SYLLABUS (For the Academic year 2016 2017) V IIand V I Semester...

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M.S. RAMAIAH INSTITUTE OF TECHNOLOGY

BANGALORE

(Autonomous Institute, Affiliated to VTU)

SYLLABUS

(For the Academic year 2016 – 2017)

Information Science and Engineering

VII and VIII Semester B.E.

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History of the Institute:

M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our Founder

Chairman who was a renowned visionary, philanthropist, and a pioneer in creating several landmark

infrastructure projects in India. Noticing the shortage of talented engineering professionals required to

build a modern India, Dr. M.S. Ramaiah envisioned MSRIT as an institute of excellence imparting

quality and affordable education. Part of Gokula Education Foundation, MSRIT has grown over the

years with significant contributions from various professionals in different capacities, ably led by

Dr. M.S. Ramaiah himself, whose personal commitment has seen the institution through its formative

years. Today, MSRIT stands tall as one of India’s finest names in Engineering Education and has

produced around 35,000 engineering professionals who occupy responsible positions across the globe.

About the Department:

Department of Information Science and Engineering (ISE) was established in the year 1992 with an

objective of producing high-quality professionals to meet the demands of the emerging field of

Information Science and Engineering. Department started with UG programme with an annual

sanctioned intake of 30 students and the intake was enhanced to 60 seats in the year 1999, to 90 seats in

the year 2001 and then to 120 seats in the year 2008. Department started M.Tech in Software

Engineering in the year 2004. Department is recognized as R&D center by VTU in the year 2012.

Department has well equipped laboratories. Some of the laboratories have also been set up in

collaboration with industries such as Intel, Apple, Honeywell, EMC2, Microsoft and IBM. Department

has highly qualified and motivated faculty members. All faculty are involved in research and technical

paper publications in reputed technical journals, conferences across the world. Department has been

accredited by the NBA in 2001, 2004, 2010 and 2015 (Tier-I). The department is successfully conducting

seminars & workshops for students and academicians in the emerging technologies.

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Faculty List

Sl.No. Name of Faculty Qualification Designation

1 Dr. Vijaya Kumar B P M.Tech (IITR), Ph.D (IISc.) Professor & Head

2 Dr. Lingaraju G M Ph.D Professor

3 Dr.Mydhili K Nair

M.Tech, Ph.D (Anna

University) Professor

4 Rajaram M Gowda MTech. (Ph.D) Associate Professor

5 Shashidhara H S M.Tech. (Ph.D) Associate Professor

6 George Philip C M.Tech. Associate Professor

7 Dr. Megha.P.Arakeri M.Tech, Ph.D (NITK) Associate Professor

8 Dr. Siddesh G M M.Tech., Ph.D Associate Professor

9 T Tamilarasi M.E (Ph.D) Assistant Professor

10 Savita K Shetty M.Tech. Assistant Professor

11 Myna A N M.Sc(Engg) , (Ph.D) Assistant Professor

12 Deepthi K M.Tech. Assistant Professor

13 Lincy Meera Mathews M.Tech (Ph.D) Assistant Professor

14 Dr. P M Krishna Raj M.Sc(Engg), Ph.D Assistant Professor

15 Rajeshwari S B M.Tech. Assistant Professor

16 Prathima M N M.E. Assistant Professor

17 Pushpalatha M N M.Tech (Ph.D) Assistant Professor

18 Mohan Kumar S M.Tech (Ph.D) Assistant Professor

19 Sumana M M.Tech (Ph.D) Assistant Professor

20 Prashanth Kambli

M.Sc, (MSc(Engg.) by

Research) Assistant Professor

21 Naresh E M.Tech, (Ph.D) Assistant Professor

22 Jagadeesh Sai D M.Tech. Assistant Professor

23 Mani Sekhar S R M.Tech.(Ph.D) Assistant Professor

24 Suresh Kumar K R M.Tech.(Ph.D) Assistant Professor

25 Sunitha R S M.Tech. Assistant Professor

26 Sandeep B L M.Tech.(Ph.D) Assistant Professor

27 Dr. Dayananda P M.Tech, Ph.D Assistant Professor

28 Koushik S M.Tech.(Ph.D) Assistant Professor

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Vision and Mission of the Institute and the Department

Vision of MSRIT

To evolve into an autonomous institution of international standing for imparting quality technical

education

Mission of MSRIT

MSRIT shall deliver global quality technical education by nurturing a conducive learning environment

for a better tomorrow through continuous improvement and customization

Quality Policy

“We at M. S. Ramaiah Institute of Technology, Bangalore strive to deliver comprehensive, continually

enhanced, global quality technical and management education through an established Quality

Management system Complemented by the Synergistic interaction of the stake holders concerned”.

Vision of Department

To evolve as an outstanding education and research center of Information Technology to create high

quality Engineering Professionals for the betterment of Society

Mission of Department

To provide a conducive environment that offers well balanced Information Technology education

and research

To provide training and practical experience in fundamentals and emerging technologies

To nurture creativity for overall personality development

Process of deriving the vision and mission of the department

Process of deriving the vision and mission of the department is shown in Figure below:

Vision &

Mission of

the

Departmen

t

Manageme

nt

Institute Vision & Mission

Parents

Alumni

Students Departme

nt Faculty

Industry

Periodic

Review

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Programme Educational Objectives (PEOs) of the programme

PEO1: Become competent Information Technology professionals with continuous progress in career or

learning

PEO2: Design and develop computing systems using modern tools and technologies

PEO3: Function effectively as professionals in a team environment

Process of Deriving Programme Educational Objectives

Academic Council &

Governing Council

Accept & Approve

PEOs

Institute Vision &

Mission

Department Vision &

Mission

Student

s

PG faculty

Parents Alumni Industry

Collect data

(Department Committee)

Deliberate, Analyze and

summarize the data

(Board of Studies)

Data Collection

Process

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Process of deriving the Programme Outcomes

POs describe what students are expected to know or be able to do by the time of graduation from the

program. They are derived by using institute Vision and Mission, Department Vision and Mission, NBA

Graduate Attributes, Professional bodies such as ACM, IEEE, CSAB, Regulatory bodies such as UGC,

AICTE, VTU as well as stakeholders such as faculty, alumni and industry as shown in the figure below.

Process Employed to Define the POs.

PO’s of the program offered

a Apply the knowledge of mathematics, science, engineering fundamentals and

computing to solve information science and engineering related problems.

b Identify, specify, formulate and analyze the computing problems using engineering

knowledge reaching substantiated conclusions.

c Design and develop solutions for computing problems considering the

environmental and societal needs.

d Design and conduct experiments, analyze the data and interpret the information and

provide valid conclusion.

e Develop and apply appropriate techniques, resources and modern software tools to

solve real world information technology related problems within constraints

applicable.

f Apply reasoning along with the contextual knowledge to assess and provide

computing solutions for societal issues.

g Understand the impact of the information technology solutions in environmental

context for sustainable development.

h Ability to acquire, apply and commit to professional ethics.

i Work individually and as a team member and/or as a leader in multidisciplinary

environment.

Review

Input Input Review

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j Communicate effectively with respect to documentation and presentation with

engineering community and society.

k Apply the knowledge of the information science and engineering and management

principles for cost effective management of projects in multidisciplinary areas.

l Enhance the knowledge and upgrade the skills independently in the ever changing

information technology environment.

Mapping of PEO’s and PO’s

The correlation between the Programme outcomes and Program Educational objectives are mapped in

the Table shown below:

Correlation between the POs and the PEOs

PEO

Programme Outcomes

PO

a

PO

b

PO

c

PO

d

PO

e

PO

f

PO

g

PO

h

PO

i

PO

j

PO

k

PO

l

To build competent engineers

with continuous professional

advancement and learning in

Information Technology

X X X X X X X X X

To make them design and

develop computing systems

using modern technologies that

cater to societal and

environmental needs

X X X X X X X

To foster them to function

effectively as professionals

having good communication

skills, leadership qualities with

ethical and social commitment

X X X X X

Curriculum breakdown structure:

The curriculum of Information Science and Engineeering programme is so structured to include all the

courses that together satisfy the requirements of the programme specific criteria prescribed by the

CSAB( Comptuter Science Accreditation Board), IEEE Computer Society/Association for Computing

Machinery (ACM). The Course code, Course title, the number of contact hours and the number of credits

for each course are given in the following table. The courses are grouped in line with the major

components of the curriculum namely: (i) Mathematics and Basic sciences, (ii) Basic Engineering

courses, (iii) Humanities and Social Sciences, (iv)Professional core courses, (v) Electives and (vi)

Industry exposure/internship.

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Breakup of Credits for BE Degree Curriculum. ( I to VIII Semester)

Semester

HSS BS ES PCS Profess

ional

Electiv

es

Other Electives Project /

Seminar/

Internship

Total

Credits

I & II 06 20 24

50

III 3 4 18 0 0 0 25

IV 4 21 0 0 0 25

V 2 0 19 4 0 0 25

VI 0 15 8 0 2 25

VII 0 14 4 3 4 25

VIII 1 0 4 20 25

Total 12 28 24 87 20 3 26 200

HSS Humanities and Social Sciences 12

BS Basic Sciences (Mathematics, Physics, Chemistry) 28

ES Engineering Sciences (Materials, Workshop, Drawing, Computers). 24

PCS Professional Core Subjects 87

Prof. Ele Professional Electives, relevant to the chosen specialization branch 20

Other Ele Elective Subjects, from other technical and / or emerging subject Areas. 03

Project /

Seminar

Project Work, Seminar and / or Internship in industry

or elsewhere.

26

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Board of Studies

S.

No. Category Name of the Person with Official Address Status

1 Head of the Department

Concerned

Dr. Vijaya Kumar B P

Head of the Department

Information Science & Engg.,

M.S. Ramaiah Institute of Technology,

MSRIT Post,

Bangalore – 560 054.

Chair Person

2

Faculty members

nominated by the

Academic Council

Shashidhara H S

Associate Professor

Dept. of Information Science & Engineering,


MSR Post, Bangalore – 560 054.

Member

Dr. Megha P Arakeri

Associate Professor




Member

Dr. Siddesh G M

Associate Professor,




Member

Dr. P. M Krishna Raj

Asst. Professor




Member

3

Experts in the subject

from outside the

College, to be

nominated by the

Academic Council

Dr. Satish Babu

Professor & Head,

Dept. of Computer Science & Engineering

Siddaganga Institute of Technology

NH 206,B.H.Road, Tumkur,

Karnataka 572103.

[email protected]

Autonomous

Institute

Member

Dr. Dilip Kumar

Professor & Head

University Visvesvaraya College of

Engineering (UVCE),

K R Circle, Dr Ambedkar Veedhi,

Bangalore

Karnataka 560001

Government

University

Member

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Dr.Y.N. SRIKANT

Professor, Department of Computer Science

and Automation,

Indian Institute of Science

Bangalore 560 012.

VTU Member

from IISc

Madhu N. Belur

Department of Electrical Engineering

Indian Institute of Technology Bombay

Powai, Mumbai 400 076

India

Special

Invitee

Dr. Chetan Kumar S

Manager Software Development

CISCO System

Cessna Business Park,

Kadubeesanahalli Village,

Varthur Hobli, Sarjapur Marathalli,

Bangalore – 560 087

Expert

Member from

Industry

Mr.Niranjan Salimath

Beaglesloft , 37/5, Ulsoor Rd, Yellappa

Chetty Layout, Sivanchetti Gardens,

Bengaluru, Karnataka 560042

Alumni

Member

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M S Ramaiah Institute of Technology


Department of Information Science & Engineering

Scheme of Teaching for Academic Year 2016-17 (2013-17 batch)

VII Semester Bachelor of Engineering in Information Science and Engineering

Sl. No Subject Code Subject Credits*

L T P Total

1 IS711 Data Mining 4 0 0 04

2 IS711L Data Mining Lab 0 0 1 01

3 IS722 Distributed Computing 4 0 0 04

4 IS722L Distributed Computing Lab 0 0 1 01

5 IS713 Software Design Patterns 4 0 0 04

6 Open Elective 3 0 0 03

7 IS715 Project Preliminaries 0 0 4 04

8 Elective D 4 0 0 04

Total 19 0 6 25

* L : Lecture T : Tutorial P : Practical

Elective D

IS71D1 System Simulation and Modelling

IS72D2 Mobile Computing

IS72D3 Machine Learning

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M S Ramaiah Institute of Technology


Department of Information Science & Engineering

Scheme of Teaching for Academic Year 2016-17 (2013-17 batch)

VIII Semester Bachelor of Engineering in Information Science and Engineering

Sl. No Subject Code Subject Credits*

L T P Total

1 Internship / Elective E 0 1 3 04

2 IS811 Senior Project 0 0 19 19

3 IS82ECA Extra/ Co-Curricular Activities 0 0 02 02

Total 0 1 24 25

* L : Lecture T : Tutorial P : Practical

Internship

IS82IN Internship

Elective E

IS82E1 Data Science

IS82E2 Cloud Computing

IS82E3 Mobile Application Development

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DATA MINING

Sub Code : IS711 Credits : 4:0:0

Total Hours : 56L Prerequisite : NIL

Course Coordinator: Sumana M

Course objectives:

Understand the basic concepts, different types of data and data pre-processing methods.

Understand basic concepts and algorithms in association mining .

Execute and implement different classification techniques .

Evaluate the accuracy of diverse classifiers and predictors .

Identify the appropriate clustering techniques for the given data sets.

To familiarize with the various applications of Data Mining

Unit-I

Data Mining- Introduction, Challenges, Data Mining Tasks, Types of Data, Data Quality-

Measurements and data collection errors, precision, bias, accuracy, missing value, inconsistent

values.noise and artifacts, outliers, duplicate data, Issues at the measurement and data collection

level to particular applications and fields.

Data Preprocessing- aggregation, sampling, dimensionality reduction, discretization and

binarization, variable transformation.

Association Analysis- Basic Concepts & Algorithms: Frequent Item set Generation.

Unit-II

Association Analysis: Rule Generation, Compact Representation of Frequent Item sets,

Alternative methods for generating Frequent Item sets, FP Growth Algorithm, Evaluation of

Association Patterns–Objective Measures of Interestingness, Measures beyond Pairs of Binary

Variables, properties of objective measures

Handling Categorical & Continuous Attributes, Mining various kinds of association Rules.

Unit-III

Classification : Basics, General approach to solve classification problem, Decision Trees Induction,

Model Overfitting- Overfitting due to presence of noise and lack of representative samples, Rule

based classifiers , Nearest Neighbour Classifiers, Bayesian Classifiers, Artificial Neural

Networks(ANN), Prediction, Accuracy and Error measures, Evaluating the accuracy of a classifier or

predictor, bagging.

Unit - IV

Clustering Techniques: Overview, Features of cluster analysis, Types of Data & Computing

Distance (Measures of Similarity &Dissimilarity), Different types of Clustering, Different types of

clusters, K-means, Agglomerative Hierarchical Clustering, DBSCAN, Cluster Evaluation-Overview.

Unit-V

Mining different types of data: Text Mining-Text data analysis &informational retrieval, text

mining approaches, Mining the world wide web-Mining web page layout structure, Mining web’s link

structures, Web usage mining. Applications: Finance, Retail Industry, Intrusion detection.

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Text Books

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

Education, 2005.

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

Morgan Kaufmann Publisher, 2006

Reference Books

1. G. K. Gupta: Introduction to Data Mining with Case Studies, 3rd Edition, PHI, New Delhi,

2009.

Course outcomes:

Students will be able to:

CO1 :Identify the data and various data preprocessing techniques.(b,c)

CO2 :Design association mining approaches and analyze them.(b,c,f)

CO3 :Design data classification methods and measures for evaluation.(b,c,f)

CO4 :Generate clusters from the data using similarity measures.(b,c)

CO5 : Apply data mining approaches in text and web and identify the applications of data

mining.(b,c)

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DATA MINING LAB

Course Code : IS711L Credits : 0:0:1

Prerequisites: NIL Contact Hours:28P

Course Coordinator: Savita Shetty

Course Objectives:

Create dataset and perform preprocessing on these dataset using Weka tool/ Rapid Miner

Tool/R Tool.

Perform association mining on the preprocessed dataset.

Perform classification on the preprocessed dataset.

Perform clustering on the preprocessed dataset.

Implement a data mining approach on a real-time dataset.

Course Contents:

Part – A

1. Working with the Weka tool/ Rapid Miner Tool/R Tool

Step 1: Dataset Creation

Step 2: Preprocessing

The preprocessing techniques that can be applied are as follows:

a. Handling of Missing values for categorical and numerical values

b. Redundancy Techniques

c. Binning

d. Normalization techniques such as the min-max normalization, z score normalization and

normalization by decimal scaling.

Step 3:

Perform the following on the preprocessed dataset:

a. Association mining

b. Decision Tree Classification

c. Naïve Bayes Classification

Cases for data sets: Hospital, Agriculture, Banking system, Social Networks, Weather Forecasting,

Aviation, Biomedical Signals etc..

2. Mini Project- Any one (Programming Language – Java/C++)

The data mining algorithms that have to be implemented are:

1. Apriori Algorithm. Indicate the rules derived due to various support and confidence levels.

2. FP growth association mining.

3. ID3 Decision tree classifier.

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4. Artificial Neural Network Classifier

5. Nearest Neighbour classifier.

6. Build a confusion matrix compute sensitivity, specificity, precision, recall, weighted accuracy and

correlation between the attributes.

PART - B

1. Write a java/c++ program to perform aggregation and discretization on a given dataset.

2. Write a java/c++ program to generate a stratified sample from a given supervised dataset.

3. Write a java/c++ program to handle missing values

a. replacing by the mean for numeric attributes.

b. replace by the mode for categorical attributes.

4. Write a java/c++ program to identify the frequent subsets from a frequent itemset.

5. Write a java/c++ program to generate rules from a frequent 4-itemset.

6. Write a java/c++ program to identify strong rules from a set of rules given the confidence

and support thresholds.

7. Write a java/c++ program to implement the information gain and gini index measures to

identify the best attribute to split.

8. Write a java/c++ program to construct a Naïve Bayesian classifier for a given dataset.

9. Write a java/c++ program to perform k-means clustering on numeric dataset.

Text Books:

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

Education, 2005.

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

Morgan Kaufmann Publisher, 2006

Reference Books:

1. Arun K Pujari: Data Mining Techniques, 2nd Edition, Universities Press, 2009.

2. G. K. Gupta: Introduction to Data Mining with Case Studies, 3rd Edition, PHI, New Delhi,

2009.

Course Outcomes

Students will be able to

CO1: Create dataset, preprocess it and mine data using data mining tool(PO b, PO d, PO e)

CO2: Design and develop preprocessing and data mining tasks by applying programming

language (PO c, PO d)

CO3: Design and implement real world problems using data mining techniques (PO b, PO d, PO e,

PO i, PO j)

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Distributed Computing

Course Code: IS722 Credits: 3:0:0

Prerequisites: Computer Organisation Contact Hours: 56

Course coordinator(s): Koushik S

Course Objectives

Students will be learning

Understand the concepts of distributed systems and computing model and Inter-Process

Communication

Identify the stub and skeleton of Remote Procedure Call.

Concept of different Clock Synchronization in Distributed System and Computing.

Understand the Distributed Systems Management Algorithms.

Concept of Heterogeneous Parallel Computing Architecture.

UNIT -1

Basic Distributed System Concepts: Distributed Computing Models, Software Concepts,

Transparency, Flexibility, Reliability, Performance, Scalability, Security, Fault Tolerance, Client-

Server Model, Case Studies.

Inter-process Communication: Message Passing, Case Study: IPC in MACH, Case Study:

CBCAST Protocol in ISIS, API for Internet Protocol, Synchronous and Asynchronous

Communications, Sockets, UDP and TCP, Java API for UDP and TCP Protocols.

UNIT -2

Remote Communication: Introduction to Remote Communication, Remote Procedural Call Basics,

RPC Implementation, RPC Communication, RPC Issues, Exception Handling and Security, RPC in

Heterogeneous Environment, Failure Handling, RPC Optimization, Complicated and Special RPCs,

Case Study: Sun RPC, Remote Method Invocation Basics, Distributed Object Concepts, RMI

Implementation, Case Study: Java RMI.

UNIT -3

Synchronization: Clock Synchronization, Physical Clocks, Use of Synchronized Clocks, Logical

Clocks, Event Ordering Implementation of logical clocks, Lamport's Timestamps, Vector

Timestamps, Global State, Mutual Exclusion, Centralized Algorithm, Distributed Algorithm, Token

Ring Algorithm, Comparison of Various Algorithms, Election Algorithms, The Bully Election

Algorithm, The Ring Election Algorithm, Election in a Wireless Network, Deadlocks in Distributed

Systems, Basic Concepts, Deadlock Modelling, Handling Deadlocks in Distributed Systems,

Distributed Deadlock Prevention, Distributed Deadlock Detection, Distributed Deadlock Recovery,

Issues in Recovery from Deadlock, Case Study: Deadlocks in Message Communication.

Simulation Basics for Formal Model Specifications: Basic Model of a Message Passing System,

Asynchronous Systems, Synchronous Systems, Basics of Formal Model Specifications, A Generic

Problem Specification, Communications System, Asynchronous Point-to-Point Message Passing

System, Asynchronous Broadcast, Processes, Admissibility Conditions, Simulations.

UNIT -4

Distributed System Management: Resource Management, Desirable Features of a Global

Scheduling Algorithm, Task Assignment Approach, Graph Theoretic Deterministic Algorithm,

Centralized Heuristic Algorithm, Hierarchical Algorithm, Load-Balancing Approach, Taxonomy of

Load-Balancing Algorithms, Issues in Designing Load-Balancing Algorithms, Load-Sharing Approach,

Issues in Designing Load-Sharing Algorithms, Process Management in a Distributed Environment,

Functions of Distributed Process Management, Desirable Features of a Process Migration

Mechanism, Process Migration, Freezing the Process on Source Node Address Space Transport

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Mechanisms, Message Forwarding Mechanism, Process Migration in Heterogeneous Systems,

Advantages of Process Migration, Threads, Process vs. Threads, Thread Models, Design Issues in

Threads, Implementing a Thread Package, Threads and Remote Execution, Fault Tolerance,

Component Faults, System Failures, Use of Redundancy

UNIT -5

Introduction: Heterogeneous Parallel Computing, Architecture of a Modern GPU, Why More Speed

or Parallelism?, Speeding Up Real Applications, Parallel Programming Languages and Models,

Overarching Goals.

History of GPU Computing: Evolution of Graphics Pipelines, GPGPU: An Intermediate Step, GPU

Computing.

Introduction to Data Parallelism and CUDA C: Data Parallelism, CUDA Program Structure, A

Vector Addition Kernel, Device Global Memory and Data Transfer, Kernel Functions and Threading.

Text Books

[1]. Distributed Computing, Second Edition, Sunita Mahajan and Seema Shah.

[2]. Programming Massively Parallel Programming, David B. Kirk, Wen-mei W. Hwu. Morgan

Kaufmann Publications.

References

[3]. Ajay D. Kshemkalyani, Mukesh Singhal, Distributed Computing: Principles, Algorithms, and

Systems, Cambridge University Press, 2011.

[4]. Carlos A. Varela, Programming Distributed Computing Systems: A Foundational Approach,

MIT Press 2013.

Course Outcomes

Students will be able to

CO1: Describe the concepts of distributed systems and computing model and Inter-Process

Communication (PO a, PO c)

CO2: Express the Remote Procedure Call Issues, Exception Handling and Securities along with its

solution. (PO a, PO b)

CO3: Compare different Clock Synchronization in Distributed Computing and Dead Lock Recovery

and Avoidance solutions. (PO b, PO d)

CO4: Describe and design Distributed Systems Management Algorithms. (PO a, PO c)

CO5: Illustrate and Explain Heterogeneous Parallel Computing Architecture. (PO a, PO c)

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Distributed Computing Lab

Course Code: IS722L Credits: 0:0:1

Prerequisites: Operating Systems Contact Hours: 14

Course coordinator(s): Mohan Kumar S

Course Objectives

To learn Basic Concepts of Distributed System Management and GPU architecture process.

Introduce the concepts of Distributed Computing and Systems along with models and

concepts.

To explore real-time distributed network applications.

Contents

1. Implementation of concurrent echo client-server application (Using TCP)

2. Implementation of concurrent echo client-server application (Using UDP)

3. Implementation of concurrent day-time client-server application

4. Implementation of iterative day-time client-server application. Calculate Request/Response

Time using TCP/UDP.

5. Implementation of Sun RPC to convert a integer number into hexadecimal and octal

6. Implementation of Incrementing a counter in shared memory

7. Configure following options on server socket and tests them: SO_KEEPALIVE, SO_LINGER,

SO_SNDBUF, SO_RCVBUF, TCP_NODELAY

8. Implementation of Lamport’s Logical Clock

9. Implementation of Ring Election Algorithm (or Mutual Exclusion Algorithms)

10. Implementation of Distributed Mutual Exclusion

Course Outcomes

CO1: Design and Develop distributed computing using concurrent concepts. (PO c, PO d)

CO2: Demonstrate the concepts of Distributed and Parallel Computing Architecture. (PO a, PO c, PO

d)

CO3: Generate an effective report on Distributed and Parallel Computing. (PO j)

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SOFTWARE DESIGN PATTERNS

Course Code : IS713 Credits : 4:0:0

Prerequisites: NIL Contact Hours : 56

Course coordinator(s): Shashidhara H S

Course objectives:

Provide an engineering basis for software design

Use standard notations to document and communicate software design

Design software architecture with patterns

Appreciate the need for aesthetic software design

Understand software design in a larger philosophical context

Course Contents:

UNIT I

AN INTRODUCTION TO OBJECT-ORIENTED SOFTWARE DEVELOPMENT, The Object-Oriented

Paradigm, The UML-The Unified Modeling Language, THE LIMITATIONS OF TRADITIONAL OBJECT-

ORIENTED DESIGN, A Problem That Cries Out for Flexible Code, A Standard Object-Oriented

Solution

UNIT II

DESIGN PATTERNS – 1, An Introduction to Design Patterns, The Facade Pattern, The Adapter

Pattern, Expanding Our Horizons, The Strategy Pattern, The Bridge Pattern, Case study – 1

UNIT III

DESIGN PATTERNS – 2 , The Abstract Factory Pattern, The Principles and Strategies of Design

Patterns, Commonality and Variability Analysis, The Analysis Matrix, The Decorator Pattern, Case

study – 2

UNIT IV

OTHER VALUES OF PATTERNS,The Observer Pattern, the Template Method Pattern. FACTORIES,

Factories, the Singleton Pattern and the Double-Checked Locking Pattern, the Object Pool Pattern,

The Factory Method Pattern, Summary of Factories, Case study – 3

UNIT V

INTRODUCTION TO SOFTWARE ARCHITECTURES – What is software Architecture, An

Engineering Discipline for Software, The Status of Software Architecture, Architectural Styles –

Pipes and Filters, Data Abstraction and Object-Oriented Organization, Event based Implicit

Invocation, Layered Systems, Repositories, Process Control, Case Studies – Keyword in Context,

Cruise Control

Text Books:

1. Alan Shalloway, James R Trott, Design Patterns Explained, A New Perspective on Object

Oriented Design, 2nd Edition, Addison Wesley

2. Mary Shaw, David Garlan, Software Architecture: Perspectives on an Emerging Discipline, PHI

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References:

1. Len Bass, Paul Clements, Rick Kazman, Softwar Architecture in Practice, 3rd Edition, Addison

Wesley

2. James W Cooper, Java Design Patterns, A Tutorial, Addison Wesley

3. Eric Freeman, Elisabeth Freeman, Head First Design Patterns, O’reilly Publications

Course Outcomes:

The student will be able to:

CO1: Design the stated problem using UML notations (POa, POb, POd)

CO2: Distinguish between different categories of design patterns (POa, POc, POd, POe)

CO3: Identify the sections of code where design patterns can be applied (POa, POc, POd, POe)

CO4: Design the software using Pattern Oriented Architectures (POa, POc, POd, POe)

CO5: Apply appropriate architectural patterns to given problem (POa, POc)

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Project Preliminaries


Prerequisites: NIL Contact Hours : 112P

Course coordinator(s): Krishna Raj P M

Course objectives:

Participate in real time software development process

Apply project management activities to co-ordinate the development process

Use modern tools for development and communication

Work with a team in a collaborative environment

Effectively communicate within and outside the team regarding the project

Project Work-flow

1. Students submit the initial details including broad area of work and choice of guide in a

prescribed format

2. The Project Co-ordinator along with Head of the department finalises the guide allocation

process

3. Students are given an option to change the guide with mutual consent by applying through

prescribed form

4. Students submit the Project WorkBook to guide on the day of registration

5. Problem statement is submitted to Project Co-ordinator within one week of registration

6. Students maintain a blog and update it on weekly basis about their work

7. Weekly meeting with guide is recorded in the workbook

8. Guide evaluates the student on a regular basis according to the rubrics defined in the

workbook for total of 50 marks which constitutes the final CIE score

9. At the end of the semester, an exam is conducted with one internal and one external

examiner for 50 marks which constitutes the final SEE score

Course outcomes:


CO 1: Schedule milestones and deliverables using appropriate project management techniques (PO

i, k)

CO 2: Compare and contrast the available literature in the context of the project (PO l)

CO 3: Formulate the requirements for the proposed system (PO b)

CO 4: Select effective communication strategies within and outside the team (PO j)

CO 5: Develop an activity for the larger good of the community (PO c, f)

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SYSTEM SIMULATION & MODELING

Sub Code : IS71D1 Credits : 4:0:0

Total Hours : 56 Prerequisite: ISMAT

Course Coordinator: Myna.A.N.

Course Objectives

To explain the concepts of system, models in general and simulation models in particular

To Develop simulation models using general purpose programming language - Java, and

simulation programming language – GPSS

Explore the various techniques to generate random numbers and and test for randomness

Develop an input model for a given system

Verify, Validate and perform output analysis of a simulation model

Syllabus

UNIT I

Introduction to Simulation: 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

UNIT II

Concepts in Discrete-Event Simulation: The Event-Scheduling / Time-Advance Algorithm, World

Views, Manual simulation Using Event Scheduling; List processing, Simulation in Java, Simulation in

GPSS; Statistical Models in Simulation: Review of terminology and concepts, Discrete distributions,

Continuous distributions-Uniform distribution, Exponential distribution, Normal distribution

UNIT III

Random-Number 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-Exponential Distribution, Uniform Distribution,

Discrete Distributions, Acceptance-Rejection technique: Poisson Distribution, Convolution method

UNIT IV

Queueing Models: Characteristics of queuing systems, Queuing notation, Long-run measures of

performance of queuing systems; Input Modeling: Data Collection, Identifying the distribution with

data, Parameter estimation, Goodness of Fit Tests, Selecting input models without data

UNIT V

Verification and Validation of Simulation Models: Model building, verification and validation,

Verification of simulation models, Calibration and validation of models, Output Analysis for a Single

Model: Types of simulations with respect to output analysis, Stochastic nature of output data;

Measures of performance and their estimation

Text Books:

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

Simulation, Fourth Edition, Pearson Education, 2007.

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References:

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

Prentice-Hall, 2006.

2. Sheldon M. Ross: Simulation, Fourth Edition, Elsevier, 2006.

3. Averill M. Law: Simulation Modeling and Analysis, Fourth Edition, Tata McGraw-Hill, 2007

Course Outcomes

The students will be able to

CO 1. Identify the concepts of simulation used to develop models. (POa, POb)

CO 2. Design and develop models using simulation algorithm techniques (POa, POb, POc)

CO 3. Design and develop techniques to generate and test random numbers (POa, POc, POd)

CO 4. Design and develop an input model for a given simulation system (POa, POb, POc, POd)

CO 5. Verify, Validate and Perform output analysis of a simulation model (POd)

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MOBILE COMPUTING

Course Code : IS72D2 Credits : 4:0:0

Prerequisites: NIL Contact Hours : 56L

Course coordinator(s): Sunitha R S

Course objectives:

1. To introduce networking concepts of Wireless transactions, spread spectrum, medium access

and access network.

2. To learn Network and Transport layer for mobile networks and the various concepts used in

telecommunication systems.

3. Exploring different techniques to handle databases and data dissemination for mobile

computing

4. To learn Data Synchronization in Mobile Computing Environment along with mobile agents.

5. Exploring various aspects of mobility support, and various mobile operating systems.

Course Contents:

UNIT I

Introduction; Types of computing; wireless transaction - Signals, antennas, signal propagation,

multiplexing modulation, spread spectrum; medium access: SDMA, FDMA, TDMA, CDMA. OFDM,

4G, 5G Networks

UNIT II

Mobile Network layer – mobile IP, Dynamic host configuration protocol, Mobile adhoc networks.

Mobile transport layer-Traditional and classical TCP, TCP over 2.5 (3.0 G wireless networks,

performance enhancing proxies, Communication Systems: GSM, DECT, TETRA, UMTS, IMIT 200,

LTE.

UNIT III

Databases: Database Hoarding Techniques, Data Caching, Transactional Models, Query Processing.

Data Dissemination and Broadcasting Systems: Communication Asymmetry, Classification of Data-

Delivery Mechanisms, Data Dissemination Broadcast Models, Selective Tuning and Indexing

Techniques.

UNIT IV

Data Synchronization in Mobile Computing Systems: Synchronization, Synchronization software for

mobile devices, Synchronization protocols, SyncML - Synchronization language for mobile

computing, Sync4J (Funambol), Synchronized Multimedia Markup Language (SMIL).

Mobile Devices: Server and Management: Mobile agent, Application server, Gateways, Portals,

Service Discovery, Device management, Mobile file systems, security.

UNIT V

Support for Mobility- File Systems, Mobile operating systems: PalmOs, Windows, Symbian OS,

Android, iOS, Linux for Mobile devices.

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References:

1. Mobile Computing, RajKamal, Oxford University Press, 2nd Edition, 2012

2. Jochen Schiller, Mobile Communications, 2nd Edition, Pearson 2003

3. Reza B, Mobile Computing Principles, Cambridge University press 2005

Course outcomes:


Describe the basic principles and techniques in mobile networks. (PO a)

Explain the concept of network and transport layer for mobile networks and

telecommunication systems. (PO b)

Analyze the database handling, data dissemination, Synchronization with respect to different

Mobile Operating Systems. (PO b)

Describe and illustrate the mobility support using different file systems and platforms. (PO b)

Demonstrate the different operating systems with their benefits and problems. (PO e, PO i,

PO j)

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INTERNSHIP

Sub Code : IS82IN Credits : 0:0:4

The student can do the Internship during the summer semester between 4th -5th

semester or between 6th -7th semester

Student should take prior permission from the departmental committee before carrying

out the internship

The duration of the Internship is one month

The report of the Internship need to be submitted during the odd semester within one

week of the commencement of the semester

The department will constitute a committee for the evaluation process of internship of

student.

The marks awarded for Internship are considered/added in the 8th semester

The students who do not take up the internship need to study a department elective

subject

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Machine Learning

Subject Code: IS81D3 Credits: 4:0:0

Prerequisite: Nil Contact Hours : 56

Course Coordinator: Deepthi K

Course Objectives

To explain the concepts of machine learning and its applications.

To discuss supervised learning

To make rational decisions to minimize expected risk using Bayesian decision theory

To estimate the probabilities from a given training set using parametric and non

parametric methods

To discuss learning algorithms used for linear discrimination

To analyze multilayer perceptron used for classification and regression

Course Contents:

Unit 1

Introduction: What is machine learning? Examples of machine learning applications Supervised

Learning: Learning a Class from Examples, VC-dimension, PAC learning, Noise, Learning multiple

classes, Regression, Model selection and generalization, Dimensions of a Supervised Machine

Learning Algorithm Bayesian Learning: Classification, losses and risks, Discriminant functions,

Utility Theory

Unit 2

Parametric Methods: Maximum Likelihood Estimation, Evaluating an estimator, Bayes’ estimator,

parametric classification, Regression Multivariate methods: Multivariate Data, Parameter

Estimation, Estimation of Missing Values, Multivariate Normal Distribution

Unit 3

Dimensionality Reduction: Subset Selection, Principal components Analysis, Linear Discriminant

Analysis Clustering: Introduction, Mixture Densities, k-Means Clustering, Expectation-Maximization

Algorithm, Mixtures of Latent Variable Models, Supervised Learning after Clustering, Hierarchical

Clustering, Choosing the Number of Clusters

Unit 4

Nonparametric Methods: Nonparametric Density Estimation, Generalization to Multivariate Data,

Nonparametric Classification Decision Trees: Introduction, Univariate Trees, Pruning, Rule

Extraction from Trees

Unit 5

Linear Discrimination: Introduction, Generalizing the Linear Model, Geometry of the Linear

Discriminant, Pairwise Separation, Support Vector Machines: Optimal separating Hyperplane

Multilayer Perceptrons: Introduction, The Perceptron, Training a Perceptron, Learning Boolean

Functions, Multilayer Perceptrons, Backpropagation Algorithm: Nonlinear regression

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Text Books:

1. Ethem Alpaydin "Introduction To Machine Learning" 2nd Edition, PHI Pvt. Ltd-New Delhi,

2010

Reference Books:

1. Christopher Bishop "Pattern Recognition and Machine Learning", CBS Publishers &

Distributors-New Delhi

2. Tom M Mitchell, “Machine Learning”, McGraw-Hill, Inc. New York, NY, USA ©1997,

ISBN:0070428077 9780070428072

Course Outcomes:

At the end of the course, a student should be able to

CO 1. Identify the concepts of machine learning and specify solutions in the supervised learning

framework (PO a, PO b)

CO 2. Design and develop solutions for classification problems using Bayesian Decision Theory by

estimating probabilities using parametric and multivariate methods (PO a, PO b, PO c)

CO 3. Apply dimensionality reduction techniques and develop clustering methods (PO a, PO b, PO c,

PO d)

CO 4. Develop nonparametric approaches for solving learning problems and implement decision

trees (PO a, PO b, PO c)

Estimate parameters using linear discrimination techniques and design a multilayer perceptron (PO

b, PO c, PO d)

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Data Sciences

Course Code : IS82E1 Credits : 0:1:3

Prerequisites: NIL Contact Hours : 56L

Course coordinator(s): Siddesh G M

Course Objectives:

Introduction to Big Data and Analytics

Working with R data analysis tool

Incorporating machine learning with data analytics

Introduction to data organization and structure of big data

Introduction to crowdSourcing and data visualization

UNIT-1

Introduction to Big Data Analytics: Big Data Overview, Data Structures, Analyst Perspective on

Data Repositories, State of the Practice in Analytics, BI Versus Data Science, Current Analytical

Architecture, Drivers of Big Data, Emerging Big Data Ecosystem and a New Approach to Analytics,

Key Roles for the New Big Data Ecosystem, Examples of Big Data Analytics. Data Analytics

Lifecycle: Data Analytics Lifecycle Overview, Key Roles for a Successful Analytics Project,

Background and Overview of Data Analytics Lifecycle, Six phases in Data Analytics lifecycle.

UNIT-2

Review of Basic Data Analytic Methods Using R: Introduction to R, R Graphical User Interfaces,

Data Import and Export, Attribute and Data Types, Descriptive Statistics, Exploratory Data Analysis,

Visualization Before Analysis, Dirty Data, Visualizing a Single Variable, Examining Multiple Variables,

Data Exploration Versus Presentation, Statistical Methods for Evaluation, Hypothesis Testing,

Difference of Means, Wilcoxon Rank-Sum Test, Type I and Type II Errors, Power and Sample Size,

ANOVA.

UNIT-3

Advanced Analytics—Technology and Tools: MapReduce and Hadoop: Analytics for

Unstructured Data, Use Cases, MapReduce, Apache Hadoop, The Hadoop Ecosystem, Pig, Hive,

HBase, Mahout, NoSQL. Exploring Hive: Introducing Hive, getting started, Datatypes in Hive,

Buitin Functions, DDL, DML, Data retrieval queries, using Joins.

UNIT-4

NOSQL Data Management, Advanced Analytics—Technology and Tools: In-Database

Analytics: SQL Essentials, Joins, Set Operations, Grouping Extensions, In-Database Text Analysis,

Advanced SQL, Window Functions, User-Defined Functions and Aggregates, Ordered Aggregates, MADlib.

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UNIT-5

Data Visualization: Basics, Key Points Supported with Data, Evolution of a Graph, Common

Representation Methods, How to Clean Up a Graphic, Additional Considerations.

Visualilizing Big data, Tools used in data visualization.

Books/ References:

1. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting

Data. John Wiley & Sons, 2015.

2. Online materials.

Course Outcomes

The students will be able to

CO 1. Understand big data and its life cycle (POb, POf)

CO 2. Analyse data using R – tool (POe)

CO 3. Apply machine learning concepts in big data analysis (POa, POb, POc)

CO 4. Analyse data using advanced tools like SQL, data integration (POb, POe)

CO 5. Understand CrowdSourcing and data visualization (POa, POf)

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CLOUD COMPUTING



Course coordinator: Koushik S

Course objectives:

Introduce different aspects of cloud computing like; service models, challenges &

infrastructure.

Explore the various cloud computing applications & paradigms.

Analyze different cloud virtualization, resource management, data storage and security

issues with case studies.

Study the literature and exercise a project.

Apply features of cloud platforms in programming and software environments.

Course Contents:

UNIT I

Introduction: Network centric computing and network centric content, Peer-to-peer systems,

Cloud Computing: an old idea whose time has come, Cloud Computing delivery models & Services,

Ethical issues, Cloud vulnerabilities, Challenges. Cloud Infrastructure: Amazon, Google, Azure &

online services, open source private clouds. Storage diversity and vendor lock-in, intercloud, Energy

use & ecological impact of data centers, service level and compliance level agreement,

Responsibility sharing, user experience, Software licensing.

UNIT II

Cloud Computing: Applications & Paradigms, Challenges, existing and new application

opportunities, Architectural styles of cloud applications, Workflows coordination of multiple

activities, Coordination based on a state machine model -the Zoo Keeper, The Map Reduce

programming model, Apache Hadoop, A case study: the GrepTheWeb application, Clouds for

science and engineering, High performance computing on a cloud, cloud for biological research,

Social computing, digital content, and cloud computing.

UNIT III

Cloud Resource Virtualization: Virtualization, Layering and virtualization, Virtual machine

monitors, Virtual machines Performance and security isolation, Full virtualization and

paravirtualization, Hardware support for virtualization Case study: Xen -a VMM based on

paravirtualization, Optimization of network virtualization in Xen 2.0, vBlades -paravirtualization

targeting a x86-64 Itanium processor, A performance comparison of virtual machines, Virtual

machine security, The darker side of virtualization, Software fault isolation.

UNIT IV

Cloud Resource Management and Scheduling: Policies and mechanisms for resource

management, Applications of control theory to task scheduling on a cloud, Stability of a two-level

resource allocation architecture, Feedback control based on dynamic thresholds, Coordination of

specialized autonomic performance managers, A utility-based model for cloud-based web services,

Resource bundling, combinatorial auctions for cloud resources, Scheduling algorithms for computing

clouds, fair queuing, Start time fair queuing, Cloud scheduling subject to deadlines, Scheduling

mapreduce applications subject to deadlines.

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UNIT V

Storage systems: Storage models, file systems, databases, DFS, General parallel File system,

GFS, Apache Hadoop, Locks & Chubby, TPS & NOSQL databases, Bigdata, Mega store. Cloud

security: Risks, Security, privacy and privacy impacts assessments, Trust, VM Security, Security

risks in shared images.

Text Book:

1. Dan Marinescu, Cloud Computing: Theory and Practice, 1st edition, MK Publishers, 2013.

2. Anthony T. Velte, Toby J. Velete, Robert Elsenpeter, Cloud Computing: A Practical Approach,

Tata McGraw Hill, 2010.

References:

1. Kai Hwang, Jack Dongarra, Geoffrey Fox, Distributed and Cloud Computing, From Parallel

Processing to the Internet of Things, 1st edition, MK Publishers, 2012.

Course outcomes:

CO 1. Introduce different aspects of cloud computing like; service models, challenges &

infrastructure (POa, POd)

CO 2. Explore the various cloud computing applications & paradigms (POb, POd, POl)

CO 3. Analyze different cloud virtualization, resource management, data storage and security

issues with case studies (POa, POb, POc, POk)

CO 4. Study the literature and exercise a project (POc, POe, POk)

CO 5. Apply features of cloud platforms in programming and software environments (POb, POc,

POe, POi)

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Mobile Application Development



Course coordinator(s): Shashidhara H S

Course Objectives:

Understand the need of mobile applications and web

Develop mobile applications using Android

Course contents:

UNIT I

What Is Android?, Android Versions, Features of Android, Architecture of Android, Android Devices

in the Market, The Android Market, Obtaining the Required Tools, Eclipse, Android SDK, Android

Development Tools (ADT), Creating Android Virtual Devices (AVDs), Creating Your First Android

Application, Anatomy of an Android Application. Understanding Activities, Applying Styles and

Themes to Activity, Hiding the Activity Title, Displaying a Dialog Window, Displaying a Progress

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

Intent, Passing Data Using an Intent Object, Calling Built-In Applications Using Intents,

Understanding the Intent Object, Using Intent Filters, Adding Categories, Displaying Notifications

UNIT II

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, Creating the User Interface Programmatically, Listening for UI

Notifications, Overriding Methods Defined in an Activity, Registering Events for Views. Basic Views -

TextView View,Button, ImageButton, EditText, CheckBox, ToggleButton, RadioButton, and

RadioGroup Views, ProgressBar View, AutoCompleteTextView View, Picker Views, TimePicker View,

Displaying the TimePicker in a Dialog Window, DatePicker View, Displaying the DatePicker View in a

Dialog Window, List Views, ListView View, Customizing the ListView, Using the Spinner View

UNIT III

Using Image Views to Display Pictures - Gallery and ImageView Views, ImageSwitcher, GridView,

Using Menus with Views - Creating the Helper Methods, Options Menu, Context Menu, Some

Additional Views - AnalogClock and DigitalClock Views, WebView. Saving and Loading User

Preferences - Using getSharedPreferences(), Using getPreferences(), Persisting Data to Files -

Saving to Internal Storage, Saving to External Storage (SD Card), Choosing the Best Storage

Option, Using Static Resources, Creating and Using Databases - Creating the DBAdapter Helper

Class, Using the Database Programmatically, Adding Contacts, Retrieving All the Contacts,

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Retrieving a Single Contact, Updating a Contact, Deleting a Contact, Upgrading the Database, Pre-

Creating the Database, Bundling the Database with an Application

UNIT IV

Sharing Data in Android, Using a Content Provider - Predefined Query String Constants, Projections,

Filtering, Sorting, Creating Your Own Content Providers - Using the Content Provider. SMS

Messaging - Sending SMS Messages Programmatically, Getting Feedback After Sending the

Message, Sending SMS Messages Using Intent, Receiving SMS Messages, Updating an Activity from

a BroadcastReceiver, Invoking an Activity from a BroadcastReceiver, Caveats and Warnings,

Sending E‑ Mail, Networking - Downloading Binary Data, Downloading Text Files - Accessing Web

Services, Performing Asynchronous Calls

UNIT V

Displaying Maps - Creating the Project, Obtaining the Maps API Key, Displaying the Map, Displaying

the Zoom Control, Changing Views, Navigating to a Specific Location, Adding Markers, Getting the

Location That Was Touched, Geocoding and Reverse Geocoding, Getting Location Data - Monitoring

a Location. Creating Your Own Services - Performing Long-Running Tasks in a Service, Performing

Repeated Tasks in a Service, Executing Asynchronous Tasks on, Separate Threads Using

IntentService, Communicating between a Service and an Activity, Binding Activities to Services.

Preparing for Publishing, Versioning, Digitally Signing Your Android Applications, Deploying APK

Files - Using the adb.exe Tool, Using a Web Server, Publishing on the Android Market, Creating a

Developer Profile, Submitting Your Apps

Text Books:

1. Wei – Meng Lee, Beginning Android Application Development, Wiley publications

2. Reto Meier, Professional Android 4 Application Development, Wiley publications

Course Outcomes:

At the end of the Course, the student will be able to

CO 1. Create Activities in Android (POf, POg)

CO 2. Design user interface using components and views in Android (POf, POg)

CO 3. Create and use databases for Android Applications (POa, POb, POc, POd)

CO 4. Implement messaging services in Android (POc, POd, POe)

CO 5. Deploy mobile applications in various marketplace for distribution (POc, POd, POe)

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SENIOR PROJECT


Prerequisites: NIL Contact Hours : 448P

Course coordinator(s): Krishna Raj P M

Course objectives:

Participate in real time software development process

Design, develop and test software applications using appropriate tools

Prepare project report by applying the principles of technical writing

Work with a team in a collaborative environment

Effectively communicate within and outside the team regarding the project

Project Work-flow

1. The work done in the previous semester (IS715 – Project Work) is continued

2. Students maintain a blog and update it on weekly basis about their work

3. Weekly meeting with guide is recorded in the workbook

4. Guide evaluates the student on a regular basis according to the rubrics defined in the

workbook for total of 50 marks which constitutes the final CIE score

5. At the end of the semester, an exam is conducted with one internal and one external

examiner for 50 marks which constitutes the final SEE score

Course outcomes:


CO 1: Design the software by applying the relevant guidelines (PO a, b, c)

CO 2: Develop the software by implementing the design (PO a, b, c, e)

CO 3: Evaluate the quality of software by testing it using appropriate techniques (PO d)

CO 4: Demonstrate the project before general public and appraise its effectiveness(PO j)

CO 5: Develop an activity for the larger good of the community (PO c, f)

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Extra-Curricular/ Co-Curricular Activities

Course Code : IS82ECA

Credits : 0:0:2

• Co-curricular activities

(i) Participation/Won Prizes in any event of the Technical Festivals at MSRIT/Outside in other

institutes of State/National/International level

(ii) Delivering lectures/handling hands on sessions on technical topics in any technical event at

MSRIT/Outside in other institutes of State/National/International level

(iii) Invitation to participate as a representative from the Institute to any technical event at

MSRIT/Outside in other institutes of State/National/International level

(iv) Invitation to participate as referee/Expert/Judge to any technical event at MSRIT/Outside in

other institutes of State/National/International level

(v) Won merit scholarships/Consultancy/award with monetary benefits/grants for any technical

event at MSRIT/Outside in other institutes of State/National/International level

(vi) Entrepreneur activity like setting up a company in association with EDC cell of MSRIT

(vii) Team member/individual representing the State/Country in any technical event at the

National/International events

(viii) Passing with good percentile the examinations like GATE/GRE/TOEFL etc and any other

examinations of National/International repute

(ix) Placement in Tier-1 Company

• Extra Curricular activities

(i) Participation/Won Prizes in any event of the Sports Festivals/Cultural in MSRIT/Outside the

other institutes of State/National/International level

(ii) Representing the Institute in any sports event/cultural event at the

University/State/National/International level

(iii) Invitation to participate as referee/expert advisor/official/Captain in any sports

event/cultural event within/outside institute at State/National/International level

(iv) Won scholarships/grants for any sports event/cultural event within institute/outside in other

institutes/State/National/International level

(v) Team member/individual representing the State/Country in any sports event/cultural event

at the National/International events

(vi) Participation/Won awards/received grants through NCC/NSS/NGO activities

• Each student need to submit the evidence for the claims for the relevant categories mentioned

above for evaluation to the proctor during 8th sem.

• If any student has a significant contribution in any category other than the above mentioned need

to submit the report with proof

SYLLABUS (For the Academic year 2016 2017) V IIand V I Semester...

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