters 1, 2, 4, 5, 7, 9)

2. Frank Buschmann, RegineMeunier, Hans Rohnert, Peter Sommerlad, Michael Stal:

Pattern-Oriented Software Architecture, A System of Patterns, Volume 1, John

Wiley and Sons, 2006. (Chapters 2, 3.1 to 3.4)

3. Mary Shaw and David Garlan: Software Architecture- Perspectives on an Emerging

Discipline, Prentice-Hall of India / Pearson Education, 2007. (Chapters 1.1, 2, 3)

Reference Books: 1. E. Gamma, R. Helm, R. Johnson, J. Vlissides: Design Patterns- Elements of

Reusable Object-Oriented Software, Addison-Wesley, 1995.

Web site for Patterns: http://www.hillside.net/patterns/

NIE, Mysuru – 8. Department of MCA

Information and Network Security (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Plan to provide security for the information by designing models and also familiarize

with the security technologies

2. Apply cryptographic techniques using various tools

3. Recognize different services and mechanisms for Network Security applications

4. Analyze the email security formats

5. Understand the key management techniques and design of IP security technology

6. Analyze the different web security technologies

UNIT – 1

Planning for Security Introduction; Information Security Policy, Standards, and Practices;

The Information Security Blue Print; Contingency plan. Security Technology Introduction;

Physical design; Firewalls; Protecting Remote Connections. Introduction; Intrusion

Detection Systems (IDS); Honey Pots, Honey Nets, and Padded cell systems.

Self-Learning Exercise: Model for contingency plan, Scanning and Analysis Tools.

UNIT – 2

Cryptography Introduction; A short History of Cryptography; Principles of

Cryptography; Cryptography Tools.

Self-Learning Exercise: Attacks on Cryptosystems.

UNIT – 3

Introduction to Network Security, Authentication Applications Attacks, services, and

Mechanisms; Security Attacks; Security Services; A model for Internetwork Security;

Internet Standards and RFCs. Kerberos.

Self-Learning Exercise: X.509 Directory Authentication Service.

UNIT – 4

Electronic Mail Security Pretty Good Privacy (PGP); S/MIME.

Self-Learning Exercise: S/MIME Functionality, S/MIME Messages


UNIT – 5

IP Security IP Security Overview; IP Security Architecture; Authentication Header;

Encapsulating Security Payload; Combining Security Associations.

Self-Learning Exercise: Key Management.

UNIT – 6

Web Security Web security requirements; Secure Socket layer (SSL) and Transport layer

Security (TLS).

Self-Learning Exercise: Secure Electronic Transaction (SET).

Text Books:

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

Edition, Thomson, 2013. (Chapters 5, 6, 7, 8; Exclude the topics not mentioned in the

syllabus)

2. William Stallings: Network Security Essentials: Applications and Standards, Pearson

Education .4th Edition, 2011. (Chapters: 1, 4, 5, 6, 7, 8)

Reference Book:

1. Behrouz A. Forouzan: Cryptography and Network Security, Special Indian Edition,

Tata McGraw-Hill, 2007.


Software Testing and Practices (4:0:0)




Pre-requisite : NA

Course outcomes:

The students on successful completion of this course should be able to

1 Define the testing and test generation strategies.

2 Classify test cases from the requirement.

3 Identify advantages and disadvantages of dataflow model and data flow testing.

4 Analyze the quality process in testing.

5 Compare which type of interaction and regression testing has to be performed and

when to release the software.

UNIT – 1

Basics of Software Testing Human Errors and Testing; Software Quality; Requirements,

Behavior and Correctness; Correctness versus Reliability; Testing and Debugging; Test

Metrics; Software and Hardware Testing; Testing and Verification; Defect Management;

Execution History; Test-generation Strategies, Static Testing. Model-Based Testing and

Model Checking; Control-Flow Graph;.

Self-Learning Exercise: Types of Testing; The Saturation Effect

UNIT – 2

Test Generation from Requirements, Structural Testing Introduction; The Test-Selection

Problem; Equivalence Partitioning; Boundary Value Analysis; Category-Partition Method.

Cause-Effect Graphing, Test Generation from Predicates.Structural Testing: Overview;

Statement testing; Path testing; Procedure call testing; Comparing structural testing

criteria; The infeasibility problem.

Self-Learning Exercise: Branch testing; Condition testing

UNIT – 3

Dependence, Data Flow Models, and Data Flow Testing Definition-Use pairs; Data flow

analysis; Classic analyses; From execution to conservative flow analysis; Data flow analysis

with arrays and pointers; Inter-procedural analysis; Overview of data flow testing;

Definition-Use associations; Data flow testing criteria; Data flow coverage with complex

structures.

Self-Learning Exercise: The infeasibility problem


UNIT – 4

Test Case Selection and Adequacy, Test ExecutionOverview; Test specification and

cases; Adequacy criteria; Comparing criteria; Overview of test execution; From test

case specification to test cases; Scaffolding; Generic versus specific scaffolding; Capture

and replay.

Self-Learning Exercise: Test oracles; Self-checks as oracles

UNIT – 5

Process Test and analysis activities within a software process: The quality process; Planning

and monitoring; Quality goals; Analysis; Testing; Improving the process; Organizational

factors.

Self-Learning Exercise: Dependability properties

UNIT – 6

Integration and component-based software testingOverview; Integration testing

strategies; Testing components and assemblies. System, Acceptance and Regression Testing:

Overview; System testing; Acceptance testing; Usability; Regression testing; Test case

prioritization and selective execution.

Self-Learning Exercise: Regression test selection techniques

Text Books: 1. Aditya P Mathur: Foundations of Software Testing, 2nd edition Pearson Education.

(Chapters 1 excluding 1.15, 1.16, 1.17, 2, 6)

2. Mauro Pezze, Michal Young: Software Testing and Analysis – Process, Principles

and Techniques, John Wiley & Sons, 2008. (Chapters 4, 6, 9, 12, 13, 17, 21, 22)

Reference Books: 1.Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices,

2nd Edition, Pearson, 2007.

2.Ron Patton: Software Testing, 2nd edition, Pearson, 2004.

3.Brian Marrick:The Craft of Software Testing, Pearson, 1995.


Services Oriented Architecture (4:0:0)




Pre-requisite : NA

Course outcome:-

The students on successful completion of this course should be able to:

1. Understand the Service oriented architecture.

2. Explain the web services primitives with the SOA

3. Distinguish the different principles of SOA and SOA platforms

4. Analyze different service layers in SOA

5. Understand web service design process.

UNIT – 1

Introduction to SOA, Evolution of SOA Fundamental SOA; Common Characteristics

of contemporary SOA; Common tangible benefits of SOA; An SOA timeline (from XML

to Web services to SOA); The continuing evolution of SOA (Standards

organizations and Contributing vendors).

Self-Learning Exercise: The roots of SOA (comparing SOA to Past architectures).

UNIT – 2

Web Services and Primitive SOA The Web services framework, Services (as Web

services), Service descriptions (with WSDL).

Self-Learning Exercise: Messaging (with SOAP).

UNIT – 3

Web Services and Contemporary SOA Message exchange patterns; Service activity;

Coordination; Atomic Transactions; Business activities; Orchestration; Choreography.

Addressing; Reliable messaging; Correlation; Polices; Metadata exchange; Security;

Notification and eventingPrinciples of Service – Orientation Services-orientation and

the enterprise; Anatomy of a service-oriented architecture; Common Principles of

Service-orientation.

.

Self-Learning Exercise: How service orientation principles inter-relate.

UNIT – 4 Web Services: Service-orientation and object-orientation; Native Web service support for


service-orientation principles

Service Layers Business service layer, Orchestration service layer; Agnostic services;

Self-Learning Exercise: Service layer configuration scenarios

UNIT – 5

Business Process Design WS-BPEL language basics; WS-Coordination overview; Service-

oriented business process design; WS - addresing language basics.

Self-Learning Exercise: WS-Reliable Messaging language basics

UNIT – 6

SOA Platforms SOA platform basics; SOA support in J2EE; SOA support in .NET;

Service-orientation and contemporary SOA; Service layer abstraction.

Self-Learning Exercise: Application service layer,

Text Books: 1. Thomas Erl: Service-Oriented Architecture – Concepts, Technology, and Design,

Pearson Education, 2005.

Reference Books:

1. Eric Newcomer, Greg Lomow: Understanding SOA with Web Services, Pearson

Education, 2005.

2. Web Service Contract Design and Versioning for SOA by Thomas Erl,

AnishKarmarkar, Priscilla Walmsley, Hugo Haas, L. UmitYalcinalp, Kevin Liu,

David Umit Orchard, Andre Tost, James Pasley ,Published Sep 24, 2008 by Prentice

Hall.

3. SOA Principles of Service Design by Thomas Erl, Published Jul 18, 2007 by Prentice

Hall.

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Mobile Computing and Wireless Communication (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Classify the mobile systems

2. Analyze various access methods

3. Analyze mobile Network and transport layer and database requirements

4. Explain the broad casting methods involved

5. Explain synchronization among mobile devices

6. Analyze the Internet connectivity requirement in mobile devices

UNIT – 1

Mobile Devices and Systems, Architectures Mobile phones, Digital Music Players,

Handheld Pocket Computers, Handheld Devices, Operating Systems, Smart Systems,

Limitations of Mobile Devices, Automotive Systems GSM – Services and System

Architectures, Radio Interfaces, Protocols, Localization, Calling, Handover.

Self-Learning Exercise: General Packet Radio Service

UNIT – 2

Wireless Medium Access Control and CDMA – based Communication Medium Access

Control, Introduction to CDMA – based Systems.

Self-Learning Exercise: OFDM

UNIT – 3

Mobile IP Network Layer, Mobile Transport Layer IP and Mobile IP Network Layers

Packet Delivery and Handover Management, Registration, Tunneling and Encapsulation,

Route Optimization, Dynamic Host Configuration Protocol Indirect TCP, Snooping TCP,

Mobile TCP, Other Methods of TCP – layer Transmission for Mobile Networks. Databases

Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation,

Transactional Models, Query Processing, Data Recovery Process.

Self-Learning Exercise: Issues relating to Quality of Service


UNIT – 4

Data Dissemination and Broadcasting Systems Communication Asymmetry, Classification

of Data – Delivery Mechanisms, Data Dissemination Broadcast Models, Selective Tuning

and Indexing Techniques, Digital Audio Broadcasting.

Self-Learning Exercise: Digital video Broadcasting.

UNIT – 5

Data Synchronization in Mobile Computing Systems Synchronization, Synchronization

Protocols, SyncML – Synchronization Language for Mobile Computing.

Self-Learning Exercise: Synchronized Multimedia Markup Language (SMIL).

UNIT – 6

Mobile Devices, Server and Management, Wireless LAN, Mobile Internet Connectivity

and Personal Area Network Mobile agent, Application Server, Gateways, Portals, Service

Discovery, Device Management, Mobile File Systems. Wireless LAN (WiFi) Architecture

and Protocol Layers, WAP 1.1 and WAP 2.0 Architectures, Bluetooth – enabled Devices

Network, Zigbee. Mobile Application languages – XML, Java, J2ME and JavaCard,

Mobile Operating Systems Introduction, XML, JAVA, Java 2 Micro Edition (J2ME),

JavaCard Operating System, PalmOS, Windows CE, Symbian OS.

Self-Learning Exercise: Linux for Mobile Devices

Text Book:

1. Raj Kamal: Mobile Computing, Oxford University Press, 2007.

Reference Books:

1 AsokeTalkukder, Roopa R Yavagal: Mobile Computing – Technology, Applications

and Service Creation, Tata McGraw Hill, 2005.

2 Reza B’Far: Mobile Computing Principles – Designing and Developing Mobile

Applications with UML and XML, 5th Edition, Cambridge University press, 2006.

3 UweHansmann, LothatMerk, Martin S Nicklous and Thomas Stober: Principles of

Mobile Computing, 2nd Edition, Springer International Edition, 2003.

4 Schiller: Mobile Communication, Pearson Publication, 2004.


Principles of User Interface Design (4:0:0)

Sub Code: MCA0438 CIE: 50%

Hrs/Week: 04 SEE: 50 %

SEE Hours: 3Hrs Max Marks: 100

Prerequisite: NA

Course Outcome:


1. Explain the Concept of Usability; design Principles, Guidelines of Human Computer

Interface.

2. Describe the Interaction design pattern and their applicability.

3. Illustrate a set of Requirements in terms of its user – interface implications.

4. Describe the usage scenario for a given set of user requirements and available

technologies.

5. Explain User Documentation and Information Search.

UNIT – 1

Usability of Interactive System, Guidelines , Principles, and Theories- Introduction,

usability goals and measures, usability Motivations, Universal usability and Goals for our

Profession. Guidelines, Principles and Theories. Managing Design processes –Introduction,

Organizational Design to Support Usability, The four pillars of Design, development

Methodologies, Ethnographic Observation.

Self-Learning Exercise: Participatory Design Scenario development.

UNIT – 2

Evaluating Interface Designs- Introduction, Expert Review, Usability Testing and

Laboratories, Survey Instruments, Acceptance Test, Evaluation during Active Use. Direct

Manipulation and Virtual Environment- Introduction, Examples of Direct Manipulation,

Discussion of Direct Manipulation, 3D Interfaces.

Self-Learning Exercise: Teleportation.

UNIT – 3

Menu Selection, Form Fill-in and Dialog Boxes- Introduction, Task-Related Menu

Organization, single Menus, Combinations of Multiple Menus, Content Organization, Fast

Movement Through Menus, Data Entry with Menus- Form Fill-in, Dialog Boxes, and

Alternatives, Audio menus and Menus for small Display. Command and Natural Languages-

Introduction, Command –organization Functionality, Strategies, and structure, Naming and

Abbreviations and Natural Languages in Computing.

Self-Learning Exercise: Adventure games and Instructional Systems.


UNIT – 4

Interaction Devices- Introduction, Keyboards and Keypads, Pointing Devices , Speech and

Auditory Interfaces and Display – Small and Large. Collaborations and Social Media

participation- Introduction, Goals of Collaboration and Participation, Asynchronous and

Synchronous Distributed Interfaces- Different Faces and Different time.

Self-Learning Exercise: Face-to-Face Interfaces: Same Place, Same time.

UNIT - 5

User Documentation and Online help- Introduction, online Versus Paper Documentation,

Reading from paper Versus from Displays, Shaping the content of the Documentation,

Accessing the Documentation, Online Tutorials and Animated Demonstrations,

Demonstrations, online Communities for user Assistance.

Self-Learning Exercise: The Development Process.

UNIT - 6

Information Search – Introduction, Searching in Textual Documents and Database

querying, Multimedia Document Searches, Advanced Filtering and search interfaces.

Self-Learning Exercise: Challenges for information Visualization.

Text Book:

1. Ben Shneiderman: Designing the User Interface, 5th edition, Pearson Educaions.,2010

Reference Books:

1. Alan J Dix et. al.: Human-Computer Interaction, II Edition, Prentice- Hall, India,

1998

2. Eberts: User Interface Design, Prentice-Hall, 1994.


Web 2.0 and Rich Internet Applications (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Understanding the basics of web services

2. Design and implement Rich Internet Applications using AJAX

3. Understand the usage of basics of XML and HTTP

4. Able to work with FLEX

5. Design and implement User Interface techniques

6. Understand the concepts of web 2.0 technology.

UNIT – 1

Introduction, Web Services What is Web 2.0?, Folksonomies and Web 2.0, Software As a

Service (SaaS), Data and Web 2.0, Convergence, Iterative development, Rich User

experience, Multiple Delivery Channels, Social Networking. Web Services: SOAP, RPC

Style SOAP, Document style SOAP, WSDL, REST services, JSON format, What is JSON?,

Array literals, Object literals, Mixing literals.

Self-Learning Exercise: JSON Syntax, JSON Encoding and Decoding, JSON versus XML.

UNIT – 2

Building Rich Internet Applications with AJAX Building Rich Internet Applications with

AJAX: Limitations of Classic Web application model, AJAX principles, Technologies behind

AJAX, Examples of usage of AJAX, Dynamic web applications through Hidden frames for

both GET and POST methods.IFrames, Asynchronous communication.

Self-Learning Exercise: AJAX application model,

UNIT – 3

XMLHTTP Object – properties and methods, handling different browser implementations

of XMLHTTP, The same origin policy, Cache control, AJAX Patterns (Only algorithms –

examples not required): Predictive fetch pattern, Submission throttling pattern, Periodic

refresh, Multi stage download.

Self-Learning Exercise: Fall back patterns.

UNIT – 4

Building Rich Internet Applications with Flex Flash player, Flex framework, MXML and

Actionscript, Working with Data services, Understanding differences between HTML and


Flex applications, Understanding how Flex applications work, Understanding Flex and Flash

authoring, MXML language, a simple example. Using Actionscript, MXML and Actionscript

correlations. Understanding Actionscript 3.0 language syntax: Language overview, Objects

and Classes, Packages and namespaces, Variables & scope of variables, case sensitivity and

general syntax rules, Operators, Conditional, Looping, Functions, Nested functions,

Functions as Objects, Function scope, OO Programming in Actionscript: Classes, Interfaces,

Inheritance, Working with String objects, Working with Arrays, Error handling in

Actionscript: Try/Catch, Working with XML Framework fundamentals, Understanding

application life cycle, Differentiating between Flash player and Framework, Bootstrapping

Flex applications, Loading one flex application in to another, Understanding application

domains, Understanding the preloader.

Self-Learning Exercise: Managing layout, Flex layout overview, Working with children,

Container types, Layout rules, Padding, Borders and gaps, Nesting containers, Making fluid

interfaces.

UNIT – 5

Working with UI components: Understanding UI Components, Creating component

instances, Common UI Component properties, Handling events, Button, Value selectors, Text

components, List based controls, Data models and Model View Controller, Creating

collection objects, Setting the data provider, Using Data grids, Using Tree controls, Working

with selected values and items, Pop up controls, Navigators, Control bars Working with data:

Using data models, Using XML,

Self-Learning Exercise: Using Actionscript classes, Data Binding.

UNIT – 6

Building Advanced Web 2.0 applications Definition of mash up applications, Mash up

Techniques, Building a simple mash up application with AJAX, Remote data communication,

strategies for data communication, Simple HTTPServices, URLLoader in Flex, Web Services

in Flex, Examples: Building an RSS reader with AJAX.

Self-Learning Exercise: Building an RSS reader with Flex.

Text Books: 1. Nicholas C Zakas et al: Professional AJAX, Wrox publications, 2006. (Chapters 1

to 4, Chapter 6 pp157-166, Chapter 7 pp191-196)

2. ChaficKazoun: Programming Flex 2, O’Reilly publications, 2007. (Chapter 1,

Chapters 3 to 7, Chapter 12, Chapter 16 pp380-403)

3. Francis Shanahan:Mashups, Wrox, 2007. (Chapters 1,6)

Reference Books: 1. Thomas A. Powel: Ajax The Complete reference, McGraw Hill, 2008.

2. Gottfried Vossen, Stephan Hagemann: Unleashing Web 2.0 From Concepts to

Creativity, Elsevier, 2007.

3. Colin Moock: Essential Actionscript 3.0, O’Reilly Publications, 2007.Steven Holzner

: Ajax Bible Wiley India , 2007.

4. Eric Van derVlist et al: Professional Web 2.0 Programming, Wiley India, 2007.


5. Justin Gehtland et al: A Web 2.0 primer Pragmatic Ajax, SPD Publications, 2006


Digital Image Processing (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Understand digital image fundamentals

2. Apply the concepts of image enhancement with various filters

3. Describe image restoration techniques

4. Appraise image encoding and compression techniques

UNIT - 1

Introduction Origins of Digital Image Processing, examples, Fundamental Steps in Digital

Image Processing.

Self-Learning Exercise: Components of an Image Processing System

UNIT - 2

Digital Image Fundamentals Elements of Visual Perception, Basic Concepts in Sampling

and Quantization, A Simple Image Formation Model, Representing Digital Images, Zooming

and Shrinking Digital Images, Some Basic Relationships Between Pixels.

Self-Learning Exercise: Linear and Nonlinear Operations.

UNIT - 3

Image Enhancement in the Spatial Domain Some Basic Gray Level Transformations,

Histogram Processing, Enhancement Using Arithmetic/Logic Operations, Basics of Spatial

Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters

Self-Learning Exercise: Combining Spatial Enhancement Methods

UNIT - 4

Image Restoration A Model of the Image degradation / Restoration process, Noise Models,

Restoration in the Presence of Noise Only–Spatial Filtering, Periodic Noise Reduction by

Frequency Domain Filtering, Linear, Position-Invariant Degradations, Estimating the

Degradation Function

Self-Learning Exercise: Minimum Mean Square Error (Wiener) Filtering


UNIT - 5

Color Fundamentals Color Models, Pseudo-color Image Processing, Basics of Full-Color

Image Processing, Color Transformations, Smoothing and Sharpening.

Self-Learning Exercise: Color Segmentation, Noise in Color Images

UNIT - 6

Image Compression Fundamentals, Image Compression Models, Error-Free Compression,

Lossy Compression, Image Compression Standards

Self-Learning Exercise: Elements of information theory

Text Book

1. Rafel C Gonzalez and Richard, Digital Image Processing, Second Edition, Prentice

Hall.


Advanced Computer Networks (4:0:0)




Pre-requisites : Computer Networks

Course Outcomes:


1. Understand the fundamentals of layering using OSI model and principles of network

layering.

2. Analyze the behavior of physical layer and their topology with the help of particular

technologies.

3. Identify the need for use of IPV6, and various address translation mechanisms in

computer networks.

4. Compare Unicast, multicast and broadcast routing protocols and also learn the

working of SCTP protocol.

5. Decide how congestion occurs in a network and different techniques to improve QOS.

6. Discuss the different applications of multimedia.

UNIT – 1

Review of Network Models Layered tasks; The OSI model and layers in the OSI model;

TCP / IP protocol suite. SONET / SDH Architecture; SONET layers; SONET frames; STS

multiplexing; SONET networks.

Self-Learning Exercise: Addressing, Virtual tributaries.

UNIT – 2

Frame Relay and ATM Frame relay; ATM.

Self-Learning Exercise: ATM LANs

UNIT – 3

IPv6, Address Mapping and Error Reporting IPv6: Advantages, Packet format, and

Extension headers, Dual stack, Tunneling, and Header translation; Address mapping: ARP,

RARP, BOOTP, and DHCP; Error reporting: ICMP.

Self-Learning Exercise: Transition fromIPv4 to IPv6:


UNIT – 4

Multi-cast Routing Protocols Unicast, multicast and broadcast, Multicasting routing;

Routing protocols. SCTP: SCTP services; SCTP features; Packet format; An SCTP

association; Flow control; Error control; Congestion control.

Self-Learning Exercise: Applications Multicast and broadcast.

UNIT – 5

Congestion Control and Quality of Service Data traffic; Congestion and congestion

control; Congestion control in TCP, Frame relay; Quality of Service; Techniques to improve

QoS; Integrated services.

Self-Learning Exercise: Differentiated services,

UNIT – 6

Multimedia Digitizing audio and video; Audio and video compression; Streaming stored

audio / video; Real-time interactive audio / video; RTP; RTCP; VoIP.

Self-Learning Exercise: Streaming live audio / video.

Text Books: 1. Behrouz A. Forouzan, Data Communications and Networking, 5th Edition, Tata

McGraw-Hill.

2. Nader F. Mir: Computer and Communication Networks, Pearson, 2007. (Chapters 19,

20 excluding 20.5)

References: 1. William Stallings, Data and Computer Communication, 5th Edition, Prentice Hall

India.

2. William A. Shay, Understanding Data Communications and Networks, 2nd Edition,

Thomson.

3. Godbole, Data Communications and Networks, Tata McGraw-Hill 2002.

4. Micael A. Gallo & William M. Handcock, Computer

5. Communications and Networking Technologies, 2003 Edition, Thomson.


Cloud Computing (4:0:0)

Sub code : MCA0446 CIE : 50 %

Hrs /week : 04 SEE : 50 %

SEE Hrs : 3 Hrs Max. Marks : 100

Pre-requisite : NA Course Outcomes:

On successful completion of the course the students will be able to

1. Classify different cloud computing models.

2. Describe basic cloud architecture and cloud services

3. Use the concepts of abstraction and virtualization to plan a cloud environment

4. Demonstrate cloud management using Google web services

5. Discuss cloud security issues

6. Contrast the relationship between SOA and cloud computing by moving application to

Cloud.

UNIT – 1

Introduction Defining Cloud Computing, Cloud Types: NIST Model, Cloud Cube Model,

Deployment models, Service Models, Characteristics of Cloud Computing: Paradigm shift,

Benefits of Cloud computing, Disadvantages of cloud computing, Role of Open Standards,

Assessing the Value Proposition: Measuring the Cloud's Value, Avoiding Capital

Expenditures, Computing the Total Cost of Ownership, Specifying Service Level

Agreements, Defining Licensing Models.

Self-Learning Exercise: Measuring Cloud Computing Cost

UNIT - 2

Understanding Cloud Architecture Exploring the Cloud Computing Stack: Composability,

Infrastructure, Platforms, Virtual Appliances, Communication Protocols, Applications,

Connecting to the cloud: JoliCloud Netbook OS.

Self-Learning Exercise: Chromium OS: The Browser as an Operating System.

UNIT – 3

Understanding Services, Applications, Abstraction and Virtualization Defining

Infrastructure as Service (IaaS), Defining Platform as a Service (PaaS), Defining Software as

a Service (SaaS), Defining Identity as a Service (IDaaS), Using Virtualization Technologies,

Load Balancing and Virtualization, Understanding Hypervisors, Understanding Machine

Imaging.

Self-Learning Exercise: Defining Compliance as a Service (CaaS), Porting Applications


UNIT - 4

Using Google Web Services and Managing the Cloud Exploring Google Applications,

Surveying the Google Application Portfolio: Indexed Search, The dark Web, Aggregation

and disintermediation, Productivity applications and services, Enterprise offerings, Google

Analytics, Google Translate, Administrating the Clouds, Cloud Management Products, and

Emerging Cloud Management Standards.

Self-Learning Exercise: Google Web Toolkit, Google App Engine

UNIT - 5

Understanding Cloud Security Securing the Cloud: The security boundary, Security service

boundary, Security mapping. Security Data: Brokered cloud storage access, Storage location

and tenancy, Encryption, Auditing and compliance, Establishing Identity and Presence:

Identity protocol standards.

Self-Learning Exercise: Windows Azure identity standards, Presence.

UNIT - 6

Understanding Service Oriented Architecture and Moving Applications to the Cloud

Introducing Service Oriented Architecture: Event-driven SOA or SOA 2.0, Enterprise Service

Bus, Service Catalogs, Defining SOA Communications: Business Process Execution

Language, Business Process modeling. Managing and Monitoring SOA: SOA Management

tools, SOA security, The Open Cloud Consortium, Moving Applications to the Cloud:

Applications in the Clouds.

Self-Learning Exercise: Relating SOA and Cloud Computing, Applications and Cloud

APIs, Development of Cloud Applications using SOA

Text Book:

1. Cloud Computing Bible by Barrie Sosinsky, Wiley India, Publication Year 2011

Reference Books: 1. Cloud Computing for Dummies by Judith Hurwitz, R. Bloor, M. Kanfman, F. Halper (Wiley

India Edition) 2. Cloud Security by Ronald Krutz and Russell Dean Vines, Wiley-India. 3. Cloud Computing: A Practical Approach, Anthony T Velte. 4. Google Apps by Scott Granneman, Pearson A Brief Guide to Cloud Computing: An Essential

Introduction to the Next Revolution in Computing, Christopher Barnatt.


Introduction to Python Programming (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Recall the need for a “programming language” like Python. Also differentiate the

differences b/w Python versions.

2. Understand and demonstrate how to execute basic code in Python.

3. Apply various python data types to create and design some simple python scripts.

4. Compare and classify the complex python data types to execute python scripts.

5. Discuss and develop python scripts using the concept of basic python statements.

6. Create, design and develop scripts using python statements, loops and expressions.

UNIT - 1

PYTHON Q & A SESSION Why do people use Python? What can I do with Python? Intro

to python: how is python developed and supported? Advantages of using python: What are

python's technical strengths? How Python runs programs: Introducing the Python interpreter,

Program execution, Python's view

Self-Learning Exercise: Installation of Python in Windows and Linux Environment,

alternatives to python.

UNIT - 2

HOW TO RUN PYTHON PROGRAMS The interactive prompt, starting an interactive

session, why the interactive prompt, System Command lines and files – a first script, Module

imports and reloads, GUI to python programming and other IDEs, Other launch options.

Introducing python object types – The python conceptual hierarchy, why use built in types?,

Numbers, strings, lists, dictionaries, tuples, files, Other core types.

Self-Learning Exercise: Test your knowledge section.

UNIT - 3

GETTING DEEPER INTO DATA TYPES – I Numeric types – Numeric type basics,

numeric literals, built in numeric tools, Numbers in action, other numeric types. The

dynamic typing interlude – the case of the missing declaration statements, shared references,

dynamic typing is everywhere.

Self-Learning Exercise: Weak references, Test your knowledge section.


UNIT - 4

GETTING DEEPER INTO DATA TYPES – II String fundamentals – basics, literals,

string basic operations, methods, formatting expressions, formatting method calls. Lists and

dictionaries – Lists in action, dictionaries in action.

Self-Learning Exercise: Some examples of strings, lists and dictionaries.

UNIT - 5

INTRODUCTION TO PYTHON STATEMENTS – I Introducing python statements –

The python conceptual hierarchy revisited, Python's statements, a tale of two ifs, syntaxes.

Assignments, expressions, and prints – assignment statement forms, sequence assignments,

extended sequence unpacking in 3.x, multiple target assignments, Expression statements,

python print statements, print stream redirection.

Self-Learning Exercise: Test your knowledge section.

UNIT - 6

INTRODUCTION TO PYTHON STATEMENTS – II if Tests and syntax rules - Python

syntax revisited, truth values and boolean tests, the if-else ternary expression, some

programs/scripts. While and for loops – while loops, break, continue, pass and the loop else,

for loops, loop coding.

Self-Learning Exercise: Largest of 2 numbers and other basic scripts.

Text Books: 1. Mark Lutz, “Learning Python”, 5th edition, O'Reilly publications, 2013 (Chapters –

1,2,3,4,5,6,7,8,10,11,12,13)

Reference Books: 1. Mark Lutz, “Programming Python”, 4th edition, O'Reilly publications, 2010

2. Zed A Shaw, “Learn Python the hard way”, Hard Way Series, 2013.


Big Data Analytics (4:0:0)




Pre-requisite : NA

Course Outcomes:


1. Define and relate several key technologies used in manipulating, storing, and

analyzing big data.

2. Outline a clear understanding of R & Hadoop.

3. Experiment with Integrating R &Hadoop for solving big data problems.

4. Analyze and get a clear understanding of Hadoop Streaming and its importance.

5. Measure Big Data and analyze Big Data.

6. Design and develop applications to apply tools and techniques for analyzing Big Data.

UNIT - 1

INTRODUCTION TO BIG DATA Big Data and its Importance – Four V’s of Big Data –

Drivers for Big Data –Introduction to Big Data Analytics – Big Data Analytics applications.

Self-Learning Exercise: Big Data Analytics Applications

UNIT - 2

INTRODUCTION TO R & HADOOP Getting Ready to Use Rand Hadoop, Installing R,

Installing RStudio, Understanding the features of R language, Installing Hadoop,

Understanding Hadoop features, Learning the HDFS and MapReduce architecture, Writing

HadoopMapReduce Programs, Introducing HadoopMapReduce, Understanding the

HadoopMapReduce fundamentals, Writing a HadoopMapReduce example, Learning the

different ways to write HadoopMapReduce in R

Self-Learning Exercise: Installing R Studio

UNIT - 3

INTEGRATION OF R & HADOOP Integrating R and Hadoop, Introducing RHIPE

,Understanding the architecture of RHIPE Understanding RHIPE samples, Understanding the

RHIPE function reference, Introducing RHadoop, Understanding the architecture of

RHadoop, Understanding RHadoop examples, Understanding the RHadoop function

reference.

Self-Learning Exercise: Installation of RHadoop


UNIT - 4

HADOOP STREAMING WITH R Using Hadoop Streaming with R - Introduction,

Understanding the basics of Hadoop streaming, Understanding how to run Hadoop

streaming with R, Understanding a MapReduce application, Exploring the

HadoopStreaming R package

Self-Learning Exercise: Hadoop Streaming Packages.

UNIT - 5

DATA ANALYTICS WITH R AND HADOOP Understanding the data analytics project

life cycle – Introduction, Identifying the problem, Designing data requirement, Preprocessing

data, Performing analytics over data, Visualizing data, Understanding data analytics

problems, Exploring web pages categorization Case Studies: Computing the frequency of

stock market change, Predicting the sale price of blue book for bulldozers.

Self-Learning Exercise: Case Study: Stock Market change

UNIT - 6

UNDERSTANDING BIG DATA ANALYSIS WITH MACHINE LEARNING

Introduction to machine learning, Types of machine-learning algorithms, Supervised

machine-learning algorithms, Unsupervised machine learning algorithm, Recommendation

algorithms, Steps to generate recommendations in R, Generating recommendations with R

and Hadoop.

Self-Learning Exercise: Unsupervised machine learning algorithm

Textbooks:

1. Arvind Sathi, “Big Data Analytics: Disruptive Technologies for Changing the Game”,

1st Edition, IBM Corporation, 2012 (Chapter 1,2,3 UNIT 1)

2. Big Data Analytics with R and Hadoop, Vignesh Prajapati, Packt Publishing 2013

(Chapters 1,2,3,4,5,6 UNIT 2,3,4,5,6)

Reference Books:

1. Michael Minelli, Michehe Chambers, “Big Data, Big Analytics: Emerging Business

Intelligence and Analytic Trends for Today’s Business”, 1st Edition, AmbigaDhiraj,

Wiely CIO Series, 2013.

2. Bill Franks, “Taming the Big Data Tidal Wave: Finding OpportUNITies in Huge

3. Data Streams with Advanced Analytics”, 1st Edition, Wiley and SAS Business Series,

2012.

4. Tom White, “Hadoop: The Definitive Guide”, 3rd Edition, O’reilly, 2012.


Introduction to Android Programming




Pre-requisite: Java Programming

Course Outcomes:

On completion of this course students will be able to

1. Understand the basic versions, the evolution of Android as a Mobile OS, create

simple apps and apply different styles.

2. Explain the User Interface of Android.

3. Manipulate the UI with different common elements.

4. Apply the knowledge to a real time Internet download app.

5. Describe persist data using preferences.

6. Apply the knowledge of DBMS to persist data.

UNIT – 1

GETTING STARTED WITH ANDROID PROGRAMMING:What Is Android? Android

Versions, Features of Android, Architecture of Android, Android Devices in the Market, The

Android Market, The Android Developer CommUNITy, Obtaining the Required

Tools,Android SDK, Installing the Android SDK Tools, Configuring the Android SDK

Manager, Eclipse, Android Development Tools (ADT), Creating Android Virtual Devices

(AVDs), Creating Your First Android Application, Anatomy of an Android Application

ACTIVITIES, FRAGMENTS AND INTENTS:Understanding Activities, Applying Styles

and Themes to an Activity, Hiding the Activity Title, Displaying a Dialog Window,

Displaying a Progress Dialog, Displaying a More Sophisticated Progress Dialog, Linking

Activities Using Intents, Resolving Intent Filter, Collision, Returning Results from an Intent,

Passing Data Using an Intent Object, Fragments, Adding Fragments Dynamically, Life Cycle

of a Fragment, Interactions between Fragments, Calling Built-In Applications Using Intents.

Self-Learning Exercise: Understanding the Intent Object, Using Intent Filters, Adding

Categories, Displaying Notifications

UNIT – 2

GETTING TO KNOW THE ANDROID USER INTERFACE:Understanding the

Components of a Screen, Views and ViewGroups, LinearLayout, AbsoluteLayout,

TableLayout, RelativeLayout, FrameLayout, ScrollView, Adapting to Display Orientation,

Anchoring Views, Resizing and Repositioning, Managing Changes to Screen Orientation,

Persisting State Information during Changes in Configuration, Detecting Orientation

Changes, Controlling the Orientation of the Activity, Utilizing the Action Bar, Adding Action

Items to the Action Bar, Customizing the Action Items and Application Icon, Creating the

User Interface Programmatically, Listening for UI Notifications. DESIGNING YOUR

USER INTERFACE WITH VIEWS: Using Basic Views, TextView, View, Button,

ImageButton, EditText, CheckBox, ToggleButton, RadioButton, and RadioGroup Views,

ProgressBar View, AutoCompleteTextView, View Using Picker Views, TimePicker View,


DatePicker View, Using List Views to Display Long List, ListView View, Using the Spinner

View, Understanding Specialized Fragments, Using a ListFragment, Using a

DialogFragment.

Self-Learning Exercise: Overriding Methods Defined in an Activity, Registering Events for

Views, Using a PreferenceFragment.

UNIT – 3

DISPLAYING PICTURES AND MENUS WITH VIEWS: Using Image Views to Display

Pictures, Gallery and ImageView Views, ImageSwitcher, GridView, Using Menus with

Views, Creating the Helper Methods, Options Menu, Context Menu.

Self-Learning Exercise:Some Additional Views, AnalogClock and DigitalClock Views,

WebView

UNIT – 4

USING INTERNET RESOURCES: Downloading and Parsing Internet Resources,

Connecting to an Internet Resource, Parsing XML Using the XML Pull Parser, Creating an

Earthquake Viewer, Using the Download Manager, Downloading Files, Customizing

Download Manager Notifications, Specifying a Download Location, Cancelling and

Removing Downloads, Querying the Download Manager, Using Internet Services.

Self-Learning Exercise: Connecting to Google App Engine, Best Practices for Downloading

Data Without Draining the Battery

UNIT – 5

FILES, SAVING STATE, AND PREFERENCES: Saving Simple Application Data,

Creating and Saving Shared Preferences, Retrieving Shared Preferences, Creating a Settings

Activity for the Earthquake Viewer,Introducing the Preference Framework and the

Preference Activity, Defining a Preference Screen Layout in XML, Native Preference

Controls, Using Intents to Import System Preferences into Preference Screens, Introducing

the Preference Fragment, Defining the Preference Fragment Hierarchy, Using Preference

Headers, Introducing the Preference Activity, Backward Compatibility and Preference

Screens, Finding and Using the Shared Preferences Set by Preference Screens, Introducing

On Shared Preference Change Listeners, Creating a Standard Preference Activity for the

Earthquake Viewer, Persisting the Application Instance State, Saving Activity State Using

Shared Preferences, Saving and Restoring Activity Instance State, Using the Lifecycle

Handlers, Saving and Restoring Fragment Instance State, Using the Lifecycle Handlers,

Including Static Files as Resources, Working with the File System, File-Management Tools

Using Application-Specific Folders to Store Files, Creating Private Application Files.

Self-Learning Exercise: Using the Application File Cache, Storing Publicly Readable File.

UNIT – 6

DATABASES AND CONTENT PROVIDERS: Introducing Android Databases, SQLite

Databases, Content Providers, Introducing SQLite, Content Values and Cursors, Working


with SQLite Databases, Introducing the SQLiteOpenHelper, Opening and Creating Databases

Without the SQLite Open Helper, Android Database Design Considerations, Querying a

Database, Extracting Values from a Cursor, Adding, Updating, and Removing Rows,

Inserting Rows, Updating Rows, Deleting Rows, Creating Content Providers, Registering

Content Providers, Publishing Your Content Provider’s URI Address, Creating the Content

Provider’s Database, Implementing Content Provider Queries, Content Provider

Transactions, Storing Files in a Content Provider, A Skeleton Content Provider

Implementation, Using Content Providers, Introducing the Content Resolver, Querying

Content Providers, Querying for Content Asynchronously Using the Cursor Loader,

Introducing Loaders, Using the Cursor Loader, Adding, Deleting, and Updating Content,

Inserting Content, Deleting Content, Updating Content, Accessing Files Stored in Content

Providers.

Self-Learning Exercise: Creating a To-Do List Database and Content Provider.

Text Books: 1. Wei-Meng Lee, Beginning Android 4 Application Development, Wrox, Wiley India

Edition. (Chapters: 1, 2, 3, 4, 5)

2. Reto Meier, Professional Android 4 Application Development, Wrox, Wiley India

Edition(Chapters: 6, 7, 8)

Reference Books: 1. Mark Murphy, The Busy Coder's Guide to Android Development, version 4.2.

2. Android Programming: The Big Nerd Ranch Guide (Big Nerd Ranch Guides)

3. Paul Deitel, Harvey Deitel, Abbey Deitel, and Michael Morgano, Android for

Programmers: An App-Driven Approach

4. AnubhavPradhan, Anil V Deshpande, Composing Mobile Apps: Learn / Explore /

Apply using Android 1stEdition.


Information Retrieval and Search Engines (4:0:0)

Subject Code :MCA0428 CIE : 50%



Course Outcomes:

On successful completion of the course, students will be able to

1. The genesis and variety of information retrieval situations. 2. The variety of information retrieval models and techniques. 3. Design principles for information retrieval systems. 4. Methods for implementing information retrieval systems. 5. Characteristics of operational and experimental information retrieval systems. 6. Methods and principles for the evaluation of information retrieval systems.

UNIT 1

Introduction to Information Retrieval

Boolean retrieval,an example information retrieval problem, a first take at building an

inverted index, Processing Boolean queries, The extended Boolean model versus ranked

retrieval ,The term vocabulary and postings lists , Document delineation and character

sequence decoding , Obtaining the character sequence in a document , Choosing a document

UNIT , Determining the vocabulary of terms , Tokenization , Dropping common terms: stop

words , Normalization (equivalence classing of terms) , Stemming and lemmatization , Faster

postings list intersection via skip pointers , Positional postings and phrase queries , Biword

indexes , Positional indexes , Combination schemes

Self-Learning Exercise:Web Search Engine and web crawling

UNIT 2

Dictionaries and tolerant retrieval Search structures for dictionaries, Wildcardqueries, General wildcard queries, k-gram indexes

for wildcard queries,spellingcorrection,implementing spelling correction, Forms of spelling

correction, Edit distance, k-gram indexes for spelling correction, Context sensitive spelling

correction, Phonetic correction

Self-Learning Exercise: Basic Text Retrieval Strategies

UNIT 3

Index Construction and Compression

Index construction, Hardware basics, blocked sort-based indexing, Single-pass in-memory

indexing, distributed indexing, Dynamic indexing, Other types of indexes, Index

compression, Statistical properties of terms in information retrieval, Heaps’ law: Estimating

the number of terms, Zipf’s law: Modeling the distribution of terms, Dictionary compression,

Dictionary as a string, Blocked storage, Postings file compression, Variable byte codes, γ


codes

Self-Learning Exercise: Indexing

UNIT 4

Vector Space Modelling and Term Weighting

Parametric and zone indexes, Weighted zone scoring, Learning weights,the optimal weight g,

Term frequency and weighting, Inverse document frequency, Tf-idf weighting, the vector

space model for scoring, Dot products, Queries as vectors, Computing vector scores, Variant

tf-idf functions, Sublinear scaling, Maximum tf normalization, Document and query

weighting schemes, Pivoted normalized document length

Self-Learning Exercise: Different Retrieval models

UNIT 5

Probabilistic information retrieval

The Probability Ranking Principle,the 1/0 loss case, The PRP with retrieval costs, The Binary

Independence Model, Deriving a ranking function for query terms, Probability estimates in

theory, Probability estimates in practice, Probabilistic approaches to relevance feedback, An

appraisal and some extensions, An appraisal of probabilistic models, Tree-structured

dependencies between terms, Okapi BM25: a non-binary model, Bayesian network

approaches to IR

Self-Learning Exercise: Basic probability theory

UNIT 6

Evaluation in information retrieval

Computing scores in a complete search system , Efficient scoring and ranking , Inexact top K

document retrieval , Index elimination , Champion lists , Static quality scores and ordering ,

Impact ordering , Cluster pruning , Components of an information retrieval system , Tiered

indexes , Query-term proximity , Designing parsing and scoring functions , Putting it all

together , Vector space scoring and query operator interaction , Evaluation in information

retrieval , Information retrieval system evaluation , Standard test collections Evaluation of

unranked retrieval sets , Evaluation of ranked retrieval results , Assessing relevance ,

Critiques and justifications of the concept of relevance , A broader perspective: System

quality and user utility , System issues , User utility , Refining a deployed system , Results

snippets

Self-Learning Exercise: Digital library, Wireless information access

Text Book

1. C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval,

Cambridge University Press, 2008.

Reference Books: 1. David A. Grossman, Ophi rFrieder: Information Retrieval Algorithms and Heuristics,

2nd Edition, Springer, 2004.


2. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson

Education, 1999