BE Electronics Computer Science_1 Syllabus Mumbai University

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Copy to : -
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AC – 11 July, 2022
Item No. – 6.33 (R)





University of Mumbai








Revised Syllabus for
B.E. (Electronics & Computer Science )
(Sem. - VII to VIII)
(Choice Based Credit System)





(With effect from the academic year 2022 -23)

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Preamble

To meet the challenge of ensuring excellence in engineering education, the issue of quality needs to be addressed,
debated and taken forward in a systematic manner. Accreditation is the principal means of quality assurance in higher
education. The major emphasis of accreditation process is to measure the outcomes of the program that is being
accredited. In line with this Faculty of Science and Techn ology (in particular Engineering)of University of Mumbai
has taken a lead in incorporating philosophy of outcome based education in the process of curriculum development.

Faculty resolved that course objectives and course outcomes are to be clearly define d for each course, so that all
faculty members in affiliated institutes understand the depth and approach of course to be taught, which will enhance
learner’s learning process. Choice based Credit and grading system enables a much -required shift in focus f rom
teacher -centric to learner -centric education since the workload estimated is based on the investment of time in
learning and not in teaching. It also focuses on continuous evaluation which will enhance the quality of education.
Credit assignment for co urses is based on 15 weeks teaching learning process, however content of courses is to be
taught in 12 -13 weeks and remaining 2 -3 weeks to be utilized for revision, guest lectures, coverage of content beyond
syllabus etc. There was a concern that the earlier revised curriculum more focused on providing information and
knowledge across various domains of the said program, which led to heavily loading of students in terms of direct
contact hours. In this regard, faculty of science and technology resolved that to minimize the burden of contact hours,
total credits of entire program will be of 170, wherein focus is not only on providing knowledge but also on building
skills, attitude and self -learning. Therefore in the present curriculum skill based laborat ories and mini projects are
made mandatory across all disciplines of engineering in second and third year of programs, which will definitely
facilitate self-learning of students. The overall credits and approach of curriculum proposed in the present revisi on is
in line with AICTE model curriculum.

The present curriculum will be implemented for Second Year of Engineering from the academic year 2020 -21.
Subsequently this will be carried forward for Third Year and Final Year Engineering in the academic years 2021 -22,
2022 -23, respectively.


Dr. S. K. Ukarande Dr. Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

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Incorporation and implementation of Online
Contents from NPTEL/ Swayam Platform

The curriculum revision is mainly focused on knowledge component, skill based activities and project based
activities. Self -learning opportunities are provided to learners. In the revision process this time in particular
Revised syllabus of ‘C‘scheme wherever possible additional resource links of platforms such as NPTEL,
Swayam are appropriately provided. In an earlier revision of curriculum in the y ear 2012 and 2016 in
Revised scheme ‘A' and ‘B' respectively, efforts were made to use online contents more appropriately as
additional learning materials to enhance learning of students.
In the current revision based on the recommendation of AICTE model c urriculum overall credits are reduced
to 171, to provide opportunity of self -learning to learner. Learners are now getting sufficient time for self -
learning either through online courses or additional projects for enhancing their knowledge and skill sets.
The Principals/ HoD’s/ Faculties of all the institute are required to motivate and encourage learners to use
additional online resources available on platforms such as NPTEL/ Swayam. Learners can be advised to take
up online courses, on successful completion they are required to submit certification for the same. This will
definitely help learners to facilitate their enhanced learning based on their interest.





Dr. S. K. Ukarande Dr. Anuradha Muzumdar
Associate Dean Dean
Faculty of Science and Technology Faculty of Science and Technology
University of Mumbai University of Mumbai

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Preface
Technical education in the country is undergoing a paradigm shift in current days. Think tank at national level are deliberat ing on
the issues, which are of utmost importance and posed challenge to all the spheres of technical education. Eventually, impact of
these developments was visible and as well adopted on bigger scale by almost all universities across the country. These are
primarily an ad option of CBCS (Choice base Credit System) and OBE (Outcome based Education) with student centric and
learning centric approach. Education sector in the country, as well, facing critical challenges, such as, the quality of grad uates,
employability, basic s kills, ability to take challenges, work ability in the fields, adoption to the situation, leadership qualities,
communication skills and ethical behaviour. On other hand, the aspirants for admission to engineering programs are on decline
over the years. An overall admission status across the country is almost 50%; posing threat with more than half the vacancies in
various colleges and make their survival difficult. In light of these, an All India Council for Technical Education (AICTE), the
national regulat or, took initiatives and enforced certain policies for betterment, in timely manner. Few of them are highlighted
here, these are design of model curriculum for all prevailing streams, mandatory induction program for new entrants, introduc tion
of skill base d and inter/cross discipline courses, mandatory industry internships, creation of digital contents, mandate for use of
ICT in teaching learning, virtual laboratory and so on.
To keep the pace with these developments in technical education, it is mandatory for the Institutes & Universities to adopt these
initiatives in phased manner, either partially or in toto. Hence, the ongoing curriculum revision process has a crucial role to play.
The BoS of Electronics Engineering under the faculty of Science & Technol ogy, under the gamut of Mumbai University has
initiated a step towards adoption of these initiatives. We, the members of Electronics Engineering Board of Studies of Mumbai
University feel privileged to present the revised version of curriculum for Electron ics Engineering program to be implemented
from academic year 2020 -21. Some of the highlights of the revision are;
i. Curriculum has been framed with reduced credits and weekly contact hours, thereby providing free slots to the students
to brain storm, debate, explore and apply the engineering principles. The leisure provided through this revision shall
favour to inculcate innovation and research attitude amongst the students.
ii. New skill-based courses have been incorporated in curriculum keeping in view AICTE model curriculum.
iii. Skill based Lab courses have been introduced, which shall change the thought process and enhance the programming
skills and logical thinking of the students
iv. Mini -project with assigned credits shall provide an opportunity to work in a grou p, balancing the group dynamics,
develop leadership qualities, facilitate decision making and enhance problem solving ability with focus towards socio -
economic development of the country. In addition, it shall be direct application of theoretical knowledg e in practice,
thereby, nurture learners to become industry ready and enlighten students for Research, Innovation and Entrepreneurship
thereby to nurture start-up ecosystem with better means.
v. An usage of ICT through NPTEL/SWAYAM and other Digital initiatives of Govt. of India shall be encouraged,
facilitating the students for self -learning and achieve the Graduate Attribute (GA) specified by National Board of
accreditation (NBA) i.e. lifelong learning.

Thus, this revision of curriculum aimed at cr eating deep impact on the teaching learning methodology to be adopted by affiliated
Institutes, thereby nurturing the student fraternity in multifaceted directions and create competent technical manpower with
legitimate skills. In times to come, these grad uates shall shoulder the responsibilities of proliferation of future technologies and
support in a big way for 'Make in India' initiative, a reality. In the process,
BoS, Electronics Engineering got whole hearted support from all stakeholders including faculty, Heads of department of
affiliating institutes, experts faculty who detailed out the course contents, alumni, industry experts and university official
providing all procedural support time to time. We put on record their involvement and sincerely t hank one and all for contribution
and support extended for this noble cause.

Boards of Studies in Electronics Engineering
Sr. No. Name Designation Sr. No. Name Designation
1 Dr. R. N. Awale Chairman 5 Dr. Rajani Mangala Member
2 Dr. Jyothi Digge Member 6 Dr. Vikas Gupta Member
3 Dr. V. A. Vyawahare Member 7 Dr. D. J. Pete Member
4 Dr. Srija Unnikrishnan Member 8 Dr. Vivek Agarwal Member

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Program Structure for Final Year Electronics and Computer Science
UNIVERSITY OF MUMBAI
(With Effect from 2022 -2023)

Semester VII

Course
Code Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
TH PR Tut TH Pract Tut Total
ECC 701 VLSI Design 3 - - 3 - - 3
ECC 702 Internet of Things 3 - - 3 - - 3
ECC
DO701 Department Level Optional
Course - III 3 - - 3 - - 3
ECC
DO702 Department Level Optional
Course - IV 3 - - 3 - - 3
ECC
IO701 Institute Level Optional Course
- I 3 - - 3 - - 3
ECL701 VLSI Design Lab - 2 1 1
ECL702 Internet of Things Lab - 2 - - 1 - 1
ECL703 Department Level Optional
Course - III Lab - 2 - - 1 - 1
ECP701 Major Project - I - 6 - - 3 - 3
Total 15 12 - 15 6 - 21


Course
Code Course
Name Examination Scheme
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
TW Pract/
Oral
Total Test 1 Test 2 Av
ECC
701 VLSI Design 20 20 20 80 03 - - 100
ECC
702 Internet of Things 20 20 20 80 03 - - 100
ECC
DO701 Department Level
Optional Course - III 20 20 20 80 03 - - 100
ECC
DO702 Department Level
Optional Course - IV 20 20 20 80 03 - - 100
ECC
IO701 Institute Level Optional
Course - I 20 20 20 80 03 - - 100
ECL701 VLSI Design Lab - - - - - 25 25 50
ECL702 Internet of Things Lab - - - - - 25 25 50
ECL703 Department Level
Optional Course - III
Lab
-
-
-
-
-
25
25
50
ECP701 Major Project - I - - - - - 50 - 50
Total 100 400 - 125 75 700
Department Level Optional Courses:
Department Level Optional Course -III (DO701) Department Level Optional Course -IV (DO702)
1. Deep Learning 1. Cloud Computing
2. Image Processing 2. Mobile Communication
3. Big Data Analytics 3. Cyber Security
4. Advanced Database Management Systems 4. Blockchain Technology

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Program Structure for Final Year Electronics and Computer Science
UNIVERSITY OF MUMBAI
(With Effect from 2022 -2023)

Semester VIII

Course
Code Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
TH PR Tut TH Pract Tut Total
ECC 801 Robotics 3 - - 3 - - 3
ECC
DO801 Department Level Optional
Course -V 3 - - 3 - - 3
ECC
DO802 Department Level Optional
Course -VI 3 - - 3 - - 3
ECC
IO801 Institute Level Optional Course
- II 3 - - 3 - - 3
ECL 801 Robotics Lab - 2 - - 1 - 1
ECL 802 Department Level Optional
Course - V Lab - 2 - 1 1
ECP 801 Major Project II - 12 - - 6 - 6
Total 12 16 - 12 8 - 20



Course
Code Course
Name Examination Scheme
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
TW Pract/
Oral
Total Test 1 Test 2 Av
ECC 801 Robotics 20 20 20 80 03 - - 100
ECC
DO801 Department Level
Optional Course -V 20 20 20 80 03 - - 100
ECC
DO802 Department Level
Optional Course -VI 20 20 20 80 03 - - 100
ECC
IO801 Institute Level Optional
Course - II 20 20 20 80 03 - - 100
ECL 801 Robotics Lab - - - - 03 25 25 50
ECL 802 Department Level
Optional Course - V
Lab
-
-
-
-
-
25
25
50
ECP 801 Major Project II - - - - - 50 100 150
Total 80 320 - 100 150 650

Department Level Optional Courses:

Department Level Optional Course -V (DO801) Department Level Optional Course -VI (DO802)
1. MEMS Technology 1. Advanced Networking Technologies
2. Natural Language Processing 2. Multimedia and Virtual Reality
3. 3-D Printing and Design 3. Quantum Computing
4. Advanced Algorithms 4. System Security

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Note:
1. Students group and load of faculty per week.
Mini Project 1 and 2:
Students can form groups with minimum 2 (Two) and not more than 4 (Four)
Faculty Load: 1 hour per week per four groups

Major Project 1 and 2:
Students can form groups with minimum 2 (Two) and not more than 4 (Four)
Faculty Load: In Semester VII – ½ hour per week per project group
In Semester VIII – 1 hour per week per project group

2. Out of 4 hours/week allotted for the mini -projects 1 -A and 1 -B, an expert lecture of at least
one hour per week from industry/institute or a field visit to nearby domain specific industry
should be arranged.

3. Mini -projects 2-A and 2-B should be based on DLOs.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
701 VLSI Design 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
701 VLSI Design 20 20 20 80 03 25 25 -- 150



Course Pre-requisite: Electronic Devices (ECC 302)
Electronic Circuits (ECC402)
Digital Electronics (ECC 303)

Course Objectives:

1. To understand VLSI Design flow and technology trends.
2. To realise MOS based circuits using different design styles.
3. To study semiconductor memories using MOS logic.
4. To study adder, multiplier and shifter circuits for realizing data path design.
5. Understand the Backend flow of the IC Fabrication

Course Outcomes:

After successful completion of the course students will be able to:

1. Demonstrate a clear understanding of VLSI Design flow, technology trends, scaling and MOSFET
models.
2. Design and analyse MOS based inverters.
3. Realise MOS based circuits using different design styles.
4. Realise semiconductor memories, adder, multiplier and shifter circuits using CMOS logic.
5. Understand the flow of IC Fabrication

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Contents Hrs.












































No. No.

1.1 VLSI Design flow and Technology Trends
VLSI Design Flow: Full custom and Semicustom IC design flow 05
1.2 Semiconductor Manufacturing: Semiconductor technology trend,clean rooms,
Wafer cleaning and Gettering. Fabrication flowchart for steps in IC fabrication
1.3 Scaling: Types of scaling, comparison of MOSFET Model levels
1.4 Technology Comparison: Comparison of BJT and MOS technologies,
long channel and short channel MOS devices
2 MOSFET Inverters 08
2.1 Introduction to MOS inverters: Active and passive load nMOS inverters, CMOS
inverter and their comparison
2.2 Circuit Analysis of MOS InvertersStatic Analysis of Resistive nMOS and
CMOS Inverters: Calculation of critical voltages and noise margins
2.3 Design of symmetric CMOS inverter
2.4 Analysis of CMOS inverter: Calculation of rise time, fall time and propagation
delay
2.5 Various components of power dissipation in CMOS circuits
MOS Circuit Design Styles 07
3.1 Static CMOS
3.2 Pseudo NMOS design styles
3.3 Pass transistor, Transmission gate
3.4 Dynamic: C2MOS
3.5 Significance of Stick diagram and Design rules, Layout of CMOS NAND,
CMOS NOR
Combinational and Sequential Circuit Realization 07
4.1 Analysis and design of 2-I/P NAND, 2-I/P NOR and complex Boolean function
realization using equivalent CMOS inverter for simultaneous switching,
Complex Boolean function realization using various design stylesand Basic
gates and MUX realization using pass transistor and transmission gate logic
4.2 SR Latch, JK FF, D FF, 1 Bit Shift Register realization using CMOS logic
Semiconductor Memories 06
5.1 SRAM: 6T SRAM operation, design strategy, read/write circuits, sense
amplifier,
5.2 DRAM: 1TDRAM, operation modes, leakage currents, refresh operation,
physical design
5.3 ROM Array: NAND and NOR based ROM array
5.4 Flash memory: F-N tunnelling
Data Path Design 06
6.1 Adder: CLA adder, MODL, Manchester carry chain, High -speed adders: carry
skip, carry select and carry save
6.2 Multipliers and shifter: Array multiplier and barrel shifter
Total 39

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Text Books:

1. CMOS Digital Integrated Circuits Analysis and Design, Sung -Mo Kang and Yusuf Leblebici, Tata
McGraw Hill, Revised 4thEdition.
2. Introduction to VLSI Circuits and Systems, John P. Uyemura, Wiley India Pvt. Ltd.
3. Sorab K. Gandhi, “VLSI Fabrication Principles”, Wiley, Student Edition.

Reference Books:

1. Digital Integrated Circuits: A Design Perspective, Jan M. Rabaey, Anantha ChandrakasanBorivoje
Nikolic, Pearson Education, 2nd Edition
2. Basic VLSI Design, Douglas A Pucknell, Kamran Eshraghian, Prentice Hall of India Private Ltd.
3. Logical Effort: Designing Fast CMOS Circuits, Ivan Sutherlan and Bob Sproull
4. Basics of CMOS Cell Design, Etienne Sicard and Sonia Delmas Bendhia, Tata McGraw Hill
5. CMOS VLSI Design: A Circuits and Systems Perspective, Neil H. E. Weste, David Harris and Ayan
Banerjee, Pearson Education
6. Analysis and Design of Digital Integrated Circuits, David Hodges, Horace Jackson, Resve Saleh,
McGraw -Hill, Inc.
7. Advanced Semiconductor Memories: Architectures, Designs, and Applications, Ashok K. Sharma, Wiley
Publication
8. Magnetic Memory Technology: Spin‐ Transfer‐ Torque MRAM and Beyond, Denny D.Tang, Chi-Feng
Pai, Wiley online Library
9. Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device
Applications, Daniele Ielmini , Rainer Waser, Wiley online Library




Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both the test
will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks will
be asked.
4. Remaining questions will be selected from all the module

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC 702 Internet of Things 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
702 Internet of
Things 20 20 20 80 03 25 ----- 25 150



Course Pre-requisite: Computer Networks, Embedded Systems, Web Technologies
Course Objectives:
1. To understand the basic building blocks of IoT
2. To understand various IoT protocols.
3. To introduce data handling in IoT
4. To understand the Design Methodology in IoT through case studies.

Course Outcomes:

After successful completion of the course students will be able to:

1. Understand concepts, functional blocks and communication methodology relevant to IoT.
2. Identify various components of IoT
3. Compare various communication protocols for IoT.
4. Understand various methods for data handling in IoT-based systems.
5. Design basic applications based on IoT using specific components.
6. Introduce various security issues in IoT

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Module
No. Unit
No.
Contents
Hrs.
1 Introduction to IoT 5
1.1 Definition and Characteristics of IoT
1.2 IoT Protocols
1.3 IoT Functional Blocks
1.4 IoT Communication Models
1.5 IoT Communication APIs :- REST and WebSockets
1.6 IoT Enabling Technologies
1.7 Introduction to M2M and Difference between IoT and M2M
2 Components(Things) in IoT 5
2.1 Sensor Technology, Examples of Sensors
2.2 Actuators
2.3 Applications of RFID and WSN in IoT
2.4 Exemplary Device: - R–Pi and its Interfaces, PCDuino, BeagleBone
3 Data Handling in IoT 9
3.1 Data Acquiring and Storage, Organizing the Data, Transactions and Business
Processes, Analytics
3.2 Data Collection, Storage and Computing Using Cloud Platform,
Introduction to Cloud Computing, Virtualization, Cloud Models
Cloud Services
IoT Cloud -based Data Collection, Storage, Computing using Xively
4 Design Principles for Web Connectivity 10
4.1 Communication Technologies – A comparison
4.2 Web Communication Protocols for connected devices: - CoRE Environment,
CoAP, LWM2M, MQTT, XMPP, HTTP, SOAP Protocols
4.3 LPWAN Fundamentals: LORA and NBIoT
5 IoT Design Methodology 6
5.1 Defining Specifications About : - Purpose & requirements, process, domain
model, information model, service, IoT level, Functional view, Operational
view, Device and Component Integration: - Case Studies of Home
automation,
Weather Monitoring
5.2 IoT Levels and Deployment Templates
5.3 Supply Chain Management
6 IoT Security and Vulnerabilities Solutions 4
6.1 Iot Security Tomography and Layered Attacker Model
6.2 Identity Management, Establishment, Access Control and Secure Message
Communication
6.3 Security Protocols

Total 39

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Text Books:

1. Arshdeep Bahga and Vijay Madisetti, “Internet of Things: A Hands -on Approach,
Universities Press.
2. Raj Kamal, “ Internet of Things: Architecture and Design Principles”, McGraw Hill
Education ,First edition
3. David Hanes ,Gonzalo salgueiro“IoT Fundamentals Networking Technologies, Protocols and
Use Cases for Internet of Things”, Cisco Press, Kindle 2017 Edition
4. Andrew Minteer ,”Analytics for the Internet of Things(IoT)”, Kindle Edition


Reference Books:

1. Adrian McEwen, Hakim Cassimally, : Designing the Internet of Things”, Paperback, First Edition
2. Yashavant Kanetkar, Shrirang Korde: Paperback “21 Internet of Things (IOT) Experiments”, BPB
Publications




Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the module

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Subject
Code Subject Name Teaching Scheme Credits Assigned
ECC
DO701 Department Level
Optional Course - III
(Deep Learning) Theory Practical Tutorial Theory Practical Tutorial Total
03 02 -- 03 01 -- 04




Subjec
t Code

Subject Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test
1 and Test 2

ECC
DO701 Department
Level Optional
Course - III
(Deep Learning) 20 20 20 80 03 25 25 -- 150



Course Prerequisite: Basic Mathematics, Linear Algebra, Machine Learning

Course Objectives:
1. To develop mathematical concepts required for Deep Learning algorithms
2. To gain an in-depth understanding of training Deep Neural Networks.
3. To acquire knowledge of advanced concepts of Convolution Neural Networks, Autoencoders and
Recurrent Neural Networks
4. To get familiarised with the recent trends in Deep Learning.

Course Outcomes:
After successful completion of the course students will be able to:

1. Understand the basic knowledge of Neural Networks
2. Explain the process of training, optimization and Regularization of Deep Neural Networks
3. Design supervised models for DNN
4. Design unsupervised model for DNN
5. Select suitable DNN model for a given application

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Module
No. Unit
No.
Contents
Hrs.
1 Introduction 5
1.1 Biological neuron, Mc -Culloch Pitts Neuron, Perceptron, Perceptron Learning,
Delta learning, Multilayer Perceptron: Linearly separable, linearly non-separable
classes.
1.2 Deep Networks: Fundamentals, Brief History, Three Classes of Deep Learning
Basic Terminologies of Deep Learning

2 Training, Optimization and Regularization of Deep Neural Network
2.1 Training Feedforward DNN
Multi Layered Feed Forward Neural Network, Learning Factors, Activation
functions: Tanh, Logistic, Linear, Softmax, ReLU, Leaky ReLU, Loss functions:
Squared Error loss, Cross Entropy, Choosing output function and loss function 6
2.2 Optimization
Learning with backpropagation, Learning Parameters: Gradient Descent (GD),
Stochastic and Mini Batch GD, Momentum Based GD, Nesterov Accelerated GD,
AdaGrad, Adam, RMSProp
2.3 Regularization
Overview of Overfitting, Types of biases, Bias Variance Tradeoff
Regularization Methods: L1, L2 regularization, Parameter sharing, Dropout,
Weight Decay, Batch normalization, Early stopping, Data Augmentation, Adding
noise to input and output.
3 Convolutional Neural Networks (CNN): Supervised Learning
3.1 Convolution Operation, Motivation, Basic structure of a convolutional neural
network: Padding, strides, pooling, fully connected layers, interleaving between
layers
3.2 Training a convolutional network: Backpropagation through convolution,
Backpropagation as convolution with inverted filter, Convolution/backpropagation
as matrix multiplication
3.3 Modern Deep Learning Architectures:
LeNet, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet
4 Recurrent Neural Networks (RNN)
4.1 Sequence Learning Problem, Unfolding Computational graphs, Recurrent Neural
Network, Bidirectional RNN, Back propagation Through Time (BTT), Vanishing
and Exploding Gradients, Truncated BTT
4.2 Long Short Term Memory: Selective Read, Selective write, Selective Forget,
Gated Recurrent Unit

Page 19

Page 17 of 140 University of Mumbai (B.E. Electronics and Computer Science)
17

5 Autoencoders: Unsupervised Learning
5.1 Introduction, Linear Autoencoder, Undercomplete Autoencoder, Overcomplete
Autoencoders, Regularization in Autoencoders
5.2 Denoising Autoencoders, Sparse Autoencoders, Contractive Autoencoders
6 Recent Trends and Applications
6.1 Generative Adversarial Network (GAN): Architecture
6.2 Applications: Image Compression, Brain Tumour Detection, Fraud Detection,
Expression identification
Total 39

Text Books:

1. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning. An MIT Press book.
2016.
2. Li Deng and Dong Yu, “Deep Learning Methods and Applications”, now publishers Inc (30 June 2014)
3. Satish Kumar "Neural Networks A Classroom Approach" Tata McGraw -Hill.
4. J M Zurada “Introduction to Artificial Neural Systems”, Jaico Publishing House
5. M. J. Kochenderfer, Tim A. Wheeler. “Algorithms for Optimization”, MIt Press.

Reference Books:
1. Jon Krohn, Grant Beyleveld, Aglae Bassens, “Deep Learning Illustrated: A Visual, Interactive
Guide to Artificial Intelligence”, Pearson Education.
2. Buduma, N. and Locascio, N., “Fundamentals of deep learning: Designing next-generation machine
intelligence algorithms" 2017. O'Reilly Media, Inc.".
3. François Chollet, “Deep Learning with Python”, Manning Publications, 2018.
4. Douwe Osinga. “Deep Learning Cookbook”, O’REILLY, SPD Publishers, Delhi.
5. Simon Haykin, Neural Network - A Comprehensive Foundation - Prentice Hall International, Inc


Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of the syllabus. The average marks of
both the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on the entire syllabus wherein sub questions of 2 to 5
marks will be asked.
4. Remaining questions will be selected from all the modules.

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18

Online References:

Sr. No. Website Links
1 https://nptel.ac. https://deeplearning.cs.cmu.edu/S21/index.html
2 http://www.cse.iitm.ac.in/~miteshk/CS6910.html
3 https://nptel.ac.in/courses/106/106/106106184/
4 https:// www.deeplearningbook.org/
5 http://introtodeeplearning.com /
6 http://vlabs.iitb.ac.in/vlabs -dev/labs/machine_learning/labs/index.php

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19





Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECCDO701 Image Processing 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Practical/
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECCDO
701 Image
Processing 20 20 20 80 03 25 25 150



Course Pre-requisite: Engineering Mathematics, Digital Signal Processing

Course Objectives:

1. To learn the fundamental concepts of image processing for image enhancement.
2. To learn image compression, segmentation techniques with practical applications.
3. To provide basic concepts of computer vision and its applications.

Course Outcomes:

After successful completion of the course students will be able to:

1. Represent image in its numerical and graphical form.
2. Perform different image enhancement approaches for improving image quality.
3. Elucidate the mathematical modelling of image segmentation and morphology.
4. Apply the concept of image compression.
5. Understand computer vision system elements.
6. Develop a computer vision system based on requirement.

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20



Module
No. Unit
No.
Contents
Hrs.
1 Fundamentals of Digital Image Processing
4 1.1. Introduction: Background, Representation of a Digital Image, Fundamental
steps in Image Processing, Elements of a Digital Image Processing System.
1.2. Digital Image Fundamentals: Elements of Visual Perception, A Simple Image
Model, Two-dimensional Sampling and Quantization, Tonal and Spatial
Resolutions, Introduction to ColorModel(RGB, CMYK, YIQ, HSI).
2 Enhancement in Spatial and Frequency domain 10
2.1. Enhancement in the spatial domain: Intensity Transformations, Histogram
Processing, Arithmetic and logical operations.
2.2. Spatial domain filters: Smoothing Filters, Sharpening Filters, High boost
Filter.
2.3. Image Transforms: 2D-DFT, FFT, DCT and Haar Transform.
Frequency domain enhancement, Homomorphic filtering.
3 Image Segmentation and Morphology 6
3.1. Image Segmentation: Detection of discontinuities, Edge linking and
Boundary detection, Thresholding, Region based segmentation.
3.2 Image Morphology: Dilation, Erosion, Opening, Closing, Hit-Or-Miss
Transformation, Boundary Detection, Thinning, Thickening.
4 Image Compression 6
4.1 Need of compression, Redundancy, Objective and subjective fidelity criteria.
Lossless compression: Run Length Coding, VLC (Huffman coding),
Arithmetic coding, LZW Coding, Vector Quantization.
Lossy compression: Bit plane coding, Predictive Coding, Transform Coding,
JPEG Compression standard.
5 Computer Vision Basics 9
5.1 Introduction, definition, Computer vision components,
Boundary Pre-processing: Chain code, Boundary approximation, Signatures,
Skeletonization.
5.2 Image Feature Extraction: Corners - Harris and Hessian Affine, Orientation
Histogram, SIFT, SURF, HOG, Gabor Filters and DWT.
Boundary feature descriptors: Basic descriptors, Shape descriptor, Fourier
descriptors, Statistical moments.
Region feature descriptors: color, intensity and texture.
6 Computer Vision Applications 4
6.1 Computer Vision applications: Visual inspection of equipments, object
detection like locating pedestrians, face detection and recognition, counting
vehicles, content -based image retrieval, applications of computer vision in
agriculture, health care, industry, sports etc.
Total 39

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21



Text Books:

1. Rafael C. Gonzalez and Richard E.Woods, “Digital Image Processing,” Pearson Education, edition 4,
2018.
2. Anil K.Jain, “Fundamentals of Digital Image Processing,” Pearson Education, 2010.
3. S. Jayaraman, T. Veerakumar, A. Esakkirajan, "Digital Image Processing," First Edition, McGraw Hill
Education, 2017
4. Robert J. Schallkoff , “Digital Image Processing and Computer Vision”, John Wiley and Sons,
1989.
5. J. R. Parker , “Algorithms for Image Processing and Computer Vision” John Wiley and Sons,
1997.

Reference Books:

1. Computer Vision: A Modern Approach, D. A. Forsyth, J. Ponce, Pearson Education, 2003
2. B.Chanda and D.Dutta Majumder, “Digital Image Processing and Analysis,” Prentice Hall of India, 2002
3. William K. Pratt, “Digital Image Processing,” John Wiley & Sons, 2nd edition, 2004
4. Alan C. Bovik, "Handbook of Image and Video Processing," Elsevier Science Publishing Co Inc, 2009
5. Richard Szeliski, "Computer Vision: Algorithms and Applications," 2nd edition, The University of
Washington, 2022
6. Kenneth R. Castleman, "Digital Image Processing," Pearson Education, 2006.




Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the modules

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22

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECCDLO
7014 Big Data
Analytics 03 -- -- 03 -- -- 03

Course
Code Course
Name Examination Scheme
Theory Marks Exam
Duration
(Hrs.) Term
Work Practical
and Oral Total
Internal Assessment End Sem.
Exam. Test1 Test2 Avg.
ECCDLO
7014 Big Data
Analytics
20
20
20
80
03
--
--
100

Course Prerequisite:

Basic knowledge of Database Management System

Course Objectives:

1. To Provide an Overview of an exciting growing field of Big Data Analytics.
2. To introduce the tools required to manage and analyze big data like Hadoop, NoSql, Map Reduce.
3. To teach the fundamental techniques in achieving big data analytics with scalability and streaming capability

Course Outcomes:

After successful completion of the course student will be able to:

1. Understand the key issues in big data management and its associated applications in intelligent
business andscientific computing.
2. Acquire fundamental enabling techniques and scalable algorithms like Hadoop, MapReduce & NoSQL
in big data analytics.
3. Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
4. Achieve adequate perspectives of big data analytics in various applications like recommender systems,
social media applications etc.
5. Develop applications for Big Data analysis using Hadoop and NoSQL etc.

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23

Module
No. Unit
No.
Topics
Hrs
1 Introduction to Big Data Analytics 03
1.1 Introduction to Big Data, Big Data characteristics, Types of Big Data, Traditional vs. Big
Data a business approach
1.2 Technologies Available for Big Data, Infrastructure for Big Data, Big Data Challenges, Case
Study of Big Data Solutions.
2 Hadoop 05
2.1 Introduction to Hadoop. Core Hadoop Components, Hadoop Ecosystem -Apache HBase,
Hive, HCatalog, Pig, Mahout, Oozie, Zookeeper, Sqoop, Physical Architecture, Hadoop
limitations.
3 NoSQL 06
3.1 Introduction to NoSQL, NoSQL business drivers, NoSQL database case studies.
3.2 NoSQL data architecture patterns: Key-value stores, Graph stores, Column family
(Bigtable) stores, Document stores, Variations of NoSQL architectural patterns
3.3 Using NoSQL to manage big data: What is a big data NoSQL solution? Understanding the
types of big data problems; Analyzing big data with a shared -nothing architecture; Choosing
distribution models: master -slave versus peer -to-peer; Four ways that NoSQL systems handle
big data problems, Managing MongoDB database with CRUD operations.
4 MapReduce 06
4.1 MapReduce and The New Software Stack: Distributed File Systems, Physical Organization
of Compute Nodes, Large Scale File-System Organization.
4.2 MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks, Combiners, Details of
MapReduce Execution, Coping With Node Failures
4.3 Algorithms Using MapReduce: MapReduce WordCount Program,
Matrix -Vector Multiplication by MapReduce , Relational -Algebra Operations by
MapReduce, Matrix Operations,
Matrix Multiplication by MapReduce.
5 Techniques in Big Data Analytics 13
5.1 Finding Similar Item: Nearest Neighbor Search, Similarity of Documents, Distance Measures:
Euclidean, Jaccard , Cosine , Edit and Hamming Distance with its Examples
5.2 Mining Data Streams: Data Stream Management Systems, Data Stream Model, Examples of
Data Stream Applications: Sensor Networks, Network Traffic Analysis
Filtering streams: The Blooms filter.
5.3 Link Analysis: PageRank Definition, Structure of the web, dead ends, Using Page rank in a

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24

search engine, Efficient computation of Page Rank: Page Rank Implementation Using
MapReduce
5.4 Frequent Itemset Mining: Market -Basket Model, Apriori Algorithm, Algorithm of Park-
Chen -Yu
6 Big Data Analytics Applications 06
6.1 Recommendation Systems: Introduction, A Model for Recommendation Systems:
Collaborative -Filtering System, Content based system and its Examples.
6.2 Mining Social -Network Graphs: Social Networks as Graphs, Types of Social -Network.
Clustering of Social Graphs: Applying Standard Clustering Techniques, Counting triangles
using MapReduce.
Total 39
Textbooks:
1. Radha Shankarmani and M Vijayalakshmi ―Big Data Analytics‖, Wiley
2. Alex Holmes ―Hadoop in Practice‖, Manning Press, Dreamtech Press.
3. Dan McCreary and Ann Kelly ―Making Sense of NoSQL – A guide for managers
and the rest of us,Manning Press.
Reference Books:
1. Bill Franks, ―Taming The Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with
Advanced Analytics‖, Wiley
2. Chuck Lam, ―Hadoop in Action‖, Dreamtech Press
E-Resources:
1. https:// www.analyticsvidhya.com/blog/2014/05/hadoop -simplified
2. https:// www.analyticsvidhya.com/blog/2014/05/introduction -mapreduce/
3. https:// www. pdfdrive.com/big -data-analytics -a-hands -on-approach -e158549112.html
4. https:// www.pdfdrive.com/data -science -and-big-data-analytics -e58447171.html


Internal Asse ssment (20-Marks):

Internal Assessment (IA) consists of two class tests of 20 marks each. IA -1 is to be conducted on
approximately 40% of the syllabus and IA -2 will be based on remaining contents (approximately
40% syllabus but excluding contents covered in IA -I). Duration of each test shall be one hour.
Average of the two tests will be considered as IA marks.

End Semester Examination (80-Marks):
Weightage to each of the modules in end -semester examination will be proportional to number of
respective lect ure hours mentioned in the curriculum.
1. Question paper will comprise of total 06 questions, each carrying 20 marks .
2. Question No: 01 will be compulsory and based on entire syllabus wherein 4 to 5 sub-questions
will be asked.
3. Remaining questions will be mixed in nature and randomly selected from all the modules.
4.Total 04 questions need to be attempted.

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25




Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO701 Advanced
Database
Management
Systems
03
02
--
03
01
--
04




Subject
Code


Subject Name Examination Scheme
Theory Marks
Term
Work
Prac
tical

Oral

Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Te
st
1 Test
2 Avg of Test
1 and Test 2

ECC
DO 701 Advanced
Database
Management
Systems 20 20 20 80 03 25 – 25 125




Course Prerequisite: Database Management System.

Course Objectives:

1. To impart knowledge related to query processing and query optimizer phases of a database
management system
2. To introduce concepts of advanced access control techniques like role based and discretionary methods
3. To impart knowledge related to indexing in database management system
4. To introduce advanced database models like parallel and distributed databases
5. To impart an overview of emerging data models like temporal, mobile and spatial databases.
6. To introduce concepts of Big data

Course Outcomes:

After successful completion of the course students will be able to

1. Measure query costs and design alternate efficient paths for query execution.
2. Apply sophisticated access protocols to control access to the database.
3. Implement alternate models like Parallel and Distributed databases and Design applications using
advanced models like mobile, spatial databases
4. Apply indexing techniques on large data in database
5. Apply Big data concepts in real world applications

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26



Module
No. Unit
No.
Contents
Hrs.


1 Query Processing and Optimization

6 1.1 Query Optimization Overview, Measures of Query Cost Selection Operation
1.2 Sorting, Join Operation, Other Operations, Evaluation of Expressions
1.3 Transformation of Relational Expressions Estimating Statistics of Expression
Results, Choice of Evaluation Plans


2 Advanced Data Management Techniques

6 2.1 Advanced Database Access protocols: Discretionary Access Control Based on
Granting and Revoking Privileges;
2.2 Mandatory Access Control and Role - Based Access Control.
2.3 Overview of Advanced Database models like Mobile databases, Temporal
databases, Spatial databases.


3 Indexing

7 3.1 Indexed Sequential Access Method (ISAM) B+ Trees: A Dynamic Index
Structure, Format of a Node
3.2 Search, insert, delete operations in B+ Tree
3.3 Hashing Techniques; Types of Indexes: Single Level Ordered Indexes;
Multilevel Indexes; Overview of B-Trees and B+-Trees


4 Parallel Databases

6 4.1 Architectures for Parallel Databases, Parallel Query Evaluation and
Optimization,
4.2 Data Partitioning, Parallelizing Sequential Operator Evaluation Code
4.3 Parallelizing Individual Operations: Bulk Loading and Scanning, sorting, joins




5 Distributed Databases



6 5.1 Introduction: Distributed Data Processing, What is a Distributed Database
System?
5.2 Design Issues, Distributed DBMS Architecture.
5.3 Distributed Database Design: Top-Down Design Process, Distribution Design
Issues, Fragmentation, Allocation.
5.4 Overview of Query Processing: Query Processing Problem, Objectives of Query
Processing
5.5 Characterization of Query Processors, Layers of Query Processing, Query
Optimization in Distributed Databases;



6 Introduction to Big Data


8 5.1 Sources and Uses of Big Data, Querying Big Data
6.2 Big Data Storage Systems: Distributed File Systems, Key-Value Storage
Systems
6.3 The MapReduce Paradigm: MapReduce in Hadoop with the example of word
count
6.4 Streaming Data: Querying Streaming Data Introduction to Graph Databases
Total 39

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27



Text Books:
1. Korth, Slberchatz,Sudarshan, :”Database System Concepts”, 6th Edition, McGraw – Hill
2. Elmasri and Navathe, “Fundamentals of Database Systems”, 6th Edition, PEARSON
Education.
3. Raghu Ramakrishnan and Johannes Gehrke, “Database Management Systems” 3rd Edition
McGraw Hill
Reference Books:

1. Hector Garcia -Molina, Jeffrey D. Ullman, Jennifer Widom “Database System
Implementation”, Pearson Ltd. 1/e
2. Thomas M. Connolly Carolyn Begg, Database Systems: A Practical Approach to Design
Implementation and Management, 4/e, Pearson Ltd.



Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of the syllabus. The average marks of
both the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5
marks will be asked.
4. Remaining questions will be selected from all the modules.

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28






Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECCDO702 Cloud Computing 03 -- -- 03 -- -- 03




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Practi
cal

Oral

Total Internal assessment End
Sem.
Exam Exam
durati
on
Hours Test
1 Test
2 Avg of
Test 1 and
Test 2
ECCDO702 Cloud
Computing 20 20 20 80 03 -- -- -- 100




Course Pre-requisites: Computer networks, Basics of operating system (O.S.)


Course Objectives:
1. To provide an overview of cloud computing fundamentals.
2. To make students familiar with thekey concepts of virtualization.
3. To explore various cloud computing services.
4. To create an opensource cloud.
5. To identify risks and provide cloud security.
6. To analyze several cloud applications and recent trends in cloud computing.

Course Outcomes:
After successful completion of the course students will be able to:

1. Define cloud computing and understand different cloud services and deployment models.
2. Implement different types of virtualization.
3. Use several cloud computing services.
4. Design of opensource cloud.
5. Identification of threats and cloud -based risks for cloud security.
6. Understand cloud applications and recent trends.

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29


Module
No. Unit
No.
Contents
Hrs.
0 Pre-requisites: Basics of operating system (O.S.), ISO-OSI model and its layers 2
1 Introduction to Cloud Computing



4 1.1 Definitionofcloudcomputing and cloud data centre, NIST model and cloud cube
model, andcharacteristics of cloud computing.
1.2 Cloud deployment models (private, public, hybrid, and community) and service
models (SaaS, PaaS, and IaaS).
1.3 Impact of cloud computing on business,key drivers for cloud computing.
1.4 Advantages and disadvantages of cloud computing.
Self-learning topics: Comparison between cloud service providers with traditional IT
service providers.
2 Virtualization



8 2.1 Introduction and benefits of virtualization, implementation levels of virtualization,
VMM.

2.2 Virtualization at O.S.level, middleware support for virtualization, virtualization
structure/tools and mechanisms, hypervisor and xen architecture, binary translation
with full virtualization, para virtualization with compiler support.
2.3 CPU virtualization, memory virtualization and I/O virtualization, virtualization in
multicore processors,demonstration of virtualization usingtype II hypervisor.
Self-learning topics: Comparison between virtualization and containerization
(docker).
3 Cloud Computing Services




5
3.1 Exploring different cloud computingservices: Software -as-a-Service (SaaS) (e.g.,
Dropbox, Google Workspace, Salesforce, etc.), Platform -as-a-Service(PaaS) (e.g.,
AWS Elastic Beanstalk, Windows Azure, Heroku, Google App Engine, etc.),
Infrastructure -as-a-Service (IaaS) (e.g., DigitalOcean, AWS, Microsoft Azure, Google
Compute Engine (GCE), etc.).
3.2 Anything -as-a-Service or Everything -as-a-Service (XaaS), Security -as-a-Service,
Identity Management -as-a-Service, and Database -as-a-Service.

3.3 Storage -as-a-Service, Collaboration -as-a-Service, Compliance -as-a-
Service,Monitoring -as-a-Service, Communication -as-a-Service,Network -as-a-
Service Disaster Recovery -as-a-Service, Analytics -as-a-Service, and Backup -as-a-
Service
Self-learning topics: Explore any 10 services offered by AWS/Microsoft Azure.
4 Open Source Cloud Implementation of OpenStack and Eucalyptus


7 4.1 OpenStack Cloud Architecture, Features of OpenStack, Components of OpenStack,
Mode of Operations of OpenStack
4.2 Eucalyptus Architecture, Features of Eucalyptus, Components of Eucalyptus, Mode of
Operations of Eucalyptus
4.3 Installation and configuration process of OpenStack and Eucalyptus
Self-learning topics: Exploreopen source cloud and edge computing platform for an
enterprise: OpenNebula.
5 Cloud Security


7 5.1 Security overview, cloud security challenges and risks, SaaS security, cloud
computing security architecture, architectural considerations.
5.2 General issues in securing cloud, securing data, application, and virtual machine
security.

5.3 AAA model, automatic security establishing trusted cloud computing, secure
execution environments and communications, access control, disaster recovery in
clouds.

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30


Self-learning topics: Cloud security in AWS/Microsoft Azure/Google Cloud
Platform.

6 Cloud Applications and Recent Trends




6

6.1 Cloud Applications:
Scientific Applications :
Healthcare: ECG analysis in cloud
IoT-enabled Cloud Applications: Smart Agriculture
Business and Consumer Applications:
CRM and ERP, Productivity, networking, media applications, multiplayer online
gaming.

6.2 Recent Trends:
Mobile cloud computing, autonomic cloud computing, multimedia cloud, energy
aware cloud computing.
Self-learning topics: Jungle computing, Fog computing, Quantum computing
39

Text Books:

1) Mastering Cloud Computing by Rajkumar Buyya, Christian Vecchiola, S. Thamarai Selvi, McGraw Hill
Education
2) Cloud Computing and Services by Arup Vithal, Bhushan Jadhav, StarEdu Solutions, SYBGEN Learning
India Pvt. Ltd.
3) Cloud Computing: A Practical Approach forLearning and Implementation by A. Srinivasan, J.Suresh,
Pearson.
4) Cloud Security: A Comprehensive Guide to Secure Cloud Computing by Ronald L. Krutz,Russell Dean
Vines, Wiley & Sons.
5) Cloud Computing Bible by Barrie Sosinsky, Wiley Publishing.

Reference Books:
1) Cloud Computing Black Book by Kailash Jayaswal, Jagannath Kallakurchi, Donald J. Houde, Deven Shah,
Dreamtech Press.
2) Amazon Web Services in Action by Michael Wittig, Andreas Wittig, Manning Publisher.
3) To the cloud: cloud powering an Enterprise, Arora Pankaj, Tata Mc Graw Hill Education.
4) Distributed and Cloud Computing: From Parallel Processing to the Internet of Things, Kai Hwang, Morgan
Kaufmann.


Useful Digital Links:
1) NPTEL: https://onlinecourses.nptel.ac.in/noc22_cs20/preview
2) OpenStack Installation Guide: https://docs.openstack.org/install -guide/
3) Eucalyptus Installation: https://docs.eucalyptuscloud.org/eucalyptus/4.4.4/install -guide -4.4.4.pdf
4) AWS Management Console: https://aws.amazon.com/console/
5) https://ndl.iitkgp.ac.in NOC: Cloud Computing https://rb.gy/wyjtjx
6) https://ndl.iitkgp.ac.in NOC :Cloud Computing and Distributed Computing –Virtualization
https://rb.gy/uuyzq3

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31



Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the module

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32




Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO702 Mobile
Communication 03 -- -- 03 -- -- 03




Subject
Code

Subject Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
DO702 Mobile
Communication 20 20 20 80 03 -- -- -- 100



Course Pre-requisite:
1. Analog and Digital Communication
2. Communication Networks
3. Digital Electronics

Course Objectives:
1. To introduce the concepts and design fundamentals of Cellular communication systems.
2. To understand the architecture and services of 2G cellular technologies.
3. To study the evolution of cellular technology from 2G to 5G.
4. To understand the services provided by network, transport and application layer in mobile
communication.

Course Outcomes:
After successful completion of the course students will be able to:
1. Analyse the design parameters of cellular communication system
2. Examine various multiple access techniques and design PN-sequence generator.
3. Describe and compare GSM and IS -95 CDMA technologies.
4. Summarize the underlying fundamentals of cellular technologies from 2G to 4G.
5. Explain services provided by network, transport and application layer in mobile communication

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33




Module
No. Unit
No.
Contents
Hrs.
1 Concept of Cellular Communication 07
1.1 Introduction to cellular communications, basic propagation mechanisms and
multipath fading
1.2 Cellular System design fundamentals : Cluster, f requenc y
reuse, Call setup, Handoff strategies, interference and system capacity,
cluster size and system capacity and Channel assignment strategies
1.3 Traffic Theory: Trunking and Grade of service, Improving Coverage and
capacity
2 Multiple Access Techniques 06
2.1 Multiplexing and Multiple Access: Time Division Multiple Access, Frequency
Division Multiple Access, Space division multiple access technique
2.2 Spread spectrum Multiple Access: Need for and concept of spread spectrum
(SS) modulation, PN-sequence generation, properties of PN-sequence, Gold -
sequence generation, Direct -sequence SS, Frequency -hopping SS
3 2G Technologies 10
3.1 GSM: Services and features, GSM air specifications, GSM network architecture,
Physical andLogical Channels, Frame structure, Identifiers, Authentication and
security, call procedure, Hand -off procedure
3.2 IS-95 (CDMA): Air specifications of IS -95, Forward and Reverse CDMA
channels, Forward and Reverse CDMA channel modulation process block
diagram, power control subchannel, Handoffs in IS 95 CDMA and RAKE
receiver.
4 Evolution from 2G to 5G 06
4.1 GPRS, EDGE technologies, W-CDMA (UMTS), CDMA2000: features and
network architectures
4.2 LTE: LTE System Overview, Evolution from UMTS to LTE, LTE/SAE
Requirements, SAE Architecture, EPS: Evolved Packet System, E-UTRAN,
Voice over LTE (VoLTE), Introduction to 5G
5 Mobile Network Layer 05
5.1 Mobile IP: IP Packet Delivery, Agent Advertisement and Discovery,
Registration, Tunneling and Encapsulation
5.2 Mobility Management: Introduction, IP Mobility, Optimization, IPv6, Micro
Mobility: CellularIP
6 Mobile Transport and Application Layer 05
6.1 Mobile TCP: Traditional TCP, Classical TCP Improvements like Indirect TCP,
Snooping TCP & Mobile TCP, Selective Retransmission
6.2 WAP: Architecture, WDP, WTLS, WTP, WSP, WAE, WML
Total 39

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34




Text Books:

1. Theodore Rappaport, “Wireless Communications: Principles and Practice, 2nd Edition, Pearson
Publication
2. Jochen Schiller, “Mobile Communications”, 3e, Pearson Publication.
3. William Stallings, “Wireless Communication and Networking”, PHI Publication.
4. Vijay Garg, “IS-95 CDMA and CDMA 2000: Cellular/PCS System Implementation”, Pearson Publication.

Reference Books:
1. T.L Singal, “Wireless Communication”, Tata McGraw Hill ,2010.
2. Upena Dalal, “Wireless Communication”, Oxford University Press, 2009.
3. Andreas F Molisch, "Wireless Communication", John Wiley, India 2006.
4. Vijay Garg, “Wireless communication and Networking”, Pearson Publication.
5. Christopher Cox, “An Introduction to LTE: LTE, LTE -Advanced, SAE and 4G Mobile Communications”, Wiley
publications.




Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the module

Page 37

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35







Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO702 Cyber Security 03 -- -- 03 -- -- 03




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test
1 and Test 2
20 20 20 80 03 ---- -- -- 100




Course Prerequisite:


Course Objectives:

1. To understand the need for Cyber Security Awareness.
2. To understand the flow and methodology of an attack
3. To learn and explore various static and web vulnerability analysis tools.
4. To understand the various IPR, privacy and security compliances.


Course Outcomes:

After successful completion of the course students will be able to:

1. Understand the need of Cyber Security and its aspects.
2. Illustrate the various tools and techniques used by attackers to launch their attacks.
3. Identify cyber attacks and its countermeasures.
4. Identify various web application and Network vulnerability scanning techniques and
defence methodologies.
5. Describe the various Privacy and standard compliances with the help of real world
application.

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Module
No. Unit
No.
Contents
Hrs.
C
O
0 Prerequisite
Computer Networks 2
1 Introduction to Cyber space 8




CO
1

1 Cyber Crime: Cybercrime definition, Types of Cybercrime.
Classifications of cybercrime, Cyber Hygiene, Types of Hackers - Hackers
and Crackers - Cyber -Attacks and Vulnerabilities - Malware threats -
Sniffing - Gaining Access - Escalating Privileges - Executing Applications
- Hiding Files - Covering Tracks - Worms - Trojans - Viruses - Backdoors

2 Cyber Attacks :Cyber attack Lifecycle,social engineering, Cyber stalking,
Cyber café and Cybercrimes, Botnets, Attack vector, Attacks on Wireless
and mobile Networks.
2 Cyber crime Attacks and Techniques 8



CO
2

3. Attacks Techniques :Password Cracking, Key loggers and Spywares
Steganography,Identity Theft (ID Theft),, Banner Grabbing Techniques,
ransom wares,Crypto wares
network information gathering, vulnerability scanning, Virtual Private
Networks (VPN), Open Port Identification, Social engineering, Types
of social engineering, How cyber criminal works?, Prevention from
being
victim of social engineering.
3

4 Cyber Attacks and Preventions 6
CO
3 attacks on WIFI and prevention, traditional techniques, theft of internet
hours, Wi-Fi measures
attacks on Mobile phone and prevention, mobile phone theft,mobile virus
,mishing,vishing,smishing,hacking bluetooth
4 Web and Network Security 8

CO
4

5 Web Security:OWASP, Web Security Considerations, Management,
Cookies, Privacy on Web, Web Browser Attacks,Web Bugs, Clickjacking,
Session Hijacking and Management, Phishing and Pharming Techniques,
Web Service Security

6. Network security:Syn -DOS:,DDOS,defences against Denial of Service
Attacks.Virtual Private Networks(VPN)
5 Cyber Laws 4

7. Information Security Privacy and Standard Compliances (WR)
HIPPA,FISMA, PCI DSS, GDPR, Intellectual Property Aspect of Cyber
Law, Creative Commons Library, Data Protection Laws in India.
CO
5
6 Cyber Security Initiatives -(case studies) 3
CO
5
8. Online Banking, Mobile Banking Security , Security of Debit and Credit
Card,UPI Security
Role of AI/ML in Cyber Security
Total 39

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Text Books:

1. Nina Godbole,Sunit Belapure, “Cyber Security -Understanding Cyber Crimes,Computer
Forensics and Legal Perspective”,Wiley -India,2011.
2. The Complete Cyber Security Course -Volume 1- Nathan House
3. Network Security Bible, Eric Cole, Second Edition, Wiley


Reference Books:

1. The Information technology Act, 2000; Bare Act - Professional Book Publishers, New
Delhi
2. James Graham,Richard Howard ,Ryan Olson. “ Cyber Security Essentials,CRC
Press,2018 print.
3. Build your own Security Lab, Michael Gregg, Wiley India
4. Computer Security, Dieter Gollman, Third Edition, Wiley



Web References:

● Virtual Penetration Testing Labs - https://pentesterlab.com
● OWASP - https://owasp.org/
● DVWA - https://dvwa.co.uk
● FISMA - https://csrc.nist.gov/projects/risk -management/fisma -background
● PCI DSS ttps:// www.itgovernance.eu/blog/en/a -guide -to-the-4-pci-dss-compliance -levels
● GDPR -https://gdpr.eu/what -is-gdpr/









Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the module

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38




Subject
Code
Subject
Name
Teaching Scheme
Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO702 Block Chain
Technologies 03 02 --- 03 --- --- 03





Subject
Code


Subject Name Examination Scheme
Theory Marks

Term
Work

Practical

Oral

Total Internal assessment
End
Sem.
Exam
Exam
duration
Hours Test
1 Test
2 Avg. of Test
1 and Test 2
ECC
DO702 Block Chain
Technologies 20 20 20 80 03 --- --- --- 100



Course Prerequisite: Computer Network, Operating System, Cryptography
Course Objectives:
1. To learn the fundamentals of Blockchain
2. To obtain knowledge about technologies of Blockchain
3. To incorporate the models of Blockchain - Ethereum
4. To learn the models of Hyperledger Fabric
5. To explore various applications of Blockchain.

Course Outcomes:

After successful completion of the course students will be able to:

1. Describe the primitives of the cryptography related to blockchain.
2. Understand and explore the working of Blockchain technology
3. Illustrate the concepts of Bitcoin and their usage.
4. Implement Ethereum block chain contract.
5. Explore Hyperledger Fabric and its working.
6. Investigate security features in blockchain technologies

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39



Module
No.
Unit
No.
Contents
Hrs.
1 Introduction of Cryptography 4
Basic Cryptographic primitives used in Blockchain - Public Key cryptosystem,
Cryptographic Hash functions: Properties of Hash, MD5, SHA 256, Hash Pointers
and Data Structures, Digital Signatures: ECDSA, Public Keys as Identities,
Cryptocurrencies: Goofycoin
2 Introduction to Blockchain 7
Centralization vs. Decentralization, What is Blockchain, History of Blockchain,
Blockchain defined - peer to peer, Distributed Ledger, Cryptographically Secure,
Append -only, Updatable via consensus, How Blockchain Works, Benefits and
Limitations of Blockchain, Types of Blockchain, The Structure of a Block, Block
header, Genesis block, Mining , Rewards, Consensus, Types of Consensus
Mechanisms, Consensus in Blockchain.

3 Bitcoin and Cryptocurrency 6
What is Bitcoin, Private keys in Bitcoin, Public Keys in Bitcoin, Addresses in
Bitcoin, Transactions, The Bitcoin Network,Bitcoin Wallets, Scripting language in
Bitcoin, Bitcoin Mining - task of Bitcoin miners, Mining Hardware, Crypto
Currencies, Anonymity and Pseudonymity in Bitcoin
Self Study - Alt Coins

4
Introduction to Ethereum 10

Introduction to Ethereum, Ethereum’s Consensus Mechanisms, MetaMask Setup,
Ethereum Accounts, Ethers, Gas, Introduction to Smart Contracts, Remix IDE,
Writing smart contracts using Solidity

Self Study - Geth, Ganache -Creating Wallets

5 Introduction to Hyperledger 6
What is Hyperledger? Distributed Ledger Technology & its Challenges, Hyperledger
& Distributed Ledger Technology, Hyperledger Fabric, Hyperledger Composer,
Transaction Flow.

Self study: Case Study of Supply Chain Management using Hyperledger
6 Privacy, Security issues in Blockchain 6
Pseudo -anonymity vs. anonymity, Zcash and Zk-SNARKS for anonymity
preservation, attacks on Blockchains (Sybil attacks, selfish mining, 51% attacks),
prevention of attacks

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40




Self Study: Corda, Ripple, Quorum platforms and its security
Total 39


Text Books:

1. A. Narayanan, J. Bonneau, E. Felten, A. Miller, S. Goldfeder, “Bitcoin and Cryptocurrency Technologies: A
Comprehensive Introduction”, Princeton University Press, 2016.
2. Imran Bashir, “Mastering Blockchain: Distributed Ledger Technology, Decentralization and Smart Contracts
Explained”, Second Edition, Packt Publishing, 2018.

Reference Books:

1. Mastering Bitcoin: Unlocking Digital Cryptocurrencies, by Andreas M Antonopoulos 2018
2. D. Drescher, Blockchain Basics. Apress, 2017.
3. Merunas Grinčelaitis, “Mastering Ethereum: Implement Advanced Blockchain Applications Using
Ethereum -supported Tools, Services, and Protocols”, Packt Publishing.
4. Blockchain with Hyperledger Fabric,Luc Desrosiers, Nitin Gaur, Salman A. Baset, Venkatraman
Ramakrishna, Packt Publishing.

OTHER ONLINE REFERENCES

1. http://www.coursera.org/learn/ibm -blockchain -essentials -for-developers
2. https:// www.nptel.ac.in/courses/106105184/
3. https:// www .tutorialspoint.com/blockchain/index.htm
4. https:// www.udemy.com/course/build -your-blockchain -az/
5. https:// www.ibm.com/downloads/cas/3EGWKGX7 .
6. https:// www.hyperledger.org/use/fabric
7. https://onlinecourses.nptel.ac.in/noc19_cs63/preview
8. https://andersbrownworth.com/blockchain/blockchain



Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of the syllabus. The average marks
of both the test will be considered as final IA marks
End Semester Examination:
● Question paper will comprise 6 questions, each of 20 marks.
● Total 4 questions need to be solved.
● Question No.1 will be compulsory and based on the entire syllabus wherein sub questions of
2 to 5 marks will be asked.
● Remaining questions will be selected from all the module

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41

Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL701 VLSI Design Lab -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work
Pract
ical
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL701 VLSI Design Lab -- -- -- -- -- 25 25 -- 50




Term Work:

At least 10 experiments covering entire syllabus of VLSI Design ECC 701 should be set to have well predefined
inference and conclusion. The experiments should be student centric and attempt should be made to make
experiments more meaningful, interesting. Simulation experiments are also encouraged. Experiment must be graded
from time to time. The grades should be converted into marks as per the Credit and Grading System manual and
should be added and averaged. The grading and term work assessment should be done based on this scheme. The
final certification and acceptance of term work ensures satisfactory performance of laboratory work and minimum
passing marks in term work. Practical and Oral exam will be based on the entire syllabu s.
Laboratory Outcomes:

After successful completion of the course students will be able to:

1. Demonstrate transfer, dynamic characteristics of various digital circuits.
2. Understand the circuit design using various simulation tools
3. Demonstrate layouts for various circuits and doing simulations.
4. Understand the variation in the behaviour after extraction.

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42
Suggested List of Experiments

Sr.No. Experiment Name
1 Plot Transfer and output characteristics of NMOS and PMOS
2 For NMOS as well as PMOS devices use the ITRS technology node 32nm, 45nm, 65nm and
90nm. Simulate the device and download the customized nominal model cards
3 Design CMOS inverter. Carry out static as well as transient simulation with different aspect ratio
of pull up and pull-down devices
4 Comparative analysis of the NMOS Inverter with different types of loads.
5 Find the equivalent CMOS inverter for the given 2-input NAND and NOR gates
6 Implement the given equation using various logic design style
7 Implementation of any Flip- Flop using various logic design styles
8 Simulate Minimum Sized CMOS INVERTER circuit to calculate τPHL and τPLH
9 Design and Simulate 4:1 multiplexer using NMOS pass transistor
10 Design and simulate 4-bit adder/subtractor
11 Design CMOS transmission gate and perform all the analysis to verify its Characteristics.
12 Design and Simulate 4-bit multiplier
13 Simulate and carry out comparative analysis for 6T SRAM cell with a) β= 1.5 and α=1, and b) β=
1 and α=1
14 Draw the CMOS schematic and Layout of the inverter circuit, simulate layout
15 Extraction of CMOS layout and simulation of the extracted Inverter
16 Draw and simulate layout for CMOS NAND and CMOS NOR gate



Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor
to design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to
the suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to
give greater clarity to the students and they can be motivated to think differently.

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43

Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 702 Internet of Things -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name
Theory
Marks
Term
Work
Practical/
Oral
Total Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL 702 Internet of Things -- -- -- -- -- 25 25 50


Prerequisites: 1. Programming Using Arduino IDE
2. Python programming


Laboratory Outcomes:

After successful completion of the course students will be able to:
1. Interface various sensors to any IoT device and push data onto cloud.

2. Remotely control various devices using Blynk App and Node -red environment.

3. Implement IoT protocols to control devices remotely.

4. Implement services like Google Assistance, Adafruit I/O, IFTTT, Firebase etc in IoT.

5. Configure AWS Cloud and its Application in IoT

Term Work:

At least 10 experiments covering entire syllabus of Internet of Things ECC702 should be set to have well
predefined inference and conclusion. The experiments should be student centric and attempt should be made to
make experiments more meaningful, interesting. Simulation experiments are also encouraged. Experiment must be
graded from time to time. The grades should be converted into marks as per the Credit and Grading System manual
and should be added and averaged. The g rading and term work assessment should be done based on this scheme.
The final certification and acceptance of term work ensures satisfactory performance of laboratory work and
minimum passing marks in term work. Practical and Oral exam will be based on the entire syllabus.

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44



Suggested List of Experiments

Sr.No. Experiment Name
1 Interfacing Various Sensors like LDR, ultrasonic, DHT etc (data collection) and pushing data on
to Thingspeak Cloud
2 Controlling IoT devices/sensors remotely using Node -red and rpi.
3 Application of MQTT in node red
4 Control a LED Remotely & Monitor Temperature values with a Raspberry Pi using Node -RED
5 Controlling IoT devices using Blynk App.
6 Temperature and Humidity monitor using Blynk
7 ESP8266 Voice Control with Google Assistant and Adafruit IO and IFTTT.
8 Implementing Publish -Subscribe model using MQTT protocol and DHT11 sensor
9 Google Firebase: - controlling LED using Android App
10 Publishing sensor data from ESP32 to AWS IoT Cloud.
11 Device controlling over cloud on android mobile app :- Monitoring sensor and different data on mobile
phone
12 Creating an emergency push button to upload status on Facebook
13 To send Push notification to IoT device (R-pi to smart phone)
14 Google Assistant Controlled Switch Using NodeMCU
15 AWS and SNS service




Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor
to design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to
the suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to
give g reater clarity to the students and they can be motivated to think differently.

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45




Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL
703 Department Level
Optional Course - III
Lab (Deep Learning) -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Ter
m
Wor
k
Pract
ical
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duratio
n Hours Te
st
1 Test
2 Avg of Test 1
and Test 2
ECL703 Department
Level
Optional
Course – III
Lab (Deep
Learning) -- -- -- -- -- 25 25 -- 50



Term Work:

At least 10 experiments covering entire syllabus should be set to have well predefined inference and conclusion. The
experiments should be student centric and attempt should be made to make experiments more meaningful,
interesting. Simulation experiments are also encouraged. Experiment must be graded from time to time. The grades
should be converted into marks as per the Credit and Grading System manual and should be added and averaged.
The grading and term work assessme nt should be done based on this scheme. The final certification and acceptance
of term work ensures satisfactory performance of laboratory work and minimum passing marks in term work.
Course Pre-requisite: Python Programming, Engineering Mathematics


Laboratory Outcomes:

After successful completion of the laboratory, students will be able to:

1. Implement basic neural network models to learn logic functions.
2. Design and train feed-forward neural networks using various learning algorithms.
3. Build and train deep learning models such as Auto -encoders, CNNs, RNN, LSTM etc.

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46



Suggested List of Experiments

Sr.No. Experiment Name

1 Based on Module 1 (Any two) using Virtual Lab

Implement Mc-Culloch Pitts model for binary logic functions.
Implement Perceptron algorithm to simulate any logic gate.
Implement Multilayer Perceptron algorithm to simulate XOR gate.
To explore python libraries for deep learning e.g. Theano, TensorFlow etc.
2 Module 2 (Any Two)
Apply any of the following learning algorithms to learn the parameters of the supervised single
layer feed-forward neural network.
a. Stochastic Gradient Descent
b. Mini Batch Gradient Descent
c. Momentum GD
d. Nestorev GD
e. Adagrad GD
Adam Learning GD
3 Module 3 (Any One)
Implement a back-propagation algorithm to train a DNN with at least 2 hidden layers.
Design and implement a fully connected deep neural network with at least 2 hidden layers for a
classification application. Use appropriate Learning Algorithm, output function and loss function
4 Module 4 (Any One)
Design and implement a CNN model for digit recognition application.
Design and implement a CNN model for image classification.
5 Module 5 (Any One)
Design the architecture and implement the auto-encoder model for Image Compression.
Design the architecture and implement the auto-encoder model for Image denoising.
6 Module 6 (Any One)
Design and implement LSTM for Text / Image / Audio / Video / etc.
Design and implement GRU for Text / Image / Audio / Video / etc.
Design and implement RNN for Text / Image / Audio / Video / etc.

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47



Online References:

Sr. No. Website Links
1 https://nptel.ac. https://deeplearning.cs.cmu.edu/S21/index.html
2 http://www.cse.iitm.ac.in/~miteshk/CS6910.html
3 https://nptel.ac.in/courses/106/106/106106184/
4 https:// www.deeplearningbook.org/
5 http://introtodeeplearning.com /
6 http://vlabs.iitb.ac.in/vlabs -dev/labs/machine_learning/labs/index.php


Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course
instructor to design and introduce new, innovative and challenging experiments, (limited to maximum
30% variation to the suggested list) from within the curriculum, so that, the fundamentals and
applications can be explored to give greate r clarity to the students and they can be motivated to think
differently.

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48

Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL703 Image Processing
Lab -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work Practical
/Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL703 Image Processing Lab -- -- -- -- -- 25 25
-- 50

Laboratory Outcomes;

After successful completion of the laboratory, students will be able to:

1. Enhance the quality of image in spatial and frequency domain.
2. Apply lossless or lossy compression techniques to reduce the size of an image.
3. Segment image components based on discontinuity and similarity criteria.
4. Extract various features from the scene for specified computer vision application.

Term Work:

At least 8 experiments covering entire syllabus of should be set to have well predefined inference and conclusion.
The experiments should be student centric and attempt should be made to make experiments more meaningful,
interesting. Experiments must be graded from time to time. One presentation on a case-study or mini project based
on the topic in Digital Image Processing need to be submitted. The grades should be converted into marks as per the
Credit and Grading System manual and should be added and averaged. The grading and term work assessment
should be done based on this scheme . The final certification and acceptance of term work ensures satisfactory
performance of laboratory work and minimum passing marks in term work. Practical and Oral exam will be based
on the entire syllabus. The Term work assessment can be carried out base d on the different tools and the rubrics
decided by the concerned faculty members and need to be conveyed to the students well in advanced.

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49


Suggested List of Experiments

Sr.No. Experiment Name
1 Gray level transformation: Negative, Log, Power law, gray level slicing, Contrast stretching.
2 Histogram Equalization.
3 Neighborhood Processing.
4 Filtering in Frequency domain - Smoothing and sharpening.
5 2D-DFT and DCT spectrum analysis.
6 Compression using Transform Coding (JPEG Baseline coding) with parameter evaluation (CR,
MSE, PSNR etc.).
7 Morphological Operations: erosion, dilation, opening, closing, boundary detection
8 Segmentation based on discontinuity and similarity.
9 Use of transforms for face recognition.
10 Object detection using statistical moment.
11 CBIR using color, shape and texture (as an application).
12 Feature Extraction using HOG.



Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor
to design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to
the suggested list) from within the curricul um, so that, the fundamentals and applications can be explored to
give greater clarity to the students and they can be motivated to think differently.

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50

Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 703 Big Data Analytics -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name
Theory
Marks
Term
Work
Practical/
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL 703 Big Data
Analytics -- -- -- -- -- 25 25 50


Laboratory outcomes:

After successful completion of the laboratory, students will be able to:


1. Develop problem solving and critical thinking skills in fundamental enabling techniques like Hadoop, Mapreduce
and NoSQL in big data analytics.
2. Collect, manage, store, query and analyze various forms of Big Data.
3. Interpret business models and scientific computing paradigms, and apply software tools for big data analytics.
4. Adapt adequate perspectives of big data analytics in various applications like recommender systems, social media
applications etc.


Term Work:

At least 8 experiments covering entire syllabus of Big Data Analytics (ECC DO701) should be set to have well
predefined inference and conclusion. Additionally, a Mini Project on a real-life large data application to be
implemented (Use standard Datasets available on the web). The experiments should be student centric and attempt
should be made to make experiments more meaningful, interesting. Simulation experiments are also encouraged.
Experiment must be graded from time to time. The grades should be converted into marks as per the Credit and
Grading System manual and should be added and averaged. The grading and term work assessment should be done
based on this scheme. The final certification and acceptance of term work ensures satisfactory performance of
laboratory work and minimum passing marks in term work. Practical and Oral exam will be based on the entire
syllabus.

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51


Suggested List of Experiments

Sr. No. Experiment Name
1 Hadoop HDFS Practical: -HDFS Basics, Hadoop Ecosystem Tools Overview. -Installing
Hadoop. -Copying File to Hadoop. -Copy from Hadoop File system and deleting file. -Moving
and displaying files in HDFS.
2 To install and configure MongoDB/ Cassandra/ HBase/ Hypertable to execute NoSQL
commands.
3 Implementing simple algorithms in Map-Reduce: Matrix multiplication, Aggregates, Joins,
Sorting, Searching, etc.
4 Write a program to implement word count program using MapReduce.
5 Implement PageRank using Map-Reduce.
6 Implementing any one Clustering algorithm (K -Means/CURE) using Map-Reduce.
7 Implement Bloom Filter using any programming language
8 Perform CRUD operations in MongoDB
9 To demonstrate use of recommendation system for movie rating prediction
10 To find common friends in social network graph using Map-Reduce.




Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor to
design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to the
suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to give
greater clarity to the students and they can be motivated to think differently.

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52



Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 703 Advanced
Database
Management
Systems Lab -- 02 -- -- 01 -- 01




Subject Code

Subject Name Examination
Scheme
Theory
Marks
Term
Work Practical
Oral
Tota
l Internal assessment End
Sem.
Exam Exam
duration
Hours Tes
t
1 Tes
t
2 Avg of Test
1
and Test 2

ECL 703 Advanced
Database
Manageme -
nt Systems
Lab -- -- -- -- -- 25 -- 25 50



Term Work:

At least 10 experiments covering entire syllabus of Advanced Database Systems (ECL 703) should be set to have
well predefined inference and conclusion. The experiments should be student centric and attempts should be made to
make experiments more meaningful, interesting. Simulation experiments are also encouraged. Experiments must be
graded from time to time. The grades should be converted into marks as per the Credit and Grading System manual
and should be added and averaged. The grading and term work assessment should be done based on this scheme.
The final certification and acceptance of term work ensures satisfactory performance of laboratory work and
minimum passing marks in term work. Practical and Oral exam will be based on the entire syllabus.
Prerequisite: Database Management System

Laboratory Outcomes:

After successful completion of the course students will be able to:

1. Build index on database.
2. Analyze time series data using open source tools.
3. Perform partitioning tasks on the database.
4. Write codes using map-reduce technique.

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53
Suggested List of Experiments

Sr. No. Experiment Name
1 Create an index on the given tables, observe execution time of queries and record your
observations.
2 Demonstrate use of grant and revoke access
3 Find the cost of queries using DBMS tools (Postgresql, MySQL, Oracle etc)
4 Data distribution and partitioning using Apache Ignite
5 Collocating computations with data using Apache Ignite
6 Time series data analysis using Temporal database like TimescaleDB
7 Count number of words in a large file using map reduce
8 Compare the cost required for query execution and obtain the optimized query
9 Develop a distributed database application. (FileServer Implementation using RMI)
10 Create a node and relationships using neo4j
11 Select and display data using neo4j
12 Create index and add constraints using neo4j






References:

1. https://docs.timescale.com/timescaledb/latest/tutorials/nyc -taxi-cab/#introduction -to-iot-new-york-city-
taxicabs
2. https://neo4j.com/developer/get -started/
3. https://docs.timescale.com/install/latest/
4. https://hadoop.apache.org/

Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor to
design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to the
suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to give
greater clarity to the students and they can be motivated to think differently.

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54





Course Code Course Name Credits
ILO7011 Product Life Cycle Management 03


Objectives:

1. To familiarize the students with the need, benefits and components of PLM
2. To acquaint students with Product Data Management & PLM strategies
3. To give insights into new product development program and guidelines for designing and
developing a product
4. To familiarize the students with Virtual Product Development

Outcomes: Learner will be able to…

1. Gain knowledge about phases of PLM, PLM strategies and methodology for PLM
feasibility study and PDM implementation.
2. Illustrate various approaches and techniques for designing and developing products.
3. Apply product engineering guidelines / thumb rules in designing products for moulding,
machining, sheet metal working etc.
4. Acquire knowledge in applying virtual product development tools for components,
machining and manufacturing plant
Module Detailed Contents Hrs



01 Introduction to Product Lifecycle Management (PLM): Product Lifecycle
Management (PLM), Need for PLM, Product Lifecycle Phases, Opportunities of
Globalization, Pre -PLM Environment, PLM Paradigm, Importance & Benefits of PLM,
Widespread Impact of PLM, Focus and Application, A PLM Project, Starting the PLM
Initiative, PLM Applications
PLM Strategies: Industrial strategies, Strategy elements, its identification,
selection and implementation, Developing PLM Vision and PLM Strategy ,
Change management for PLM 10




02 ProductDesign: Product Design and Development Process, Engineering Design,
Organization and Decomposition in Product Design, Typologies of Design Process
Models, Reference Model, Product Design in the Context of the Product Development
Process, Relation with the Development Process Planning Phase, Relation with the Post
design Planning Phase, Methodological Evolution in Product Design, Concurrent
Engineering, Characteristic Features of Concurrent Engineering, Concurrent
Engineering and Life Cycle Approach, New Product Development (NPD) and
Strategies, Product Configuration and Variant Management, The Design for X System,
Objective Properties and Design for X Tools, Choice of Design for X Tools and Their
Use in the Design Process 09

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03 Product Data Management (PDM): Product and Product Data, PDM systems
and importance, Components of PDM, Reason for implementing a PDM system,
financial justification of PDM, barriers to PDM implementation 05

04 Virtual Product Development Tools: For components, machines, and
manufacturing plants, 3D CAD systems and realistic rendering techniques,
Digital mock -up, Model building, Model analysis, Modeling and simulations in
Product Design, Examples/Case studies 05


05 Integration of Environmental Aspects in Product Design: Sustainable Development,
Design for Environment,Need for Life Cycle Environmental Strategies, Useful Life
Extension Strategies, End -of-Life Strategies, Introduction of Environmental Strategies
into the Design Process, Life Cycle Environmental Strategies and Considerations for
Product Design 05


06 Life Cycle Assessment and Life Cycle Cost Analysis: Properties, and Framework of
Life Cycle Assessment, Phases of LCA in ISO Standards, Fields of Application and
Limitations of Life Cycle Assessment, Cost Analysis and the Life Cycle Approach,
General Framework for LCCA, Evolution of Models for Product Life Cycle Cost
Analysis 05


Assessment :



Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.


End Semester Theory Examination:

Some guidelines for setting up the question paper.Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

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56



REFERENCES:

1. John Stark, “Product Lifecycle Management: Paradigm for 21st Century Product
Realisation”, Springer -Verlag, 2004. ISBN: 1852338105
2. Fabio Giudice, Guido La Rosa, AntoninoRisitano, “Product Design for the environment -
A life cycle approach”, Taylor & Francis 2006, ISBN: 0849327229
3. SaaksvuoriAntti, Immonen Anselmie, “Product Life Cycle Management”, Springer,
Dreamtech, ISBN: 3540257314
4. Michael Grieve, “Product Lifecycle Management: Driving the next generation of lean
thinking”, TataMcGrawHill,2006,ISBN:0070636265

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Course Code Course Name Credits
ILO7012 Reliability Engineering 03


Objectives:

1. To familiarize the students with various aspects of probability theory
2. To acquaint the students with reliability and its concepts
3. To introduce the students to methods of estimating the system reliability of simple and complex
systems
4. To understand the various aspects of Maintainability, Availability and FMEA procedure

Outcomes: Learner will be able to…

1. Understand and apply the concept of Probability to engineering problems
2. Apply various reliability concepts to calculate different reliability parameters
3. Estimate the system reliability of simple and complex systems
4. Carry out a Failure Mode Effect and Criticality Analysis



Module
Detailed Contents
Hrs



01 Probability theory: Probability: Standard definitions and concepts; Conditional
Probability, Baye’s Theorem.
Probability Distributions: Central tendency and Dispersion; Binomial, Normal,
Poisson, Weibull, Exponential, relations between them and their significance.
Measures of Dispersion: Mean, Median, Mode, Range, Mean Deviation, Standard
Deviation, Variance, Skewness and Kurtosis.


08



02 Reliability Concepts: Reliability definitions, Importance of Reliability, Quality
Assurance and Reliability, Bath Tub Curve.
Failure Data Analysis: Hazard rate, failure density, Failure Rate, Mean Time To
Failure (MTTF), MTBF, Reliability Functions.
Reliability Hazard Models: Constant Failure Rate, Linearly increasing, Time
Dependent Failure Rate, Weibull Model. Distribution functions and reliability analysis.


08
03 System Reliability: System Configurations: Series, parallel, mixed configuration, k out
of n structure, Complex systems. 05
04 Reliability Improvement: Redundancy Techniques: Element redundancy, Unit
redundancy, Standby redundancies. Markov analysis. 08

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System Reliability Analysis – Enumeration method, Cut-set method, Success

Path method, Decomposition method.


05 Maintainability and Availability: System downtime, Design for Maintainability:
Maintenance requirements, Design methods: Fault Isolation and self -diagnostics, Parts
standardization and Interchangeability, Modularization and Accessibility, Repair Vs
Replacement.
Availability – qualitative aspects.

05

06 Failure Mode, Effects and Criticality Analysis: Failure mode effects analysis,
severity/criticality analysis, FMECA examples. Fault tree construction, basic symbols,
development of functional reliability block diagram, Fau1t tree analysis and Event tree
Analysis
05


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. L.S. Srinath, “Reliability Engineering”, Affiliated East-Wast Press (P) Ltd., 1985.
2. Charles E. Ebeling, “Reliability and Maintainability Engineering”, Tata McGraw Hill.
3. B.S. Dhillion, C. Singh, “Engineering Reliability”, John Wiley & Sons, 1980.
4. P.D.T. Conor, “Practical Reliability Engg.”, John Wiley & Sons, 1985.
5. K.C. Kapur, L.R. Lamberson, “Reliability in Engineering Design”, John Wiley & Sons.
6. Murray R. Spiegel, “Probability and Statistics”, Tata McGraw -Hill Publishing Co. Ltd.

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Course Code Course Name Credits
ILO7013 Management Information System 03


Objectives:

1. The course is blend of Management and Technical field.
2. Discuss the roles played by information technology in today’s business and define
various technology architectures on which information systems are built
3. Define and analyze typical functional information systems and identify how they meet
the needs of the firm to deliver efficiency and competitive advantage
4. Identify the basic steps in systems development
Outcomes: Learner will be able to…

1. Explain how information systems Transform Business
2. Identify the impact information systems have on an organization
3. Describe IT infrastructure and its components and its current trends
4. Understand the principal tools and technologies for accessing information from
databases to improve business performance and decision making
5. Identify the types of systems used for enterprise -wide knowledge management and how
they provide value for businesses



Module
Detailed Contents
Hrs

01 Introduction To Information Systems (IS): Computer Based Information Systems,
Impact of IT on organizations, Imporance of IS to Society. Organizational Strategy,
Competitive Advantages and IS.
4


02 Data and Knowledge Management: Database Approach, Big Data, Data warehouse and
Data Marts, Knowledge Management.
Business intelligence (BI): Managers and Decision Making, BI for Data analysis and
Presenting Results

7
03 Ethical issues and Privacy: Information Security. Threat to IS, and Security Controls 7

04 Social Computing (SC): Web 2.0 and 3.0, SC in business -shopping, Marketing,
Operational and Analytic CRM, E-business and E-commerce – B2B B2C. Mobile
commerce.
7
05 Computer Networks Wired and Wireless technology, Pervasive computing, Cloud 6

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60



computing model.


06 Information System within Organization: Transaction Processing Systems, Functional
Area Information System, ERP and ERP support of Business Process.
Acquiring Information Systems and Applications: Various System development life
cycle models.

8

Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.


End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Kelly Rainer, Brad Prince,Management Information Systems, Wiley
2. K.C. Laudon and J.P. Laudon, Management Information Systems: Managing the Digital
Firm, 10th Ed., Prentice Hall, 2007.
3. D. Boddy, A. Boonstra, Managing Information Systems: Strategy and Organization,
Prentice Hall, 2008

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Course Code Course Name Credits
ILO7014 Design of Experiments 03


Objectives:

1. To understand the issues and principles of Design of Experiments (DOE)
2. To list the guidelines for designing experiments
3. To become familiar with methodologies that can be used in conjunction with
experimental designs for robustness and optimization

Outcomes: Learner will be able to…

1. Plan data collection, to turn data into information and to make decisions that lead to
appropriate action
2. Apply the methods taught to real life situations
3. Plan, analyze, and interpret the results of experiments



Module
Detailed Contents
Hrs



01 Introduction

1.1 Strategy of Experimentation

1.2 Typical Applications of Experimental Design

1.3 Guidelines for Designing Experiments

1.4 Response Surface Methodology


06






02 Fitting Regression Models

2.1 Linear Regression Models

2.2 Estimation of the Parameters in Linear Regression Models

2.3 Hypothesis Testing in Multiple Regression

2.4 Confidence Intervals in Multiple Regression

2.5 Prediction of new response observation

2.6 Regression model diagnostics

2.7 Testing for lack of fit





08

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62









03 Two-Level Factorial Designs and Analysis

3.1 The 22 Design

3.2 The 23 Design

3.3 The General2k Design

3.4 A Single Replicate of the 2k Design

3.5 The Addition of Center Points to the 2k Design,

3.6 Blocking in the 2k Factorial Design

3.7 Split-Plot Designs





07





04 Two-Level Fractional Factorial Designs and Analysis

4.1 The One-Half Fraction of the 2k Design

4.2 The One-Quarter Fraction of the 2k Design

4.3 The General 2k-p Fractional Factorial Design

4.4 Resolution III Designs

4.5 Resolution IV and V Designs

4.6 Fractional Factorial Split-Plot Designs




07





05 Conducting Tests

5.1 Testing Logistics

5.2 Statistical aspects of conducting tests

5.3 Characteristics of good and bad data sets

5.4 Example experiments

5.5 Attribute Vs Variable data sets




07


06 Taguchi Approach

6.1 Crossed Array Designs and Signal -to-Noise Ratios

6.2 Analysis Methods

6.3 Robust design examples

04

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63



Assessment :



Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.


End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.



REFERENCES:

1. Raymond H. Mayers, Douglas C. Montgomery, Christine M. Anderson -Cook, Response
Surface Methodology: Process and Product Optimization using Designed Experiment,
3rd edition,John Wiley & Sons, New York, 2001
2. D.C. Montgomery, Design and Analysis of Experiments, 5th edition, John Wiley & Sons,
New York, 2001
3. George E P Box, J Stuart Hunter, William G Hunter, Statics for Experimenters: Design,
Innovation and Discovery, 2nd Ed. Wiley
4. W J Dimond, Peactical Experiment Designs for Engineers and Scintists, John Wiley and
Sons Inc. ISBN: 0-471-39054 -2
5. Design and Analysis of Experiments (Springer text in Statistics), Springer by A.M. Dean,
and D. T.Voss
6. Phillip J Ross, “Taguchi Technique for Quality Engineering,” McGrawHill
7. Madhav S Phadke, “ Quality Engineering using Robust Design,” Prentice Hall

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64





Course Code Course Name Credits
ILO7015 Operations Research 03


Objectives:

1. Formulate a real-world problem as a mathematical programming model.
2. Understand the mathematical tools that are needed to solve optimization problems.
3. Use mathematical software to solve the proposed models.

Outcomes: Learner will be able to…

1. Understand the theoretical workings of the simplex method, the relationship between a linear
program and its dual, including strong duality and complementary slackness.
2. Perform sensitivity analysis to determine the direction and magnitude of change of a model’s
optimal solution as the data change.
3. Solve specialized linear programming problems like the transportation and assignment problems,
solve network models like the shortest path, minimum spanning tree, and maximum flow
problems.
4. Understand the applications of integer programming and a queuing model and compute
important performance measures



Module
Detailed Contents
Hrs







01 Introduction to Operations Research : Introduction, , Structure of the Mathematical
Model, Limitations of Operations Research
Linear Programming : Introduction, Linear Programming Problem, Requirements of
LPP, Mathematical Formulation of LPP, Graphical method, Simplex Method Penalty
Cost Method or Big M-method, Two Phase Method, Revised simplex method,
Duality , Primal – Dual construction, Symmetric and Asymmetric Dual, Weak Duality
Theorem, Complimentary Slackness Theorem, Main Duality Theorem, Dual Simplex
Method, Sensitivity Analysis
Transportation Problem : Formulation, solution, unbalanced Tra nsportation problem.
Finding basic feasible solutions – Northwest corner rule, least cost method and Vogel’s
approximation method. Optimality test: the stepping stone method and MODI method.
Assignment Problem : Introduction, Mathematical Formulation of the Problem,
Hungarian Method Algorithm, Processing of n Jobs Through Two Machines and m
Machines, Graphical Method of Two Jobs m Machines Problem Routing Problem,






14

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65



Travelling Salesman Problem

Integer Programming Problem : Introduction, Types of Integer Programming
Problems, Gomory’s cutting plane Algorithm, Branch and Bound Technique.
Introduction to Decomposition algorithms.

02 Queuing models : queuing systems and structures, single server and multi -server
models, Poisson input, exponential service, constant rate service, finite and infinite
population
05

03 Simulation : Introduction, Methodology of Simulation, Basic Concepts, Simulation
Procedure, Application of Simulation Monte -Carlo Method: Introduction, Monte -Carlo
Simulation, Applications of Simulation, Advantages of Simulation, Limitations of
Simulation
05


04 Dynamic programming . Characteristics of dynamic programming. Dynamic
programming approach for Priority Management employment smoothening, capital
budgeting, Stage Coach/Shortest Path, cargo loading and Reliability problems.

05

05 Game Theory . Competitive games, rectangular game, saddle point, minimax
(maximin) method of optimal strategies, value of the game. Solution of games with
saddle points, dominance principle. Rectangular games without saddle point – mixed
strategy for 2 X 2 games.
05
06 Inventory Models : Classical EOQ Models, EOQ Model with Price Breaks, EOQ with
Shortage, Probabilistic EOQ Model, 05


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

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66







REFERENCES:

1. Taha, H.A. "Operations Research - An Introduction", Prentice Hall, (7th Edition), 2002.
2. Ravindran, A, Phillips, D. T and Solberg, J. J. "Operations Research: Principles and
Practice", John Willey and Sons, 2nd Edition, 2009.
3. Hiller, F. S. and Liebermann, G. J. "Introduction to Operations Research", Tata McGraw
Hill, 2002.
4. Operations Research, S. D. Sharma, KedarNath Ram Nath -Meerut.
5. Operations Research, KantiSwarup, P. K. Gupta and Man Mohan, Sultan Chand & Sons.

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Course Code Course Name Credits
ILO7016 Cyber Security and Laws 03


Objectives:

1. To understand and identify different types cybercrime and cyber law
2. To recognized Indian IT Act 2008 and its latest amendments
3. To learn various types of security standards compliances

Outcomes: Learner will be able to…

1. Understand the concept of cybercrime and its effect on outside world
2. Interpret and apply IT law in various legal issues
3. Distinguish different aspects of cyber law
4. Apply Information Security Standards compliance during software design and
development



Module
Detailed Contents
Hrs

01 Introduction to Cybercrime: Cybercrime definition and origins of the world,
Cybercrime andinformation security, Classifications of cybercrime, Cybercrime and the
Indian ITA 2000, A global Perspective on cybercrimes.
4




02 Cyber offenses & Cybercrime: How criminal plan the attacks, Social Engg, Cyber
stalking, Cyber café and Cybercrimes, Botnets, Attack vector, Cloud computing,
Proliferation ofMobile and Wireless Devices, Trends in Mobility, Credit Card Frauds in
Mobile and Wireless Computing Era, Security Challenges Posed byMobile Devices,
Registry Settings for Mobile Devices, AuthenticationService Security, Attacks on
Mobile/Cell Phones, Mobile Devices:Security Implications for Organizations,
Organizational Measures forHandling Mobile, Devices -Related Security Issues,
OrganizationalSecurity Policies and Measures in Mobile Computing Era, Laptops



9


03 Tools and Methods Used in Cyberline

Phishing, Password Cracking, Keyloggers and Spywares, Virus and Worms,
Steganography, DoS and DDoS Attacks, SQL Injection, Buffer Over Flow, Attacks on
Wireless Networks, Phishing, Identity Theft (ID Theft)

6
04 The Concept of Cyberspace

E-Commerce , The Contract Aspects in Cyber Law ,The Security Aspect of Cyber Law 8

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68



,The Intellectual Property Aspect in Cyber Law
, The Evidence Aspect in Cyber Law , The Criminal Aspect in Cyber Law, Global
Trends in Cyber Law , Legal Framework for Electronic Data Interchange Law Relating
to Electronic Banking , The Need for an Indian Cyber Law

05 Indian IT Act.

Cyber Crime and Criminal Justice : Penalties, Adjudication and Appeals Under the IT
Act, 2000, IT Act. 2008 and its Amendments
6

06 Information Security Standard compliances

SOX, GLBA, HIPAA, ISO, FISMA, NERC, PCI.
6
Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.


End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination.
In question paper weightage of each module will be proportional to number of respective lecture
hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Nina Godbole, Sunit Belapure, Cyber Security , Wiley India, New Delhi
2. The Indian Cyber Law by Suresh T. Vishwanathan; Bharat Law House New Delhi
3. The Information technology Act, 2000; Bare Act- Professional Book Publishers, New
Delhi.
4. Cyber Law & Cyber Crimes By Advocate Prashant Mali; Snow White Publications,
Mumbai
5. Nina Godbole, Information Systems Security, Wiley India, New Delhi
6. Kennetch J. Knapp, Cyber Security &Global Information Assurance Information Science
Publishing.
7. William Stallings , Cryptography and Network Security, Pearson Publication

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69


8. Websites for more information is available on : The Information Technology ACT,
2008 - TIFR : https:// www.tifrh.res.in
9. Website for more information , A Compliance Primer for IT professional :
https:// www.sans.org/reading -room/whitepapers/compliance/compliance -primer -
professionals -33538

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Course Code Course Name Credits
ILO7017 Disaster Management and Mitigation Measures 03

Objectives:

1. To understand physics and various types of disaster occurring around the world
2. To identify extent and damaging capacity of a disaster
3. To study and understand the means of losses and methods to overcome /minimize it.
4. To understand role of individual and various organization during and after disaster
5. To understand application of GIS in the field of disaster management
6. To understand the emergency government response structures before, during and after
disaster
Outcomes: Learner will be able to…

1. Get to know natural as well as manmade disaster and their extent and possible effects on the
economy.
2. Plan of national importance structures based upon the previous history.
3. Get acquainted with government policies, acts and various organizational structure
associated with an emergency.
4. Get to know the simple do’s and don’ts in such extreme events and act accordingly.



Module
Detailed Contents
Hrs


01 Introduction
1.1 Definition of Disaster, hazard, global and Indian scenario, general
perspective, importance of study in human life, Direct and indirect effects of
disasters, long term effects of disasters. Introduction to global warming and
climate change.

03



02 Natural Disaster and Manmade disasters:
2.1 Natural Disaster: Meaning and nature of natural disaster, Flood, Flash flood,
drought, cloud burst, Earthquake, Landslides, Avalanches, Volcanic
eruptions, Mudflow, Cyclone, Storm, Storm Surge, climate change, global
warming, sea level rise, ozone depletion
2.2 Manmade Disasters: Chemical, Industrial, Nuclear and Fire Hazards. Role of
growing population and subsequent industrialization, urbanization and
changing lifestyle of human beings in frequent occurrences of manmade
disasters.


09

03 Disaster Management, Policy and Administration

3.1 Disaster management: meaning, concept, importance, objective of disaster
management policy, disaster risks in India, Paradigm shift in disaster management.
06

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71



3.2 Policy and administration:

Importance and principles of disaster management policies, command and co-
ordination of in disaster management, rescue operations -how to start with and how
to proceed in due course of time, study of flowchart showing the entire process.





04 Institutional Framework for Disaster Management in India:

4.1 Importance of public awareness, Preparation and execution of emergency
management programme.Scope and responsibilities of National Institute of Disaster
Management (NIDM) and National disaster management authority (NDMA) in
India.Methods and measures to avoid disasters, Management of casualties, set up of
emergency facilities, importance of effective communication amongst different
agencies in such situations.
4.2 Use of Internet and softwares for effective disaster management. Applications of
GIS, Remote sensing and GPS in this regard.



06



05 Financing Relief Measures:

5.1 Ways to raise finance for relief expenditure, role of government agencies and
NGO’s in this process, Legal aspects related to finance raising as well as overall
management of disasters. Various NGO’s and the works they have carried out in the
past on the occurrence of various disasters, Ways to approach these teams.
5.2 International relief aid agencies and their role in extreme events.


09





06 Preventive and Mitigation Measures:

6.1 Pre-disaster, during disaster and post-disaster measures in some events in general

6.2 Structural mapping: Risk mapping, assessment and analysis, sea walls and
embankments, Bio shield, shelters, early warning and communication
6.3 Non Structural Mitigation: Community based disaster preparedness, risk transfer
and risk financing, capacity development and training, awareness and education,
contingency plans.
6.4 Do’s and don’ts in case of disasters and effective implementation of relief aids.




06


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.

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72



End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. ‘Disaster Management’ by Harsh K.Gupta, Universities Press Publications.
2. ‘Disaster Management: An Appraisal of Institutional Mechanisms in India’ by O.S.Dagur,
published by Centre for land warfare studies, New Delhi, 2011.
3. ‘Introduction to International Disaster Management’ by Damon Copolla, Butterworth Heinemann
Elseveir Publications.
4. ‘Disaster Management Handbook’ by Jack Pinkowski, CRC Press Taylor and Francis group.
5. ‘Disaster management & rehabilitation’ by Rajdeep Dasgupta, Mittal Publications, New Delhi.
6. ‘Natural Hazards and Disaster Management, Vulnerability and Mitigation – R B Singh, Rawat
Publications
7. Concepts and Techniques of GIS –C.P.Lo Albert, K.W. Yonng – Prentice Hall (India)
Publications.
(Learners are expected to refer reports published at national and International level and updated
information available on authentic web sites)

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73





Course Code Course Name Credits
ILO 7018 Energy Audit and Management 03


Objectives:

1. To understand the importance energy security for sustainable development and the fundamentals
of energy conservation.
2. To introduce performance evaluation criteria of various electrical and thermal installations to
facilitate the energy management
3. To relate the data collected during performance evaluation of systems for identification of
energy saving opportunities.

Outcomes: Learner will be able to…

1. To identify and describe present state of energy security and its importance.
2. To identify and describe the basic principles and methodologies adopted in energy audit of an
utility.
3. To describe the energy performance evaluation of some common electrical installations and
identify the energy saving opportunities.
4. To describe the energy performance evaluation of some common thermal installations and
identify the energy saving opportunities
5. To analyze the data collected during performance evaluation and recommend energy
saving measures


Module
Detailed Contents
Hrs

01 Energy Scenario:
Present Energy Scenario, Energy Pricing, Energy Sector Reforms, Energy
Security, Energy Conservation and its Importance, Energy Conservation Act-
2001 and its Features. Basics of Energy and its various forms, Material and
Energy balance
04



02 Energy Audit Principles:
Definition, Energy audit - need, Types of energy audit, Energy management
(audit) approach -understanding energy costs, Bench marking, Energy
performance, Matching energy use to requirement, Maximizing system
efficiencies, Optimizing the input energy requirements, Fuel and energy
substitution. Elements of monitoring& targeting; Energy audit Instruments; Data
and information -analysis.
Financial analysis techniques: Simple payback period, NPV, Return on
investment (ROI), Internal rate of return (IRR)


08
03 Energy Management and Energy Conservation in Electrical System:
Electricity billing, Electrical load management and maximum demand Control; 10

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74



Power factor improvement, Energy efficient equipments and appliances, star
ratings.
Energy efficiency measures in lighting system, Lighting control: Occupancy
sensors, daylight integration, and use of intelligent controllers.
Energy conservation opportunities in: water pumps, industrial drives, induction
motors, motor retrofitting, soft starters, variable speed drives.



04 Energy Management and Energy Conservation in Thermal Systems:
Review of different thermal loads; Energy conservation opportunities in: Steam
distribution system, Assessment of steam distribution losses, Steam leakages,
Steam trapping, Condensate and flash steam recovery system.
General fuel economy measures in Boilers and furnaces, Waste heat recovery,
use of insulation - types and application. HVAC system: Coefficient of
performance, Capacity, factors affecting Refrigeration and Air Conditioning
system performance and savings opportunities.


10

05 Energy Performance Assessment:
On site Performance evaluation techniques, Case studies based on: Motors and
variable speed drive, pumps, HVAC system calculations; Lighting System:
Installed Load Efficacy Ratio (ILER) method, Financial Analysis.
04

06 Energy conservation in Buildings:

Energy Conservation Building Codes (ECBC): Green Building, LEED rating,
Application of Non-Conventional and Renewable Energy Sources
03

Assessment :



Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

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75



REFERENCES:

1. Handbook of Electrical Installation Practice, Geofry Stokes, Blackwell Science
2. Designing with light: Lighting Handbook, By Anil Valia, Lighting System
3. Energy Management Handbook, By W.C. Turner, John Wiley and Sons
4. Handbook on Energy Audits and Management, edited by A. K. Tyagi, Tata Energy Research
Institute (TERI).
5. Energy Management Principles, C.B.Smith, Pergamon Press
6. Energy Conservation Guidebook, Dale R. Patrick, S. Fardo, Ray E. Richardson, Fairmont Press
7. Handbook of Energy Audits, Albert Thumann, W. J. Younger, T. Niehus, CRC Press
8. www.energymanagertraining.com
9. www.bee -india.nic.in

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76




Subject
code
Subject Name
Teaching scheme
Credit assigned

ISP701
Major Project – I Theory Pract. Tut. Theory Pract. Tut. Total
-- 6# -- -- 3 -- 3
# Indicates workload of Learner (Not Faculty)


Sub
Code

Subject Name Examination scheme
Theory (out of 100)
Term
work Pract.
and
Oral
Oral
Total Internal Assessment End sem
Exam Test1 Test2 Avg.
ISP701 Major Project – I -- -- -- -- 50 -- 50 100

Subject Code Subject Name Credits
ISP701 Major Project – I 3



Course Objectives
The course is aimed
1. To acquaint with the process of identifying the needs and converting it into the
problem.
2. To familiarize the process of solving the problem in a group.
3. To acquaint with the process of applying basic engineering fundamentals to
attempt solutions to the problems.
4. To inculcate the process of self-learning and research.






Course Outcomes On successful completion of course learner/student will be able to:
1 Identify problems based on societal /research needs.
2 Apply Knowledge and skill to solve societal problems in a group.
3 Develop interpersonal skills to work as member of a group or leader.
4 Draw the proper inferences from available results through theoretical/
experimental/simulations.
5 Analyze the impact of solutions in societal and environmental
context for sustainable development.
6 Use standard norms of engineering practices
7 Excel in written and oral communication.
8 Demonstrate capabilities of self-learning in a group, which leads to
lifelong learning.
9 Demonstrate project management principles during project work.



Guidelines for Major Project

 Students should form groups with minimum 2(two) and not more than 4 (four)

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77


 Students should do survey and identify needs, which shall be converted into problem statement for
major project in consultation with faculty supervisor/head of department/internal committee of
faculties.

 Student shall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover
weekly activity of major project.

 A log book to be prepared by each group, wherein group can record weekly work progress,
guide/supervisor can verify and record notes/comments.

 Faculty supervisor may give inputs to students during major project activity; however, focus shall be
on self -learning.

 Students in a group shall understand problem effectively, propose multiple solution and select best
possible solution in consultation with guide/ supervisor.

 Students shall convert the best solution into working model using various components of their
domain areas and demonstrate.

 The solution to be validated with proper justification and report to be compiled in standard format of
University of Mumbai.

 With the focus on the self -learning, innovation, addressing societal problems and entrepreneurship
quality development within the students through the major Projects.


Guidelines for Assessment of Major Project:
Term Work

 The review/ progress monitoring committee shall be constituted by head of departments. The
progress of major project to be evaluated on continuous basis, minimum two reviews in the semester.

 In continuous assessment focus shall also be on each individual student, assessment based on
individual’s contribution in group activity, their understanding and response to questions.

 Distribution of Term work marks for both semesters shall be as below;

Marks awarded by guide/supervisor based on log book : 15
Marks awarded by review committee : 15
Quality of Project report : 20

Review/progress monitoring committee may consider following points for assessment.

 In VII semester entire theoretical solution shall be ready, including components/system
selection and cost analysis. Two reviews will be conducted based on presentation given by students
group.
 First shall be for finalization of problem
 Second shall be on finalization of proposed solution of problem.

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Assessment criteria of Major Project -I
Major Project -I shall be assessed based on following criteria;

1. Quality of survey/ need identification

2. Clarity of Problem definition based on need.

3. Innovativeness in solutions

4. Feasibility of proposed problem solutions and selection of best solution

5. Cost effectiveness

6. Societal impact

7. Innovativeness

Guidelines for Assessment of Major Project Practical/Oral Examination:

 Report should be prepared as per the guidelines issued by the University of Mumbai.

 Major Project shall be assessed through a presentation and demonstration of working model by the
student project group to a panel of Internal and External Examiners preferably from industry or research
organizations having experience of more than five years approved by head of Institution.

 Students shall be motiv ated to publish a paper based on the work in Conferences/students
competitions.

Major Project -I shall be assessed based on following points;

1. Quality of problem and Clarity

2. Innovativeness in solutions

3. Cost effectiveness and Societal impact

4. Full functioning of working model as per stated requirements

5. Effective use of skill sets

6. Effective use of standard engineering norms

7. Contribution of an individual’s as member or leader

8. Clarity in written and oral communication

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Program Structure for Final Year Electronics and Computer Science
UNIVERSITY OF MUMBAI
(With Effect from 2022 -2023)

Semester VIII

Course
Code Course
Name Teaching Scheme
(Contact Hours) Credits Assigned
TH PR Tut TH Pract Tut Total
ECC 801 Robotics 3 - - 3 - - 3
ECC
DO801 Department Level Optional
Course -V 3 - - 3 - - 3
ECC
DO802 Department Level Optional
Course -VI 3 - - 3 - - 3
ECC
IO801 Institute Level Optional Course
- II 3 - - 3 - - 3
ECL 801 Robotics Lab - 2 - - 1 - 1
ECL 802 Department Level Optional
Course - V Lab - 2 - 1 1
ECP 801 Major Project II - 12 - - 6 - 6
Total 12 16 - 12 8 - 20



Course
Code Course
Name Examination Scheme
Internal Assessment End
Sem
Exam Exam
Duration
(in Hrs)
TW Pract/
Oral
Total Test 1 Test 2 Av
ECC 801 Robotics 20 20 20 80 03 - - 100
ECC
DO801 Department Level
Optional Course -V 20 20 20 80 03 - - 100
ECC
DO802 Department Level
Optional Course -VI 20 20 20 80 03 - - 100
ECC
IO801 Institute Level Optional
Course - II 20 20 20 80 03 - - 100
ECL 801 Robotics Lab - - - - 03 25 25 50
ECL 802 Department Level
Optional Course - V
Lab
-
-
-
-
-
25
25
50
ECP 801 Major Project II - - - - - 50 100 150
Total 80 320 - 100 150 650

Department Level Optional Courses:

Department Level Optional Course -V (DO801) Department Level Optional Course -VI (DO802)
1. MEMS Technology 1. Advanced Networking Technologies
2. Natural Language Processing 2. Multimedia and Virtual Reality
3. 3-D Printing and Design 3. Quantum Computing
4. Advanced Algorithms 4. System Security

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80



Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC 801 Robotics 03 02 -- 03 -- -- 03



Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC 801 Robotics
20
20
20
80
03
--
--
--
100

























Course Pre-requisite: Applied Mathematics III, Applied Mathematics IV, Controls and Instrumentation

Course Objectives:

1. To get acquainted with the basics of robotics
2. To familiarize students with kinematics & dynamics of robots
3. To familiarize students with Trajectory & task planning of robots.
4. To familiarize students with robot vision

Course Outcomes:
After successful completion of the course students will be able to:
1. Describe the basics of Robotics
2. Describe and derive kinematics and dynamics of stationary and mobile robots.
3. Apply trajectory planning algorithms
4. Describe concepts of robot motion planning algorithms
5. Apply image processing in robotic vision
6. Identify suitable Robot language based on applications

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81




Module
No. Unit
No.
Contents
Hrs.
1 Fundamentals of Robotics 04
1.1 Robot Classification, Robot Components, Robot Specification, Joints,
Coordinates, Coordinate frames, Workspace, Specification
Notations, Applications.
2 Direct Kinematics 08
2.1 Dot and Cross Products, Co-ordinate frames, Rotations, Homogeneous Co-
ordinates, Link Co-ordinates, Arm Equation (3 axis and 4 axis Robots)
3 Inverse Kinematics and Work Space Analysis 07
3.1 General properties of solutions, Tool Configuration, Inverse kinematics of 3
axis, 4 axis and 5 axis Robots, Work Space Analysis of 3 axis and 4 axis
Robots, Work Envelope.
4 Trajectory planning 04
4.1 Basics of Trajectory planning , Joint -space trajectory planning, Pick and place
operations, Continuous path motion, Interpolated motion, Straight line motion
5 Task Planning 07
5.1 Task level programming, Uncertainty, Configuration Space, Gross motion Planning;
Grasp planning, Fine-motion Planning, Simulation of Planer motion, Source and goal
scenes, BUG 1, BUG 2 and Tangent Bug Algorithms
6 Robot Vision and Robot Languages 09
6.1 Image representation, Template matching, Polyhedral objects, Shape analysis,
Segmentation, Iterative processing, Perspective transform.
6.2 Robot language, Classification of Robot languages, Computer control and Robot
software, Variable Assembly Language system and language

Total 39

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82




Text Books:
1. Robert Shilling, “Fundamentals of Robotics - Analysis and control, Prentice Hall of India, 2009
2. Saeed Benjamin Niku, “Introduction to Robotics – Analysis, Control, Applications”, Wiley India Pvt. Ltd.,
Second Edition, 2011
Reference Books:

1. John J. Craig, “Introduction to Robotics – Mechanics & Control”, Third Edition, Pearson Education, India,
2009
2. Mark W. Spong , Seth Hutchinson, M. Vidyasagar, “Robot Modeling & Control ”,
3. Wiley India Pvt. Ltd., 2006
4. Mikell P. Groover et.al, ”Industrial Robots -Technology, Programming & applications”, McGraw Hill , New
York, 2008
5. S. R. Deb and Sankha Deb, “Robotics Technology and Flexible Automation” , Second Edition. TMH




Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both the test
will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks will
be asked.
4. Remaining questions will be selected from all the module

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83




Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO801 MEMS
Technology 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio n
Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
DO801 MEMS
Technology 20 20 20 80 03 25 25 -- 150



Course Pre-requisite: 1. Controls and Instrumentation
2. Embedded Systems and RTOS


Course Objectives:

1. To provide knowledge of MEMS fabrication steps.
2. To provide knowledge of MEMS Materials with respect to applications.
3. To demonstrate the use of semiconductor -based fabrication processes for sensors and actuators
4. To provide an understanding of basic design and operation of MEMS sensors, actuators
and passive structures.

Course Outcomes:

After successful completion of the course students will be able to:

1. Understand the different MEMS devices, working principles, materials and their properties.
2. Design and simulate MEMS devices using standard simulation tools.
3. Develop different concepts of MEMS sensors and actuators for real-world applications.
4. Understand the rudiments of Micro -fabrication techniques.

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Module
No. Unit
No.
Contents
Hrs.
1 Introduction to MEMS 04
1.1 Introduction to MEMS and Micro Electronics Technologies.
1.2 MEMS in Real world applications such as Air -Bag, DMD, Pressure Sensors,
MEMS Challenges, MEMS Sensors in Internet of Things (IoT), Bio -medical
applications.
2 MEMS Materials and Their Properties 07
2.1 Use of Si, SiO 2, SiN, SiC, Cr, Au, Al, Ti, SU8, PMMA, Pt in building MEMS
applications.
2.2 Material properties such as Young modulus, Poisson’s ratio, density,
piezoresistive coefficients, TCR, Thermal Conductivity, Thermoelectricity.
3 MEMS Sensors and Actuators 08
3.1 Types MEMS Sensing (Capacitive, Piezo electric Piezo resistive)
3.2 Micro Actuation Techniques (Thermal, Piezo electric, Electro static), Shape Memory
Alloys, Micro Grippers, Micro Gears, Micro Motors, Micro Valves, Micro Pumps .
4 MEMS Fabrication Processes 08
4.1 MEMS Processes & Process parameters: Bulk & Surface Micromachining,
High Aspect Ratio MEMS (LIGA) .
4.2 X-Ray Lithography, Photolithography, PVD, Wet etching, Dry etching, Plasma
etching, DRIE, Etch Stop Techniques, Die, Wire & Wafer Bonding, Dicing,
Packaging.
5 MEMS Devices 09
5.1 Construction and working and applications of basic Cantilever structure, Micro
heaters, Accelerometers, Pressure Sensor , Micromirrors in DMD, Inkjet printer
,Steps involved in fabrication of above devices.

6 MEMS Reliability 03
6.1 Reliability and various failure mechanisms for MEMS.
6.2 Reliability curve.

Total 39

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85




Text Books:

1. An Introduction to Micro -electromechanical Systems Engineering; 2 nd Ed - by N. Maluf, K Williams;
Publisher: Artech House Inc.

2. Micro -system Design - by S. Senturia; Publisher: Springer.

3. Introduction to Electromechanical system design –by James J Allen. Taylor & Francis Group, LLC publication

Reference Books:

1. Fundamentals of Micro -fabrication - by M. Madou; Publisher: CRC Press; 2nd edition.

2. Micro machined Transducers Sourcebook - by G. Kovacs; Publisher: McGraw -Hill


Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both the test will
be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks will be
asked.
4. Remaining questions will be selected from all the modules.

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86

Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECCDLO
8012 Natural
Language
Processing
03
--
--
03
--
--
03


Course
Code Course
Name Examination Scheme
Theory Marks Exam
Duration
(Hrs.) Term
Work Practical
and Oral Total
Internal Assessment End Sem.
Exam. Test1 Test2 Avg.
ECCDLO
8012 Natural
Language
Processing
20
20
20
80
03
--
--
100

Course Pre-requisite:

---

Course Objectives:

1. To understand natural language processing and to learn how to apply basic algorithms in this field.
2. To get acquainted with the basic concepts and algorithmic description of the main language levels:morphology,
syntax, semantics, and pragmatics.
3. To design and implement applications based on natural language processing

Course Outcomes:

After successful completion of the course student will be able to:

1. Have a broad understanding of the field of natural language processing.
2. Understand the mathematical and linguistic preliminaries necessary for various processes in NLP
3. Be able to Design, implement and test algorithms for NLP problems
4. Perform Word -Level, Syntax -Level and Semantic -Level Analysis
5. Develop basic understanding of Pragmatics in NLP
6. Be able to apply NLP techniques to design real world NLP applications

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87

Module
No. Unit
No. Topics Hrs.
1.0 Introduction to Natural Language Processing 06
1.1 The need of NLP. Generic NLP system, Levels of NLP 02
1.2 Stages in building a Natural Language Processing System. Challenges and ambiguities in
NLP Design 04
2.0 Mathematical and Linguistic Preliminaries 06
2.1 Probability Theory, Conditional Probability and Independence, Bayes Rule, Random
Variables, Probability Distributions, Statistics, Counting, Frequency, Mean and Variance 04
2.2 English Grammar, Parts of Speech, Phrase Structures 02
3.0 Word Level Analysis 06

3.1 Tokenization, Segmentation, Lemmatization, Edit Distance, Collocations, Porter Stemmer,
N-gram Language Model
04
3.2 Morphological Analysis, Derivational and Reflectional Morphology 02
4.0 Syntax -Analysis 08
4.1 Tag set for English, Penn Tree bank, Introduction to Parts of Speech Tagging (POST) 02
4.2 Markov Processes, Hidden Markov Models (HMM) 02
4.2 Parts of Speech Tagging using Hidden Markov Models, Viterbi Algorithm 04
5.0 Semantic Analysis 08

4.1 Lexical Semantics, ambiguous words, word senses, Relations between senses: synonym,
antonym, reversives, hyponym, hypernym, meronym, structured polysemy, metonymy,
zeugma
04
4.2 Introduction to WordNet, gloss, synset, sense relations in WordNet. Cosine distance
between documents. Word sense disambiguation. 04
6.0 Pragmatics and applications of NLP 05
6.1 Reference resolution: Discourse model, Reference Phenomenon, Syntactic and Semantic
Constraints on co reference 03
6.2 Applications of NLP: Categorization, Summarization, Sentiment Analysis, Named Entity
Recognition, Machine Translation, Information Retrieval, Question Answer System 02
Total 39

Text Books:

1. Daniel Jurafsky, James H. Martin, Speech and Language Processing‖ Second Edition, Prentice Hall.
2. Christopher D. Manning & Hinrich Schutze, Foundations of Statistical Natural Language Processing, MIT Press.

Reference Books:

1. Steven Bird, Ewan Klein, Natural Language Processing with Python, O‘Reilly
2. Alexander Clark (Editor), Chris Fox (Editor), Shalom Lappin (Editor), The Handbook of Computational
Linguisticsand Natural Language Processing

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88

NPTEL / Swayam Course:

1. Course: Natural Language Processing By Prof. Pawan Goyal, IIT Kharagpur
https://onlinecourses.nptel.ac.in/noc21_cs102/preview
2. Course: Applied Natural Language Processing By Prof. Ramaseshan R, CMI
https://onlinecourses.nptel.ac.in/noc20_cs87/preview

Internal Assessment (20-Marks):

Internal Assessment (IA) consists of two class tests of 20 marks each. IA-1 is to be conducted on approximately 40%
of the syllabus and IA -2 will be based on remaining contents (approximately 40% syllabus but excluding contents
covered in IA-I). Duration of each test shall be one hour. Average of the two tests will be considered as IA marks.

End Semester Examination (80-Marks):

Weightage to each of the modules in end -semester examination will be proportional to number of respective lecture
hours mentioned in the curriculum.

1. Question paper will comprise of total 06 questions, each carrying 20 marks .
2. Question No: 01 will be compulsory and based on entire syllabus wherein 4 to 5 sub-questionswill be asked.
3. Remaining questions will be mixed in nature and randomly selected from all the modules.
4. Total 04 questions need to be attempted.

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89





Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO801 3D Printing and
Design 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
DO801 3D Printing
and Design 20 20 20 80 03 25 25 -- 150



Course Pre-requisite: None


Course Objectives:

1. To Understand the concept of Additive Manufacturing
2. To Classify the Various different AM Processes
3. To Demonstrate the concept of Direct Digital Manufacturing
4. To Implement the Concept of Design for Additive Manufacturing
5. To Understand RE Technologies

Course Outcomes:

After successful completion of the course students will be able to:

1. Repeat the concept of Additive Manufacturing
2. Describe the Various different AM Processes
3. Demonstrate the concept of Direct Digital Manufacturing
4. Demonstrate the Concept of Design for Additive Manufacturing
5. Implement RE Technologies

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90




Module
No. Unit
No.
Contents
Hrs.
1 Introduction 5
Additive Manufacturing Fundamentals, Historical Development, Commonly
Used Terms, Definitions, AM Manufacturing Process, Classification of AM
processes (As per ASTM F42 and ISO TC 261). Subtractive Manufacturing vs
Additive manufacturing. Benefits of Additive Manufacturing. AM technology
in Product Development
Applications of AM : Engineering, Planning, Aerospace, Automotive,
Jewellery, Architecture, Arts, Medical, Bio Engineering
2 Additive Manufacturing systems 10

Vat Polymerisation, Powder Bed Fusion Based AM, Material Extrusion based,
Material Jetting Based, Binder Jetting Based, Sheet Lamination based, Direct
Energy Deposition based
3 Direct Digital Manufacturing 6
Direct Digital Manufacturing(DDM) : Concept of DDM, Applications with
Case Studies, DDM Drivers, Cost estimation: Cost Model, Build Time Model,
Life-cycle costing, Future of DDM
4 Design for Additive Manufacturing 6
Design for Additive Manufacturing
AM unique Capabilities : Shape Complexity, Heirarchical Complexity,
Functional Complexity and Material Complexity.
Core DFAM Concepts and Objective : Complex Geometry, Integrated
Assemblies, Customised Geometry, Multi -functional Design, Eliminnaaton of
Conventional Design for Manufacturing Constraints
5 Rapid Proto -typing 6
Rapid Prototyping Data Formats : STL, File Format Problems and Limitations,
Consequence of Building valid and Invalid Tessellated model, STL File repair,
Newly Proposed File Formats. RapidProto -typing software Features of various
Software.

6 Reverse Engineering 6
Reverse Engineering (RE): Introduction to Generic RE Process, RE Hadware
and Software. Integration of RE and RP for Layer Based Model Generaion,
Application and Case Studies of RE in Automotive, Aerospace, Medical,
Architectural industry, Barriers for adopting RE
Other Related technologies: Reverse Engineering , Computer Aided
Engineering, Haptic Feedback Based CAD

Total 39

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91




Text Books:
1. Fundamentals of Digital Manufacturing science, Zude Zhou, Shane (Shengquan) Xie, Dejun Chen, Springer
2012
2. Additive Manufacturing Technologies, Ian Gibson, David Rosen, Brent Stucker, Spinger

Reference Books:

1. Understanding Additive Manufacturing, Andreas Gebhardt, Hanser Publication, ISBN -13:978 -1-56990 -507-4
2. Rapid Manufacturing : An Industrial Revolution fo the digital Age, N. Hopkins, R.J.M. Hague and P.M.
Dickens (Eds.), John Wiley and Sons, 2006
3. Rapid Proto -typing Principles and Applications, Chua C. K., Leong K. F., and Lim C.S. , 2nd edition , World
Scientific, 2003
4. Rapid Proto -typing Theory and Practice, Ali Kamrani and EmadAbouel Nasr (Eds.), Springer, 2006





Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both the test
will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks will
be asked.
4. Remaining questions will be selected from all the modules.

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92




Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO801 Advanced
Algorithms 03 02 -- 03 01 -- 04




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
DO801 Advanced
Algorithms 20 20 20 80 03 25 25 -- 150



Course Pre-requisite: Data structure concepts, Discrete structures

Course Objectives:

1. To provide mathematical approaches for Analysis of Algorithms
2. To understand and solve problems using various algorithmic approaches
3. To analyse algorithms using various methods

Course Outcomes:

At the end of the course learner will be able to

1. Analyze the running time and space complexity of algorithms.
2. Describe, apply and analyze the complexity of divide and conquer, greedy and dynamic programming
strategy.
3. Identify appropriate data structures and design techniques for different problems
4. Differentiate polynomial and non-deterministic polynomial algorithms.
5. Analyze various algorithms.

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93





Module
No. Unit
No.
Contents
Hrs.
1 Introduction to analysis of algorithm 08
1.1
Mathematical background for algorithm analysis, Growth of function – Big –
Oh, Omega, Theta notation, Complexity derivations,
1.2
Solving recurrences using Substitution Method, Recursion tree method and
Master method
1.3
Complexity Classes: P, NP, NP Hard, NP Complete
1.4 Amortised Analysis -Aggregate Method, Accounting Method, Potential Method
2
Divide and Conquer approach 04
2.1
General method,Binary Search, Merge Sort, Quick Sort, Randomized quick
sort, and Min -max algorithm
3 Greedy Algorithms 06
3.1
General Method, Knapsack Problem, Huffman's Codes , Minimum Spanning
Tree, Kruskal's Algorithm , Prim's Algorithm , Dijkstra's Algorithm.
4 Dynamic Programming Approach 08
4.1 General Method, Making coin change, Principle of optimality, Knapsack
Problem, Matrix Chain Multiplication, Activity Selection Problem, Longest
common subsequence, All pair shortest path algorithm
5 Maximum Flow 07
5.1
Flow networks, Ford Fulkerson method, Max bipartite matching , Push relabel
algorithm , The relabel to front algorithm
6 Classical Problems in Algorithms 06
6.1 Travelling Salesman problem, Subset Sum Problem, Matrix Multiplication, 15
puzzle , N-queens problem
Total 39

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Text Books:

1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, “Introduction to
Algorithms”, Third Edition, The MIT Press, 2009.
2. Michael T Goodrich and Roberto Tamassia, “Algorithm Design: Foundations, Analysis and Internet
Examples”, John Wiley and Sons, 2002.
3. Sanjoy Dasgupta, Christos Papadimitriou and Umesh Vazirani, “Algorithms”, Tata McGraw -Hill, 2009
4. R.K. Ahuja, TL Magnanti and JB Orlin, “Network flows: Theory, Algorithms, and Applications”, Prentice
Hall Englewood Cliffs, NJ 1993.

Reference Books:

1. M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of
NP-Completeness, Freeman, 1979.
2. E. Horowitz and S. Sahni, Fundamentals of Computer Algorithms, Computer Science
Press,1978.
Online References:

1. NPTEL course: https://nptel.ac.in/courses/106105164
2. Coursera link: https:// www.coursera.org/specializations/algorithms



Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the modules.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutoria
l Theory Practical Tutorial Total
ECC
DO8021 Advanced
Networking
Technologies 03 - -- 03 - -- 04




Subject
Code

Subject Name Examination Scheme
Theory Marks

Term
Work

Practi
cal

Oral

Total Internal assessment End
Sem.
Exam Exam
duration
Hours Te
st
1 Test
2 Avg of Test
1 and Test 2
ECC
DO8021 Advanced
Networking
Technologies 20 20 20 80 03 - -- - 100




Course Pre-requisite: Computer Networks

Course Objectives:

1. Understand the characteristic features of Various Wireless networks.
2. Understand the characteristic features of Various Wireless networks.
3. Introduce the need for network security and safeguards

Course Outcomes:

After successful completion of the course students will be able to:

1. Appreciate the need for Wireless networks and study the IEEE 802.11 Standards
2. Comprehend the significance of Asynchronous Transfer Mode(ATM).
3. Analyze the importance of Optical networking
4. Demonstrate knowledge of network design and security and management
5. Understand the concept of multimedia networks..

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Module
No. Uni
t
No.
Contents Hrs
.
1 Wireless LAN and WAN Technologies




08 1.1 Introduction to Wireless networks : Infrastructure networks, Ad-hoc
networks
1.2 IEEE 802.11 architecture and services
1.3 Medium Access Control sub-layers
1.4 CSMA/CA, Physical Layer, 802.11 Security considerations
1.5 Asynchronous Transfer Mode (ATM): Architecture, ATM logical
connections, ATM cells , ATM Functional Layers, Congestion control and
Quality of service
2 Optical Networking 06
2.1 SONET : SONET/SDH, Architecture, Signal, SONET devices, connections,
SONET layers, SONET frames, STS Multiplexing, SONET Networks
2.2 WDM, DWDM: Frame format, DWDM architecture ,Optical Amplifier ,
Optical cross connect Performance and design considerations.
3 Routing in the Internet 08
3.1 Intra and inter domain Routing, Unicast Routing Protocols: RIP, OSPF,
BGP
3.2 Multicast Routing Protocols ,Drawbacks of traditional Routing methods
4 Network Security 08
4.1 Security goal, Security threats, security safeguards, firewall types and design,
IPTABLES
4.2 Internet Security: Network Layer Security, Transport Layer Security,
Application Layer Security
5 Multimedia Information and Networking 06
5.1 Compression Fundamentals, Digital Representation, Compression
techniques
5.2 Multimedia Communication across networks, RTP, RTSP, SIP,H..323
6 Network Design 03
6.1 3 tier Network design layers: Application layer, Access layer
6.2 Backbone layers, Ubiquitous computing and Hierarchical computing
Total 39

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Text Books:

1. Behrouz A. Forouzan, “Data communication and networking “, McGraw Hill Education,
Fourth Edition.
2. J F. Kurose & KW. Ross: Computer Networking - A Top-down Approach featuring the
Internet, 3rd edition,
3. Darren L. Spohn , “Data Network Design” , McGraw Hill Education ,Third edition
4. William Stallings, “Data and Computer communications”, Pearson Education, 10th Edition


Reference Books:

1. K. R. Rao et al: Multimedia Communication Systems, Prentice -Hall of India,.
2. Deven Shah , Ambavade, “Advanced Communication Networking”
3. Beherouz A Forouzan , “TCP /IP Protocol Suite” , Tata McGraw Hill Education ,4th edition



Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5 marks
will be asked.
4. Remaining questions will be selected from all the modules.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO802 Multimedia
System and
Virtual reality
03
-
--
03
-
--
03




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test
1 and Test 2
ECC
DO802 Multimedia
System and
Virtual
reality 20 20 20 80 03 - - -- 100



Course Pre -requisites: Computer Fundamentals, Graphics, Communication Theory, ISO -OSI Model, Java
Classes

Course Objectives:

1. To introduce students about basic fundamentals and key aspects of the multimedia system.
2. To provide knowledge of compression techniques of different multimedia components.
3. To discuss the multimedia authoring tools and security in multimedia systems.
4. To comprehend and analyse the fundamentals of virtual reality system.
5. To learn different pipeline and modelling techniques in virtual reality.
6. To understand various programming languages in virtual reality.

Course Outcomes:
After successful completion of the course students will be able to:

1. Understand the basics of multimedia and multimedia system architecture.
2. Analyse file formats and compression algorithms for different multimedia components.
3. Understand multimedia authoring system and apply different security techniques in multimedia
environment.
4. Define the fundamentals of virtual reality and its related technologies.
5. Understand typical rendering pipeline and modelling techniques.
6. Design an application with the principles of virtual reality.

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Module
No. Unit
No.
Contents
Hrs.
Pre-requisites
0 Computer Fundamentals, Graphics, Communication Theory, ISO-OSI Model,
Java Class 2
1 Introduction to Multimedia 5
1.1 Definition of Multimedia, Characteristics of Multimedia System, History of
Multimedia System, Difference between Multimedia and Hypermedia, Objects
and Elements of Multimedia, Applications of Multimedia.
1.2 Multimedia System Architecture: Workstation Architecture, IMA Architectural
Framework, Network Architecture for Multimedia Systems, Types of Medium
(Perception Media, Representation Media, Presentation Media, Storage Media,
Transmission Media, Information Exchange Media), Interaction Techniques.

2 Multimedia Types, File Formats and Compression Techniques 10
2.1 Digital Image: Representation (2D format, resolution), Types of Images
(monochrome, gray, color), File formats like BMP, JPG, Compression
Techniques: fundamentals (coding, interpixel and psychovisual redundancies),
Types –lossless and lossy, Lossless Compression; Algorithms – Shannon -Fano,
CCITT Group 4 2D, Lossy Compression Algorithm – JPEG
2.2 Digital Audio: Computer representation of sound, File Formats – WAV, MPEG
Audio, Compression: PCM, DM, DPCM
2.3 Digital Video : Digitization of Video, types of video signals (component,
composite and S-video), File Formats: MPEG Video, H.261, Compression:
MPEG
3 Multimedia Authoring and Security 6
3.1 Authoring System : Overview, Introduction to Authoring Tools, Features of
Authoring Tools, Design Issue of Multimedia Authoring, Types of Authoring
Systems.
3.2 Digital Watermarking : Concept, Visible and Invisible Watermarks,
Watermarking Classification (Spatial Domain, Transform Domain, Feature
Domain), Digital Watermarking Applications
3.2 Steganography : Concept and Types
3.3 Image Authentication : Issues and Digital Signature Based Image
Authentication
4 Introduction to Virtual Reality 4
4.1 Definition of Virtual Reality (VR), Classical Components of VR System,
Important factors in a VR System, Types of VR Systems, VR Advantages, VR
Input Output Devices, Applications of VR System

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5 VR Rendering Pipeline and Modelling 6
5.1 Graphical Rendering Pipeline, Haptic Rendering Pipeline, OpenGL Rendering
Pipeline, Geometric Modelling, Kinematic Modelling, Physical Modelling,
BehaviourModelling
6 VR Programming 6
6.1 VRML, Extensible 3D (X3D), Java 3D, OpenGL

Total 39

Text Books:

1. Prabhat K. Andleigh&KiranThakrar, “Multimedia System Design”, Pearson, 2015
2. Rajesh K. Maurya, “Computer Graphics with Virtual Reality Systems”, 3rd Edition, Wiley, 2018
3. K.R.Rao,D.Milovanovic, Multimedia Communication Systems: Techniques, Standards and Networks,
Pearson, 2012.
4. Koegel Buford, “Multimedia Systems”, Pearson, 2002.


Reference Books:

1. Steinmetz Ralf and NahrstedtKlara, “Multimedia: Computing, Communications and Applications”,
Pearson, 2008
2. AtulPuri, “Multimedia Systems, Standards, and Networks”, 1st Edition, CRC Press, 2000.
3. Frank Y. Shih, “Multimedia Security: Watermarking, Steganography and Forensics”, CRC Press, 2013.
4. Grigore C. Burdea, Philippe Coiffet, “Virtual Reality Technology”, 2nd Edition, Wiley, 2003.
5. John Vince, “Virtual Reality Systems”, Pearson, 2002.
Online References :
1. Multimedia Systems: https://nptel.ac.in/courses/117105083
2. Virtual Reality: https://nptel.ac.in/courses/106106138
3. Virtual Reality Specialization: https:// www.coursera.org/specializations/virtual -reality



Internal Assessment (IA):
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:
1. Question paper will comprise 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on the entire syllabus wherein sub questions of 2 to
5 marks will be asked.
4. Remaining questions will be selected from all the modules.

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101



Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECCDLO
7025 Quantum
Computing 03 -- -- 03 -- -- 03

Course
Code Course
Name Examination Scheme
Theory Marks Exam
Duration
(Hrs.) Term
Work Practical
and Oral Total
Internal Assessment End Sem.
Exam. Test1 Test2 Avg.
ECCDLO
7025 Quantum
Computing
20
20
20
80
03
--
--
100


Course pre-requisite:

ECC303 - Digital System Design
ECC301 -Engineering Mathematics -III
ECCDLO5014 - Data Structures and Algorithm
ECL404 -Skill Lab: Python Programming

Course Objectives:
1. To understand basics of quantum computing
2. To understand mathematics required for quantum computing.
3. To understand building blocks of quantum computing.
4. To understand quantum algorithms.
5. To understand quantum hardware principles.
6. To understand tools for quantum computing.

Course Outcome:
After successful completion of the course student will be able to :-
1. Explain basic concepts of quantum computing
2. Explain mathematical fundamentals required for quantum computing.
3. Explain building blocks of quantum computing through architecture and programming models.
4. Explain quantum algorithms.
5. Explain quantum hardware building principles.
6. Explain usage of tools for quantum computing.

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Module
No. Unit
No. Topics Hrs.
1.0 Introduction to Quantum Computing 07
1.1 Motivation for studying Quantum Computing
1.2 Origin of Quantum Computing
1.3 Quantum Computer vs. Classical Computer
1.4 Introduction to Quantum mechanics
1.5 Overview of major concepts in Quantum Computing
Qubits and multi -qubits states
Bloch Sphere representation
Quantum Superposition
Quantum Entanglement
1.6 Major players in the industry (IBM, Microsoft, Rigetti, D-Wave etc.)
2.0 Mathematical Foundations for Quantum Computing 05
2.1 Matrix Algebra: basis vectors and orthogonality, inner product and Hilbert
spaces, matrices and tensors, unitary operators and projectors, Dirac notation,
Eigen values and Eigen vectors.
3.0 Building Blocks for Quantum Program 08
3.1 Architecture of a Quantum Computing platform
3.2 Details of q-bit system of information representation:
Block Sphere
Multi -qubpits Stateys Quantum -superposAition of qpubits (vralid aind
linvalid superposition)
Quantum Entanglement
Useful states from quantum algorithmic perceptive e.g. Bell State Operation on
qubits: Measuring and transforming using gates.
Quantum Logic gates and Circuit
No Cloning Theorem and Teleportation
3.3 Programming model for a Quantum Computing Program
Steps performed on classical computer
Steps performed on Quantum Computer
Moving data between bits and qubits.
4.0 Quantum Algorithms and Error correction 06
4.1 Quantum Algorithms
Shor’s Algorithm
Grover’s Algorithm
Deutsch’s Algorithm
Deutsch -Jozsa Algorithm
4.2 Quantum error correction using repetition codes
3 qubit codes
Shor’s 9 qubit error correction Code
5.0 Quantum Hardware 10
5.1 Ion Trap Qubits
The DiVincenzo Criteria
Lagrangian and Hamiltonian Dynamics in a Nutshell: Dynamics of a Translating
Rotor
5.2 Quantum Mechanics of a Free Rotor: A Poor Person’s Atomic
Model: Rotor Dynamics and the Hadamard Gate, Two-Qubit Gates
The Cirac -Zoller Mechanism: Quantum Theory of Simple Harmonic Motion, A
Phonon -Qubit Pair Hamiltonian, Light -Induced Rotor -Phonon Interactions,

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University of Mumbai (B.E. Electronics and Computer Science) Page 103 of 140



Trapped Ion Qubits, Mølmer -Sørenson Coupling ..
5.3 Cavity Quantum Electrodynamics (cQED): Eigenstates of the Jaynes -Cummings
Hamiltonian
Circuit QED (cirQED): Quantum LC Circuits, Artificial Atoms, Superconducting
Qubits
5.4 Quantum computing with spins:
Quantum inverter realized with two exchange coupled spins in quantum dots, A
2-qubit spintronic universal quantum gate.
6.0 OSS Toolkits for implementing Quantum program 03
6.1 IBM quantum experience
6.2 Microsoft Q
Rigetti PyQuil (QPU/QVM)
Total 39

Text books:

1. Michael A. Nielsen, “Quantum Computation and Quantum Information”, Cambridge
University Press.
2. David McMahon, “Quantum Computing Explained”, Wiley ,2008
3. Qiskit textbook https://qiskit.org/textbook -beta/
4. Vladimir Silva, Practical Quantum Computing for Developers,2018
5. Bernard Zygelman, A First Introduction to Quantum Computing and Information,2018
6. Supriyo Bandopadhyay and Marc Cahy, “Introduction to Spintronics”, CRC Press, 2008.

Reference books:

1. The Second Quantum Revolution: From Entanglement to Quantum
Computing and OtherSuper -Technologies, Lars Jaeger
2. La Guardia , Giuliano Gladioli “Quantum Error correction codes” Springer,2021

Internal Assessment (20-Marks):

Internal Assessment (IA) consists of two class tests of 20 marks each. IA -1 is to be conducted on
approximately 40% of the syllabus and IA -2 will be based on remaining contents (approximately
40% syllabus but excluding contents covered in IA -I). Duration of each test shall be one hour.
Average of the two tests will be considered as IA marks.

End Semester Examination (80-Marks):

Weightage to each of the modules in end -semester examination will be proportional to number of
respective lecture hours mentioned in the curriculum.

1. Question paper will comprise of total 06 questions, each carrying 20 marks .
2. Question No: 01 will be compulsory and based on entire syllabus wherein 4 to 5 sub-questions
will be asked.
3. Remaining questions will be mixed in nature and randomly selected from all the modules.
4.Total 04 questions need to be attempted.

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100



Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECC
DO802 System Security 03 02 -- 03 -- 3




Subject
Code

Subject
Name Examination Scheme
Theory Marks
Term
Work
Prac
tical
Oral
Total Internal assessment End
Sem.
Exam Exam
duratio
n
Hours Test
1 Test
2 Avg of Test 1
and Test 2
ECC
DO802 System
Security 20 20 20 80 03 -- 100



Course Pre-requisite: Computer Networks

Course Objectives:

1. To understand the fundamentals of system security.
2. To explore the working principles and utilities of various crypto algorithms including Secret key
Cryptography and public key algorithms
3. To understand the various controls available for protection against internet attacks, including integrity
check, firewalls, intruder detection systems.
4. To understand, and evaluate different attacks on Open Web Applications and Web services
5. To describe the mechanisms used to provide security in different infrastructure and networks.
6. To perform Security Auditing and Analysis

Course Outcomes:

After successful completion of the course students will be able to:

1. Understand the concept of vulnerabilities, attacks and protection mechanisms.
2. Understand theworkingof various crypto algorithms.
3. Analyzevarious controls available for protection against internet attacks.
4. Evaluate different attacks on Open Web Applications and Web services
5. Analyze mechanisms used to provide security in different infrastructure and networks
6. Perform security monitoring and testing of system

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101





Module
No. Unit
No.
Contents
Hrs.
1 The Need for System Security 4
1.1 Risks, Threats, and Vulnerabilities, Tenets of Information Systems Security
(Confidentiality,Integrity ,Availability )
1.2 Malicious Attack
Birthday Attacks ,Brute -Force Password Attacks ,
Dictionary Password Attacks, IP Address Spoofing
Hijacking ,Replay Attacks ,Man -in-the-Middle Attacks
Masquerading ,Eavesdropping ,Social Engineering, Phreaking ,Phishing
,Pharming .
2 Cryptography 6
2.1 Cryptography : Overview of Cryptography : What is cryptography ,
encryption and decryption techniques ,Symmetric and asymmetric key
cryptography : AES, DES, RSA, Knapsack cryptosystem.
3 Network Security 9
3.1 Firewall: Need of Firewall, types of firewall - Packet Filters, Stateful Packet
Filters, Application Gateways, Circuit gateways. Firewall Policies,
Configuration, limitations, DMZ, VPN.
3.2 Intrusion Detection System Vulnerability Assessment, Misuse detection,
Anomaly Detection, Network Based IDS, Host -Based IDS, Honeypots
3.3 Kerberos: Working, AS, TGS, SS
3.4 IP Security - Overview, Protocols - AH, ESP, Modes - transport and Tunnel.
3.5 Public key infrastructure Introduction, Certificates, (PKI): Certificate Authority,
authority, Registration
3.6 X.509/PKIX certificate format.
3.7 Basic concepts of SNMP, SNMPv1 Community facility and SNMPv3
4 Web Security 7
4.1 Web Security Considerations, User Authentication and Session Management,
Cookies, SSL, HTTPS, SSH, Privacy on Web, Web Browser Attacks, Account
Harvesting, Web Bugs, Clickjacking, CrossSite Request Forgery, Session
Hijacking and Management, Secure Electronic Transaction, Email Attacks,
DNS Attacks, Web Service Security.

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5 Infrastructure Security 9
5.1 Physical Security : Managerial, Technical And Physical Controls,
Environmental Exposures And Controls, Physical Access Controls
5.2 Wireless network Security: IEEE 802.11xWireless LAN Security, Wireless
Intrusion Detection System (WIDS)
5.3 Mobile Security: Security Threats, Device Security, Cloud Security: Cloud
Security Risks and Countermeasures, Cloud Identity and Access Management,
Cloud Security as a Service, SAML, OAuth
5.4 IOT Security: IoT Concepts, IoT Attacks, IoT Hacking Methodology, IoT
Hacking Tools, IoT Countermeasures

6 Security Auditing and Analysis 4
6.1
How to define your audit plan?
What auditing benchmarks are ?
How to collect audit data?
Which post-audit activities you need to perform?
How to perform security monitoring?
Which types of log information you should capture?
How to verify security controls ?
• How to monitor and test your security systems?

Total 39

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103


Text Books:
1. Computer Security Principles and Practice, William Stallings, Sixth Edition, Pearson Education
2. Security in Computing, Charles P. Pfleeger, Fifth Edition, Pearson Education .
3. Fundamentals of Information system security, Third Edition, David Kim,Michael G. Solomon
1. Jones & Bartlett Learning
4. Network Security and Cryptography, Bernard Menezes, Cengage Learning
5. Network Security Bible, Eric Cole, Second Edition, Wiley


Reference Books :
1. Web Application Hackers Handbook by Wiley.
2. Information Security The Complete Reference, 2nd Edition , Mark Rhodes -Ousley, McGraw Hill
Education
3. Computer Security, Dieter Gollman, Third Edition, Wiley
4. CCNA Security Study Guide, Tim Boyle, Wiley
5. Introduction to Computer Security, Matt Bishop, Pearson.
6. Cloud Security and Privacy, Tim Mather, Subra Kumaraswamy, Shahed Latif ,O’Reilly



Internal Assessment (IA):

Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both
the test will be considered as final IA marks

End Semester Examination:

1. Question paper will comprise of 6 questions, each of 20 marks.
2. Total 4 questions need to be solved.
3. Question No.1 will be compulsory and based on entire syllabus wherein sub questions of 2 to 5
marks will be asked.
4. Remaining questions will be selected from all the modules.

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104
Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 801 Robotics lab -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work
Pract
ical
Oral
Total Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL 801 Robotics lab -- -- -- -- -- 25 -- 25 50

Laboratory outcomes:
After successful completion of the laboratory, students will be able to:
1. Use the acquired knowledge in solving direct and inverse kinematics problems
2. Select and Implement suitable task and trajectory planning algorithms.
3. Develop suitable programming tools for Robotic applications
4. Construct Robots/Robotic arms for automation applications

Term Work:

Term work consists of performing minimum 6 experiments covering entire syllabus of Robotics, one mini project
and two assignments. The experiments should be student centric and attempt should be made to make experiments
more meaningful and interesting. Simulation experiments are also encouraged. Experiment must be graded from
time to time. The grades should be converted into marks as per the Credit and Grading System manual and should
be added and averaged. The grading and term work assessment should be done based on this scheme. The final
certification and acceptance of term work ensures satisfactory performance of laboratory work and minimum
passing marks in term work. Practical and/or Oral exam will be based on the entire syllabus.

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105


Suggested List of Experiments


Sr.No. Experiment Name
1 Study/Demo of 4 axis robotic arm
2 Study/Demo of 5 axis robotic arm
3 Forward kinematics
4 Inverse kinematic
5 Joint -space trajectory
6 Cartesian -space trajectory
7 Template matching
8 Iterative processing
9 Segmentation
10 Mini project






Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course
instructor to design and introduce new, innovative and challenging experiments, (limited to maximum
30% variation to the suggested list) from within the curriculum, so that, the fundamentals and
applications can be explored to give greater clarity to the students and they can be motivated to think
differently.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL
802 MEMS Technology -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work
Pract
i cal
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL
802 MEMS
Technology -- -- -- -- -- 25 25 -- 50


Laboratory outcomes:

After completing laboratory sessions, students will be able to

1. Determine various parameters for MEMS devices.
2. Plot characteristics of MEMS devices.
3. Select particular device for specific application.
4. Observe effect of device parameters variation on its performance.

Term Work:

At least 10 experiments covering entire syllabus of MEMS Technology ECC DO801 should be set to have well
predefined inference and conclusion. The experiments should be student centric and attempt should be made to
make experiments more meaningful, interesting. Simulation experiments are also encouraged. Case study
experiment can be given to cover advance development in the subject. Experiment must be graded from time to
time. The grades should be converted into marks as per the Credit and Grading System manual and should be added
and averaged. The grading and term work assessment should be done based on this scheme. The final certification
and acceptance of term work ensures satisfactory performance of laboratory work and minimum passing marks in
term work. Practical and Oral exam will be based on the entire syllabus.

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Suggested List of Experiments

Sr. No. Experiment Name
1 Modeling and simulation of cantilever .
2 Modeling and simulation of pressure sensor .
3 Modeling and simulation of accelerometer .
4 Modeling and simulation of thermal actuator. .
5 Modeling and simulation of SMA .
6 Modeling and simulation of Piezoelectric sensor. .
7 Hardware experiment on MEMS devices.
8 Hardware experiment on MEMS devices
9 Case study on advance topic.
10 Case study of recent development in the subject.



Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor to
design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to the
suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to give
greate r clarity to the students and they can be motivated to think differently.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 802 Natural Language
processing -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name
Theory
Marks
Term
Work
Practical/ Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL 802 Natural
Language
processing -- -- -- -- -- 25 25 50



Laboratory outcomes:

After successful completion of the laboratory, students will be able to:

1. Design, implement and test algorithms for NLP problems.
2. Understand the mathematical and linguistic foundations underlying approaches to the various areas in NLP.
3. Apply NLP techniques to design real world NLP applications such as machine translation, text categorization,
text summarization, information extraction...etc.


Term Work:

At least 8 experiments covering entire syllabus of Natural Language processing ECC DO801 should be set to have
well predefined inference and conclusion. Additionally, a Case Study/ Mini Project based on any Application is
mandatory. The experiments should be student centric and attempt should be made to ma ke experiments more
meaningful and interesting. Simulation experiments are also encouraged. Experiment must be graded from time to
time. The grades should be converted into marks as per the Credit and Grading System manual and should be added
and averaged. The grading and term work assessment should be done based on this scheme. The final certification and
acceptance of term work ensures satisfactory performance of laboratory work and minimum passing marks in term
work. Practical and Oral exam will be based on the entire syllabus.

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Suggested List of Experiments

Sr. No. Experiment Name
1 Preprocessing of text (Tokenization, Filtration)
2 Preprocessing of text ( Script Validation)
3 Preprocessing of text ( Stop Word Removal, Stemming)
4 Understanding the morphology of a word by the use of Add -Delete table.
5 N-gram model
6 POS tagging
7 Calculation of emission and transition matrix which will be helpful for tagging Parts of Speech
using Hidden Markov Model.
8 Find POS tags of words in a sentence using Viterbi decoding.
9 Chunking
10 Named Entity Recognition


Note:
1. Possible tools / language: R tool/ Python programming Language
2. Although it is not mandatory, the experiments can be conducted with reference to any Indian regional language.



Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor to
design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to the
suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to give
greater clarity to the students and they can be motivated to think differently.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL
802 3D printing and
Design Lab -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work
Pract
ical
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL
802 3D Printing and Design Lab -- -- -- -- -- 25 25 -- 50



Term Work:

At least 10 experiments covering entire syllabus of 3D Printing and Design ECC DO801 should be set to have
well predefined inference and conclusion. The experiments should be student centric and attempt should be made to
make experiments more meaningful, interesting. Simulation experiments are also encouraged. Experiment must be
graded from time to tim e. The grades should be converted into marks as per the Credit and Grading System manual
and should be added and averaged. The grading and term work assessment should be done based on this scheme.
The final certification and acceptance of term work ensures satisfactory performance of laboratory work and
minimum passing marks in term work. Practical and Oral exam will be based on the entire syllabus.

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Suggested List of Experiments

Sr.No. Experiment Name
1 Study of Specifications & Processes for 3d Printing Machines
2 2D Modeling Using CAD Software
3 Assembly Modeling Involving 2 or 3 Components
4 Creation of STL Files and Slicing Files as Preprocessing for 3D printing
5 Study of Meshing and its Influence on the accuracy of component
6 Reverse Engineering using a 3 D scanner / CMM
7 Design of Supports For Thin Section / Overhang Features of a Component
8 Study workflow, Material Requirements, Design Considerations, Post Processing of Fused
Deposition Modeling FDM 3D printer
9 Design for additive Manufacturing : one or Two experiments
10 Mini Project on New Product Design




Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course
instructor to design and introduce new, innovative and challenging experiments, (limited to maximum
30% variation to the suggested list) from within the curriculum, so that, the fundamentals and
applications can be explored to give greater clarity to the students and they can be motivated to think
differently.

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Subject
Code Subject Name Teaching Scheme Credits Assigned
Theory Practical Tutorial Theory Practical Tutorial Total
ECL 802 Advanced Algorithms
Lab -- 02 -- -- 01 -- 01




Subject
Code

Subject
Name Examination
Scheme
Theory
Marks
Term
Work
Pract
ical
Oral
Total
Internal assessment End
Sem.
Exa
m Exam
duration
Hours Te st 1 Test
2 Avg of Test 1
and Test 2
ECL 802 Advanced
Algorithms Lab 20 20 20 80 3 25 25 -- 50


Laboratory Outcomes:

After completing laboratory sessions, students will be able to

1. Understand the methods of designing and analyzing algorithms.
2. Design and implement efficient algorithms for a specified application.
3. Strengthen the ability to identify and apply the suitable algorithm for the given real -world
problem.
4. Analyze worst -case running time of algorithms and understand fundamental algorithmic problems.

Term Work:

At least 10 experiments covering entire syllabus should be set to have well predefined inference and conclusion. The
experiments should be student centric and attempt should be made to make experiments more meaningful,
interesting. Simulation experiments are also encouraged. Experiment must be graded from time to time. The grades
should be c onverted into marks as per the Credit and Grading System manual and should be added and averaged.
The grading and term work assessment should be done based on this scheme. The final certification and acceptance
of term work ensures satisfactory performance of laboratory work and minimum passing marks in term work.
Practical and Oral exam will be based on the entire syllabus.

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Suggested List of Experiments

Sr.No. Experiment Name
1. Implementation of Merge and Quick sort using divide and conquer approach
2. Implementation of Fractional Knapsack
3. Implementation of Djikstras’s algorithms
4. Implementation of 0/1 Knapsack using dynamic programming
5 Implementation of Longest Common Subsequence
6. Implementation of Floyd’s Warshall’s algorithm
7. Implementation of Ford Fulkerson algorithm
8. Implementation of Maximum Bipartite matching algorithm
9. Implementation of n-queen using backtracking
10. Implementation of sum of subsets algorithm
11. Implementation of 15 puzzle problem
12. Implementation of Travelling salesman’s problem.




Note: Suggested List of Experiments is indicative. However, flexibilities lie with individual course instructor to
design and introduce new, innovative and challenging experiments, (limited to maximum 30% variation to the
suggested list) from within the curriculum, so that, the fundamentals and applications can be explored to give
greater clarity to the students and they can be motivated to think differently.

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e Code Course Name Credits
ILO8021 Project Management 03


Objectives:

1. To familiarize the students with the use of a structured methodology/approach for each
and every unique project undertaken, including utilizing project management concepts,
tools and techniques.
2. To appraise the students with the project management life cycle and make them
knowledgeable about the various phases from project initiation through closure.

Outcomes: Learner will be able to…

1. Apply selection criteria and select an appropriate project from different options.
2. Write work break down structure for a project and develop a schedule based on it.
3. Identify opportunities and threats to the project and decide an approach to deal with them
strategically.
4. Use Earned value technique and determine & predict status of the project.
5. Capture lessons learned during project phases and document them for future reference



Module
Detailed Contents
Hrs



01 Project Management Foundation:

Definition of a project, Project Vs Operations, Necessity of project management, Triple
constraints, Project life cycles (typical & atypical) Project phases and stage gate
process. Role of project manager. Negotiations and resolving conflicts. Project
management in various organization structures. PM knowledge areas as per Project
Management Institute (PMI).


5


02 Initiating Projects:

How to get a project started, Selecting project strategically, Project selection models
(Numeric /Scoring Models and Non -numeric models), Project portfolio process, Project
sponsor and creating charter; Project proposal. Effective project team, Stages of team
development & growth (forming, storming, norming & performing), team dynamics.

6


03 Project Planning and Scheduling:

Work Breakdown structure (WBS) and linear responsibility chart, Interface
Co-ordination and concurrent engineering, Project cost estimation and budgeting, Top
down and bottoms up budgeting, Networking and Scheduling techniques. PERT, CPM,

8

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GANTT chart. Introduction to Project Management Information System (PMIS).



04 Planning Projects:

Crashing project time, Resource loading and leveling, Goldratt's critical chain, Project
Stakeholders and Communication plan.
Risk Management in projects: Risk management planning, Risk identification and risk
register. Qualitative and quantitative risk assessment, Probability and impact matrix.
Risk response strategies for positive and negative risks


6






05 5.1 Executing Projects:

Planning monitoring and controlling cycle. Information needs and reporting, engaging
with all stakeholders of the projects.
Team management, communication and project meetings.

5.2 Monitoring and Controlling Projects:

Earned Value Management techniques for measuring value of work completed; Using
milestones for measurement; change requests and scope creep. Project audit.
5.3 Project Contracting

Project procurement management, contracting and outsourcing,





8





06 6.1 Project Leadership and Ethics:

Introduction to project leadership, ethics in projects.
Multicultural and virtual projects.
6.2 Closing the Project:

Customer acceptance; Reasons of project termination, Various types of project
terminations (Extinction, Addition, Integration, Starvation), Process of project
termination, completing a final report; doing a lessons learned analysis; acknowledging
successes and failures; Project management templates and other resources; Managing
without authority ; Areas of further study.




6


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.

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End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Jack Meredith & Samuel Mantel, Project Management: A managerial approach, Wiley
India, 7thEd.
2. A Guide to the Project Management Body of Knowledge (PMBOK ® Guide), 5th Ed,
Project Management Institute PA, USA
3. Gido Clements, Project Management, Cengage Learning.
4. Gopalan, Project Management, , Wiley India
5. Dennis Lock, Project Management, Gower Publishing England, 9 th Ed.

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Course Code Course Name Credits
ILO8022 Finance Management 03


Objectives:

1. Overview of Indian financial system, instruments and market
2. Basic concepts of value of money, returns and risks, corporate finance, working capital and its
management
3. Knowledge about sources of finance, capital structure, dividend policy

Outcomes: Learner will be able to…

1. Understand Indian finance system and corporate finance
2. Take investment, finance as well as dividend decisions



Module
Detailed Contents
Hrs





01 Overview of Indian Financial System: Characteristics, Components and Functions of
Financial System.
Financial Instruments: Meaning, Characteristics and Classification of Basic Financial
Instruments — Equity Shares, Preference Shares, Bonds -Debentures, Certificates of
Deposit, and Treasury Bills.
Financial Markets: Meaning, Characteristics and Classification of Financial Markets
— Capital Market, Money Market and Foreign Currency Market

Financial Institutions: Meaning, Characteristics and Classification of Financial
Institutions — Commercial Banks, Investment -Merchant Banks and Stock Exchanges




06



02 Concepts of Returns and Risks: Measurement of Historical Returns and Expected
Returns of a Single Security and a Two-security Portfolio; Measurement of Historical
Risk and Expected Risk of a Single Security and a Two-security Portfolio.
Time Value of Money: Future Value of a Lump Sum, Ordinary Annuity, and Annuity
Due; Present Value of a Lump Sum, Ordinary Annuity, and Annuity Due; Continuous
Compounding and Continuous Discounting.


06

03 Overview of Corporate Finance: Objectives of Corporate Finance; Functions of
Corporate Finance —Investment Decision, Financing Decision, and Dividend Decision.
Financial Ratio Analysis: Overview of Financial Statements —Balance Sheet, Profit
and Loss Account, and Cash Flow Statement; Purpose of Financial Ratio Analysis;
09

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Liquidity Ratios; Efficiency or Activity Ratios; Profitability Ratios; Capital Structure
Ratios; Stock Market Ratios; Limitations of Ratio Analysis.




04 Capital Budgeting: Meaning and Importance of Capital Budgeting; Inputs for Capital
Budgeting Decisions; Investment Appraisal Criterion —Accounting Rate of Return,
Payback Period, Discounted Payback Period, Net Present Value(NPV), Profitability
Index, Internal Rate of Return (IRR), and Modified Internal Rate of Return (MIRR)
Working Capital Management: Concepts of Meaning Working Capital; Importance of
Working Capital Management; Factors Affecting an Entity’s Working Capital Needs;
Estimation of Working Capital Requirements; Management of Inventories;
Management of Receivables; and Management of Cash and Marketable Securities.



10



05 Sources of Finance: Long Term Sources —Equity, Debt, and Hybrids; Mezzanine
Finance; Sources of Short Term Finance —Trade Credit, Bank Finance, Commercial
Paper; Project Finance.
Capital Structure: Factors Affecting an Entity’s Capital Structure; Overview of
Capital Structure Theories and Approaches — Net Income Approach, Net Operating
Income Approach; Traditional Approach, and Modigliani -Miller Approach. Relation
between Capital Structure and Corporate Value; Concept of Optimal Capital Structure


05

06 Dividend Policy: Meaning and Importance of Dividend Policy; Factors Affecting an
Entity’s Dividend Decision; Overview of Dividend Policy Theories and Approaches —
Gordon’s Approach, Walter’s Approach, and Modigliani -Miller Approach
03

Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

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119





REFERENCES:

1. Fundamentals of Financial Management, 13th Edition (2015) by Eugene F. Brigham and Joel F.
Houston; Publisher: Cengage Publications, New Delhi.
2. Analysis for Financial Management, 10th Edition (2013) by Robert C. Higgins; Publishers:
McGraw Hill Education, New Delhi.
3. Indian Financial System, 9th Edition (2015) by M. Y. Khan; Publisher: McGraw Hill Education,
New Delhi.
4. Financial Management, 11th Edition (2015) by I. M. Pandey; Publisher: S. Chand (G/L) &
Company Limited, New Delhi.

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Course Code Course Name Credits
ILO8023 Enterpreneurship Development and Management 03


Objectives:

1. To acquaint with entrepreneurship and management of business
2. Understand Indian environment for entrepreneurship
3. Idea of EDP, MSME

Outcomes: Learner will be able to…

1. Understand the concept of business plan and ownerships
2. Interpret key regulations and legal aspects of entrepreneurship in India
3. Understand government policies for entrepreneurs



Module
Detailed Contents
Hrs



01 Overview Of Entrepreneurship: Definitions, Roles and Functions/Values of
Entrepreneurship, History of Entrepreneurship Development, Role of Entrepreneurship
in the National Economy, Functions of an Entrepreneur, Entrepreneurship and Forms of
Business Ownership
Role of Money and Capital Markets in Entrepreneurial Development: Co ntribution of
Government Agencies in Sourcing information for Entrepreneurship


04



02 Business Plans And Importance Of Capital To Entrepreneurship: Preliminary and
Marketing Plans, Management and Personnel, Start -up Costs and Financing as well as
Projected Financial Statements, Legal Section, Insurance, Suppliers and Risks,
Assumptions and Conclusion, Capital and its Importance to the Entrepreneur
Entrepreneurship And Business Development: Starting a New Business, Buying an
Existing Business, New Product Development, Business Growth and the Entrepreneur
Law and its Relevance to Business Operations


09

03 Women’s Entrepreneurship Development, Social entrepreneurship -role and need, EDP
cell, role of sustainability and sustainable development for SMEs, case studies,
exercises
05

04 Indian Environment for Entrepreneurship: key regulations and legal aspects ,
MSMED Act 2006 and its implications, schemes and policies of the Ministry of MSME,
role and responsibilities of various government organisations, departments, banks etc.,
Role of State governments in terms of infrastructure developments and support etc.,
08

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Public private partnerships, National Skill development Mission, Credit Guarantee
Fund, PMEGP, discussions, group exercises etc

05 Effective Management of Business: Issues and problems faced by micro and small
enterprises and effective management of M and S enterprises (risk management, credit
availability, technology innovation, supply chain management, linkage with large
industries), exercises, e-Marketing
08

06 Achieving Success In The Small Business: Stages of the small business life cycle, four
types of firm -level growth strategies, Options – harvesting or closing small business
Critical Success factors of small business
05
Assessment :
Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Poornima Charantimath, Entrepreneurship development - Small Business Enterprise, Pearson
2. Education Robert D Hisrich, Michael P Peters, Dean A Shapherd, Entrepreneurship, latest
edition, The McGrawHill Company
3. Dr TN Chhabra, Entrepreneurship Development, Sun India Publications, New Delhi
4. Dr CN Prasad, Small and Medium Enterprises in Global Perspective, New century Publications,
New Delhi
5. Vasant Desai, Entrepreneurial development and management, Himalaya Publishing House
6. Maddhurima Lall, Shikah Sahai, Entrepreneurship, Excel Books
7. Rashmi Bansal, STAY hungry STAY foolish, CIIE, IIM Ahmedabad
8. Law and Practice relating to Micro, Small and Medium enterprises, Taxmann Publication Ltd.
9. Kurakto, Entrepreneurship - Principles and Practices, Thomson Publication
10. Laghu Udyog Samachar
11. www.msme.gov.in
12. www.dcmesme.gov.in
13. www.msmetraining.gov.in

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Course Code Course Name Credits
ILO8024 Human Resource Management 03


Objectives:

1. To introduce the students with basic concepts, techniques and practices of the human resource
management.
2. To provide opportunity of learning Human resource management (HRM) processes, related with
the functions, and challenges in the emerging perspective of today’s organizations.
3. To familiarize the students about the latest developments, trends & different aspects of HRM.
4. To acquaint the student with the importance of inter -personal & inter -group behavioral skills in
an organizational setting required for future stable engineers, leaders and managers.

Outcomes: Learner will be able to…

1. Understand the concepts, aspects, techniques and practices of the human resource management.
2. Understand the Human resource management (HRM) processes, functions, changes and
challenges in today’s emerging organizational perspective.
3. Gain knowledge about the latest developments and trends in HRM.
4. Apply the knowledge of behavioral skills learnt and integrate it with in inter personal and
intergroup environment emerging as future stable engineers and managers.



Module
Detailed Contents
Hrs



01 Introduction to HR

 Human Resource Management - Concept, Scope and Importance,
Interdisciplinary Approach Relationship with other Sciences, Competencies of
HR Manager, HRM functions.
 Human resource development (HRD): changing role of HRM – Human
resource Planning, Technological change, Restructuring and rightsizing,
Empowerment, TQM, Managing ethical issues.


5



02 Organizational Behavior (OB)

 Introduction to OB Origin, Nature and Scope of Organizational Behavior,
Relevance to Organizational Effectiveness and Contemporary issues
 Personality: Meaning and Determinants of Personality, Personality
development, Personality Types, Assessment of Personality Traits for
Increasing Self Awareness
 Perception: Attitude and Value, Effect of perception on Individual Decision -


7

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making, Attitude and Behavior.
 Motivation: Theories of Motivation and their Applications for Behavioral
Change (Maslow, Herzberg, McGregor);
 Group Behavior and Group Dynamics: Work groups formal and informal
groups and stages of group development. Team Effectiveness: High performing
teams, Team Roles, cross functional and self-directed team.
 Case study



03 Organizational Structure &Design

 Structure, size, technology, Environment of organization; Organizational Roles
& conflicts: Concept of roles; role dynamics; role conflicts and stress.
 Leadership: Concepts and skills of leadership, Leadership and managerial roles,
Leadership styles and contemporary issues in leadership.
 Power and Politics: Sources and uses of power; Politics at workplace, Tactics
and strategies.


6



04 Human resource Planning

 Recruitment and Selection process, Job-enrichment, Empowerment - Job-
Satisfaction, employee morale.
 Performance Appraisal Systems: Traditional & modern methods, Performance
Counseling, Career Planning.
 Training & Development: Identification of Training Needs, Training Methods


5



05 Emerging Trends in HR

 Organizational development; Business Process Re-engineering (BPR), BPR as a
tool for organizational development , managing processes & transformation in
HR. Organizational Change, Culture, Environment
 Cross Cultural Leadership and Decision Making : Cross Cultural
Communication and diversity at work , Causes of diversity, managing diversity
with special reference to handicapped, women and ageing people, intra
company cultural difference in employee motivation.


6




06 HR & MIS

Need, purpose, objective and role of information system in HR, Applications in HRD in
various industries (e.g. manufacturing R&D, Public Transport, Hospitals, Hotels and
service industries
Strategic HRM

Role of Strategic HRM in the modern business world, Concept of Strategy, Strategic
Management Process, Approaches to Strategic Decision Making; Strategic Intent –
Corporate Mission, Vision, Objectives and Goals



10

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Labor Laws & Industrial Relations

Evolution of IR, IR issues in organizations, Overview of Labor Laws in India; Industrial
Disputes Act, Trade Unions Act, Shops and Establishments Act

Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCES:

1. Stephen Robbins, Organizational Behavior, 16th Ed, 2013
2. V S P Rao, Human Resource Management, 3rd Ed, 2010, Excel publishing
3. Aswathapa, Human resource management: Text & cases, 6th edition, 2011
4. C. B. Mamoria and S V Gankar, Dynamics of Industrial Relations in India, 15th Ed, 2015,
Himalaya Publishing, 15thedition, 2015
5. P. Subba Rao, Essentials of Human Resource management and Industrial relations, 5th Ed, 2013,
Himalaya Publishing
6. Laurie Mullins, Management & Organizational Behavior, Latest Ed, 2016, Pearson Publications

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Course Code Course Name Credits
ILO8025 Professional Ethics and Corporat Social Responsibility (CSR) 03


Objectives:

1. To understand professional ethics in business
2. To recognized corporate social responsibility
Outcomes: Learner will be able to…

1. Understand rights and duties of business
2. Distinguish different aspects of corporate social responsibility
3. Demonstrate professional ethics
4. Understand legal aspects of corporate social responsibility



Module
Detailed Contents
Hrs

01 Professional Ethics and Business: The Nature of Business Ethics; Ethical Issues in
Business; Moral Responsibility and Blame; Utilitarianism: Weighing Social Costs and
Benefits; Rights and Duties of Business
04


02 Professional Ethics in the Marketplace: Perfect Competition; Monopoly Competition;
Oligopolistic Competition; Oligopolies and Public Policy
Professional Ethics and the Environment: Dimensions of Pollution and Resource
Depletion; Ethics of Pollution Control; Ethics of Conserving Depletable Resources

08


03 Professional Ethics of Consumer Protection: Markets and Consumer Protection;
Contract View of Business Firm’s Duties to Consumers; Due Care Theory; Advertising
Ethics; Consumer Privacy
Professional Ethics of Job Discrimination: Nature of Job Discrimination; Extent of
Discrimination; Reservation of Jobs.

06


04 Introduction to Corporate Social Responsibility: Potential Business Benefits —Triple
bottom line, Human resources, Risk management, Supplier relations; Criticisms and
concerns —Nature of business; Motives; Misdirection.
Trajectory of Corporate Social Responsibility in India

05
05 Corporate Social Responsibility: Articulation of Gandhian Trusteeship 08

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Corporate Social Responsibility and Small and Medium Enterprises (SMEs) in India,
Corporate Social Responsibility and Public -Private Partnership (PPP) in India

06 Corporate Social Responsibility in Globalizing India: Corporate Social
Responsibility Voluntary Guidelines, 2009 issued by the Ministry of Corporate Affairs,
Government of India, Legal Aspects of Corporate Social Responsibility —Companies
Act, 2013.
08


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Business Ethics: Texts and Cases from the Indian Perspective (2013) by Ananda Das Gupta;
Publisher: Springer.
2. Corporate Social Responsibility: Readings and Cases in a Global Context (2007) by Andrew
Crane, Dirk Matten, Laura Spence; Publisher: Routledge.
3. Business Ethics: Concepts and Cases, 7th Edition (2011) by Manuel G. Velasquez; Publisher:
Pearson, New Delhi.
4. Corporate Social Responsibility in India (2015) by BidyutChakrabarty, Routledge, New Delhi.

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Course Code Course Name Credits
ILO8026 Research Methodology 03


Objectives:

1. To understand Research and Research Process
2. To acquaint students with identifying problems for research and develop research strategies
3. To familiarize students with the techniques of data collection, analysis of data and interpretation

Outcomes: Learner will be able to…

1. Prepare a preliminary research design for projects in their subject matter areas
2. Accurately collect, analyze and report data
3. Present complex data or situations clearly
4. Review and analyze research findings



Module
Detailed Contents
Hrs




01 Introduction and Basic Research Concepts

1.1 Research – Definition; Concept of Construct, Postulate, Proposition, Thesis,
Hypothesis, Law, Principle. Research methods vs Methodology
1.2 Need of Research in Business and Social Sciences
1.3 Objectives of Research
1.4 Issues and Problems in Research
1.5 Characteristics of Research: Systematic, Valid, Verifiable, Empirical and Critical



09





02 Types of Research

2.1. Basic Research

2.2. Applied Research

2.3. Descriptive Research

2.4. Analytical Research

2.5. Empirical Research

2.6 Qualitative and Quantitative Approaches




07

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03 Research Design and Sample Design
3.1 Research Design – Meaning, Types and Significance
3.2 Sample Design – Meaning and Significance Essentials of a good sampling Stages in
Sample Design Sampling methods/techniques Sampling Errors

07










04 Research Methodology
4.1 Meaning of Research Methodology
4.2. Stages in Scientific Research Process:
a. Identification and Selection of Research Problem
b. Formulation of Research Problem
c. Review of Literature
d. Formulation of Hypothesis
e. Formulation of research Design
f. Sample Design
g. Data Collection
h. Data Analysis
i. Hypothesis testing and Interpretation of Data
j. Preparation of Research Report









08

05 Formulating Research Problem

5.1 Considerations: Relevance, Interest, Data Availability, Choice of data, Analysis of
data, Generalization and Interpretation of analysis
04


06 Outcome of Research

6.1 Preparation of the report on conclusion reached

6.2 Validity Testing & Ethical Issues

6.3 Suggestions and Recommendation

04

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129


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or at least 6 assignment on complete syllabus or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Dawson, Catherine, 2002, Practical Research Methods, New Delhi, UBS Publishers Distributors.
2. Kothari, C.R.,1985, Research Methodology -Methods and Techniques, New Delhi, Wiley Eastern
Limited.
3. Kumar, Ranjit, 2005, Research Methodology -A Step-by-Step Guide for Beginners, (2nded),
Singapore, Pearson Education

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130





Course Code Course Name Credits
ILO8027 IPR and Patenting 03


Objectives:

1. To understand intellectual property rights protection system
2. To promote the knowledge of Intellectual Property Laws of India as well as International treaty
procedures
3. To get acquaintance with Patent search and patent filing procedure and applications

Outcomes: Learner will be able to…

1. understand Intellectual Property assets
2. assist individuals and organizations in capacity building
3. work for development, promotion, protection, compliance, and enforcement of Intellectual
Property and Patenting



Module
Detailed Contents
Hr


01 Introduction to Intellectual Property Rights (IPR) : Meaning of IPR, Different
category of IPR instruments - Patents, Trademarks, Copyrights, Industrial Designs,
Plantvariety protection, Geographical indications, Transfer of technology etc.
Importance of IPR in Modern Global Economic Environment: Theories of IPR,
Philosophical aspects of IPR laws, Need for IPR, IPR as an instrument of development

05



02 Enforcement of Intellectual Property Rights: Introduction, Magnitude of problem,
Factors that create and sustain counterfeiting/piracy, International agreements,
International organizations (e.g. WIPO, WTO) activein IPR enforcement
Indian Scenario of IPR: Introduction, History of IPR in India, Overview of IP laws in
India, Indian IPR, Administrative Machinery, Major international treaties signed by
India, Procedure for submitting patent and Enforcement of IPR at national level etc.


07
03 Emerging Issues in IPR: Challenges for IP in digital economy, e-commerce, human
genome,biodiversity and traditional knowledge etc. 05

04 Basics of Patents: Definition of Patents, Conditions of patentability, Patentable and non-
patentable inventions, Types of patent applications (e.g. Patent of addition etc), Process
Patent and Product Patent, Precautions while patenting, Patent specification
Patent claims, Disclosures and non-disclosures, Patent rights and infringement, Method
07

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131



of getting a patent

05 Patent Rules: Indian patent act, European scenario, US scenario, Australia scenario,
Japan scenario, Chinese scenario, Multilateral treaties where India is a member (TRIPS
agreement, Paris convention etc.)
08


06 Procedure for Filing a Patent (National and International): Legislation and Salient
Features, Patent Search, Drafting and Filing Patent Applications, Processing of patent,
Patent Litigation, Patent Publicationetc, Time frame and cost, Patent Licensing, Patent
Infringement
Patent databases: Important websites, Searching international databases

07
Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or at least 6 assignments on complete syllabus or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

REFERENCE BOOKS:

1. Rajkumar S. Adukia, 2007, A Handbook on Laws Relating to Intellectual Property Rights in
India, The Institute of Chartered Accountants of India
2. Keayla B K, Patent system and related issues at a glance, Published by National Working Group
on Patent Laws
3. T Sengupta, 2011, Intellectual Property Law in India, Kluwer Law International
4. Tzen Wong and Graham Dutfield, 2010, Intellectual Property and Human Development: Current
Trends and Future Scenario, Cambridge University Press
5. Cornish, William Rodolph & Llewelyn, David. 2010, Intellectual Property: Patents, Copyrights,
Trade Marks and Allied Right, 7th Edition, Sweet & Maxwell
6. Lous Harns, 2012, The enforcement of Intellactual Property Rights: A Case Book, 3rd Edition,
WIPO
7. Prabhuddha Ganguli, 2012, Intellectual Property Rights, 1st Edition, TMH
8. R Radha Krishnan & S Balasubramanian, 2012, Intellectual Property Rights, 1st Edition, Excel
Books

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132


9. M Ashok Kumar and mohd Iqbal Ali, 2-11, Intellectual Property Rights, 2nd Edition, Serial
Publications
10. Kompal Bansal and Praishit Bansal, 2012, Fundamentals of IPR for Engineers, 1st Edition, BS
Publications
11. Entrepreneurship Development and IPR Unit, BITS Pilani, 2007, A Manual on In tellectual
Property Rights,
12. Mathew Y Maa, 2009, Fundamentals of Patenting and Licensing for Scientists and Engineers,
World Scientific Publishing Company
13. N S Rathore, S M Mathur, Priti Mathur, Anshul Rathi , IPR: Drafting,Interpretation of Patent
Specifications and Claims , New India Publishing Agency
14. Vivien Irish, 2005, Intellectual Property Rights for Engineers,IET
15. Howard B Rockman, 2004, Intellectual Property Law for Engineers and scientists, Wiley -IEEE
Press

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Course Code Course Name Credits
ILO8028 Digital Business Management 03


Objectives:

1. To familiarize with digital business concept
2. To acquaint with E-commerce
3. To give insights into E-business and its strategies

Outcomes: The learner will be able to …..

1. Identify drivers of digital business
2. Illustrate various approaches and techniques for E-business and management
3. Prepare E-business plan

Module Detailed content Hours





1 Introduction to Digital Business -

Introduction, Background and current status, E-market places, structures,
mechanisms, economics and impacts
Difference between physical economy and digital economy,

Drivers of digital business - Big Data & Analytics, Mobile, Cloud Computing,
Social media, BYOD, and Internet of Things(digitally intelligent
machines/services)
Opportunities and Challenges in Digital Business,




09







2 Overview of E-Commerce

E-Commerce - Meaning, Retailing in e-commerce -products and services,
consumer behavior, market research and advertisement
B2B-E-commerce -selling and buying in private e -markets, public B2B exchanges
and support services, e -supply chains, Collaborative Commerce, Intra business EC
and Corporate portals
Other E -C models and applications, innovative EC System -From E-government
and learning to C2C, mobi le commerce and pervasive computing
EC Strategy and Implementation -EC strategy and global EC, Economics and
Justification of EC, Using Affiliate marketing to promote your e-commerce
business, Launching a successful online business and EC project, Legal, Ethics
and Societal impacts of EC






06

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3 Digital Business Support services : ERP as e –business backbone, knowledge
Tope Apps, Information and referral system
Application Development: Building Digital business Applications and
Infrastructure

06



4 Managing E -Business -Managing Knowledge, Management skills for e -business,
Managing Risks in e –business
Security Threats to e -business -Security Overview, Electronic Commerce Threats,
Encryption, Cryptography, Public Key and Private Key Cryptography, Digital
Signatures, Digital Certificates, Security Protocols over Public Networks: HTTP,
SSL, Firewall as Security Control, Public Key Infrastructure (PKI) for Security,
Prominent Cryptographic Applications


06


5 E-Business Strategy -E-business Strategic formulation - Analysis of Company’s
Internal and external environment, Selection of strategy,
E-business strategy into Action, challenges and E-Transition
(Process of Digital Transformation)

04
6 Materializing e-business: From Idea to Realization -Business plan preparation

Case Studies and presentations
08

Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or at least 6 assignment on complete syllabus or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of ea ch module will be proportional
to number of respective lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.

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135


References:
1. A textbook on E-commerce , Er Arunrajan Mishra, Dr W K Sarwade,Neha Publishers &
Distributors, 2011
2. E-commerce from vision to fulfilment, Elias M. Awad, PHI-Restricted, 2002
3. Digital Business and E-Commerce Management, 6th Ed, Dave Chaffey, Pearson, August 2014
4. Introduction to E-business -Management and Strategy, Colin Combe, ELSVIER, 2006
5. Digital Business Concepts and Strategy, Eloise Coupey, 2nd Edition, Pearson
6. Trend and Challenges in Digital Business Innovation, VinocenzoMorabito, Springer
7. Digital Business Discourse Erika Darics, April 2015, Palgrave Macmillan
8. E-Governance -Challenges and Opportunities in : Proceedings in 2nd International Conference
theory and practice of Electronic Governance
9. Perspectives the Digital Enterprise –A framework for Transformation, TCS consulting journal
Vol.5
10. Measuring Digital Economy -A new perspective -DOI: 10.1787/9789264221796 -enOECD
Publishing

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136





Course Code Course Name Credits
ILO8029 Environmental Management 03


Objectives:

1. Understand and identify environmental issues relevant to India and global concerns
2. Learn concepts of ecology
3. Familiarise environment related legislations

Outcomes: Learner will be able to…

1. Understand the concept of environmental management
2. Understand ecosystem and interdependence, food chain etc.
3. Understand and interpret environment related legislations



Module
Detailed Contents
Hrs


01 Introduction and Definition of Environment: Significance of Environment
Management for contemporary managers, Career opportunities.
Environmental issues relevant to India, Sustainable Development, The Energy
scenario.

10

02 Global Environmental concerns : Global Warming, Acid Rain, Ozone Depletion,
Hazardous Wastes, Endangered life-species, Loss of Biodiversity, Industrial/Man -
made disasters, Atomic/Biomedical hazards, etc.
06
03 Concepts of Ecology: Ecosystems and interdependence between living organisms,
habitats, limiting factors, carrying capacity, food chain, etc. 05

04 Scope of Environment Management, Role & functions of Government as a planning
and regulating agency.
Environment Quality Management and Corporate Environmental Responsibility
10
05 Total Quality Environmental Management, ISO-14000, EMS certification. 05
06 General overview of major legislations like Environment Protection Act, Air (P & CP)
Act, Water (P & CP) Act, Wildlife Protection Act, Forest Act, Factories Act, etc. 03

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137


Assessment :

Internal:

Assessment consists of two tests out of which; one should be compulsory class test and the other is either
a class test or assignment on live problems or course project.
End Semester Theory Examination:

Some guidelines for setting up the question paper. Minimum 80% syllabus should be covered in question
papers of end semester examination. In question paper weightage of each module will be proportional
to number of respect ive lecture hours as mention in the syllabus.
1. Question paper will comprise of total six question
2. All question carry equal marks
3. Questions will be mixed in nature (for example supposed Q.2 has part (a) from module 3 then
part (b) will be from any module other than module 3)
4. Only Four question need to be solved.


REFERENCES:

1. Environmental Management: Principles and Practice, C J Barrow, Routledge Publishers London,
1999
2. A Handbook of Environmental Management Edited by Jon C. Lovett and David G. Ockwell,
Edward Elgar Publishing
3. Environmental Management,T V Ramachandra and Vijay Kulkarni, TERI Press
4. Indian Standard Environmental Management Systems — Requirements With Guidance For Use,
Bureau Of Indian Standards, February 2005
5. Environmental Management: An Indian Perspective, S N Chary and Vinod Vyasulu, Maclillan
India, 2000
6. Introduction to Environmental Management, Mary K Theodore and Louise Theodore, CRC Press
Environment and Ecology, Majid Hussain, 3rd Ed. Access Publishing.2015

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138



Subject
code Subject Name Teaching scheme Credit assigned

ISP801
Major Project – II Theory Pract. Tut. Theory Pract. Tut. Total
-- 12# -- -- 6 -- 6
# Indicates workload of Learner (Not Faculty)



Sub
Code

Subject Name Examination scheme
Theory (out of 100)
Term
work Pract.
and
Oral
Oral
Total Internal Assessment End
sem
Exam Test1 Test2 Avg.
ISP801 Major Project – II -- -- -- -- 100 -- 50 150

Subject Code Subject Name Credits
ISP801 Major Project – II 3



Course Objectives The course is aimed
1. To acquaint with the process of identifying the needs and converting it into
the problem.
2. To familiarize the process of solving the problem in a group.
3. To acquaint with the process of applying basic engineering fundamentals to
attempt solutions to the problems.
4. To inculcate the process of self-learning and research.






Course Outcomes On successful completion of course learner/student will be able to:
1 Identify problems based on societal /research needs.
2 Apply Knowledge and skill to solve societal problems in a group.
3 Develop interpersonal skills to work as member of a group or leader.
4 Draw the proper inferences from available results through theoretical/
experimental/simulations.
5 Analyze the impact of solutions in societal and environmental
context for sustainable development.
6 Use standard norms of engineering practices
7 Excel in written and oral communication.
8 Demonstrate capabilities of self-learning in a group, which leads to
lifelong learning.
9 Demonstrate project management principles during project work.

Guidelines for Major Project
 Students should form groups with minimum 2(two) and not more than 4 (four)

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 Students should do survey and identify needs, which shall be converted into problem statement for
major project in consultation with faculty supervisor/head of department/internal committee of
faculties.

 Student shall submit implementation plan in the form of Gantt/PERT/CPM chart, which will cover
weekly activity of major project.

 A log book to be prepared by each group, wherein group can record weekly work progress,
guide/supervisor can verify and record notes/comments.

 Faculty supervisor may give inputs to students during major project activity; however, focus shall
be on self-learning.

 Students in a group shall understand problem effectively, propose multiple solution and select best
possible solution in consultation with guide/ supervisor.
 Students shall convert the best solution into working model using various components of their
domain areas and demonstrate.

 The solution to be validated with proper justification and report to be compiled in standard format
of University of Mumbai.

 With the focus on the self-learning, innovation, addressing societal problems and entrepreneurship
quality development within the students through the major Projects.

Guidelines for Assessment of Major Project:
Term Work
 The review/ progress monitoring committee shall be constituted by head of departments of
each institute. The progress of major project to be evaluated on continuous basis, minimum two
reviews in the semester.

 In continuous assessment focus shall also be on each individual student, assessment based on
individual’s contribution in group activity, their understanding and response to questions.

 Distribution of Term work marks for both semesters shall be as below;
o Marks awarded by guide/supervisor based on log book 30
o Marks awarded by review committee 30
o Quality of Project report 40
Review/progress monitoring committee may consider following points for
assessment.
 In VIII semester expected work shall be procurement of component’s/systems, building of
working prototype, testing and validation of results based on work completed in an earlier
semester.

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140

 First review is based on readiness of building working prototype to be conducted.
 Second review shall be based on poster presentation cum demonstration of working
model in last month of the said semester.
Assessment criteria of Major Project -II
Major Project -II shall be assessed based on following criteria;

1. Cost effectiveness and Societal impact

2. Full functioning of working model as per stated requirements

3. Effective use of skill sets

4. Effective use of standard engineering norms

5. Contribution of an individual’s as member or leader

6. Clarity in written and oral communication

Guidelines for Assessment of Major Project Practical/Oral Examination:
 Report should be prepared as per the guidelines issued by the University of Mumbai.

 Major Project shall be assessed through a presentation and demonstration of working model by the
student project group to a panel of Internal and External Examiners preferably from industry or research
organizations having experience of more than five years approved by head of Institution.

 Students shall be motivated to publish a paper based on the work in Conferences/students
competitions.
Major Project shall be assessed based on following points:

1 Quality of problem and Clarity

2 Innovativeness in solutions

3 Cost effectiveness and Societal impact

4 Full functioning of working model as per stated requirements

5 Effective use of skill sets

6 Effective use of standard engineering norms

7 Contribution of an individuals as member or leader

8 Clarity in written and oral communication


****************