curriculum - pda college of engineering

CURRICULUM

FOR THE ACADEMIC YEAR 2021-2022

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

M. Tech. (Computer Science & Engineering)

Syllabus I to IV SEMESTER

POOJYA DODDAPPA APPA COLLEGE OF ENGINEERING

(An autonomous college under VTU)

KALABURAGI

About the institution: The Hyderabad Karnataka Education (HKE) society founded by Late

Shri Mahadevappa Rampure, a great visionary and educationist.The HKE Society runs 46

educational institutions. Poojya Doddappa Appa College of Engineering, Gulbarga is the first

institution established by the society in 1958. The college is celebrating its golden jubilee year,

setting new standards in the field of education and achieving greater heights. The college was

started with 50% central assistance and 50% state assistance, and a desire to impart quality

technical education to this part of Karnataka State. The initial intake was 120 with degree offered

in three branches of engineering viz, Civil, Mechanical and Electrical Engineering. Now, it

houses 11 undergraduate courses, 10 post Graduate courses and 12 Research centers, established

in Civil Engg., Electronics & Communication Engg, Industrial & Production Engg, Mechanical

Engg, Electrical Engg., Ceramic Cement Tech., Information Science & Engg.,Instrumentation

Technology, Automobile Engg., Computer Sc. and Engg., Mathematics and Chemistry All the

courses are affiliated to Visveswaraya Technological University, Belgaum. At present the total

intake at UG level is 980 and PG level 193.

The college receives grant in aid funds from state government. A number of projects have been

approved by MHRD /AICTE, Govt. of India for modernization of laboratories. KSCST, Govt. of

Karnataka is providing financial assistance regularly for the student's projects.

The National Board of Accreditation, New Delhi, has accredited the College in the year 2005-08

for 09 UG Courses out of which 08 courses are accredited for three years and 01 course is

accredited for five years. And second time accredited for Six Course in the year 2009-2012

Our college is one among the 14 colleges selected under TEQIP, sponsored by World Bank. It

has received a grant of Rs 10.454 Crores under this scheme for its development. The institution

is selected for TEQIP phase II in year 2011 for four years. Institution is receiving a grant of Rs

12.50 Crores under TEQIP Phase -II scheme for its development and selected for TEQIP-III as

mentoring Institute for BIET Jhansi(UP).

Recognizing the excellent facilities, faculty, progressive outlook, high academic standards and

record performance, the VTU Belgaum reposed abundant confidence in the capabilities of the

College and the College was conferred Autonomous Status from the academic year 2007-08, to

update its own programme and curriculum, to devise and conduct examinations, and to evaluate

student's performance based on a system of continuous assessment. The academic programmers

are designed and updated by a Board of Studies at the department level and Academic Council at

the college level. These statutory bodies are constituted as per the guidelines of the VTU

Belgaum. A separate examination section headed by a Controller of Examinations conducts the

examinations.

At present the college has acquired the Academic autonomous status for both PG and UG

courses from the academic year 2007-08 and it is one among the six colleges in the state of

Karnataka to have autonomous status for both UG and PG courses.

One of the unique features of our college is, it is the first college in Karnataka State to start the

Electronics and Communication Engineering branch way back in the year 1967, to join NIT

Surathkal and IISc, Bangalore. Also, it is the only college in the state and one among the three

colleges across the country, offering a course in Ceramic and Cement Technology. This is the

outcome of understanding by faculty and management about the basic need of this region,

keeping in view of the available raw material and existing Cement Industries.

Bharatiya Vidya Bhavan National Award for an Engineering College having Best Overall

Performance for the year 2017 by ISTE (Indian Society for Technical Education). In the year

2000, the college was awarded as Best College of the year by KSCST, Bangalore in the state

level students projects exhibition.

The college campus is spread over 71 acres of land on either side of Mumbai-Chennai railway

track and has a sprawling complex with gardens and greenery all around.

Vision of the institute:

To be an institute of excellence in technical education and research to serve the needs of the

industry and society at local and global levels.

Mission of the institute:

To provide a high quality educational experience for students with values and ethics that

enables them to become leaders in their chosen professions.

To explore, create and develop innovations in engineering and science through research

and development activities.

Toprovidebeneficialservicetothenationalandmultinationalindustriesandcommunitiesthrou

gheducational, technical and professional activities.

About the department: The Computer Science and Engineering department was started in the

year 1984 with an intake of 40 students for UG. The department has seen phenomenal growth

and now the department has increased UG intake to 120 students and offering two Post

Graduation programmes : PG (Computer Science and Engineering with an intake of 25 students)

and PG(Computer Network and Engineering with an intake of 18 students). The department is

offering research program under its recognized research center. The department is having state-

of-the-art computing facilities with high speed internet facilities and laboratories. The

department library provides useful resources like books and journals. The department has well

qualified and experienced teaching faculty. The department has been conducting several faculty

development programs and student training programs.

Department Vision

To become pioneers in computer education and research and to prepare highly competent

IT professionals to serve Industry and Society at local and global levels.

Department Mission

To impart high quality professional education to become a leader in Computer Science

and Engineering.

To achieve excellence in Research for contributing to the development of the society.

To inculcate professional and ethical behavior to serve the Industry.

Program Educational Objectives (PEOs) are

PEO1: To enable the graduates to acquire strong foundation in mathematics, science and

engineering disciplines.

PEO2: To prepare for successful career in software industry or related fields and contribute to

the profession with ethical responsibility.

PEO3: To involve in research and engage in sustained learning and adapt to ever changing

technological and societal requirements.

PEO4: Engage in multidisciplinary projects leading to entrepreneurship.

PROGRAM OUTCOMES

Engineering Graduates will be able to:

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering

problems.

2. Problem analysis: Identify, formulate, review research literature, and analyze

complex engineering problems reaching substantiated conclusions using first principles of

mathematics, natural sciences, and engineering sciences.

3. Design/development of solutions: Design solutions for complex engineering problems

and design system components or processes that meet the specified needs with

appropriate consideration for the public health and safety, and the cultural, societal, and

environmental considerations.

4. Conduct investigations of complex 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, and apply appropriate techniques, resources, and

modern engineering and IT tools including prediction and modeling to complex engineering

activities with an understanding of the limitations.

6. The Engineer and society: Apply reasoning informed by the contextual knowledge to

assess societal, health, safety, legal and cultural issues and the consequent responsibilities

relevant to the professional engineering practice.

7. Environment and sustainability: Understand the impact of the professional engineering

solutions in societal and environmental contexts, and demonstrate the knowledge

of, and need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and

norms of the engineering practice.

9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

10. Communication: Communicate effectively on complex engineering activities with the

engineering community and with society at large, such as, being able to comprehend and

write effective reports and design documentation, make effective presentations, and give and

receive clear instructions.

11. Project management and finance: Demonstrate knowledge and

understanding of the engineering and management principles and apply these to one’s own

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

environments.

12. Life-long learning: Recognize the need for, and have the preparation and ability to

engage in independent and life-long learning in the broadest context of technological

change.

PROGRAM SPECIFIC OUTCOMES (PSOs)

1. Demonstrate understanding of the principles and working of the hardware and software aspects of Computer systems and manage storage of voluminous data.

2. Ability to apply mathematical methodologies to solve computation task. Model real world

problem using appropriate data structures and suitable algorithms and evolving trends in

computer communication. Ability to understand the structure and development methodologies

of software systems.

3. Possess professional skills and knowledge of software design process ethically, leadership

quality, learn cutting edge technologies to identify open research issues by critical thinking and

cooperative learning. Familiarity and practical competence with a broad range of programming

languages and open source platforms.

Scheme for M.Tech Computer Science & Engineering 2020-2021

Year : I Semester : I

Code No. Course Hours/Week Maximum Marks

Lecture Tutorial Practical Credits CIE SEE Total

SEMESTER I

THEORY

19PCS11

Advances in Database Management

Systems 4 2 0 5 50 50 100

19PCS12 Digital Image Processing 4 0 0 4 50 50 100

19PCS13 Research Methodology 4 0 0 5 50 50 100

19PCS14X Elective - I 4 0 0 4 50 50 100

19PCS15X Elective – II 4 0 0 4 50 50 100

PRACTICAL

19PCS16 Mobile Application Development Lab 0 0 3 2 50 50 100

19PCS17 Mini Project 0 0 3 2 50 50 100

Total 20 2 6 26 350 350 700

ELECTIVES – I

1. Internet of Things 19PCS141

2. Embedded Computing Systems 19PCS142

3. Service Oriented Architecture 19PCS143

ELECTIVES – II

1. Wireless Sensor Networks 19PCS151

2. Parallel Computing 19PCS152

3. Artificial Intelligence 19PCS153

Scheme for M.Tech. Computer Science & Engineering 2021-2022

Year : I Semester : II


Lecture Tutorial Practical Credits CIE SEE Total

SEMESTER II

THEORY

19PCS21 Cloud Computing 4 0 0 4 50 50 100

19PCS22 Managing Big – Data 4 2 0 5 50 50 100

19PCS23 Machine Learning 4 0 0 5 50 50 100

19PCS24X Elective – III 4 0 0 4 50 50 100

19PCS25X Elective - IV 4 0 0 4 50 50 100

PRACTICAL

19PCS26 Cloud Computing Lab 0 0 3 2 50 50 100

19PCS27 Mini Project 0 0 3 2 50 50 100

Total 20 2 6 26 350 350 700

Elective – III

1 Software Engineering &

Quality Assurance 19PCS241

2 Storage Area Network 19PCS242

3 Block Chain Technology 19PCN243

Elective - IV

1 Cyber Security 19PCS251

2 Soft Computing 19PCS252

3 Advanced Data

Structures & Algorithms 19PCS253


Year : II Semester : III


Year : II

Semester : IV

Sl.

No


Practical Credits CIE SEE Total

1 19PCS31 Industrial Internship 6 Weeks 06 50 50 100

2 19PCS32 Project Phase-I 07 50 50 100

3 19PCS33 Seminar 01 100 -- 100

4 19PCS34 Certificate Course 02

16 200 100 300

Sl.

No Code No. Course

Hours/Week Maximum Marks

Practical Credits CIE SEE Total

1 19PCS41

Project Phase-II

20 50 50 100

20 50 50 100

AUTONOMOUS SYLLABUS FOR M.Tech I SEMESTER 2021-2022

Course Title: ADVANCES IN DATABASE MANAGEMENT SYSTEMS

Subject Code : 19PCS11 Credit : 5 CIE: 50

Number of Lecture Hours/Week 4 h (Theory)+ 2 hrs (Tut) SEE: 50

Total Number of Lecture Hours 52 SEE Hours: 03

Prerequisites: Database Management Systems

Course Objectives:

Understand Relational data model and Object Oriented Database.

Gain Knowledge about parallel Database, Data mining and Data warehouse

Compare Advanced applications of the Database.

MODULES Teaching Hours

Module-I

Review of Relational Data Model and Relational Database Constraints:

Relational model concepts, Relational model constraints and relational

database schemas; Update operations, transactions and dealing with constraint

violations. Object and Object-Relational Databases: Overview of Object-

Oriented Concepts – Objects, Encapsulation, Type and class hierarchies.

10 Hrs

Module-II

Complex objects; Object model of ODMG, Object definition Language ODL;

Object Query Language OQL; Overview of C++ language binding; Conceptual

design of Object database. Overview of object relational features of SQL;

Object-relational features of Oracle; Implementation and related issues for

extended type systems, the nested relational model.

11 Hrs

Module-III

Enhanced Data Models for Some Advanced Applications: Active database

concepts and triggers; Temporal, Spatial, and Deductive Databases – Basic

concepts, Parallel and Distributed Databases: Architectures for parallel

databases; Parallel query evaluation; Parallelizing individual operations;

Parallel query optimizations.

10 Hrs

Module-IV

Introduction to distributed databases: Distributed DBMS architectures; Storing

data in a Distributed DBMS; Distributed catalog management; Distributed

Query processing; Updating distributed data; Distributed transactions;

Distributed Concurrency control and Recovery. Data Warehousing, Decision

Support and Data Mining: Introduction to decision support; OLAP,

multidimensional model; Window queries in SQL; Finding answers quickly;

Implementation techniques for OLAP; Data Warehousing; Views and Decision

support; View materialization; Maintaining materialized views.

11 hrs

Module-V

Introduction to Data Mining: Counting co-occurrences; Mining for rules;

Tree-structured rules; Clustering; Similarity search over sequences;

Incremental mining and data streams; Additional data mining tasks. More

Recent Applications: Mobile databases; Multimedia databases; Geographical

Information Systems; Genome data management.

10 Hrs

Question paper pattern:

The question paper will have ten questions.

There will be 2 questions from each module, covering all the topics from a module. The students will have to answer 5 full questions, selecting one full question from each module.

Text Books:

1. Elmasri and Navathe, Fundamentals of Database Systems, Pearson Education, 2007.

2. Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems, 3rd

Edition,

McGraw-Hill, 2003.

Reference Books:

1. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, Database System Concepts, 6th

Edition,

McGraw Hill, 2010.

2. Connolly and Begg, Database Systems, 4th

Edition, Pearson Publications, 2005.

Course outcomes: On completion of the course, the student will have the ability to:

Course

Code

CO # Course Outcome (CO)

19PCS11

CO1 Demonstrate Relational Data Model and Relational Database Constraints

CO2 Design ODBMS using Object Query Language

CO3 Develop Data models using active Database and Parallel and Distributed

Databases

CO4 Demonstrate Data ware housing concepts

CO5 Discuss Rules and Applications on Data mining

Course Title: DIGITIAL IMAGE PROCESSING


Number of Lecture Hours/Week 4 hrs (Theory) SEE: 50


Prerequisites: Concepts of Digital Signal Processing

Course Objectives:

To Study the Image fundamental and mathematical transformations necessary for

image processing

Understand the image enhancement techniques, image restoration and compression

techniques


Module – I

Digital image fundamentals: Introduction, Examples of fields that use Digital

image processing, Fundamental steps in Digital image Processing, Image

sensing and acquisition, A simple image formation model, Image Sampling &

Quantization, basic relationships between pixels.

10 Hrs

Module – II

Background on MATLAB and image processing toolbox: Background on

MATLAB and Image processing Toolbox, MATLAB working environment,

Digital image representation, Reading, displaying and writing images, data

classes, image types, converting between data classes and image types, array

indexing, some important standard arrays, introduction to M-function

programming.

10 Hrs

Module – III

Image Enhancement: Background, Some basic gray level transformations:

Image negatives, Log transformations, Power-law transformation, piecewise

linear transformation, Histogram processing: Histogram equalization and

matching, Local enhancement, Use of Histogram statistics, Basics of spatial

filtering: Smoothing linear filters, Order statistics filters, Sharpening Spatial

filters: Use of Second and First derivative for enhancement.

Image enhancement in frequency domain: Background, Introduction to

Fourier transform and the frequency domain: 1-D Fourier transform and its

inverse, 2-D DFT and its inverse Filtering in frequency domain, Smoothing and

sharpening frequency domain filters.

12 Hrs

Module – IV

Image Restoration and Compression: A model of the Image

degradation/Restoration Process, Noise models, Restoration in the presence of

noise only spatial filtering, Estimating the degradation function, inverse

filtering, Minimum mean square error (wiener) filtering, geometric

transformation.

Image compression: Fundamentals, Image compression models, error free

compression, lossy compression.

10 Hrs

Module - V

Image segmentation, Representation and Description: Detection of

10 Hrs

discontinuities, edge linking and boundary detection, Thresholding, Region

based segmentation.

Representation and description: Various schemes for representation,

boundary descriptors, and regional descriptors.




Text Books:

1. Rafael C., Gonzalez & Richard E. Woods, Digital Image Processing, Pearson Education, 2007.

2. Rafael C. Gonzalez, Richard E wood, Digital Image Processing Using MATLAB, Pearson

Education Publisher, 2007.

Reference books:

1. Anil K Jain, Fundamentals of Digital Image Processing, Pearson Education/Prentice-Hall of India

Pvt. Ltd., 1997. 2. B.Chanda, D Dutta Majumder, Digital 1mage Processing and Analysis, Prentice-Hall, India, 2002.


Course

Code


19PCS12

CO1 Review the fundamental concepts of Digital Image Processing System

CO2 Analyse Images in the Space and Frequency domain using various transformations.

CO3 Explain the techniques for Image enhancement in frequency domain.

CO4 Demonstrate various image restoration and compression techniques.

CO5 Interpret Image segmentation, representation and description techniques.

Course Title: RESEARCH METHODOLOGY


Number of Lecture Hours/Week 4 Hrs SEE: 50


Pre-requisite: Statistics , Operational Research

Course Objectives:

Give overview of RM and techniques in defining research problem

Conduct literature survey.

MODULES Teaching Hours Module – I

Meaning, Objectives and characteristics of Research - Research Methods Vs Methodology, Types of research – Descriptive Vs Analytical , Applied Vs Fundamental , Quantitative Vs Qualitative, Conceptual Vs Empirical – Research process - Criteria of Good Research- Developing a research plan.

10 Hrs

Module – II

Defining the Research Problem - Selecting the Problem, Necessity of Defining the Problem - Technique Involved in Defining a Problem - Importance of the literature review in defining a problem – Survey of literature – Primary and secondary sources – Review ,treatise, monographs patents – web as a source – searching the web – Identifying gap areas from literature review – Development of working hypothesis.

10 Hrs

Module – III

Research Design and Methods - Research Design – Basic Principles - Need for Research Design - Features of a Good Design - Important concepts relating to Research Design - Observation and Facts ,Laws and Theories, Predication and Explanation, Induction, Deduction, Development of Models – Developing a research plan – Exploration Description, Diagnosis and Experimentation – Determining experimental and sample designs.

10 Hrs

Module – IV

Sampling design – Steps in sampling design –characteristics of a good sample design – Types of sample designs – Measurement and Scaling techniques- Methods of data collected – Collection of primary data - Data Collection instrument.

11 hrs

Module- V

Testing of Hypotheses – Basic Concepts – Procedure for hypotheses testing flow diagram for hypotheses – Data analysis with statistical packages – Correlation and Regression – Important parametric test – Chi-square test – Analysis of variance and covariance.

11 Hrs




TEXT BOOKS/ REFERENCES:

1. Garg.B.L., Karadia, R., Agarwal,F. and Agarwal, U.K., 2002. An introduction to

Research Methodology, RBSA Publishers.

2. Kothari, C.R.(2008). Research Methodology: Methods and Techniques. Second Edition.

New Age International Publishers, New Delhi.

3. Anderson T. W, An introduction to multivariate statistical analysis, Wiley Eastern Pvt Ltd,

New Delhi.

4. Sinha, S.C. and Dhiman, A.K., 2002. Research Methodology, Ess Ess Publications. 2

volumes.

5. Trochim, W.M.K., 2005. Research Methods: the concise knowledge base, Atomic Dog

Publishing. 270 p.

6. Day RA (1992) How to write and publish a scientific paper. Cambridge University press.

London

7. Fink A, 2009, Conducting Research Literature Reviews: From the Internet to Paper, Sage

Publication.

8. Coley S M. and Scheinberg C A , -1990 Proposal Writing”, Sage Publication.

9. Intellectual Property rights in the global economy : Keith Eugene Maskus, Institute for

International Economics , Washington DC ,2000

10. Subbarau NR - Handbook on Intellectual Property Law and Practice – S Vishwanath Printers

and Publishing Private Limited , 1998.

Course outcomes:

On completion of the course, the student will have the ability to:

Course

Code


19PCS13

CO1 Explain key research concepts and identify and discuss the role and

importance of research in the social sciences.

CO2 Identify and discuss the issues and concept salient to the research process.

CO3 Identify and discuss the complex issues inherent in selecting a research

problem, selecting an appropriate research design and implementing a

research project.

CO4 Identify and discuss the concepts and procedures of sampling , data

collection, analysis and reporting.

CO5 Read , Comprehend and explain research articles in their academic

discipline.

Course Title: INTERNET OF THINGS




Pre-requisite: Computer Networking and Wireless Sensor Networks, Electronic circuits

Course Objectives:

Recognize basic issues, policy and challenges in the IoT

Illustrate Mechanism and Key Technologies in IoT

Clarify the Standard of the IoT , Resources in the IoT and Data analytics for IoT.

To gain knowledge of the Layer 1/2 and 3 connectivity of Mobile IPv6 technologies.

Identify research opportunities in IoT technology, applications and services.


Module I

Internet of Things overview Definitions And Frameworks: Motivations,

Examples of Applications, IPv6 Role, Areas of Development and

Standardization, Scope of the Present Investigation. IoT Definitions, General

Observation, ITU-T Views, Working Definition, IoT Frameworks, Basic Nodal

Capabilities. Internet Of Things Application Examples: Overview, Smart

Metering Advanced Metering Infrastructure , e-Health Body Area Networks ,

City Automation, Automotive Applications, Home Automation, Smart Cards,

Tracking (Following and Monitoring Mobile Objects), Over-The-Air-Passive

Surveillance Ring of Stee, Control Application Examples, Myriad Other

Applications.

10 Hrs

Module II

Fundamental IoT Mechanisms And Key Technologies: Identification of IoT

Objects and Services, Structural Aspects of the IoT, Environment

Characteristics, Traffic Characteristics, Scalability, Interoperability , Security

and Privacy, Open Architecture, Key IoT Technologies, Device Intelligence,

Communication Capabilities, Mobility Support, Device Power, Sensor

Technology, RFID Technology, Satellite Technology.

10 Hrs

Module III

Evolving IoT Standards: Overview and Approaches, IETF IPv6 Routing

Protocol for RPL Roll , Constrained Application Protocol (CoAP), Background,

Messaging Model, Request Response Model , Intermediaries and Caching,

Representational State Transfer (REST), ETSI M2M , Third-Generation

Partnership Project Service Requirements for Machine-Type Communications,

Approach , Architectural Reference Model for MTC, CENELEC , IETF IPv6

Over Low power WPAN (6LoWPAN), ZigBee IP (ZIP) , IP in Smart Objects

(IPSO).

10 Hrs

Module IV

Layer 1/2 Connectivity: Wireless Technologies For The Iot : WPAN

Technologies for IoTM2M , ZigbeeIEEE 802.17.4, Radio Frequency for

Consumer Electronics (RF4CE), Bluetooth and its Low-Energy Profile , IEEE

802.17.6 WBANs, IEEE 802.17 WPAN TG4j MBANs , ETSI TR 101 557 ,

NFC, Dedicated Short-Range Communications (DSRC) and Related Protocols,

Comparison of WPAN Technologies , Cellular and Mobile Network

Technologies for IoTM2M, overview and Motivations Universal Mobile

Telecommunications System, LTE.

11 hrs

Module V

Layer 3 Connectivity: Ipv6 Technologies For The IoT :Overview and

Motivations, Address Capabilities, IPv4 Addressing and Issues,IPv6 Address

Space, IPv6 Protocol Overview, IPv6 Tunneling, IPsec in IPv6, Header

Compression Schemes, Quality of Service in IPv6, Migration Strategies to

IPv6, Technical Approaches, Residential Broadband Services in an IPv6

Environment Deployment Opportunities.

Layer 3 Connectivity: Mobile Ipv6 Technologies For The IoT: Overview,

Protocol Details, Generic Mechanisms, New IPv6 Protocol, Message Types,

and Destination Option, Modifications to IPv6 Neighbor Discovery,

Requirements for Various IPv6 Nodes, Correspondent Node Operation, HA

Node Operation, Mobile Node Operation, Relationship to IPV4 Mobile IPv4 (MIP).

11 Hrs




Text books:

1. “Building the Internet of Things with IPv6 and MIPv6: The Evolving World of M2M

Communications” Author(s): Daniel Minoli.

Reference Books:

1. Designing the Internet of Things Adrian McEwen, Hakim Cassimally ISBN: 978-1-118-43062-0

November 2013, Wiley.

2. Charalampos Doukas , "Building Internet of Things with the Arduino", Create space, April 2002

http://postscapes.com/

3. “ Architecting the Internet of Things” Uckelmann, Dieter, Harrison, Mark, Michahelles, Florian

(Eds.) 2011, XXXI, springerEbooks.

Course outcomes:


Course

Code


19PCS141

CO1 Explain internet of Things technology and relate it ubiquitous computing.

CO2 Identify the Definitions of IoT, frameworks and examples of

applications.

CO3 Demonstrate the fundamentals of IoT mechanism, Key technologies and evolving IoT Standards

CO4 Illustrate the Layer 1/2 and 3 connectivity of wireless technologies

CO5 Compare IPv4 with IPv6 technologies and determine the Mobile IPv6

technologies for the IoT.

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Adrian%2BMcEwen

http://as.wiley.com/WileyCDA/Section/id-302477.html?query=Hakim%2BCassimally

http://postscapes.com/

Course Title: EMBEDDED COMPUTING SYSTEMS

Subject Code : 19PCS142 Credits : 4 CIE: 50

Number of Lecture Hours/Week 4 Hrs (Theory) SEE: 50


Prerequisites: Computer Organization & Microprocessor

Course objectives:

Gain Knowledge about the characteristics of Embedded Systems and Examine

vulnerabilities of Embedded computer system

Learn designing of Embedded systems


Module – I

Introduction to Embedded systems: Embedded systems, Processor

embedded into a system, embedded hardware units and device in a system,

Embedded software in a system, Examples of embedded systems, Design

process in embedded system, Formalization of system design, Design process

and design examples, Classification of embedded systems, skills required for

an embedded system designer.

10 Hrs

Module - II

Devices and communication buses for devices network :IO types and

example, Serial communication devices, Parallel device ports, Sophisticated

interfacing features in device ports, Wireless devices, Timer and counting

devices, Watchdog timer, Real time clock, Networked embedded systems,

Serial bus communication protocols, Parallel bus device protocols-parallel

communication internet using ISA, PCI, PCI-X and advanced buses, Internet

enabled systems network protocols, Wireless and mobile system protocols.

11 Hrs

Module - III

Device drivers and interrupts and service mechanism: Programming-I/O

busy-wait approach without interrupt service mechanism, ISR concept,

Interrupt sources, Interrupt servicing (Handling) Mechanism, Multiple

interrupts, Context and the periods for context switching, interrupt latency and

deadline, Classification of processors interrupt service mechanism from

Context-saving angle, Direct memory access, Device driver programming.

10 Hrs

Module – IV

Interprocesses communication and synchronization of processes, Threads

and tasks: Multiple process in an application, Multiple threads in an

application, Tasks, Task states, Task and Data, Clear-cut distinction between

functions. ISRS and tasks by their characteristics, concept and semaphores,

Shared data, Inter-process communication, Signal function, Semaphore

functions, Message Queue functions, Mailbox functions, Pipe functions, Socket functions, RPC functions.

10 Hrs

Module – V

Real-time operating systems: OS Services, Process management, Timer

functions, Event functions, Memory management, Device, file and IO

subsystems management, Interrupt routines in RTOS environment and

handling of interrupt source calls, Real-time operating systems, Basic design

using an RTOS, RTOS task scheduling models, interrupt latency and response

11 Hrs

of the tasks as performance metrics, OS security issues.

Introduction to embedded software development process and tools, Host

and target machines, Linking and location software.




Text Books:

1. Raj Kamal, “Embedded Systems: Architecture, Programming, and Design” 2nd edition, Tata

McGraw hill-2013.

References:

1. Marilyn Wolf ,“Computer as Components, Principles of Embedded Computing System Design”

3rd edition ,Elsevier-2014 .


Course

Code


19PCS142

CO1 Distinguish the characteristics of embedded computer systems.

CO2 Examine the various vulnerabilities of embedded computer systems.

CO3 Design an embedded system.

CO4 Design and develop modules using RTOS.

CO5 Illustrate RPC, threads and tasks

Course Title: SERVICE ORIENTED ARCHITECTURE




Prerequisites: Java , XML and Web designing

Course objectives:

To understand the basic concepts of SOA.

To learn Web services and messaging with SOA.

To learn about Service oriented Analysis and Design .NET, Java and WS Specification

Standards.


Module – I

INTRODUCTION Roots of SOA , Characteristics of SOA , Comparing SOA

to client-server and distributed internet architectures , Anatomy of SOA, How

components in an SOA interrelate , Principles of service orientation

10 Hrs

Module - II

SERVICE ORIENTED ARCHITECTURE IN WEB SERVICES : Web

services , Service descriptions , Messaging with SOAP , Message exchange

Patterns , Coordination , Atomic Transactions , Business activities , 43

Orchestration , Choreography , Service layer abstraction, Application Service

Layer , Business Service Layer , Orchestration Service Layer

11 Hrs

Module - III

BUILDING SOA :Service oriented analysis , Business-centric SOA , Deriving

business services, service modeling , Service Oriented Design , WSDL basics ,

SOAP basics , SOA composition guidelines , Entity-centric business service

design , Application service design , Task centric business service design.

10 Hrs

Module – IV

SOA PLATFORMS : SOA platform basics , SOA support in J2EE :Java API

for XML-based web services (JAX-WS) , Java architecture for XML binding

(JAXB) , Java API for XML Registries (JAXR) , Java API for XML based

RPC (JAX-RPC), Web Services Interoperability Technologies (WSIT) , SOA

support in .NET: Common Language Runtime ,ASP.NET web forms ,

ASP.NET web services , Web Services Enhancements (WSE).

10 Hrs

Module – V

SOA DESIGN Web Service, BPEL-process, elements, functions , Web

Service Coordination overview elements, web service business activity &

atomic transaction coordination type , Business process design Web Service ,

Choreography, Web Service, Policy-elements , Web Service Security, XML –

Signature element

11 Hrs




Textbook:

1. Thomas Erl, “Service-Oriented Architecture: Concepts, Technology, and Design”,Pearson

Education, 2009.

REFERENCE BOOK

1. Newcomer, Lomow, “Understanding SOA with Web Services”, Pearson Education, 2005.

2. Sandeep Chatterjee, James Webber, “Developing Enterprise Web Services, An Architect’s

Guide”, Pearson Education, 2005


Course

Code


19PCS143

CO1 Gain the knowledge of basic concepts of SOA.

CO2 Explain advanced concepts of service composition, orchestration,

choreography and Web Service frame-work CO3 Discuss various Service Oriented analysis techniques and service design.

CO4 Experiment on creation of SOA Web Service using various technologies

CO5 Describe Open Standards available for developing SOA compliant Web

Services

Course Title: WIRELESS SENSOR NETWORKS




Prerequisite: Data Communication, Computer Networks

Course Objectives

• Comprehend sensor networks for various application setups.

• Review coverage and conduct node deployment planning.

• Devise appropriate data dissemination protocols and model links cost.

• Determine suitable medium access protocols and radio hardware.


Module –I

Introduction, Overview and Applications of Wireless Sensor Networks

Introduction, Basic overview of the Technology, Applications of Wireless

Sensor Networks: Introduction, Background, Range of Applications, Examples

of Category 2 WSN Applications, Examples of Category 1 WSN Applications,

Another Taxonomy of WSN Technology.

10 Hrs

Module –II

Basic Wireless Sensor Technology and Systems: Introduction, Sensor Node

Technology, Sensor Taxonomy, WN Operating Environment, WN Trends,

Wireless Transmission Technology and Systems: Introduction, Radio

Technology Primer, Available Wireless Technologies

10 Hrs

Module -III

MAC and Routing Protocols for Wireless Sensor Networks: Introduction,

Background, Fundamentals of MAC Protocols, MAC Protocols for WSNs,

Sensor-MAC case Study, IEEE 802.15.4 LR-WPANs Standard Case Study.

Routing Protocols for Wireless Sensor Networks: Introduction, Background,

Data Dissemination and Gathering, Routing Challenges and Design Issues in

WSNs, Routing Strategies in WSNs.

11 Hrs

Module -IV

Transport Control and Middleware for Wireless Sensor Networks: Traditional

Transport Control Protocols, Transport Protocol Design Issues, Examples of

Existing Transport Control Protocols, Performance of Transport Control

Protocols. Middleware for Wireless Sensor Networks: Introduction, WSN

Middleware Principles, Middleware Architecture, Existing Middleware.

10 Hrs

Module -V

Network Management and Operating System for Wireless Sensor Networks:

Introduction, Network Management Requirements, Traditional Network

Management Models, Network Management Design Issues. Operating Systems

for Wireless Sensor Networks: Introduction, Operating System Design Issues,

Examples of Operating Systems.

11 Hrs




Text book:

1. Kazem Sohraby, Daniel Minoli, Taieb Znati, “Wireless Sensor Networks: Technology, Protocols

and Applications:, WILEY , Second Edition (Indian) , 2014.

Reference Books:

1. Ananthram Swami et. el, Wireless Sensor Networks, Signal Processing and Communication

Perspectives, John Wiley, 2007.

2. Feng Zhao, Leonidas Guibas, Wireless Sensor Networks – An Information Processing Approach,

Elsevier, 2004


Course

Code


19PCS151

CO1 Explain existing applications and categories of wireless sensor actuator

networks.

CO2 Apply in the context of wireless sensor networks and explain elements of

distributed computing and network protocol design

CO3 Contrast various hardware, software platforms and transport protocols that

exist for sensor networks

CO4 Summarize various network level protocols for MAC, routing, time

synchronization, aggregation, consensus and distributed tracking

CO5 Describe the knowledge of Network management and Operating system for

wireless sensor network.

Course Title : PARALLEL COMPUTING




Prerequisite :

Knowledge of the C and/or C++ programming language and the corresponding Linux development

environment including editors, compilers, debuggers, and some profiling tools.

Knowledge of computer architecture.

Course objectives:

Increase Graphics Processing Unit (GPU) awareness and know GPU computing platforms.

Become familiar with massively parallel computing hardware and programming models, with specific

Compute Unified Device Architecture (CUDA) architecture.

Become familiar with contemporary parallel programming paradigms and the systems on which they

are used.


Module-I

Introduction and History :GPUs as Parallel Computers; Architecture of a

Modem GPU; Parallel Programming Languages and Models; Overarching

Goals; Evolution of Graphics Pipelines; The Era of Fixed- Function; Graphics

Pipelines; Evolution of Programmable Real-Time Graphics; Unified Graphics

and Computing Processors; GPGPU; An Intermediate Step; GPU Computing;

Scalable GPUs Recent Developments; Future Trends.

11 Hrs

Module-II

CUDA Memories, Performance Considerations and Floating Point

Considerations: Importance of Memory Access Efficiency; CUDA Device

Memory Types; A Strategy for Reducing Global Memory Traffic; Memory as a

Limiting Factor to Parallelism; Global Memory Bandwidth; Dynamic

Partitioning of SM Resources; Data Prefetching; Instruction Mix; Thread

Granularity; Measured Performance; More on thread execution, Global

memory bandwidth, dynamic partitioning of SM resources, Floating point

format, Arithmetic Accuracy and rounding.

11 Hrs

Module-III

Floating Point Considerations: Floating-Point Format, Normalized

Representation of M, Excess Encoding of E, Representable Numbers, Special

Bit Patterns and Precision, Arithmetic Accuracy and Rounding, Algorithm

Considerations.

10 Hrs

Module-IV

Introduction to OPENCL :Introduction to OPENCL; Background; Data

Parallelism Model; Device Architecture; Kernel Functions; Device Management

and Kernel Launch; Electrostatic Potential Map in OpenCL

10 Hrs

Parallel Programming and Computational Thinking: Goals of Parallel

Programming, Problem Decomposition, Algorithm Selection, Computational

Thinking.

Module –V

Introduction to Embedded GPU Computing : Architecture, Programming

Model, Programs, Configuration etc.

Case Study /Projects: Concepts of Game Design, Applications like Matrix

multiplication, MRI reconstruction Molecular Visualization and Gaming.

10 Hrs




Text Books:

1. “Programming Massively Parallel Processors: A Hands on Approach”; David B. Kirk, Wen-mei W. Hwu; Morgan Kaufmann /Elsevier India reprint 2010

Reference Books:

1. Heterogeneous Computing with OpenCL, by Benedict R. Gaster, Lee Howes, David R. Kaeli,

Perhaad Mistry & Dana Schaa; Morgan Kaufmann 2011


Course

Code


19PCS152

CO1 Explain GPU architecture and its programming.

CO2 Illustrate programs on GPUs, debugging and profiling parallel programs.

CO3 Discuss performance and floating point consideration of CUDA memories.

CO4 Design data parallelism model using OPENCL.

CO5 Develop parallel computing application like game design, matrix multiplication

etc.

Course Title: ARTIFICIAL INTELLIGENCE

Subject Code: 19PCS153 Credits: 4 CIE: 50



Prerequisite: Working on Discrete mathematics, Programming ability and exposure to probability.

Course Objectives :

To have an understanding of the basic issues of knowledge representation and heuristic

search, as well as an understanding of other topics such as minimax, resolution, etc. that play

an important role in AI programs.

To have a basic understanding of advanced topics of AI such as learning, natural language

processing, agents and robotics, expert systems, and planning.


Module I

What is Artificial Intelligence: The AI Problems, The Underlying

assumption, What is an AI Technique? The Level of the model, Criteria for

success, some general references, One final word and beyond. Problems,

problem spaces, and search: Defining, the problem as a state space search,

Production systems, Problem characteristics, Production system

characteristics, Issues in the design of search programs, Additional Problems.

Intelligent Agents: Agents and Environments, The nature of environments,

The structure of agents.

10 Hrs

Module II

Heuristic search techniques: Generate-and-test, Hill climbing, Best-first

search, Problem reduction, Constraint satisfaction, Mean-ends analysis.

Knowledge representation issues: Representations and mappings,

Approaches to knowledge representation, Issues in knowledge representation,

The frame problem.

Using predicate logic: Representing simple facts in logic, representing

instance and ISA relationships, Computable functions and predicates,

Resolution, Natural Deduction.

Logical Agents: Knowledge –based agents, the Wumpus world, Logic-

Propositional logic, Propositional theorem proving, Effective propositional

model checking, Agents based on propositional logic.

11 Hrs

Module III

Symbolic Reasoning Under Uncertainty: Introduction to nonmonotonic

reasoning, Logic for nonmonotonic reasoning, Implementation Issues,

Augmenting a problem-solver, Implementation: Depth-first search,

Implementation: Breadth-first search.

Statistical Reasoning: Probability and bayes Theorem, Certainty factors and

rule-based systems, Bayesian Networks, Dempster-Shafer Theory, Fuzzy

logic.

Quantifying Uncertainty: Acting under uncertainty, Basic probability

notation, Inference using full joint distributions, Independence, Bayes’ rule

and its use, The Wumpus world revisited

11 Hrs

Module IV Weak Slot-and-filter structures: Semantic Nets, Frames.

10 Hrs

Strong slot-and –filler structures: Conceptual dependency, scripts, CYC.

Adversarial Search: Games, Optimal Decision in Games, Alpha-Beta

Pruning, Imperfect Real-Time Decisions, Stochastic Games, Partially

Observable Games, State-Of-The-Art Game Programs, Alternative Approaches, Summary.

Module V

Learning From examples: Forms of learning, Supervised learning, Learning

decision trees, Evaluating and choosing the best hypothesis, The theory of

learning ,PAC, Regression and Classification with linear models,

Nonparametric models, Support vector machines, Ensemble learning.

Learning Probabilistic Models: Statistical learning, learning with complete

data, learning with hidden variables: The EM algorithm.

10 Hrs




Text Books.

1. Elaine Rich,Kevin Knight, Shivashanka B Nair: Artificial Intelligence, Tata CGraw Hill 3rd

edition. 2013

2. Stuart Russel, Peter Norvig: Artificial Intelligence A Modern Approach, Pearson 3rd edition 2013.

REFERENCE BOOKS:

1.Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, ISBN-13: 9780934613101

Course

Code


19PCS153

CO1 Differentiate optimal reasoning Vs human like reasoning and Apply AI

search Models

CO2 Discuss of state space representation, exhaustive search, heuristic search

along with the time and space complexities CO3 Explain Logic for representing Knowledge and Reasoning of AI systems.

CO4 Design different learning algorithms for improving the performance of AI

systems.

CO5 Apply techniques of AI in speech recognition, Expert Systems, Machine Learning and Natural Language Processing and Robotics.

Course Title: MOBILE APPLICATION DEVELOPMENT LABORATORY


Number of Practical Hours/Week/batch 3 Hrs SEE: 50

SEE Hours: 03

Pre-requisite: Programming language – C++, Java

Course Objectives:

Write simple GUI applications, use built-in widgets and components, work with the database

to store data locally

Design, develop, and deploy mobile apps on Android devices

List of Programs

1. Create an application that takes the name from a text box and shows hello

message along with the name entered in text box, when the user clicks the

OK button.

2. Create an application that takes the text from the text box and shows using

Toast.

3. Create an Android application to implement simple arithmetic calculator.

Use Buttons for each arithmetic operation.

4. Create a screen with linear layout that has input boxes for User Name,

Password, Address, Gender (radio buttons), Age (numeric), State (Spinner)

and a Submit button. On clicking the submit button, print all the data as

Toast.

5. Create an application to show details of students on selecting a name from

student list with help native database – SQLite.

6. Create a user registration application that stores the user details in a database

table using native database-SQLite.

7. Develop an application that uses a menu with 3 options for dialing a

number, opening a website and to send an SMS. On selecting an option, the

appropriate action should be invoked using intents.

8. Develop an application for handling notifications.

9. Develop an application that shows all contacts of the phone along with

details like name, phone number, mobile number etc. Create an application

that saves user information like name, age, gender etc. in shared preference

and retrieves them when the program restarts.

10. Create an alarm that rings every Sunday at 8:00 AM. Modify it to use a

time picker to set alarm time.

11. Develop an application that shows the current location’s latitude and

longitude continuously as the device is moving (tracking).

12. Create an application that shows the current location on Google maps.

Note: Every exercise should be demonstrated on android handset.


For SEE similar question related to the above programs will be asked.


Course

Code


19PCS16

CO1 Demonstrate understanding of android application development concepts

CO2 Exhibit expertise and proficiency in using android services and

mechanisms for developing android applications.

CO3 Exhibit the skills of writing programs related to Android programming in

order to generate and deploy the application on android handsets

following the ethical standards.

CO4 Demonstrate the skill of oral communication to present his /her views on

Android programming concepts.

CO5 Prepare report covering details about various APIs used in the

Experiments.

24

Course Title: MINI PROJECT



SEE Hours: 03

Pre-requisite: Programming languages, Tools and Operating Systems

Course Objectives:

Understand formulation of problem

Design and develop simple applications using any domain of Computer Science

Understand the procedure of documentation and presentation of Mini-project.

Guidelines for Mini project:

Student has to carry out literature survey to identify and formulate the

problem

Student has to design and develop a H/W or S/W model using any

domains of Computer Science.

Timely evaluation of the mini project will be conducted by concerned guide

and reviewer for CIE assessment.

At the end of the semester students has to prepare and submit a project

report.


Course Code


19PCS17

CO1 Demonstrate skill to identify and formulate the given problems.

CO2 Apply basic engineering knowledge learnt in developing

system

CO3 Evaluate current research status by conducting literature

survey.

CO4 Design and develop real time applications

CO5 Apply the programming language for Software Development

Life Cycle model for the implementation of the Mini-project

and prepare well organized report.

25

II SEMESTER

Course Title: CLOUD COMPUTING




Prerequisites: Networking, Distributed system and Database management system.

Course Objectives:

Understand cloud computing, infrastructure, services.

Learn Virtualization and scheduling techniques.


Module –I

Introduction: Cloud Computing Overview, Applications, Internets and the

cloud, First movers in the cloud, Your organization and cloud Computing,:

When you can Use Cloud Computing, Benefits, Network- centric computing

and network centric content, peer-peer systems, Cloud computing an old idea

whose time has come , Cloud computing delivery models and services, Ethical

issues, Cloud vulnerabilities, Major challenges faced by cloud computing.

Cloud Infrastructure: Cloud computing at Amazon, Cloud computing the

Google perspective Microsoft Windows Azure and online services, Open-

source software platforms for private clouds, Cloud storage diversity and

vendor lock-in, Energy use and ecological impact, Service level agreements,

User experience and software licensing. Exercises and problems.

11 Hrs

Module-II

Cloud Computing: Application Paradigms: Challenges of cloud computing,

Architectural styles of cloud computing, Workflows: Coordination of multiple

activities, Coordination based on a state machine model: The Zookeeper.

The Map Reduce programming model, A case study: The GrepTheWeb

application, Cloud for science and engineering, High-performance computing

on a cloud, Cloud computing for Biology research.

10 Hrs

Module-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, paravirtualization, Optimization

of network virtualization, vBlades,

10 Hrs

Module- IV

Cloud Resource Management and Scheduling : Policies and mechanisms for

resource management, Application 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,

10 hrs

26

Resourcing bundling Combinatorial auctions for cloud resources.

Module – V

Scheduling algorithms for computing clouds: Fair queuing, Start-time fair

queuing, Borrowed virtual time, Cloud scheduling subject to deadlines,

Scheduling Map Reduce applications subject to deadlines, Resource

management and dynamic scaling, Exercises and problems.

Cloud Security: Cloud security risks, Security: The top concern for cloud

users, Privacy and privacy impact assessment, Trust, Operating system security,

Virtual machine Security, Security of virtualization.

11 Hrs



There will be 2 questions from each module, covering all the topics from a module.

The students will have to answer 5 full questions, selecting one full question from each module.

Text books:

1. Antony T Velte, “Cloud Computing : A Practical Approach”, McGrawHill.

2. Dan C Marinescu: “Cloud Computing Theory and Practice”, Elsevier(MK) 2013.

Reference Books:

1. Larry L. Peterson and Bruce S Davie: Computer Networks: A Systems Approach, Fifth Edition, Elsevier, 2011.

2. Tanenbaum: Computer Networks, 4th

Ed, Pearson Education/PHI, 2003.

3. William Stallings: Data and Computer Communications, 8th

Edition, Pearson Education, 2012.

Course outcomes:


Course

Code


19PCS21

CO1 Describe Cloud delivery Models, Cloud Infrastructure.

CO2 Discuss the key dimensions and challenges of Cloud Computing and

Architecture and Workflow.

CO3 Demonstrate Scheduling and Resource management in Cloud.

CO4 Explain Network Support and Storage Systems in Cloud

CO5 Illustrate Security in Cloud and Design Cloud Applications

27

Course Title: MANAGING BIG- DATA


Number of Lecture Hours/Week 4 Hrs(Theory) + 2 Hrs (Tut) SEE: 50


Pre-requisite: Database Management system and Distributed System

Course Objectives:

To understand basic of Big-data and machine language

Ability to use NoSQL tools for Big-data applications.


Module –I

UNDERSTANDING BIG DATA : Types of digital data –classification of data,

introduction to big data –characteristics of big data , evolution of big data,

definition of big data ,challenges with big data what is big data – why big data,

traditional business intelligence (BI) versus big data, what is new today. big data

analytics – where do we begin? ,what is big data analytics, what is big data

analytics isn’t ,classification of big data analytics ,greatest challenges that prevent

business from capitalization on big data, top challenges facing big data why big

data analytics important, data sciences, data scientist, terminologies, used in big

data.

10 Hrs

Module-II

NOSQL DATABASE: Introduction to NoSQL types of NoSQL Databases, why

NoSQL, advantages of NoSQL and its use of NoSQL in industry SQL verus

NoSQL ,nes SQL ,comparison of SQL , NoSQL,newSQL.

Introduction to MongoDB, why and what MongoDB ,RDBMS and

MongoDB,data types in MongoDB, MongoDB query language.

10 Hrs

Module-III

BASICS OF HADOOP: Data format – analyzing data with Hadoop – scaling out

– Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system

(HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data

integrity – compression – serialization Avro – file-based data structures

10 Hrs

Module- IV

MAPREDUCE APPLICATIONS : Map Reduce workflows - anatomy of

MapReduce job run – classic Map-reduce – YARN – failures in classic Map-

reduce and YARN – job scheduling – shuffle and sort – task execution –

MapReduce types – input formats – output formats .

10 hrs

Module – V

HADOOP RELATED TOOLS: Hbase – data model and implementations –

Hbase clients – Hbase examples – praxis. Hadoop integration. Pig – Grunt – pig

data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data

types and file formats – HiveQL data definition – HiveQL data manipulation –

HiveQL queries.

12 Hrs

28





Textbooks:

1. Seema Acharaya and Subhashini Chellappan “Big Data and analytics”,Wiley2017

2. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.

Reference:

1. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot

Persistence", Addison-Wesley Professional, 2012.

2. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.

3. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

4. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

5. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.

6. Alan Gates, "Programming Pig", O'Reilley, 2011.

Course outcomes:


Course

Code


19PCS22

CO1 Describe big data and use cases from selected business domains

CO2 Explain NoSQL big data management.

CO3 Install, configure, and run Hadoop and HDFS

CO4 Perform map-reduce analytics using Hadoop.

CO5 Discuss related Hadoop tools such as Hbase , Pig and Hive,

29

Course Title: MACHINE LEARNING

Subject Code : 19PCS23 Credit :5 CIE: 50



Pre-requisite: Mathematical Foundation of Computer Networks, Analysis and Design of Algorithm

Course Objectives:

Understand types , concepts of learning Design a system using various types of learning.


Module –I

Introduction, Concept Learning And Decision Trees : Learning Problems –

Designing Learning systems, Perspectives and Issues – Concept Learning –

Version Spaces and Candidate Elimination Algorithm – Inductive bias –

Decision Tree learning – Representation – Algorithm – Heuristic Space Search.

10 Hrs

Module-II

Neural Networks And Genetic Algorithms: Neural Network Representation –

Problems – Perceptrons – Multilayer Networks and Back Propagation

Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space

Search – Genetic Programming – Models of Evolution and Learning.

10 Hrs

Module-III

Bayesian And Computational Learning: Bayes Theorem – Concept Learning

– Maximum Likelihood – Minimum Description Length Principle – Bayes

Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian

Belief Network – EM Algorithm – Probably Learning – Sample Complexity for

Finite and Infinite Hypothesis Spaces – Mistake Bound Model.

11 Hrs

Module- IV

Instant Based Learning And Learning Set Of Rules: K- Nearest Neighbor

Learning – Locally Weighted Regression – Radial Basis Functions – Case-

Based Reasoning – Sequential Covering

Algorithms – Learning Rule Sets – Learning First Order Rules – Learning Sets

of First Order Rules – Induction as Inverted Deduction – Inverting Resolution.

11 hrs

Module – V

Analytical Learning And Reinforced Learning: Perfect Domain Theories –

Explanation Based Learning – Inductive-Analytical Approaches - FOCL

Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal

Difference Learning.

10 Hrs





30

Text Books:

1. Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (INDIAN EDITION), 2013.

Reference Books:

1. Ethem Alpaydin, “Introduction to Machine Learning”, 2nd Ed., PHI Learning Pvt. Ltd., 2013.

2. T. Hastie, R. Tibshirani, J. H. Friedman, “The Elements of Statistical Learning”, Springer;

1st edition, 2001.

Course outcomes:


Course

Code


19PCS23

CO1 Choose the learning techniques with this basic knowledge.

CO2 Apply effectively neural networks and genetic algorithms for designing an

application.

CO3 Illustrate bayesian techniques and derive effectively learning rules.

CO4 Describe instant based learning and rule sets.

CO5 Choose and differentiate reinforcement and analytical learning techniques

31

Course Title: SOFTWARE ENGINEERING AND QUALITY ASSURANCE


Number of Lecture Hours/Week 4 Hrs(Theory) SEE: 50


Pre-requisite: Software Engineering and Testing.

Course Objectives:

To understand software development, management , deployment of software

To demonstrate software testing methods

Discuss quality assurance and management of software product. MODULES Teaching Hours

Module –I

Software Processes and Project Management & S/W requirement: Software

Process Models, Process Iteration, Process Activities, The Rational Unified Process,

Computer Aided Software Engineering. Project Management: Management

Activities, Project Planning, Project Scheduling, Risk Management.

Requirements Engineering: Software Requirements: Functional and Non-functional

requirements, User Requirements, System Requirements, Interface specification, The

software requirement Document. Requirements Engineering Processes: Feasibility

studies, Requirements elicitation and analysis, Requirements validation,

Requirements management.

10 Hrs

Module-II

Design Engineering & requirement Engg process :Architectural Design –

Architectural Design Decisions, System Organization, Modular decomposition styles,

Control Styles. Object-oriented design : Object and Object classes, An Object-

oriented design process, Design Evolution. Real-Time Software design : System

design, Real-time operating systems, Monitoring and control systems, Data

acquisition systems.

10 Hrs

Module-III

Software Development and Management: Rapid Software Development- Agile

methods, Extreme Programming, Rapid Application Development, Software

Prototyping. Computer Based software engineering: Components and Component

models, The CBSE process, Component composition. Managing People : Selecting

Staff, Motivating People, Managing groups, The people Capability Maturity Model.

Software cost estimation: Software productivity, Estimation techniques, Algorithmic

Cost modeling, Project duration and staffing.

10 Hrs

Module- IV

Testing Strategies: A strategic approach to Software Testing, Strategic Issues, Unit

Testing, Integration Testing, Validation Testing, System Testing, The art of

Debugging.

Testing Techniques: Software testing fundamentals, Test case Design, White Box

testing, Basis Path testing, Control Structure Testing, Black Box Testing, Testing for

11 hrs

32

Specialized Environments, Architectures, and Applications.

Module – V

Product Metrics: Software Quality, A framework for Product Metrics, Metrics for

Analysis Model, Metrics for the Design Model, Metrics for Source Code, Metrics for

Testing, Metrics for Maintenance.

Quality Management: Quality Concepts, Software Quality Assurance, Software

Reviews, Formal Technical Reviews, Formal approaches to SQA, Statistical

Software Quality Assurance, Software Reliability, The ISO 9000 Quality Standards,

The SQA Plan.

11 Hrs





Text books:

1. Software Engineering, Sommerville, Eighth Edition, Pearson Education, 2009

2. Software Engineering: A Practitioner’s Approach, Roger S Pressman, Tata McGraw-Hill Sixth

edition, 2005.

Reference Books:

1. Richard Fairley, “Software Engineering Concepts” –, Tata Mcgraw Hill, 2008.

2. Ian Sommerville, “Software Engineering”, Seventh Edition, Pearson Education Asia, 2007.

3. Gopalaswamy Ramesh, Ramesh Bhattiprolu, “Software Maintenance” Tata Mcgraw Hill,2003.

4. Shari Lawarence Pfleeger, Joanne M.Atlee “Software Engineering Theory and Practice”, Third

Edition, Pearson Education, 2006.

5. Alistair Cockburn, "Agile Software Development”, First Edition, Pearson Education Asia, 2001.

6. Hans Van Vliet “Software Engineering: Principles and Practices” –, Wiley; 3 edition, 2008.

Course outcomes:


Course

Code


19PCS241

CO1 Select and implementation of different software development process models.

Extract and analyze software requirements specifications for real time

problems.

CO2 Develop some basic level of software architecture/design and Defining the

basic concepts and importance of Software project management concepts like

cost estimation, scheduling and reviewing the progress

CO3 Apply different testing and debugging techniques and analyzing their

effectiveness.

CO4 Describe the Knowledge of software risks and risk management strategies

CO5 Demonstrate software quality measurement metrics.

33

Course Title: STORAGE AREA NETWORK




Prerequisites: Operating System and Computer Networks.

Course Objectives:

Understand fundamentals of storage centric and server centric systems.

Learn metrics used for designing storage area networks, RAID concepts.

Describe backup and remote mirroring concepts


Module –I

Introduction: Server Centric IT Architecture and its Limitations; Storage –

Centric IT Architecture and its advantages. Case study: Replacing a server with

Storage Networks The Data Storage and Data Access problem; The Battle for

size and access. Intelligent Disk Subsystems: Architecture of Intelligent Disk

Subsystems; Hard disks and Internal I/O Channels; JBOD, Storage

virtualization using RAID and different RAID levels; Caching: Acceleration of

Hard Disk Access; Intelligent disk subsystems, Availability of disk subsystems.

11 Hrs

Module-II

I/O Techniques: The Physical I/O path from the CPU to the Storage System;

SCSI; Fibre Channel Protocol Stack; Fibre Channel SAN; IP Storage. Network

Attached Storage: The NAS Architecture, The NAS hardware Architecture,

The NAS Software Architecture, Network connectivity, NAS as a storage

system. File System and NAS: Local File Systems; Network file Systems and

file servers; Shared Disk file systems; Comparison of fibre Channel and NAS.

10 Hrs

Module-III

Storage Virtualization: Definition of Storage virtualization; Implementation

Considerations; Storage virtualization on Block or file level; Storage

virtualization on various levels of the storage Network; Symmetric and

Asymmetric storage virtualization in the Network.

10 Hrs

Module- IV

SAN Architecture and Hardware devices: Overview, Creating a Network for

storage; SAN Hardware devices; The fibre channel switch; Host Bus Adaptors;

Putting the storage in SAN; Fabric operation from a Hardware perspective.

Software Components of SAN: The switch’s Operating system; Device

Drivers; Supporting the switch’s components; Configuration options for SANs.

11 Hrs

Module – V

Management of Storage Network: System Management, Requirement of

management System, Support by Management System, Management Interface,

Standardized Mechanisms, Property Mechanisms, In-band Management, Use of

SNMP, CIM and WBEM, Storage Management Initiative Specification (SMI-

10 Hrs

34

S), CMIP and DMI, Optional Aspects of the Management of Storage Networks,

Summary.





Text books:

1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley India,

2013.

Reference Books:

1. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2011.

2. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,

Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.

3. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to

Understanding and Implementing SANs”, Wiley India, 2006.

Course outcomes:


Course

Code


19PCS242

CO1 Learn sever storage-centric architecture and types of intelligent

subsystems and their usage.

CO2 Describe I/O techniques, NAS and File system.

CO3 Demonstrate storage virtualization on various levels of the storage

networks.

CO4 Illustrate Hardware and software components of storage Area network.

CO5 Explain various mechanism of managing SAN.

35

Course Title: BLOCKCHAIN TECHNOLOGY

Subject Code 19PCS243 Credit : 4 CIE: 50



Pre-requisite: Data structures, Distributed Systems, Cryptography

Course Objectives:

To understand Blockchain and its applications

To be able to explain the different components involved within Blockchain

To know when and why you may want to use Blockchain within your environment

To master at a high level cryptocurrency is.


Module-I

Introduction: Basic Cryptographic primitives used in Blockchain – Secure,

Collison-resistant hash functions, digital signature, public key cryptosystems,

zero-knowledge proof systems. Need for Distributed Record Keeping,

Modelling faults and adversaries, Byzantine Generals problem, Consensus

algorithms and their scalability problems, Why Nakamoto Came up with Block

chain based cryptocurrency?

11 Hrs

Module-II

Technologies Borrowed in Blockchain – hash pointers, Consensus, Byzantine

Models of fault tolerance, digital cash etc.Bitcoin blockchain - Wallet - Blocks

- Merkley Tree - hardness of mining - transaction verifiability - anonymity -

forks - double spending - mathematical analysis of properties of Bitcoin.

Bitcoin, the challenges, and solutions 20082020 / 18

10 Hrs

Module-III

Abstract Models for blockchain - GARAY model - RLA Model - Proof of Work

(PoW) as random oracle - formal treatment of consistency, liveness and fairness

- Proof of Stake (PoS) based Chains - Hybrid models (PoW + PoS).Bitcoin

scripting language and their use

10 Hrs

Module- IV

Ethereum - Ethereum Virtual Machine (EVM) - Wallets for Ethereum - Solidity

- Smart Contracts - The Turing Completeness of Smart Contract Languages and

verification challenges, Using smart contracts to enforce legal contracts,

10 hrs

36

comparing Bitcoin scripting vs. Ethereum Smart Contracts. Some attacks on

smart contracts.

Module – V

Hyperledger fabric, the plug and play platform and mechanisms in

permissioned blockchain. Beyond Cryptocurrency – applications of blockchain

in cyber security, integrity of information, E-Governance and other contract

enforcement mechanisms. Limitations of blockchain as a technology, and

myths vs. reality of blockchain technology

11 Hrs





Text books:

1 Blockchain Technology: Cryptocurrency and Applications S. Shukla, M. Dhawan, S. Sharma, S.

Venkatesan Oxford University Press 2019

2 Bitcoin and cryptocurrency technologies: a comprehensive introduction Arvind Narayanan et. Al.

Princeton University Press 2016

Reference Books:

1 Research perspectives and challenges for Bitcoin and cryptocurrency Joseph Bonneau et al, SoK

IEEE Symposium on security and Privacy 2015

2 The bitcoin backbone protocol - analysis and applications J.A.Garay et al, EUROCRYPT LNCS

VOl 9057, ( VOLII ), pp 281-310 2015

3 Analysis of Blockchain protocol in Asynchronous networks R.Pass et al EUROCRYPT 2017

4 Fruitchain, a fair blockchain R.Pass et al , PODC 2017

5 Blockchain: The Blockchain for Beginnings, Guild to Blockchain Technology and Blockchain

Programming’ Josh Thompson Create Space Independent Publishing Platform 2017

Course outcomes:


Course

Code


19PCS243

CO1 Define and Explain the fundamentals of Blockchain

CO2 Illustrate the technologies of blockchain

CO3 Describe the models of blockchain

CO4 Analyse and demonstrate the Ethereum

CO5 Analyse and demonstrate Hyperledger fabric

37

Course Title: CYBER SECURITY




Pre-requisite: Discrete Mathematics, Computer Networks.

Course Objectives:

1. Understanding the basic concepts, major issues and technologies in Cyber security.

2. Develop basic understanding of cryptography, encryption techniques and web security policies.


Module –I

Introduction to Cyber crime: Introduction, Cybercrime and Information

security, who are cybercriminals, Classifications of Cybercrimes, Cybercrime:

The legal Perspectives and Indian Perspective, Cybercrime and the Indian ITA

2000, A Global Perspective on Cybercrimes. Cyber offenses: How criminals

Plan Them Introduction, How Criminals plan the Attacks

10 Hrs

Module-II

Cyber offenses: Social Engineering, Cyber stalking, Cyber cafe and

Cybercrimes, Botnets: The Fuel for Cybercrime, Attack Vector, Cloud

Computing. Cybercrime: Mobile and Wireless Devices: Introduction,

Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit card

Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by

Mobile Devices, Registry Settings for Mobile Devices, Authentication service

Security, Attacks on Mobile/Cell Phones, Mobile Devices: Security

Implications for Organizations, Organizational Measures for Handling Mobile.

Organizational security policies and measures in mobile computing Era,

laptops.

10 Hrs

Module-III

Tools and Methods Used in Cybercrime: Introduction, Proxy Servers and

Anonymizers, Phishing, Password Cracking, Keyloggers and Spywares, Virus

and Worms, Trojan Horses and Backdoors, Steganography, DoS and DDoS

Attacks, SQL Injection, Buffer Overflow, Attacks on Wireless Networks.

10 Hrs

Module- IV

Understanding Computer Forensics: Introduction, Historical background of

Cyber forensics, Digital Forensics Science, The Need for Computer Forensics,

Cyber Forensics and Digital evidence, Forensics Analysis of Email, Digital

10 hrs

38

Forensics Lifecycle, Chain of Custody concept, Network Forensics,

Approaching a computer Forensics Investigation, Setup a Computer Forensics

Laboratory, Computer forensics and steganography, Relevance of the OSI

Layer Model to Forensics, Forensics and Social Networking Sites: The

Security/Privacy Threats.

Module – V

Understanding Computer Forensics: Challenges in Computer Forensics,

Special Tools and Techniques, Forensics Auditing, Antiforensics, Cyber

Security: Introduction, Cost of Cybercrimes and IPR Issues: Lessons for

Organizations, Web Threats for Organization: The Evils and Perils, Protecting

People`s Privacy in the Organization, Organization Guidelines for Internet

Usage, Safe Computing, Incident Handling: An Essential Component of Cyber

Security, Forensics Best Practices for Organizations.

12 Hrs





Textbooks:

1. “Cyber Security: Understanding Cyber Crimes, Computer Forensics and Legal

Perspectives”, Nina Godbole and Sunil Belapure, Wiley INDIA.,2017

2. “Introduction to Cyber Security” , Chwan-Hwa(John) Wu,J.David Irwin.CRC Press T&F Group

2015.

Reference:

1. Cyber Security Essentials, James Graham, Richard Howard and Ryan Otson, CRC Press.2010

2. Cyber Security : Nina Godbole, Sunit Belapure, Wiley India.2014

Course outcomes:


Course

Code


19PCS251

CO1 Describe cyber attacks and Classification of CyberCrime.

CO2 Explain Botnets and Cyber offences in mobile and wireless devices.

CO3 Illustrate tools and methods used in cyber crime.

CO4 Discuss computer and network forensics and steganography.

CO5 Identify challenges in computer forensics, cost of cyber crime, IPR issues

and antiforensics.

39

Course Title: SOFT COMPUTING




Pre-requisites: XML, Web page development Skills

Course objectives:

The primary objective of this course is to provide an introduction to the basic principles, techniques,

and applications of soft computing. Upon successful completion of the course, students will have an

understanding of the basic areas of Soft Computing including Artificial Neural Networks, Fuzzy

Logic and Genetic Algorithms. Provide the mathematical background for carrying out the

optimization associated with neural network learning.

MODULES Teaching

Hours

Module I

Introduction to Soft Computing: Introduction, hardware computing versus

software computing, constituents of soft computing.

Crisp and Fuzzy Sets: Introduction, Classical or Crisp Set Theory, Crisp

Relations and Operations, Fuzzy Sets Theory, Important Concept of Fuzzy Set: α-

cut.

Fuzzy Logic and Inference Rules: Introduction, Classical logic, Multi-valued

Logic, Fuzzy Logic , Fuzzy Propositions, Inference Rules for Fuzzy Propositions.

10 Hrs

Module II

Fuzzy Inference Systems: Introduction, Fuzzy Inference Systems, Types of

Fuzzy Inference Engines, Implementation of Fuzzy Inference Engines, Neuro-

fuzzy System.

Rough Set and Possibility Theory: Introduction, Rough Set, Approximations

using Rough membership function, Information System, Possibility Theory,

Enhancements to Logic. to Logic.

10 Hrs

Module III

Single-Layer Feed-forward Neural Network-Perception: Introduction,

Biological Neurons, Artificial Neural Network, Artificial Neural Network Model,

Single-Layer Feed-forward Neural Network, Applications for Neural Network.

Multi-layer Feed-forward Neural Network: Introduction, Multi-layer FFNN

Architecture, Learning Methods, Back propagation Method, Design Issues of

Artificial Neural Network, Applications of Feed-forward Network.

10 Hrs

Module IV

Radial Basis Function Neural Network: Introduction, Radial Basis Function

Network,

Architecture of Radial Basis Function Network, Radial Basis function network

learning, RBFN for the XOR Problem, Comparison of RBF Network with FFNN,

RBFN Applications, MATLAB Implementation of RBFN.

Recurrent Neural Networks: Introduction, Recurrent Neural Networks

Architecture, Training of Recurrent Neural Networks, Hopfeild Network,

Boltzmann Machines, Self-organizing Neural Networks.

11 hrs

40

Module – V

Introduction to Evolutionary Computing: Introduction, Biological

Evolutionary Process, Paradigms of Evolutionary Computing, Evolutionary

strategies, Evolutionary Programming, Advantages of Evolutionary Computing,

Applications of Evolutionary Algorithms.

Genetic Algorithm Processes: Introduction, Genetic Algorithm, Selection of

Parents, Encoding of Genetic Operators, Classification of Genetic Algorithm, GA

Example and Implementation, Applications, Advantages and Disadvantages of

GA, GA using MATLAB for TSP.

Introduction to Swarm Intelligence: Introduction, Overview of Swarm

Intelligence, Particle Swarm Intelligence, Ant colony Optimization, MATLAB

Implementation of ACO.

11Hrs





Textbook:

1. J.S.R.Jang, C.T.Sun and E.Mizutani, “Neuro-Fuzzy and Soft Computing”, PHI, 2004, Pearson

Education 2004.

2. Simon O. Haykin “Artificial Neural Network”, PHI, 2003 .

3. Charu C. Aggarwal ,Neural Networks and Deep Learning: Springer International Publishing,

2018

4. Elaine Rich, Kevin Knight, Artificial Intelligence TMH, 2009.

REFERENCE BOOK

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

2. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”,

Addison Wesley, N.Y., 1989.

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

2003.

4.R.Eberhart, P.Simpson and R.Dobbins, “Computational Intelligence - PC Tools”, AP

Professional, Boston, 1996.

5. Dan W. Patterson, Introduction to AI and Expert System, PHI, 2009.

Course outcomes:


Course

Code


19PCS252

CO1 Describe the fundamentals of Soft computing, Classification, Clustering

and applications of soft computing

CO2 Explain how intelligent system works using soft computing approaches.

CO3 Apply basics of neural networks, learning algorithms and deep learning

techniques to solve problems.

CO4 Discuss the ideas of fuzzy sets, fuzzy logic and use of heuristics based

on human experience

CO5 Describe genetic algorithms and self-learning algorithms to solve real

life applications using soft Computing Techniques.

41

Course Title: ADVANCED DATA STRUCTURES & ALGORITHMS




Pre-requisites : Object Oriented Programming Language

Course objectives

Perform analysis of an algorithms

Impart concepts of data structures like stacks, queues, list, trees, heaps etc.

Learn to solve problems using Graph and other algorithm design techniques. MODULES Teaching Hours

Module – I

Algorithm Analysis: Mathematical Background, Model, What to Analyze,

Running Time Calculations.

List, Stacks, and Queues: Abstract Data Types (ADTs), The List ADT,

Vector and List in the STL, Implementation of Vector, Implementation of List,

The Stack ADT, The Queue ADT.

11 Hrs

Module - II

Trees: Preliminaries, Binary Trees, The Search Tree ADT – Binary Search

Trees.

Hashing: General Idea, Hash Function, Separate Chaining, Hash Tables

Without Linked Lists, Rehashing, Hash Tables in the Standard Library.

10 Hrs

Module – III

Priority Queues (Heaps): Model, Simple Implementation, Binary Heap,

Applications of Priority Queues, Priority Queues in the standard Library.

Sorting: Preliminaries, Insertion Sort, Merge sort, Quicksort.

10 Hrs

Module – IV

Graph Algorithms: Definitions, Topological Sort, Shortest-Path Algorithms,

Minimum Spanning Tree, Applications of Depth-First Search approaches.

10 Hrs

Module - V

Algorithm Design Techniques: Greedy Algorithms, Divide and Conquer,

Dynamic Programming, Backtracking Algorithms.

11 Hrs





TEXT BOOKS:

1. Mark Allen Weiss: Data Structures and Algorithm Analysis in C++, 3rd Edition, Pearson, 2007.

REFERENCE BOOKS:

1. Yedidyah, Augenstein, Tannenbaum: Data Structures Using C and C++, 2nd Edition, Pearson

Education, 2003.

2. Sartaj Sahni: Data Structures, Algorithms and Applications in C++, 2nd Edition, Universities Press

42

Course outcomes:


Course

Code


19PCS253

CO1 Analyze asymptotic performance of an algorithm and describe List, Stacks

and Queues in data structures.

CO2 Solve problems using trees Hashing.

CO3 Implement priority queues and demonstrate sorting techniques.

CO4 Solve problems using graph algorithm and other techniques.

CO5 Apply various Algorithm Design techniques for problem solving.

43

Course Title: CLOUD COMPUTING LABORATORY



SEE Hours: 03

Pre-requisite: Operating System, Computer Network, Distributed System

Course Objectives:

Understand the Cloud and Virtualization Concepts

List of Programs

1. Create a Virtual machine with following parameter.

a) 1 vCPU

b) 20GB Hard Disk

c) 2048 MB RAM

d) Bridge Network

e) Ubuntu desktop 16.04 as Guest Machine

f) Login to the Virtual Machine, Show the working of network by opening

the browser.

2. Create a Virtual machine, with the following parameter.

a) 1 vCPU

b) 30 GB Hard Disk

c) 2048 MB RAM

d) NAT

e) Windows 10 Client as guest VM OS

f) Power on/off the VM.

g) Login to Virtual Machine, Open Command prompt and show that ping to

www.google.com.

3. Create a Virtual machine with following parameter.

a) 1 vCPU

b) 20GB Hard Disk

c) 2048 MB RAM

d) Bridge Network

e) Ubuntu desktop 16.04 as Guest Machine

g) Login to the Virtual Machine, Show the network by opening the

browser.

f) Power off the VM.

http://www.google.com/

44

g) Go to edit settings for the above VM.

h) Increase CPU to 2.

i) Increase HARD Disk to 40.

j) Increase RAM to 4096.

k) Install Ubuntu Server edition instead of desktop.

l) Login to guest VM to check if VM is Working Properly.

4. Take a Snapshot of one of the virtual machines and restore at a

later point of time. Note your observation if restoration of snapshot

has the regular changes.

5. Export an OVF file (image of the virtual machine) and deploy

another virtual machine and check the functionality.

Experiments on Openstack

6. Familiarize with Openstack dashboard.

Part 1 - Log on to openstack dashboard

Open a web browser and enter the URL of openstack dashboard.

Part 2 – Browse different sections in dashboard

Three main sections

- Project

- Admin

- Identity

Explore the various options inside them.

7. User and Project Management in Openstack

• Create projects

• Edit / Modify projects

• Delete projects

• Create user

• Edit user

• Delete user

• Monitor resource usage of Projects

8. Perform Common Cloud management tasks

Creating and managing Quotas, flavours, containers and monitoring

different users as Admins.

9. Implementation of Deployment of a virtual machine instance on

45

openstack cloud

- Enable SSH on default security group

- Create / import key pair

- Import images to openstack to cloud

- Deploy on to the virtual instance

10. Deploy a Linux VM from ISO image

Creating virtual instances on openstack cloud using ISO image

- Create volumes

- Create openstack cloud image from ISO file

- Deploy / Launch new virtual instance from ISO

- Attach volume to instance

11. Exercise on openstack CLI options

Instance creation using CLIs on openstack cloud:

- launching an ISO instance in Openstack cloud.

- Boot from ISO, a volume has to be created and assigned to the instance while

booting.

- Creating volumes and attaching it to instances.


For SEE similar question related to the above programs will be asked.

Course outcomes:


Course

Code


19PCS26

CO1 Perform virtualization by considering various components

CO2 Manage Projects and User created using Openstack

CO3 Perform common Cloud management tasks.

CO4 Deploy VM Instance on Openstack

CO5 Create Instance using CLIs

46

Course Title: MINI PROJECT



SEE Hours: 03

Pre-requisite: Programming languages, Tools and Operating Systems

Course Objectives:

Understand formulation of problem

Design and develop simple applications using any domain of Computer Science

Understand the procedure of documentation and presentation of Mini-project.

Guidelines for Mini project:

Student has to carry out literature survey to identify and formulate the

problem

Student has to design and develop a H/W or S/W model using any

domains of Computer Science.

Timely evaluation of the mini project will be conducted by concerned

guide and reviewer for CIE assessment.

At the end of the semester students has to prepare and submit a project

report.

Course outcomes:


Course

Code


19PCS27


CO2 Apply basic engineering knowledge learnt in developing system

CO3 Evaluate current research status by conducting literature survey.


CO5 Apply the programming language for Software Development Life Cycle

model for the implementation of the Mini-project and prepare well

organized report.

47

III SEMESTER

Course Title: INDUSTRIAL INTERNSHIP


Number of Practical Hours/Week/batch 6 WEEKS SEE: 50

SEE Hours: 03

Course Objectives:

Expose Students to the Industry Practices for development of S/W or H/W Product

Make students to acquire knowledge of cutting edge technology and develop management and

communication skills

Guidelines for Internship:

Student has to choose the Industry and Topic of Internship relevant to

their branch of PG course.

Student has to complete the internship in stipulated period of Time.

Timely evaluation of the Internship will be conducted by concerned

guide and Review Committee for CIE assessment.

At the end of the semester students has to prepare and submit a well

organized Report of Internship.

Course outcomes:


Course

Code


19PCS31

CO1 Acquire the knowledge of Industry Practices

CO2 Learn working on Real time system development

CO3 Demonstrate management and communication skills

CO4 Analyse and Test System Performance using standard tools

CO5 Design well organized documentation of the product design

48

Course Title: PROJECT PHASE – I



SEE Hours: 03

Pre-requisite: Knowledge of All Subjects of the Programme

Course Objectives:

Acquire the knowledge of Computer Engineering and apply this knowledge to develop a

Project Model.

Understand and analyse the Engineering Problem

Prepare a well organized Documentation for the Project Developed.

Guidelines for Project:

Student has to Identify and formulate problem

Students has to survey 20 to 30 Research Papers of Reputed Publication

Proposed Methodology and Objectives of the Project should be defined

Adopt appropriate method to design selected problem

Timely evaluation of the project will be conducted by concerned guide

and Review Committee for CIE assessment.

At the end of the semester students has to prepare and submit a well

organized project report.

Course outcomes:


Course

Code


19PCS32



individually or in group



CO5

Apply the programming language for Software Development Life Cycle

model for the implementation of the project and prepare well organized

report.

49

Course Title: SEMINAR


Course Objectives:

Learn about the topics requires for current IT needs.

Develop documentation and presentation skills.

Each student shall present a seminar on any topic of interest related to

the core / elective courses offered in the first / second semester of the M.

Tech. Programme. He / she shall select the topic based on the references from

reputed International Journals, preferably IEEE/ACM/Springer/Elsevier

journals. They should get the paper approved by the Programme Coordinator

/ Faculty member in charge of the seminar and shall present it in the class.

Every student shall participate in the seminar. The students should undertake

a detailed study on the topic and submit a report at the end of the semester.

Marks will be awarded based on the topic, presentation, participation in the

seminar and the report submitted.

Course outcomes:


Course

Code


19PCS33

CO1 Apply effective strategies for Performing literature survey.

CO2 Identify, understand and discuss upcoming technologies.

CO3 Apply principles of ethics while preparing the report.

CO4 Develop oral and written communication skills.

CO5 Prepare well designed documentation and present effectively

50

Course Title: CERTIFICATION COURSE

Subject Code : 19PCS34 Credit : 2

Student has to complete a certification course of 8 -12 Weeks from

NPTEL, MOOCS, SWAYAM etc. and submit the evaluation report and the

Certificate to earn the allocated credits.

51

IV SEMESTER

Course Title: PROJECT PHASE - II

Subject Code : 19PCS41 Credit : 20

CIE: 50 SEE Hours: 03 duration SEE: 50

Pre-requisite: Knowledge of All Subjects of the Programme

Course Objectives:

Acquire the knowledge of Computer Engineering and apply this knowledge to develop a

Project Model.

Understand and analyse the Engineering Problem

Prepare a well organized Documentation for the Project Developed.

Main project phase II is a continuation of project phase-I started in the third

semester. There would be two reviews in the fourth semester, first in the middle

of the semester and the second at the end of the semester. First review is to

evaluate the progress of the work, presentation and discussion. Second review

would be a pre -submission presentation before the evaluation committee to

assess the quality and quantum of the work done. It is encouraged to prepare at

least one technical paper for possible publication in journals or conferences.

The papers received acceptance before the M. Tech evaluation will carry

specific weightage. The project report (and the technical paper(s)) shall be

prepared without any plagiarised content and with adequate citations, in the

standard format specified by the Department /University.

Course outcomes:


Course

Code


19PCS41

CO1 Demonstrate skill to identify formulate the given problems.


individually or in group



CO5

Apply the programming language for Software Development Life Cycle

model for the implementation of the project and prepare well organized

report.

curriculum - pda college of engineering

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