master of engineering in electronics & communication

Syllabi and Scheme of Teaching

Master of Engineering in

Electronics & Communication Engineering (Artificial Intelligence)

(2020-2022)

Electronics & Communication Engineering Department

National Institute of Technical

Teachers Training & Research

Chandigarh

SCHEME OF EXAMINATION FOR

MASTER OF ENGINEERING - ELECTRONICS AND COMMUNICATION ENGG.

(ARTIFICIAL INTELLIGENCE)

Year: First Semester I

S. No.

Course Code

Course Name

Scheme of Teaching Scheme of Examination

L-T-P Contact hrs/week

Credits Theory Practical

Internal Assessment

University Assessment

Total

1 ECEAI 1101 Data Structures and Programming

4-0-0 4 4 50 50 100 -

2 ECEAI 1102 Natural Language Processing

4-0-0 4 4 50 50 100 -

3 CSEI 8106*

Machine Learning 3-0-0 3 3 50 50 100 -

4 CSEI 8107*

Fundamentals of Internet of Things

3-0-0 3 3 50 50 100 -

5 Elective - 1 4-0-0 4 4 50 50 100 -

6 ECEAI 1107 AI Laboratory -I 0-0-4 4 2 - - - 100

7 Audit Course 2-0-0 2 - 50 - 50 -

Total 20-0-4 24 20 250 250 500 100

* Industry Core subjects – handled by Computer Science and Engineering Department

Elective I (SELECT ANY ONE)

ECEAI 1103 : Cloud Computing & Virtualization

ECEAI 1104 : Industrial Robotics (Common with M.E. in Mechanical Engineering with specialization in Robotics - MER 601)

ECEAI 1105 : Wireless Sensor Networks

ECEAI 1106 : Parallel and Distributed Computing

Audit Course

1. Technical Report Writing

2. Start-up/ Venture Capitalism

3. Digital Pedagogy

4. Stress Management by Yoga

Year: First Semester II

S. No.

Course Code Course Name




Internal Assessment


Total

1 ECEAI 1201 Computer Vision 4-0-0 4 4 50 50 100 -

2 ECEAI 1202 Deep Learning 4-0-0 4 4 50 50 100 -

3 CSEI 8206**

Industrial Internet of Things

3-0-0 3 3 50 50 100 -

4 CSEI 8207** Big Data Analytics 3-0-0 3 3 50 50 100 -

5 Elective -II 4-0-0 4 4 50 50 100 -

6 ECEAI 1207 AI Laboratory -II 0-0-4 4 2 100*

Total 18-0-4 22 20 250 250 500 100

** Industry Core subjects - Common with Computer Science & Engineering

Elective II (SELECT ANY ONE)

ECEAI 1203 : Research Methodology

ECEAI 1204 : Bio-Inspired Computation ECEAI 1205 : Embedded System Design and Architecture ECEAI 1206 : Fuzzy Systems and Applications

* Practical marks are for continuous and end semester evaluation

Year: Second Semester III

S. No.

Course Code

Course Name




Internal Assessment


Total

1 ECEAI 1301 MOOCS – I** - - 2 100 - 100 -

2 ECEAI 1302 MOOCS – II** - - 2 100 - 100 -

3 ECEAI 1303 Preliminary Thesis - - 10 100 - 100

Total - - 14 300 - 300

** Tentative list attached. However, the Dept. should prepare the list only from the MOOCS which conduct

proctored examinations like NPTEL. Depending upon the availability of online MOOCS courses, students will

be intimated one month prior to the commencement of the course.

Year: Second Semester IV

S. No.

Course Code

Course Name**




Internal Assessment


Total

1 ECEAI 1401 Thesis 0-0-25 25 16 100 100 200 -

Total 0-0-25 25 16 100 100 200 -

** Candidate shall make a presentation along with a demo of work done in the presence of panel of experts

and nominees as per Panjab University, Chandigarh norms.

Total M.E. Credits = 70

Programme Outcomes of PG Program in Artificial Intelligence At the end of the program, a student is expected to have: PO1: An ability to independently carry out research/investigation and development work to solve

practical problems.

PO2: An ability to write and present a substantial technical report / document.

PO3: An ability to demonstrate mastery over the emerging area of Artificial Intelligence and allied

specialization of the program.

PO4: To understand apply, analyze, evaluate and synthesize existing and new knowledge

related to Artificial Intelligence.

PO5: To use advanced computing techniques and tools.

List of MOOCS Courses

MOOCS-I (Choose one from the list) S. No. Name of Course## Faculty Link

1. Design and Analysis of Algorithms

Madhavan Mukund https://onlinecourses.nptel.ac.in/noc19_cs11/preview

2. Parallel Algorithms Sajith Gopalan https://onlinecourses.nptel.ac.in/noc19_cs17/preview

3. Embedded System Design with ARM

Dr. Kamalika Datta https://onlinecourses.nptel.ac.in/noc19_cs22/preview

4. Industrial Automation and Control

Prof. Siddhartha Mukhopadhyay

https://onlinecourses.nptel.ac.in/noc19_me04/preview

5. Process Control - Design, Analysis and Assessment

Raghunathan Rengaswamy https://onlinecourses.nptel.ac.in/noc19_ch10/preview

6. Dynamical Systems and Control

Dr. N. Sukavanam https://onlinecourses.nptel.ac.in/noc19_ma10/preview

7. Product Design and Development

Dr. Inderdeep Singh https://onlinecourses.nptel.ac.in/noc19_me21/preview

8. Advanced Engineering Mathematics

Dr. P. N. Agrawal https://onlinecourses.nptel.ac. in/noc19_ma11/preview

9. Basics of Finite Element Analysis-I

Dr. Nachiketa Tiwari https://onlinecourses.nptel.ac.in/noc19_me02/preview

10. Introduction to Finite Volume Methods II

Dr. Ashoke De https://onlinecourses.nptel.ac.in/noc19_ae03/preview

11. Bio-Informatics: Algorithms and Applications

M Michael Gromiha https://onlinecourses.nptel.ac.in/noc19_bt01/preview

12. Bioengineering: An Interface with Biology and Medicine

https://onlinecourses.nptel.ac.in/noc19_bt13/preview

13. Basics of Software Defined Radios and practical applications

Dr. Meenakshi Rawat https://onlinecourses.nptel.ac.in/noc19_ee22/preview

https://onlinecourses.nptel.ac.in/noc19_cs11/preview








https://onlinecourses.nptel.ac.in/noc19_ch10/preview

https://onlinecourses.nptel.ac.in/noc19_ch10/preview

https://onlinecourses.nptel.ac.in/noc19_ma10/preview

https://onlinecourses.nptel.ac.in/noc19_ma10/preview





https://onlinecourses.nptel.ac.in/noc19_ae03/preview

https://onlinecourses.nptel.ac.in/noc19_ae03/preview





https://onlinecourses.nptel.ac.in/noc19_ee22/preview

https://onlinecourses.nptel.ac.in/noc19_ee22/preview

MOOCS-II (Choose one from the list) S. No. Name of Course## Faculty Link

1. Managing Learning Resources

Uday Chand Kumar National Institute of Technical Teachers’ Training and Research, Kolkatta

https://swayam.gov.in/courses/5224-managing-learning-resources

2. Outcome Based Pedagogic Principles for Effective Teaching

Shyamal Kr. Das Mandal Institute of Technology- Kharagpur

https://swayam.gov.in/courses/4898-July-2018-outcome-based-pedagogic-principles-for-effective-teaching

3. Pedagogical Innovations & Research Methodology

Vandana Punia Guru Jambheshwar University of Science and Technology

https://swayam.gov.in/courses/5269-pedagogical-innovations-reserach-methodology

4. Managing Intellectual Property in Universities

Feroze Ali IIT Madras

https://swayam.gov.in/courses/5474-jan-2019-managing-intelluctual-property-at-universities

5. Innovation, Business Models and Entrepreneurship

Rajat Agarwal IIT Roorkee

https://swayam.gov.in/courses/4816-july-2018-innovation-business-moels-and-entrepreneurship

6. Environment Natural Resources and Sustainable Development

Prabhakar Rao Jandhyala University of Hyderabad

https://swayam.gov.in/courses/3911-environment-natural-resources-and-sustainable-development

7. Critical Thinking Partha Chatterjee Shiv Nadar University

https://swayam.gov.in/courses/3912-critical-thinking

8. OER for Empowering Teachers

Malliga P National Institute of Technical Teachers Training and Research - Chennai

https://swayam.gov.in/courses/4231-oer-for-empowering-teachers

9. Enhancing Soft Skills and Personality

Dr. T. Ravichandran https://onlinecourses.nptel.ac. in/noc19_hs22/preview

## The Dept. should prepare the list only from the MOOCS which conduct proctored

examinations like NPTEL. Depending upon the availability of online MOOCS courses,

students will be intimated one month prior to the commencement of the course.










Title DATA STRUCTURES AND PROGRAMMING Credits 04

Code ECEAI 1101 Semester: Ist L T P 4 0 0

Max. Marks External: 50 Internal: 50 Elective N

Pre-requisites Programming in C, C++ Contact Hours 44

Objectives

The goal of this course is to provide an introduction to different data types, Python and JavaScript programming. The course will discuss topics necessary for the participant to be able to create and execute programs in Python and JavaScript which are essential ingredients of Artificial Intelligence. The lectures and presentations are designed to provide knowledge and experiences to students that serve as a foundation for continued learning of presented areas. The focus of the course is to provide students with an introduction to programming, I/O, and visualization using Python and JavaScript programming languages.

Course

Outcomes

At the end of the course, the students will be able to: 1. Have knowledge of arranging data in different ways 2. Describe the Numbers, Math functions, Strings, List, Tuples and

Dictionaries in Python 3. Express different Decision Making statements and Functions 4. Understand and summarize different File handling operations in Python 5. Design and develop Client Server network applications using JavaScript 6. JavaScript to program the behavior of web

Note for

Examiner

The question paper shall be of 50 marks having 7 questions of equal marks,

covering the whole syllabus. Question paper will be divided into three parts i.e.

Section A, Section B and Section C. There will be 2 questions in Section A from

Unit I, 3 questions in Section B from Unit II and 3 questions in Section C from

Unit III. The candidate is required to attempt one question from Section A and

two questions from each of Section B and Section C.

SECTION A

UNIT I: Data Structures

Arrays and Strings, Algorithm Development, Complexity analysis,

Recursion, Linear Data Structures, Stacks, Queues, Circular Queues, Links

Lists, Operation – Creations, insertion, Deletion, Circular Lists, Doubly Linked

List, Trees, Graphs.

12

SECTION B

UNIT II: Python Programming

Introduction, gitHub, Functions, Booleans and Modules, Sequences, Iteration

and String Formatting, Dictionaries, Sets, and Files, Exceptions, Testing,

Comprehensions, Advanced Argument Passing, Lambda -- functions as objects,

Object Oriented Programming, More OO -- Properties, Special methods,

Iterators, Iterables, and Generators, Decorators, Context Managers, Regular

Expressions, and Wrap Up

16

SECTION C

UNIT III: JavaScript

Basics, Functional programming, Object oriented programming, Client-side

applications, Server-side applications, Design patterns and Idioms, Popular

frameworks

16

Suggested

Books

1. Fundamental of Data Structures by Horowitz and Sahni, , 4th Ed., CSP,

1994, (Pascal, C , C++ or Generic version) 2. Learning with Python by Allen Downey, Jeff Elkner and Chris Meyers 3. Introduction to Machine Learning with Python by Andreas C. Mueller, Sarah

Guido 4. Programming Python by Mark Lutz 5. Eloquent JavaScript: A Modern Introduction to Programming‖ by Marijn

Haverbeke

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Andreas+C.+Mueller&search-alias=stripbooks

https://www.amazon.in/Sarah-Guido/e/B0788K4D8N/ref=dp_byline_cont_book_2

https://www.amazon.in/Sarah-Guido/e/B0788K4D8N/ref=dp_byline_cont_book_2

https://www.amazon.com/Eloquent-JavaScript-3rd-Introduction-Programming/dp/1593279507/ref=as_li_ss_tl?ie=UTF8&linkCode=ll1&tag=eejs-20&linkId=ae5b8f9de89a5f5b0eab39ba8c32fe12

Title NATURAL LANGUAGE PROCESSING Credits 04



Pre-requisites Programming in C Contact Hours 44

Objectives

This course introduces the fundamental concepts and techniques of natural language processing (NLP). Students will gain an in-depth understanding of the computational properties of natural languages and the commonly used algorithms for processing linguistic information. The contents of this course are devoted to the study of phonological, morphological and syntactic processing. These areas will be approached from both a linguistic and an algorithmic perspective. Focus is on the computational properties of natural languages and of the algorithms used to process them, as well as the match between grammar formalisms and the linguistic data that needs to be covered.

Course

Outcomes

At the end of the course, the students will be able to: 1. Understand approaches to syntax and semantics in NLP. 2. Understand approaches to discourse, generation, dialogue and

summarization within NLP. 3. Understand current methods for statistical approaches to machine

translation. 4. Understand machine learning techniques used in NLP, including hidden

Markov models and probabilistic context-free grammars, clustering and unsupervised methods, log-linear and discriminative models, and the EM algorithm as applied within NLP.

Note for

Examiner

The question paper shall be of 50 marks having 7 questions of equal marks.

First question, covering the whole syllabus and having questions of conceptual

nature, will be compulsory. Rest of the paper will be divided into two parts i.e.

Section A and Section B having three questions each and the candidate is

required to attempt two questions from each of Section A and Section B.

SECTION-A

Introduction: Fundamentals, challenges, usage, classical problems.

Words-Structure: spellcheck, morphology using FSTs.

Words-Semantics: Basic ideas in Lexical Semantics, WordNet and WordNet based similarity measures, Distributional measures of similarity, Concept Mining using Latent Semantic Analysis, Word Sense Disambiguation; supervised, unsupervised and semi-supervised approaches

Words-Parts of Speech: POST using Brill's Tagger and HMMs

Sentences: Basic ideas in compositional semantics, Classical Parsing (Bottom up, top down, Dynamic Programming: CYK parser), Parsing using Probabilistic Context Free Grammars and EM based approaches for learning PCFG

22

parameters.

SECTION-B

Language Modeling: Basic ideas, smoothing techniques.

Machine Translation: Rule based techniques, Statistical Machine Translation (SMT), parameter learning in SMT (IBM models) using EM.

Information Extraction: Introduction to Named Entity Recognition and Relation Extraction

Natural Language Generation: the potential of using ML for NLG

Additional topics: Advanced Language Modeling (including LDA), other applications like summarization, question answering

22

Suggested

Books

1. Daniel Jurafsky, James H. Martin: "Speech and Language Processing",

2/E, Prentice Hall, 2008. 2. James Allen, "Natural Language Understanding", 2/E, Addison-Wesley,

1994 3. Christopher D. Manning, Hinrich Schutze: "Foundations of Statistical

Natural Language Processing", MIT Press, 1999 4. Steven Bird, Natural Language Processing with Python, 1st Edition,

O'Reilly, 2009. 5. Jacob Perkins, Python Text Processing with NLTK 2.0 Cookbook, Packt

Publishing, 2010.

Title MACHINE LEARNING Credits 03

Code CSEI 8106 Semester: Ist L T P 3 0 0


Pre-requisites Basics of Probability, Linear Algebra and

Calculus Contact Hours 45

Objectives

This course will serve as a comprehensive introduction to various topics in machine learning. The objective is to familiarize the audience with some basic learning algorithms and techniques and their applications, as well as general questions related to analyzing and handling large data sets. At the end of the course the students should be able to design and implement machine learning solutions to classification, regression, and clustering problems; and be able to evaluate and interpret the results of the algorithms.

Course

Outcomes

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

1. Understand the fundamental issues and challenges of machine learning. 2. Understand the strengths and weaknesses of many popular machine

learning approaches. 3. Interpret the underlying mathematical relationships within and across

Machine Learning algorithms and the paradigms of supervised and un-supervised learning.

4. Design and implement various machine learning algorithms in a range of real-world applications.

Note for

Examiner

The question paper shall be of 50 marks having 7 questions of equal marks.

First question, covering the whole syllabus and having questions of

conceptual nature, will be compulsory. Rest of the paper will be divided into

two parts i.e. Section A and Section B having three questions each and the

candidate is required to attempt two questions from each of Section A and

Section B.

SECTION-A

Introduction: Probability, Statistics (including basics of hypothesis testing), Algebra, Calculus, Basics of Data Structures and Python Programming, databases (SQL/NoSQL), Connecting to Cloud with Linux and write scripts, virtualization with docker and setting up jupyter notebooks

7

Machine learning: supervised/unsupervised/reinforcement learning and its key terminology EDA, Data wrangling and Visualization with Pandas, Numpy and Matplotlib

8

Linear Regression, Model Training and Loss Gradient Descent and various hyperparameters First steps with TensorFlow and scikit learn

8

SECTION-B

Classification algorithms covering logistic regression, Multi-Layer perceptron, SVM, Decision trees and Random Forest Probabilistic algorithms covering Bayes classifier and Hidden Markov Models

8

Cross Validation, Performance measurement of models Feature engineering techniques to improve model performance

8

Unsupervised learning: k-means clustering, hierarchical clustering, Gaussian Mixture models and Density Based clustering Dimensionality Reduction techniques: PCA, FDA, QDA, Random Forests

6

Suggested

Books

1. Machine Learning by Tom Mitchell 2. Introduction to Machine Learning by Ethem Alpaydin 3. Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor

Hastie, Robert Tibshirani, Springer, 2013. 4. Pattern Classification, 2nd Ed., Richard Duda, Peter Hart, David Stork,

John Wiley & Sons, 2001.

Title FUNDAMENTALS OF IoT Credits 03

Code CSEI 8107 Semester: Ist L T P 3 0 0


Pre-requisites Basics of Sensors, Machine Level

Programming Contact Hours 45

Objectives

This course focuses on the latest microcontrollers with application development, product design and prototyping. This also focuses on interoperability in IoT along with various IoT Platforms for application development.

Course

Outcomes


1. Understand the various network protocols used in IoT 2. Understand the role of Big Data, Cloud Computing and Data Analytics

in a typical IoT system. 3. Design a simple IoT system made up of sensors, wireless network

connection, data analytics and display/actuators, and write the necessary control software.

4. Build and test a complete IoT system.

Note for

Examiner

The question paper shall be of 50 marks having 7 questions of equal marks. First question, covering the whole syllabus and having questions of conceptual nature, will be compulsory. Rest of the paper will be divided into two parts having three questions each of Section A and Section B and the candidate is required to attempt at least two questions from each of Section A and Section B.

SECTION-A

Introduction

Introduction to IoT, Sensing, Actuation, Basics of Networking, Communication

Protocols

6

Sensor Networks, Machine to Machine Communications. Understanding of the IoT

ecosystem, various layers in building an IoT application and interdependencies 6

Interoperability in IOT

Introduction to Arduino Programming, Integration of Sensors and Actuators with

Arduino, Introduction to Python programming

5

Introduction to Raspberry Pi, Implementation of IoT with Raspberry Pi. Build use

cases using Raspberry Pi 5

SECTION-B

SDN for IoT Introduction to SDN, SDN for IoT, Data Aggregation, Handling and Analytics

4

Cloud Computing, Sensors, Fog Computing

4

Understanding of the various protocols being used in IoT like MQTT, AMQP, REST API

4

IoT Platforms and Applications

Understanding of the IoT platforms like PTC Thingworx and IoT frameworks like MS Azure, Understanding of the usage of these platforms to build applications like Smart Cities and Smart Homes, Connected Vehicles, Smart Grid, Case Study: Agriculture, Healthcare, Activity Monitoring.

11

Suggested

Books

1. David Etter, ―IoT (Internet of Things) Programming: A Simple and Fast

Way of Learning IoT,‖ Kindle Edition. 2. Jan Holler, VlasiosTsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis

Karnouskos, and David Boyle, ―From Machine to Machine to the Internet of Things: Introduction to a New Age of Intelligence,‖ Elsevier Science Publishing Co. Inc, 2014.

3. Pethuru Raj and Anupama C. Raman, ―The Internet of Things: Enabling Technologies, Platforms, and Use Cases,‖ 1st Edition, Auerbach Publications, 2017.

4. Yasuura, H., Kyung C.M., Liu Y., and Lin Y.L., ―Smart Sensors at the IoT Frontier,‖ 1st Edition, Springer International Publishing, 2018.

Title COMPUTER VISION Credits 04

Code ECEAI 1201 Semester: 2nd L T P 4 0 0


Pre-requisites Digital Signal Processing Contact Hours 45

Objectives To introduce the different low level and high level computer vision

techniques. Students are also made aware about the different image

processing techniques

Course

Outcomes

At the end of the course, the students will be able to explain: 1. Fundamental concepts of a digital image processing system. 2. Concepts of image enhancement techniques. 3. Various spatial domain and frequency domain filters. 4. Image reconstruction in the presence of noise. 5. Color models and various applications of image processing.

Note for

Examiner


SECTION-A

Introduction to Image Processing:

Digital Image representation, Sampling & Quantization, Steps in image Processing,

Image acquisition, color image representation.

6

Image Transformation, Filtering & Restoration:

Intensity transform functions, histogram processing, Spatial filtering, Fourier

Transforms and its properties, frequency domain filters, Homomorphic Filtering,

color models, Pseudo coloring, color transforms, Basics of Wavelet Transforms,

Image Noise and Restorations, Inverse Filtering.

12

Image Compression:

Coding redundancy, Interpixel redundancy, Psychovisual redundancy, Huffman

Coding, Arithmetic coding, Lossy compression techniques, JPEG Compression.

6

SECTION-B

Image Morphological Processing:

Introduction to basic operation on binary and grayscale images: Dilation, Erosion,

Opening & Closing, Morphological Algorithms: Boundary & Region Extraction,

6

Convex Hull, Thinning, Thickening, Skeletons, Pruning.

Image Segmentation, Representation & Descriptions:

Point, Line and Edge Detection, Thresholding, Edge and Boundary linking, Hough

transforms, Region Based Segmentation, Contour following, Boundary

representations, Region Representations, shape properties, Boundary Descriptors,

Regional Descriptors, Texture representations, Object Descriptions

6

Object Recognition:

Patterns and Patterns classes, Recognition based on Decision Theoretic methods,

Structural Methods

9

Suggested

Books

1. Gonzalez and Woods: Digital Image Processing ISDN 0-201-600- 781,

Addison Wesley 1992.

2. Forsyth and Ponce: Computer Vision A Modern Approach Pearson

Education Latest Edition.

3. Pakhera Malay K: Digital Image Processing and Pattern Recognition,

PHI.

4. Trucco & Verri: Introductory Techniques for 3-D Computer Vision,

Prentice Hall, Latest Edition.

5. Jayaraman and Veerakumara: Digital Image Processing, McGraw Hill.

6. Low: Introductory Computer Vision and Image Processing, McGraw-

Hill 1991, ISBN 0-07-707403-3.

7. Jain, Kasturi and Schunk: Machine Vision, McGraw-HiII. 1995 ISBN

0070320187.

8. Sonka, Hlavac, Boyle : Image -Processing, Analysis and Machine

Vision 2nd ed. ISBN 0-534-95393-X, PWS Publishing,1999

Title Technical Report Writing Credits Audit

Course

Code ECEAI C1 Semester: Ist L T P 2 0 0

Max. Marks External: - Internal: 50 Elective N

Pre-requisites NIL Contact Hours 22

Objectives

The course develops technical writing skills necessary to communicate information gained through a process of technical or experimental work. The course highlights the factors that determine the degree of technicality of the language and concepts involved. Students will learn how to write different technical reports, e.g., laboratory reports, research reports, design and feasibility reports, progress reports, consulting reports, etc. The course also approaches several language, structure, style, and content issues that you can encounter while reporting the results of research.

Note for

Conducting

Internal

Examination

The student should write a technical paper, give a seminar and try to publish

the technical paper in a conference / journal / edited book. A rubrics for award

of 50 Marks will be developed by the Department.

Unit -I Introduction to Technical Writing: how differs from other types of written communication Purpose of technical writing, Correspondence: prewriting, writing and rewriting Objectives of Technical Writing. Audience Recognition: High-tech audience, Low tech audience, Lay audience, Multiple Audience. Unit – II Correspondence: Memos, Letters, E-mails, Its differentiation, types of letters, Docum ent Design, its importance, Electronic Communication: Internet, Intranet, extranet, Writing effective e-mail. Unit – III Summary: Report Strategies, Effective style of technical report writing: Structures: content, introduction, conclusions, references, etc., Presentation, Writing first draft, revising first draft, diagrams, graphs, tables, etc. report lay -out. Unit -IV Report Writing: Criteria for report writing, Types of Report: Trip report, Progress report, lab report, Feasibility report, project report, incident report, etc. Case Studies. Unit -V Proposals & Presentation: Title page, Cover letter, Table of Content, list of illustrations, summary, discussion, conclusion, references, glossary, appendix, Case Studies. Oral Presentation/ Seminar

Suggested

Books

Text Books: 1. Sharon J. Gerson & Steven M. Gerson ―Technical Writing – Process& Product‖, Pearson Education.

Reference Books: 1. Sunita Mishra, ―Communication Skills for Engineers‖ Pearson Education 2. Davies J.W. ―Communication for engineering students‖, Longman 3. Eisenberg, ―Effective Technical Communication‖, Mc. Graw Hill.

Title Start-up / Venture Capitalism Credits Audit

Course




Objectives

The course provides a broader view on all relevant aspects of Startup and Innovation policy and eco-system. It gives an understanding of the general policy framework in which government is trying to promote innovation and start up in India. The course also takes students through the important institutions, concepts and terms which can help learners to take advantage of the policy framework. The framework of this module comes from the 'Start-up India' programme of Government of India which is intended to build a robust eco-system for nurturing innovation and start-ups in turn will drive sustainable economic growth and generate employment.

Course

Outcomes

Students will learn the policy framework of innovation and start-ups in India. This course aims to serve the following objectives:

To help students understand the framework of policies to promote Innovation and start up

To help students understand the eco system and institutions which help companies in Innovation and entrepreneurship

To help them be able to do comparative analysis of the eco system of innovation and start-ups of different countries

To help students understand the link between economic development, innovation, entrepreneurship and public policy framework

To enable students to figure out how to make use of the framework for the growth of the firms.

Note for

Conducting

Internal

Examination

The student should write a technical paper, give a seminar, develop a business

plan and try to publish the white paper. A rubric for award of 50 Marks will be

developed by the Department.

Innovation and Innovation Eco-System The Policy Framework Startup Landscape and Innovation Hubs Digital India and Make in India Linking Innovation with IPR Raising Finance for Startups in India Innovation in Indian Context Writing a business plan Case Study: The Government of India has come up with several initiatives like Digital India, Start-up India and Make in India. It is also promoting Incubators, Accelerators, Venture Capital financing, industry clusters and investment in Research labs and world class universities. The Global institutions have given new frameworks of Global Competitiveness report, Global Innovation Index, The World bank’s Ease of doing business help understand the important

factors in creating thriving Innovation and start up landscape.

Suggested

Books

Innovation and Entrepreneurship by Peter F. Drucker (Classic Drucker Collection, 2007)

Joseph A. Schumpeter’s views on entrepreneurship and innovation by Perihan Hazel

HBR/Forbes/Mckinsey/BCG/Knowledge@Wharton /ISB Insight/ IBEF/ Innosigh

Title Digital Pedagogy Credits Audit

Course




Objectives

The ways in which we teach and learn in the 21st century are shifting, and the emergence of digital production tools and networked technologies both reflects and shapes these shifts. In many cases, however, university-level pedagogy remains the same as it has been for more than a century. Rather than simply adopting digital technologies for their own sake, or blindly mapping conventional teaching approaches onto digital space, we will examine the ways in which the pedagogy might drive the technology, and experiment with digital applications that serve an inquiry-driven, project-based approach.

Course

Outcomes

Students will learn the policy framework of innovation and start-ups in India. This course aims to serve the following objectives:

How do shifts in epistemology (ways of knowing) impact pedagogy (ways of transmitting knowledge)?

What are the implications of emergent technologies for the university, its faculty and its students?

Are current institutions—governmental, legal, entertainment, journalistic, educational—which coalesced during the ascendency of print, relevant in the age of the digital?

How do we foster a sense of community and collaboration when technologies threaten to attenuate the lines of communication?

Note for

Conducting

Internal

Examination

The student should write a technical paper, give a seminar, develop a teaching

plan and try to publish the white paper. A rubric for award of 50 Marks will be

developed by the Department.

Digital Culture: Epistemology and Pedagogy: Introduction to the course. What is digital culture? Digital pedagogy? Examination of the Horizon Report + College 2020 Institutional Structures + Boundaries + Assessment Knowledge Structures + Ethics Learner Centered Classroom? Collective Action: Collaboration + Peer Review Remix: Digital Argument Remote Class: Collaborative Remix Designing Learning Games and Interactive Learning

Suggested

Books

Fyfe, Paul. "Digital pedagogy unplugged." (2011). Kivunja, C. (2013). Embedding digital pedagogy in pre-service higher

education to better prepare teachers for the digital generation. International Journal of Higher Education, 2(4), 131-142.

Clarke, T., & Clarke, E. (2009). Born digital? Pedagogy and computer‐

assisted learning. Education+ Training. Anderson, V. (2020). A digital pedagogy pivot: re-thinking higher education

practice from an HRD perspective. Human Resource Development International, 23(4), 452-467.

Title Stress Management by Yoga Credits Audit

Course




Objectives

The aim of the course is to propagate and promote yoga for positive health. This introduce basic concepts of preventive health and health promotion through yoga, concepts of Human Body to the students so as to making their, Develop clear understanding about the benefit and contraindication of Yoga practice and to train teachers on preventive health and promotion of positive health through yoga and personality development.

Note for

Conducting

Internal

Examination

The student should practice Yoga and at the end of course there will be a

practice session following by some research work. A rubric for award of 50

Marks will be developed by the Department.

Principles of Stress Management with Human Science Foundations of Yoga Yogic Lifestyle Basic Meditation with Asan, Pranayam & Mudra Soft Tissue Manipulation for Stress Chakra Yoga Practical Diseases & Remedial Therapy

Suggested

Books Online Resources

Title DEEP LEARNING Credits 04

Code ECEAI 1202 Semester: 2nd L T P 4 0 0


Pre-requisites Machine Learning, Python Programming Contact Hours 45

Objectives

This course aims to present the mathematical, statistical and computational challenges of building stable representations for high-dimensional data, such as images, text and data. Course delves into selected topics of Deep Learning, discussing recent models from both supervised and unsupervised learning. Special emphasis will be on convolutional architectures, invariance learning, unsupervised learning and non-convex optimization.

Course

Outcomes

At the end of the course, the students will be able to learn:

1. The fundamental principles, theory and approaches for learning with deep neural networks

2. The main variants of deep learning (such convolutional and recurrent architectures), and their typical applications

3. The key concepts, issues and practices when training and modeling with deep architectures as well as hands-on experience in using deep learning frameworks for this purpose

4. How to implement basic versions of some of the core deep network algorithms (such as backpropagation)

5. How deep learning fits within the context of other ML approaches and what learning tasks it is considered to be suited and not well suited to perform

Note for

Examiner


SECTION-A

History of Deep Learning, Deep Learning Success Stories, McCulloch Pitts Neuron,

Thresholding Logic, Perceptrons, Perceptron Learning Algorithm

Multilayer Perceptrons (MLPs), Representation Power of MLPs, Sigmoid Neurons,

Gradient Descent, Feedforward Neural Networks, Representation Power of

8

Feedforward Neural Networks, Backpropagation

Gradient Descent (GD), Momentum Based GD, Nesterov Accelerated GD,

Stochastic GD, Eigenvalues and eigenvectors, Eigenvalue Decomposition, Basis 6

Principal Component Analysis and its interpretations, Singular Value

Decomposition 6

Autoencoders and relation to PCA, Regularization in autoencoders, Denoising

autoencoders, Sparse autoencoders, Contractive autoencoders 6

SECTION-B

Regularization: Bias Variance Tradeoff, L2 regularization, Early stopping, Dataset

augmentation, Parameter sharing and tying, Injecting noise at input, Ensemble

methods, Dropout 6

Greedy Layerwise Pre-training, Better activation functions, Better weight

initialization methods, Batch Normalization 6

Learning Vectorial Representations of Words 3

Convolutional Neural Networks, LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet,

ResNet, Visualizing Convolutional Neural Networks, Guided Backpropagation,

Deep Dream, Deep Art, Fooling Convolutional Neural Networks. 4

Suggested

Books

1. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

and Francis Bach. 2. Neural Networks and Deep Learning By Michael Nielsen 3. Deep Learning with Python by Francois Chollet, 1st Edition 4. Hands-On Machine Learning with Scikit-Learn and TensorFlow:

Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron,1st Edition

5. Colab (Google)

https://www.amazon.com/Ian-Goodfellow/e/B01MQGN8N0/ref=dp_byline_cont_book_1

https://www.amazon.com/Yoshua-Bengio/e/B00IWC47MU/ref=dp_byline_cont_book_2

https://www.amazon.com/Aaron-Courville/e/B01N8XGWRL/ref=dp_byline_cont_book_3

https://www.amazon.com/s/ref=dp_byline_sr_book_4?ie=UTF8&text=Francis+Bach&search-alias=books&field-author=Francis+Bach&sort=relevancerank

http://neuralnetworksanddeeplearning.com/

https://www.amazon.com/Francois-Chollet/e/B077C1X8Y8/ref=dp_byline_cont_book_1

https://www.amazon.com/Aur%C3%A9lien-G%C3%A9ron/e/B004MOO740/ref=dp_byline_cont_book_1

Title INDUSTRIAL INTERNET OF THINGS Credits 03

Code CSEI 8206 Semester: 2nd L T P 3 0 0


Pre-requisites Fundamentals of IoT Contact Hours 45

Time 3 Hours

Objectives To Introduce the state of art of Industrial IoT with smart machines that performs pervasive sensing distinct from M2M communication. The course is a blend of engineering and business of IoT. It deals with connectivity in industrial networks, building systems to enable delivery of software services networked to the cloud platforms. At the end of the course, the students will be in a position to start an Industrial IoT business.

Course

Outcomes

At the end of the course, the students will be able to: 1. Understand Industry 4.0 Standards and IIOT Architecture. 2. Apply Intelligent algorithms for IIOT based Applications. 3. Analyse the security threats of IIOT. 4. Evaluate various components of Cyber Physical Systems in the context

of Industry 4.0

Note for

Examiner


SECTION-A

Industry 4.0: Globalization and Emerging Issues, The Fourth Revolution, LEAN

Production Systems, Smart and Connected Business Perspective, Smart Factories

4

Cyber Physical Systems and Next Generation Sensors, Collaborative Platform and

Product Lifecycle Management

5

Augmented Reality and Virtual Reality, Artificial Intelligence, Big Data and

Advanced Analysis. Cybersecurity in Industry 4.0

5

Basics of Industrial IoT: Industrial Processes, Industrial Sensing & Actuation,

Industrial Internet Systems

4

IIoT Introduction, Business Models-Part I, Part II, Reference Architecture 3

IIoT Layers, IIoT Sensing, IIoT Processing, IIoT Communication, IIoT Networking 3

SECTION-B

Big Data Analytics and Software Defined Networks 2

IIoT Analytics: Introduction, Machine Learning and Data Science, and Julia

Programming, Data Management with Hadoop 6

Data Center Networks, Security and Fog Computing, Cloud Computing in IIoT 3

Application Domains: Factories and Assembly Line, Food Industry, Healthcare,

Power Plants, Inventory Management & Quality Control, Plant Safety and Security

(Including AR and VR safety applications), Facility Management

6

IIoT Applications: Oil, chemical and pharmaceutical industry, Applications of UAVs

in Industries, Real case studies 4

Suggested

Books

1. Enterprise IoT Strategies and Best Practice for Connected Products and Services. – Dirk Slama, Frank Puhlmann, Jim Mirrish, Rishi M Bhatnagar

2. The Internet of Things: Key Applications and Protocols - David Boswarthick

3. The Silent Intelligence, the Internet of Things. By – Daniel Kellmereit, Daniel Obodovski

4. ―Industry 4.0: The Industrial Internet of Things‖, by Alasdair Gilchrist (Apress)

5. ―Industrial Internet of Things: Cyber manufacturing Systems‖ by Sabina Jeschke, Christian Brecher, Houbing Song, Danda B. Rawat (Springer)

Title BIG DATA ANALYTICS Credits 03

Code CSEI 8207 Semester: 2nd L T P 3 0 0


Pre-requisites Basics knowledge of Python or any Object

Oriented Programming Language Contact Hours 45

Objectives

The objective of this course is to teach the emerging concepts and case studies of Big Data with the real world case studies. In addition, the course focuses towards the coverage of data acquisition, storage, processing, querying and visualization with hands-on-practice using various big data analytics tools.

Course

Outcomes

After the completion of this course, the students will be able to:

1. Understand the concepts of Big Data Analytics with real world case studies.

2. Acquire, store and process Big Data from various sources. 3. Analyse and visualize Big Data. 4. Apply Big Data Analytics in various domains.

Note for

Examiner


SECTION-A

BIG DATA CONCEPTS, ARCHITECTURES AND ANALYTICS PATTERNS

Introduction to Big Data: Definition, various tools for Big Data, Possibilities of Big

Data storage using RDBMS, Data Warehousing and Data Marts concept, Types of

analytics - Descriptive, Diagnostic, Predictive, Prescriptive, Big Data characteristics

- Volume, Velocity, Variety, Veracity, Value, Data analysis flow, Big data examples,

applications & case studies.

4

Big Data Architectures & Patterns: MapReduce, Sharding, Bloom Filters,

Lambda Architecture, Consistency, Availability & Partition Tolerance (CAP), 5

Consensus in Distributed Systems, Leader Election and Other analytics patterns

Python Programming for Big Data Applications: Introduction to Python, Big

Data stack setup and examples, Hortonworks Data Platform/Apache Ambari,

Amazon EMR, Running Python MapReduce examples on big data stack.

5

BIG DATA ACQUISITION & STORAGE

Data Acquisition: Apache Flume; Apache Sqoop; Publish - Subscribe Messaging

Frameworks; Big Data Collection Systems, Messaging queues, Custom connectors,

Implementation examples

4

Big Data Storage: HDFS, HBase, Kudu

NoSQL Databases: Key-value databases, Document databases, Column Family

databases, Graph databases

3

Standard ETL Tools: Standard Industry tools. 2

SECTION-B

BATCH ANALYTICS, REAL-TIME ANALYTICS & INTERACTIVE QUERYING

Batch Data Analysis: Hadoop & YARN, MapReduce & Pig, Spark core, Batch data

analysis examples & case studies

4

Real-time Analysis: Stream processing with Storm, In-memory processing with

Spark Streaming, Real-time analysis examples & case studies

3

Interactive Querying: Hive, Spark SQL, Interactive querying examples & case

studies

4

BIG DATA VISUALIZATION & APPLICATION DEPLOYMENT

Cloud Computing Platforms: Amazon Web Services (AWS), Deploying Big Data

applications in the cloud

4

Web Frameworks & Serving Databases: Django - Python web framework, Using

different serving databases with Django

3

Data Visualization: Building visualizations with Lightning, pyGal & Seaborn 4

Suggested

Books

1. Arshdeep Bahga, Vijay Madisetti, "Big Data Analytics: A

Hands-On Approach", VPT Publishers, 2018

2. Big Data Black Book, D T editorial service, Dreamtech

Press, Wiley India; 1st edition, 2016.

3. Baesens Bart, "Analytics in A Big Data World - The Essential

Guide To Data Science and Its Applications", Wiley, 2014

4. Radha Shankarmani, M. Vijayalakshmi, "Big Data Analytics",

Wiley, 2016

5. Acharya Seema, Subhashini Chellappan, "Big Data and

Analytics", Wiley, 2015

6. NPTEL Course on ―The Joy of Computing using Python‖ by

Dr. Sudarshan Iyengar



7. NPTEL Course on ―Programming, Data Structures and

Algorithms in Python‖ by Dr. Madhavan Mukund


8. NPTEL Course on ―Big Data Computing‖ by Dr. Rajiv Misra


Title CLOUD COMPUTING AND VIRTUALIZATION Credits 04


Max. Marks External: 50 Internal: 50 Elective Y

Pre-requisites Basic Knowledge of Distributed Computing Contact Hours 45

Objectives This course will enable students to understand cloud computing concepts and prepares students to be in a position to design cloud based applications for distributed systems.

Course Outcomes

After the completion of this course, the students will be able to:

1. Learn core concepts of cloud computing paradigm 2. Apply virtualization in the cloud ecosystem 3. Design and Implement scheduling algorithms for cloud 4. Illustrate the fundamental concepts of cloud storage and demonstrate

their use in storage systems such as Amazon S3 and HDFS 5. Analyse various security issues in the cloud

Note for

Examiner


SECTION-A

Overview of Computing Paradigms

Recent Trends in Computing: Distributed Computing, Cluster Computing, Grid

Computing, Utility Computing, Cloud Computing

Evolution of Cloud Computing: Migrating into a Cloud

5

Cloud Computing Basics

Cloud Computing Overview; Characteristics; Applications; Benefits; Limitations;

Challenges, SOA; Cloud Computing Service Models: Infrastructure as a Service;

Platform as a Service; Software as a Service

Cloud Computing Deployment Models: Private Cloud; Public Cloud; Community

Cloud; Hybrid Cloud, Major Cloud Service providers

6

Virtualization Concepts



Overview of Virtualization Technologies, Types of Virtualization, Benefits of

Virtualization, Hypervisors

VM Provisioning & Migration: VM Lifecycle, VM Provisioning Process, VM Migration

Techniques

6

Scheduling in Cloud

Overview of Scheduling problem, Different types of scheduling, Scheduling for

independent and dependent tasks, Static vs. Dynamic scheduling, Optimization

techniques for scheduling

5

SECTION-B

Cloud Storage

Overview; Storage as a Service, Benefits and Challenges, Storage Area

Networks(SANs), Case Study of Amazon S3

5

Cloud Security

Infrastructure Security: Network Level Security, Host Level Security and Application

Level Security;

Data Security: Data Security & Privacy Issues; Identity & Access Management; Legal

Issues in Cloud Computing

6

Mobile Cloud Computing

Overview of Mobile Cloud Computing, Advantages, Challenges, Using Smartphones

with the Cloud, Offloading techniques - their pros and cons, Mobile Cloud Security.

6

SLA Management:

Overview of SLA, Types of SLA, SLA Life Cycle, SLA Management Process

4

Case Study of Implementation tools/Simulators 2

Suggested

Books

1. Rajkumar Buyya, James Broberg, AndrzejGoscinski (Editors): Cloud

Computing: Principles and Paradigms, Wiley, 2011

2. Barrie Sosinsky: Cloud Computing Bible, Wiley, 2011.

3. Anthony T. Velte, Toby J. Velte, and Robert Elsenpeter: Cloud Computing:

A Practical Approach, McGraw Hill, 2010.

4. Judith Hurwitz, Robin Bloor, Marcia Kaufman,Fern Halper: Cloud

Computing for Dummies, Wiley, 2010.

5. BorkoFurht, Armando Escalante (Editors): Handbook of Cloud Computing,

Springer, 2010.

Title INDUSTRIAL ROBOTICS Credits 04



Pre-requisites Basic Engineering Mathematics Contact Hours 45

Objectives The objective of this course is to impart knowledge about industrial robots for their control and design.

Course Outcomes

After the completion of this course, the students will be able to

Perform kinematic and dynamic analyses with simulation

Design control laws for a robot

Integrate mechanical and electrical hardware for a real prototype of robotic device.

Select a robotic system for given application.

Note for

Examiner -

1. Introduction to Robotics

Robot Subsystems, Classification of Robots, Grippers, Sensors, Industrial Applications.

7

2. Transformations

Robot Architecture, Pose of a Rigid body, Coordinate Transformation, Denavit and Hartenberg (DH) Parameters.

7

3. Kinematics

Forward Position Analysis, Inverse Position Analysis, Velocity Analysis: The Jacobian Matrix, Link Velocities, Jacobian Computations, Forward and Inverse Velocity Analysis, Acceleration Analysis.

8

4. Statics and Manipulator Design

Forces and Moments Balance, Equivalent Joint Torques, Role of Jacobian in Statics, Manipulator Design.

7

5. Dynamics

Inertia Properties, Euler-Lagrange Formulation, Newton-Euler Formulation, Recursive Newton-Euler Algorithm, Dynamic Algorithms.

8

6. Robot Trajectory Planning and Control

Multivariable Robot Control, Stability of Multi-DOF Robot, Linearized Control, Proportional Derivative (PD) Position Control, Computed-torque (Inverse Dynamics) Control, Joint Space Planning, Cartesian Space Planning, Point-to-Point vs. Continuous Path Planning

8

Suggested

Books

1. Introduction to Robotics, S. K. Saha, McGraw Hill Education (India) Pvt. Ltd. 2. Introduction to Robotics – Analysis, Control, Applications, Saeed B. Niku, Wiley

India Pvt. Ltd. 3. Introduction to Robotics – Mechanics and Control, John J. Craig, Pearson

Education Inc.

4. Robotics & Control – R.K. Mittal & I.J. Nagrath – TMH Publications 5. Robotics for Engineers - Yoram Korean- McGrew Hill Co. 6. Industrial Robotics – Technology, Programming and Applications - M.P.Groover,

M.Weiss, R.N.Nagel, N.G.Odrey.

Title WIRELESS SENSOR NETWORKS Credits 04



Pre-requisites - Contact Hours 44

Objectives

Fueled by recent advances in MEMS and wireless communication technologies, sensor actuator networks and networks of embedded devices in general have become extremely popular and have been used in several applications such as environmental monitoring, perimeter security, structural control, asset tracking and personal healthcare systems. Lectures will emphasize aspects of distributed systems such as fault-tolerance, reliability, and security. The course is aimed both at students who wish to do research in the sensor networks area, as well as at students from related disciplines, such as signal processing, wireless communications, databases, algorithms, etc., who wish to understand what new challenges sensor networks pose for their own discipline. This course provides an insight into different layers and their design considerations. A thorough knowledge of infrastructure establishment and sensor network platform is provided.

Course Outcomes


1. Identify the requirements for the real world problems. 2. Conduct a survey of available literature in the preferred field of study. 3. Study and enhance software/ hardware skills. 4. Demonstrate and build the project successfully by hardware/sensor

requirements, coding, emulating and testing. 5. To report and present the findings of the study conducted in the preferred

domain 6. Demonstrate an ability to work in teams and manage the conduct of the

research study.

Note for

Examiner


SECTION-A

Introduction: Introduction to Sensor Networks, unique constraints and challenges, Advantage of Sensor Networks, Applications of Sensor Networks, Mobile Adhoc NETworks (MANETs) and Wireless Sensor Networks, Enabling technologies for Wireless Sensor Networks

8

Sensor Node Hardware and Network Architecture: Single-node architecture, Hardware components & design constraints, Operating systems and execution environments, introduction to TinyOS and nesC, Network architecture, Optimization

8

goals and figures of merit, Design principles for WSNs, Service interfaces of WSNs, Gateway concepts.

Deployment and Configuration: Localization and positioning, Coverage and connectivity, Single-hop and multihop localization, self-configuring localization systems, sensor management

6

SECTION-B

Network Protocols: Issues in designing MAC protocol for WSNs, Classification of

MAC Protocols, S-MAC Protocol, B-MAC protocol, IEEE 802.15.4 standard and Zig

Bee, Dissemination protocol for large sensor network.

7

Routing protocols: Issues in designing routing protocols, Classification of routing protocols, Energy-efficient routing, Unicast, Broadcast and multicast, Geographic routing.

7

Data Storage and Manipulation: Data centric and content based routing, storage and retrieval in network, compression technologies for WSN, Data aggregation technique. Applications: Detecting unauthorized activity using a sensor network, WSN for Habitat Monitoring.

8

Suggested

Books

1. Protocols and Architectures for Wireless Sensor Network by Holger Kerl & Andreas Willig, John Wiley and Sons, 2005

2. Wireless Sensor Network by Raghavendra, Cauligi S, Sivalingam, Krishna M., Zanti Taieb, Springer 1st Ed., 2004

3. Wireless Sensor Network by Feng Zhao, Leonidas Guibas, Elsevier, 1st Ed. 2004

4. Wireless Sensor Network: Technology, Protocols and Application by Kazem, Sohraby, Daniel Minoli, Taieb Zanti, John Wiley and Sons, 1st Ed., 2007

5. Networking Wireless Sensors by B. Krishnamachari, Cambridge University Press.

6. Sensor Networks and Configuration: Fundamentals, Standards, Platforms, and Applications by N. P. Mahalik, Springer Verlag.

Title PARALLEL AND DISTRIBUTED COMPUTING Credits 04

Code ECEAI 1106 Semester: 1st L T P 4 0 0


Pre-requisites Software engineering, testing tools Contact Hours 45

Objectives

The course tells about programming paradigms used in parallel computation, about the organization of parallel systems, and about the application of programs and systems to solve interesting problems.

Course

Outcomes


1. Develop, test and debug RPC based client-server programs. 2. Design and build application programs on distributed systems. 3. Improve the performance and reliability of distributed programs. 4. Design and build newer distributed file systems for any OS.

Note for

Examiner

The Semester question paper of a subject will be of 50 marks having 7 questions of equal marks. First question, covering the whole syllabus and having questions of conceptual nature, will be compulsory. Rest of the paper will be divided into two parts having three questions each and the candidate is required to attempt at least two questions from each part.

SECTION-A

Introduction

Basic issues and model Asynchrony, delay, failure concurrency, Communication

topology, load balancing, scaling

6

Basic Approaches

Agreement and consensus problems, transactions, Algorithms for reduction, scans

(also non-parallel issues). Analysis: work/time complexity.

12

SECTION-B

Shared Memory

Models and primitives, PRAM, VRAM, semaphores, spin-locks, Barriers’

implementations, NESL, Threads, distributed shared memory.

11

Parallel Architectures

Survey of Architectures KSR, TMC, MasPar, workstation clusters 4

Algorithm Development and Analysis

Parallel algorithms, Connected components (dense and sparse case), Sorting,

distributed algorithms, Clock synchronization

12

Suggested 1. Kai, Hwang: Computer Architecture and parallel processing, Tata

Books McGraw Hill Co.

2. F.T.Leighto: Introduction to Parallel Algorithms and Architectures: Arrays,

Trees, Hypercubes, Morgan Kaufinann Publishers, San Mateo, California

3. Joseph JaJa: An Introduction to Parallel algorithms, Addison Wesley.

4. Patterson: Computer Architecture-Quantitative Analysis

Title RESEARCH METHODOLOGY Credits 04

Code ECEAI 1203 Semester - 2nd L T P 4 0 0

Max. Marks External - 50 Internal - 50 Elective Y

Pre-requisites Basic Mathematics Contact Hours 45

Objectives To make students familiar with various methodologies of research.

Course Outcomes

On completion of the course, the students will be able to 1. Understand the concept of research, identify research problems and

learn the basics of literature review. 2. Interpret a good research design and learn the different types of

sampling procedures. 3. Write research reports and publications that follow research ethics and

standards. 4. Distinguish between data and their methods of measurement and

collection. 5. Apply the knowledge of statistical methods of research in their field of

study using different statistical software.

Note for

Examiner


SECTION-A

Defining Research and Literature Review

Need and Significance of Research, Research Process, Different Methods of

Research, Different approaches to literature survey, difference between survey and

review, Locating and selecting a research problem, Defining a problem statement,

formulation of objectives, Retrieving literature from libraries (Online and Offline)

7

Research Design and Methodology

Concept of research design, Concept of population and sample, Selection of

sample size, Different types of Sampling, Methods of data collection, Concept of

data measurement: Nominal, Ordinal, Interval and Ratio, Ethical issues related to

data collection, Various Research Data Repositories

5

Statistical Methods of Analysis

Descriptive Statistics: Mean, Median, Mode, Range, Standard Deviation, regression

and correlation analysis.

Inferential Statistics: Estimation of parameters, Hypothesis, Types of Hypothesis,

Testing of Hypothesis, Test of Normality, Introduction to Parametric and Non

Parametric tests,

10

Test of significance: t-test, chi square test, ANOVA (1-way, 2-way), Repeated

Measures ANOVA, ANCOVA, α-correction.

SECTION-B

Introduction to Statistical software

SPSS/Minitab/Ms Excel with hands on practical session on concepts detailed in

section A3.

5

Procedure for writing a research proposal and research report

Purpose, types and Components of research reports, layout of report, Ethical

issues related to publishing, plagiarism and self-plagiarism, Introduction to ArXive,

BioarXive, Overleaf and Research Gate: Uses and Benefits.

8

Introduction of Software

Hands on practical session on software useful for technical report writing such as

MS-Word/ Open-Office (reference Management, formatting, Tracking changes,

Handling Images and tables layout etc.), Google Docs, Writing document in Latex,

Introduction to Mendeley.

Graphical presentation of results in different types of graphs and plots.

10

Suggested

Books

1. Kothari C.K. (2004), Research Methodology-Methods and Techniques

(New Age International, New Delhi) 2nd Ed.

2. Panneerselvam R., Research Methodology, PHI, 2nd Edition

3. N. Gurumani. Scientific Thesis writing and Paper Presentation. MJP

Publishers

Title BIO-INSPIRED COMPUTATION Credits 04




Objectives Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course aims to provide an understanding of the distributed architectures of natural complex systems, and how those can be used to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, complexity, computer science, informatics, cognitive science, robotics, and cybernetics. Through this course, students will be introduced to fundamental topics in bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems.

Course Outcomes

At the end of the course, the students will have: 1. An overview of algorithms that can be used for autonomous design and

adaptation of intelligent systems.

2. Insight in biologically inspired as well as traditional machine learning methods for search, optimization and classification.

3. An overview of the benefits and drawbacks of the various methods.

4. Knowledge of using the methods for real-world applications.

5. Practical assignments with experience being achieved from both using tools as well as coding your own algorithms.

Note for

Examiner


SECTION-A

Evolutionary Computation Foundation of Evolutionary theory, Evolutionary Strategies, Evolutionary programming, Evolutionary Algorithms, Evolutionary Algorithm Case Study

11

Genetic Algorithms Genetic Representations, Initial Population, Fitness Function, Selection and Reproduction, Genetic Operators (Selection, Crossover, Mutation)

11

SECTION-B

Other Optimization Methods Artificial Immune Systems, Other Algorithms Harmony Search, Honey-Bee Optimization, Memetic Algorithms, Co-evolution, Multi-objective Optimization, Artificial Life, Constraint Handling

11

Collective Systems Collective Behavior and Swarm Intelligence, Particle Swarm Optimization and Ant Colony Optimization, Artificial evolution of Competing Systems, Artificial Evolution of cooperation and competition. Recent topics from research papers.

11

Suggested

Books

1. Bio-Inspired Computing and Communication by Pietro Lio and Eiko

Yoneki 2. Bio-Inspired Models of Network, Information, and Computing Systems

by Gianni A Di Caro and Guy Theraulaz 3. Bioinspired Computation in Combinatorial Optimization: Algorithms and

Their Computational Complexity by Carsten Witt and Frank Neumann 4. Bio-Inspired Computational Algorithms and Their Applications by

Shangce Gao

Title EMBEDDED SYSTEM DESIGN & ARCHITECTURE Credits 04




Objectives

This is a course to explore in Embedded Systems hardware and firmware. The architecture and instruction sets of the ARM processor will be

discussed. The course uses a bottom‐up approach in gradually building and optimizing Embedded System Software. This course introduces basic RTOS principles and Embedded Software Development process. The skills provided by this course are necessary in designing digital control units for consumer electronics, industrial automation, telecommunication systems, etc.

Course Outcomes


1. Understand what is an embedded system. 2. Understand different components of an embedded system and their

interactions. 3. Become familiar with programming environment used to develop

embedded systems. 4. Understand key concepts of embedded systems like IO timers,

interrupts, interaction with peripheral devices 5. Learn debugging techniques for an embedded system.

Note for

Examiner


SECTION-A

INTRODUCTION TO EMBEDDED CONCEPTS Introduction to embedded systems, Application Areas, Categories of embedded systems, Overview of embedded system architecture, Specialties of embedded systems, recent trends in embedded systems, Architecture of embedded systems, Hardware architecture, Software architecture, Application Software, Communication Software.

10

OVERVIEW OF ARM AND CORTEX, M3 Background of ARM Architecture, Architecture Versions, Processor Naming, Instruction Set Development, Thumb2 Instruction Set Architecture. Cortex, M3 Basics: Registers, General Purpose Registers, Stack Pointer, Link Register, Program Counter, Special Registers, Operation Mode, Exceptions and Interrupts, Vector Tables, Stack Memory Operations, Reset Sequence. Cortex, M3 Instruction Sets: Assembly Basics, Instruction List, Instruction Descriptions, Cortex, M3 Implementation Overview: Pipeline, Block Diagram, Interfaces on Cortex, M3, I Code Bus, D Code Bus, System Bus, External PPB and DAP Bus

10

SECTION-B

CORTEX EXCEPTION HANDLING AND INTERRUPTS Exceptions: Exception Types, Priority, Vector Tables, Interrupt Inputs and Pending Behavior, Fault Exceptions, Supervisor Call and Pending Service Call. NVIC: Nested Vectored Interrupt Controller Overview, Basic Interrupt Configuration, Software Interrupts and SYSTICK Timer. Interrupt Behavior: Interrupt/Exception Sequences, Exception Exits, Nested Interrupts, Tail, Chaining Interrupts, Late Arrivals and Interrupt Latency.

10

CORTEX, M3/M4 PROGRAMMING Cortex, M3/M4 Programming: Overview, Typical Development Flow Using C, CMSIS (Cortex Microcontroller Software Interface Standard), Using Assembly. Exception Programming: Using Interrupts, Exception/Interrupt Handlers, Software Interrupts, Vector Table Relocation. Memory Protection Unit and other Cortex, M3 features: MPU Registers, Setting Up the MPU, Power Management, Multiprocessor Communication. STM32L15xxx ARM Cortex M3/M4 Microcontroller: Memory and Bus Architecture, Power Control, Reset and Clock Control. STM32L15xxx Peripherals: GPIOs, System Configuration Controller, NVIC, ADC, Comparators, GP Timers, USART. Development & Debugging Tools: Software and Hardware tools like Cross Assembler, Compiler, Debugger, Simulator, In-circuit Emulator (ICE), Logic Analyzer etc.

15

Suggested

Books

[1] Raj Kamal, ―Embedded Systems: Architecture, Programming, and Design,‖ 2nd Edition, Tata McGraw Hill, 2008.

[2] K.V. Shibu, ―Introduction to Embedded Systems,‖ Tata McGraw Hill Private Education Limited, 2009.

[3] Peter Barry and Patric Crowley, ―Modern Embedded Computing,‖ Morgan Kaufmann, 2012.

[4] Joseph Yiu, ―The Definitive Guide to the ARM Cortex, M3,‖ 2nd Edition, Elsevier Inc. 2010.

[5] Andrew N Sloss, Dominic Symes and Chris Wright, ―ARM System Developer's Guide Designing and Optimizing System Software,‖ Elsevier Publications, 2006.

[6] Steve Furber, ―ARM System on Chip Architecture‖, 2nd Edition, Pearson Education, 2015.

[7] Dr. K.V.K. Prasad, ―Embedded / Real Time Systems: Concepts, Design and Programming Black Book,‖ Paperback Format, Dreamtech Press, 2003.

[8] Ajay Deshmukh, ―Microcontroller, Theory & Applications,‖ Tata McGraw Hill, 2005.

[9] Arnold. S. Berger, ―Embedded Systems Design, An introduction to Processes, Tools and Techniques,‖1st Edition, CRC Press, 2001.

[10] Raj Kamal, ―Microcontroller, Architecture Programming Interfacing and System Design,‖ 2nd Edition, Pearson Education, 2011.

[11] Cortex, M series, ARM Reference Manual [12] STM32L152xx ARM Cortex M3 Microcontroller Reference Manual

5/97 [13] ARM Company Ltd. ―ARM Architecture Reference Manual– ARM DDI

0100E‖.

Title FUZZY SYSTEMS AND APPLICATIONS Credits 04




Objectives

The aim of this course is to equip graduate students with some state-of-the-art fuzzy-logic technology and fuzzy system design methodologies. Fuzzy logic is the only solution when we don’t have any mathematical modeling of problem solving (i.e., algorithm) and need a solution to a complex problem in real time. It has enormous applications in many application areas such as medical diagnosis, computer vision, hand written character recognition, pattern recognition, machine intelligence, weather forecasting, network optimization, VLSI design, etc.

Course Outcomes


1. Understand fuzzy logic basics and operations, Fuzzy arithmetic and representations and classical logic.

2. Apply fuzzy logic for engineering problems.

Note for

Examiner

The Semester question paper of a subject will be of 50 marks having 7

questions of equal marks. First question, covering the whole syllabus and

having questions of conceptual nature, will be compulsory. Rest of the paper

will be divided into two parts having three questions each and the candidate

is required to attempt at least two questions from each part.

SECTION-A

INTRODUCTION Fundamentals of fuzzy logic: Introduction, Classical and Fuzzy sets, Fuzzy operators, Classical Relations and Fuzzy Relations, membership functions

5

FUZZIFICATION AND DEFUZZIFICATION Fuzzy modeling of uncertainty, Fuzzy arithmetic, numbers, vectors, and the Extension Principle, Fuzzy to crisp conversions and methods for defuzzification

10

SECTION-B

FUZZY RULE BASE AND FUZZY DECISION MAKING

10

Fuzzy rule based systems, Fuzzy nonlinear simulation and fuzzy decision making

PROCESS CONTROL USING FUZZY LOGIC Fuzzy classification and Fuzzy control systems, special forms of fuzzy control

10

FUZZY LOGIC: APPLICATIONS AND IMPLEMENTATION Fuzzy pattern recognition systems, Neuro-fuzzy systems and evolutionary learning in fuzzy systems, Fuzzy logic implementation using Python scikit, fuzzy

10

Suggested

Books

1. "Fuzzy Logic with Engineering Applications" Timothy J. Ross, ISBN:

0470860758, 650 pages, paperback, published by John Wiley & Sons, 2nd edition, 2004.

2. Fuzzy Logic: Intelligence, Control, and Information, J. Yen, R. Langari, Prentice Hall, 1998.

3. Fuzzy Logic: Implementation and Applications by Marek J. Patyra and Daniel J. Mlynek

4. ―Fuzzy Control,‖ Kevin M. Passino and Stephen Yurkovich, Addison Wesley Longman, Menlo Park, CA, 1998 (later published by Prentice Hall).

5. ―Fuzzy Logic: a Practical Approach,‖ McNeill, Martin and Ellen Thro., 1994 Academic Press Professional.

https://www.amazon.in/Fuzzy-Logic-Implementation-Marek-Patyra/dp/3322889572/ref=sr_1_1?s=books&ie=UTF8&qid=1550693858&sr=1-1

https://www.amazon.in/Fuzzy-Logic-Implementation-Marek-Patyra/dp/3322889572/ref=sr_1_1?s=books&ie=UTF8&qid=1550693858&sr=1-1

master of engineering in electronics & communication

Documents