m. tech. in instrumentation engineering detailed syllabus · 2020. 11. 20. · m. tech. in...

M. Tech. in Instrumentation Engineering

Detailed Syllabus

(As per choice based semester and grading system with effect from the academic year 2020–2021)

Department of Instrumentation Engineering

Ramrao Adik Institute of Technology

Ramrao Adik Institute of Technology, Nerul, Navi Mumbai


2

Program Structure for First Year MTech. Engineering

Semester-I

Course Code

Course Name

Teaching Scheme(Contact Hours) Credits Assigned

Theory Pract. Tut. Theory Pract. Tut. Total

MT- INC101 Sensors and Measurement

Systems 3 -

3 - 3

MT- INC102 Process Dynamics and Control 3 - - 3 - - 3

MT-INDLO101X Department Level Elective -I 3 - 3 - - 3

MT-INDLO102X Department Level Elective -II 3 - 3 - 3

MT-INILO101X Institute Level Open Elective-I 3 3 3

MT-INL101 Laboratory -I 2 - 1 1

MT-INL102 Laboratory -II 2 - - 1 - 1

Total 17 4 15 2 17



3

Course Code

Course Name

Evaluation Scheme

Theory

Exam

Duration

(in Hrs.)

Term

Work

Pract. Oral Total Internal Assessment Mid Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT- INC101 Sensors and Measurement

Systems 20 20 20 20 60 2 - - 100

MT- INC102 Process Dynamics and Control 20 20 20 20 60 2 - 100

MT-INDLO101X Department Level Elective -I 20 20 20 20 60 2 - 100

MT-INDLO102X Department Level Elective -II 20 20 20 20 60 2 - 100

MT-INILO101X Institute Level Open Elective-I 20 20 20 20 60 2 - 100

MT-INL101 Laboratory -I - - - - - - 25 - 25 50

MT-INL102 Laboratory -II 25 25 50

Total Marks 600



4

Semester-II

Course Code

Course Name

Teaching Scheme (Contact Hours)

Credits Assigned


MT-INC201

Modern Control

Engineering 3 - 3 - 3

MT- INC202 Industrial Automation

3 - - 3 - - 3

MT-INDLO201X

Department Level

Elective –III 3 - - 3 - - 3

MT-INDLO202X Department Level

Elective –IV 3 - - 3 - - 3

MT-INILO202X Institute Level Open

Elective-III 3 -

- 3 - - 3

MT-INL201 Lab practice -III - 2 - -

1 - 1

MT-INL202 Lab practice -IV - 2 - - 1 -

1

Total 15 4 - 15 2 - 17



5

Course Code

Course Name

Evaluation Scheme

Theory

Exam

Duration

(in Hrs.)

Term

Work

Pract. Oral

Total Internal Assessment Mid

Sem.

Exam

End

Sem.

Exam

IA1

IA2

Avg.

MT-INC201

Modern Control

Engineering 20 20 20 20 60 2 - - - 100

MT- INC202 Industrial Automation

20 20 20 20 60 2 - - - 100

MT-INDLO201X

Department Level

Elective –III 20 20 20 20 60 2 - - - 100

MT-INDLO202X

Department Level

Elective –IV 20 20 20 20 60 2 - - - 100

MT-INILO20X

Institute Level Open

Elective-III 20 20 20 20 60 2 - - - 100

MT-INL201 Lab practice -III - - - - - - 25 - 25 50

MT-INL202 Lab practice -IV - - - - - - 25 - 25 50

Total 600



6

Department Elective Subjects

(Semester-I)

Subject Code Department Elective Course I

Subject Code Department Elective Course II

MT-INDLO1011 Computational Techniques in

Instrumentation Engineering MT-INDLO1021 Soft computing Techniques

MT-INDLO1012 Digital Image processing MT-INDLO1022 Multi Sensor Data Fusion

MT-INDLO1013 Design of Embedded System MT-INDLO1023 Advanced Biomedical Instrumentation

MT-INDLO1014 Communication Protocols for

Instrumentation MT-INDLO1024 Advanced Process Instrumentation

(Semester-II)

Subject Code Department Elective Course III Subject Code Department Elective Course IV

MT-INDLO2011 Modelling and Simulation MT-INDLO2021 Machine Learning and Deep Learning

MT-INDLO2012 IOT and Applications MT-INDLO2022 Safety In Engineering Industry

MT-INDLO2013 Robotics and Automation MT-INDLO2023 Cyber Physical System

MT-INDLO2014 Advanced Power Electronics MT-INDLO2024 Applied Industrial Instrumentation



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Institutional Elective Subjects

(Semester-I)

Subject Code Institute Level Open Elective-I Subject Code Institute Level Open Elective-I

MTILO1011 Optimization Techniques in Problem

Solving MTILO1014

Entrepreneurship Development and

Management

MTILO1012 Cyber and Data Laws

MTILO1015 Renewable Energy Systems

MTILO1013 Product Lifecycle Management

MTILO1016 Design of Experiments

(Semester-II)

Subject Code Institute Level Open Elective-I Subject Code Institute Level Open Elective-I

MTILO2021 Research Methodology

MTILO2024 Disaster Management and Mitigation

Measures

MTILO2022 Probability and Random Variables

MTILO2025 Financial Management

MTILO2023 Project Management

MTILO2026 IPR and Patenting

Note:

End Semester Examination -In all five questions to be set, each of 20 marks, out of these any three questions to be attempted by students.

Each question will comprise of mixed questions from different units of the subjects.



8

Semester III

Course Code

Course Name


Credits Assigned


MT-INS301 Seminar -- 06 -- -- 03 -- 03

MT-IND302 Dissertation – I -- 24 -- -- 12 -- 12

Total -- 30 -- -- 15 -- 15

Course Code Course Name

Examination Scheme

Theory

Term Work

Pract.

/ Oral

Total

Internal Assessment End Sem. Exam.

IA1 IA2 Avg.

MT-INS301 Seminar -- -- -- -- 50 50 100

MT-IND302 Dissertation – I -- -- -- -- 100 -- 100

Total -- -- -- -- 150 50 200



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Semester IV



Credits Assigned


MT- ISD401 Dissertation - II -- 30 -- -- 15 -- 15

Total -- 30 -- -- 15 -- 15


Examination Scheme

Theory

Term

Work

Pract.

/ Oral

Total

Internal Assessment End Sem.

Exam. IA1 IA2 Avg.

MT- ISD401 Dissertation - II -- -- -- -- 100 100 200

Total -- -- -- -- 100 100 200

Note:

o In case of Seminar, 01 Hour / week / student should be considered for the calculation of load of a teacher

o In case of Dissertation I, 02 Hour / week / student should be considered for the calculation of load of a teacher

o In case of Dissertation II, 02 Hour / week / student should be considered for the calculation of load of a teacher



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Subject Name: Sensors and Measurement Systems

Course Code

Course Name

Teaching Scheme

(Contact Hours) Credits Assigned


MT-INC101 Sensors and Measurement

Systems 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term

Work Pract. Oral Total Internal Assessment Mid

Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INC101 Sensors and Measurement

Systems 20 20 20 20 60 2 - - - 100

Prerequisite:

Overview of conventional sensors - Resistive, Capacitive, Inductive, Thermal, Piezoelectric, Magneto strictive and Hall effect sensors - Static and

Dynamic characteristics and specifications - Measurements and Measurement system.

Course Description:

This course introduces the resistive, inductive and capacitive transducers and their transduction principles. Also included acoustic, mechanical and

flow metering elements, various optical sensors, their transduction principles and their applications. This subject involved design, construct, and

verify an instrumentation system to meet desired specifications, with the aid of computer-aided design techniques.



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Course Objectives

To introduce the resistive, inductive and capacitive transducers and their transduction principles

To educate on magnetic transducer elements

Study of acoustic, mechanical and flow metering elements, their working principle

To introduce various optical sensors, their transduction principles and their applications

To introduce various advanced and miniature sensors and their applications

Course Outcomes

1. Be able to interpret and formulate design specifications for various transducers that meet accuracy and sampling speed requirements.

2. Understand the principles of operation of sensors including temperature transducers, flow transducers and pressure transducers.

3. Understand principles of analog and digital signal and data processing, including Amplifiers, filters and A-D conversion techniques. Understand

sources and measures of error in instrumentation systems.

4. Be able to design, construct, and verify an instrumentation system to meet desired specifications, with the aid of computer-aided design techniques.

5. Be familiar with safety issues concerning design of instrumentation, including the effects of electric current through tissue and defibrillation.

6. Able to aquire knowledge of various advanced and miniature sensors

.

Module No. Detailed Content Hours CO Mapping

1

Basic Measuring Sensor Dynamics

Mathematical model of variable resistive transducers: Potentiometer loading effect, Strain gauge analysis,

Variable inductive transducers and its circuit analysis, Variable capacitive transducers: Capacitive pickups,

its model, Capacitive transducer signal processing, Piezoelectric transducers: Its equivalent circuit,

Displacement to pressure system (Flapper and nozzle system).

09 CO1



12

2

Mathematical model of Measurement system

Generalized mathematical model of measurement system Mechanical translational and rotational system,

Thermal system, Liquid level system, Pneumatic system.

04 CO2

3

Process Measuring Sensor Dynamics I

Mathematical model and analysis of flow measuring device: Mass flow meter. Corriolis type mass flow meter.

Venturi meter, Vortex meter, Magnetic flow meter, orifice and rotameter.

Mathematical model, analysis and compensation of temperature measuring devices: high speed

thermocouples, RTD, pulse excitation techniques, photovoltaic sensor.

09 CO3

4

Process Measuring Sensor Dynamics II

Mathematical model and signal processing of pressure measurement: Manometer dynamics, Elastic pressure

pickups- diaphragm type, Strain gauge pressure pickups, Differential presuure transmitters, Mathematical

model and analysis of displacement measurement: Strain gauge, Potentiometer, LVDT.

09 CO4

5 Introduction to transmitters

Two wire, three wire and four wire transmitters, Smart and intelligent Transmitters. Design of transmitters. 04 CO5

6

SMART Sensors and Nanotechnology

Micro-sensors and smart sensors: Construction, characteristics and applications. Nanotechnology: Discussion

on nano sensors and MEMS application.

04 CO6

References:

1. H.K.P Neubert “Instrument Transducers Oxford Herman University Press Eighth Impression 2008.

2. B. G. Liptak, "Instrument Engineers Handbook", Vol. I and II, Third Edition, Chilton and Book Company, 1990.

3. C. D. Johnson," Process Control Instrumentation Technology", Fourth Edition, PHI, 1996.

4. Andrew and Williams, "Applied Instrumentation in Process Industries", Vol. I, II, III, IV, Gulf Publishing Company, 1979.

5. B. C. Nakra and K. K. Choudhari, "Instrumentation: Measurement and Analysis", Tata McGraw Hill Pub, 1985.

6. E.A. Doebelin, "Measurement Systems – Applications and Design", Tata McGraw Hill, New York, 2000.

7. A.K. Sawhney, "A course in Electrical & Electronic Measurement and Instrumentation", DhanpatRai and Co (P) Ltd., 2004.

8. D. Patranabis"Princples of Industrial Instrumentation" H.K.P Neubert “Instrument Transducers”, Oxford Herman University Press Eighth



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Impression 2008.

Evaluation Scheme:

1) In-Semester Assessment:

Assessment consists of two Internal Assessments (IA1, IA2) out of which; one should be compulsory class test (on minimum 02 Modules)

and the other is a class test / assignment on case studies / course project.

Mid Semester Examination (MSE) will be based on 40-50% of the syllabus.

2) End-Semester Examination:

Question paper will comprise of full syllabus.

In the question paper, weightage of marks will be proportional to the total number of lecture hours as mentioned in the syllabus



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Subject Name: Process Dynamics and Control

Course Code

Course Name

Teaching Scheme



MT-INC102 Process Dynamics and

Control 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INC102

Process Dynamics and

Control 20 20 20 20 60 2 - - - 100

Course Description:

This course discusses complex dynamic processes and the operations and control engineering tasks. The course covers the fundamental aspects of

process dynamics and control, which includes developing dynamic models of processes, control strategies for linear time-invariant systems and

instrumentation aspects. The course is presented on the integrated theory with practice model.

Course Objectives

• To make the students to familiar with different Process Dynamics & process control actions.

• Students are expected to understand various control schemes.



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• To familiarize concept of Multivariable Control & Discrete state process control Requirement.

Course Outcomes

1. Describe process objectives, characteristics of processes, need of mathematical modelling for processes.

2. Understand process dynamics and analysis of control loops with the various process variables.

3. Select controlled, manipulated and measured variable among the process variables to have control on process and safety in operation.

4. Design and tune appropriate controller for process control application.

5. Understand the concept of multivariable control and design the decentralised control for multivariable processes.

6. Describe the model based controller design procedure and design, implement model based control to processes.


1

Introduction to Process Control

Process objectives and benefits, Characteristics of processes, Dead time, Single /multi- capacity, self- Regulating

/non self-regulating, Interacting/non‐interacting, Linear/nonlinear, and Selection of control action for them.

Necessity of process modelling, degree of freedom, Mathematical modelling of simple processes like Surge tank

level, stirred tank reactor etc.

06 CO1

2

Process Dynamics and analysis of control loops

Dynamic behaviour of first order and second order systems, Pole- Zero effect on process response, Development

of empirical model using Step and PRBS inputs , Approximation of higher order models, Analysis of Flow

Control, Pressure Control, Liquid level Control, Temperature control, Steady state gain, Process gain, Valve

gain, Process time constant, Variable time Constant, Transmitter gain

06 CO2

3

Feedback Control

Block Diagram, Elements of the feedback Loop, Response for Set- point and Disturbances inputs, PID Controller

Algorithms, Stability, Control Performance Measures, Tuning and Fine tuning of controllers for Good Control

Performance, Correlations for tuning Constants, Practical Application of Feedback Control: Equipment

Specification, Input Processing, Feedback Control Algorithm, Output Processing.

07 CO3



16

4

Multi-Loop Control

SLPC and MLPC features, faceplate, functions and their comparison. Basic principles, Design Criteria,

Performance, Controller Algorithm and Tuning, Implementation issues of Cascade control, feed forward control,

feedback, feed-forward Control, Ratio control, Selective Control, Split range control, Inferential Control.

08 CO4

5

Multivariable Control

Concept of Multivariable Control: Interactions and its effects, block representation and transfer function matrix

of two input two output systems, Pairing of controlled and manipulated variables-Relative Gain Array, Singular

Value Analysis, effect of Interaction on stability. Decoupler, and decoupler design: ideal decoupler, simplified

decoupler and static decoupler. Concept of decentralized control, decentralized control of the coupled tank

system.

06 CO5

6

Advanced Control

Model based controller-design procedure for direct synthesis method, tuning relations based on integral error

criteria, Smith predictor, Internal Model control-design procedure for FOPDT, SOPDT and Inverse response

processes, Effect of model uncertainty and disturbances, design of improved disturbance rejection, IMC based

PID controller design procedure for delay free processes and Introduction to Model predictive control.

06 CO6

Text Books:

1. Seborg, “Process Dynamics and Control”, Wiley.

2. George Stephonopolous, “Chemical Process Control”, PHI.

3. B. Wayne Bequette, “Process Control: Modeling, Design and Simulation”, PHI.

Reference Books:

1. Thomas E Marlin, “Process Control‐ Designing processes and Control Systems for Dynamic”, McGraw‐Hill International.

2. B.G. Liptak, “Instrument Engineers' Handbook : Process control”, Chilton.

3. F.G. Shinskey, “Process Control Systems", TMH.

Evaluation Scheme:






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18

Subject Name: Computational Techniques in Instrumentation Engineering

Course Code

Course Name

Teaching Scheme



MT-INDLO1011

Computational

Techniques in

Instrumentation

Engineering

3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1011

Computational

Techniques in

Instrumentation

Engineering

20 20 20 20 60 2 - - - 100

Course Description:

The course enhances the ideas to analyze and solve engineering problems. The course focuses on various applications of mathematical transforms.

Course Objectives

To develop understanding and application of fundamental techniques involved in the analysis and computation of Instrumentation engineering.

To train the students with mathematics needed to analyze and solve engineering problems.



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To build up the ability to apply the concepts of Matrix theory and Linear programming.

To familiarize numerical methods pertaining to control engineering problems.

To make acquainted the students in calculus of variations and solve problems using integral transforms associated with engineering applications

Course Outcomes

1. Able to acquire adequate knowledge in matrix theory and its application.

2. Able to acquire knowledge related to vector space

3. Capable to solve numerical methods for differential equations.

4. Able to apply the basic concepts and methods of probability and statistics.

5. Demonstrate concept of integral transforms and their application areas, Improve the problem evaluation skills.

6. Able to select an appropriate method to solve a practical problem



20

References:

1. Strang G., "Linear Algebra and its Application", Fourth edition, 1996.

2. Horward Anton ―Elementary Linear Algebra‖, 11th Edition, Wiley Publications, 2013.

3. Michael T. Heath, "Scientific computing- An Introduction Survey", McGraw Hill,1997.

4. Erwin Kreyszig, "Advanced Engineering Mathematics", 9th Edition, John Wiley and Sons, INC, 2006.

5. Davies B., "Integral Transforms and their applications," 2nd edition, New York, Springer- Verlag, 1985.

6. Murray Spiegal, John Schiller and R. Alu Srinivasan, "Probability and Statistics", Tata Mc- Graw - Hill edition, New Delhi, 2004.

7. Walpole R.E., Myers R.H. and Myers S.L., "Probability and Statistics for Engineers and Scientist", Prentice Hall Inc., New Jercy, 1998.

8. G.H. Golub and C.F. Van Loan, "Matrix computations", 4th edition, John Hopkins University press, 2007.


1 Matrix computation: Determinant, rank, Norm, inverse, Transpose of matrix, Eigen values and Eigen vectors,

Decomposition: LU, QR, Cholesky, Schur decomposition, Eigenvalue decomposition, Its application 06 CO1

2 Vector spaces: Subspaces, Linear combinations and subspaces spanned by a set of vectors, Linear dependence

and Linear independence, Inner product, Orthogonality, Gram – Schmidt orthonormalization. 07 CO2

3 Probability and random variable: Probability space, Sample spaces, Conditional probability, Bayes' theorem,

Random variable, Probability distribution function, Probability density function, Distributions- Binomial,

Poisson, Normal, Exponential, Uniform

06 CO3

4 Numerical methods for algebraic and differential equations: Solution of algebraic equations- least square

method, Gauss-Jordan method, Gauss- Seidal method, Gauss elimination method, Newton- Raphson method,

Euler's method, Runge-Kutta method,

08 CO4

5

Calculus of Variation: Concept of variation and its properties – Euler‘s equation, Functional dependant on

first and higher order derivatives, Functional dependant on functions of several independent variables,

Variation problems with moving boundaries, problems with constraints, Direct methods: Ritz and

Kantorovich methods

06 CO5

6 Integral Transforms: Laplace transform, Z-Transform, Fourier transform, Fast fourier transform, Discrete

fourier transform, Wavelet transform, Mellin transform, Hankel transform. 06 CO6



21

Evaluation Scheme:








.



22

Subject Name: Digital Image Processing

Course Code

Course Name

Teaching Scheme



MT-INDLO1012 Digital Image Processing 3 2 - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1012 Digital Image Processing 20 20 20 20 60 2 - - - 100

Course Description:

The course enhances the ideas to use adequate knowledge about the color image . The Course focuses on innovating many real world applications of

image processing.

Course Objectives

• To study the image fundamentals and mathematical transforms necessary for image processing.

• To study the image enhancement techniques

• To study image restoration procedures.

• To study the image compression procedures.



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Course Outcomes

Upon completion of the course, students will be able to

1. Review the fundamental concepts of a digital image processing system.

2. Analyze images in the frequency domain using various transforms.

3. Evaluate the techniques for image enhancement and image restoration.

4. Categorize various compression techniques.

5. Interpret Image compression standards.

6. Interpret image segmentation and representation techniques


1.

Fundamentals of Image Processing: Steps in image processing, Human visual system, Sampling &

quantization, Representing digital images, Spatial &gray-level resolution, Image file formats, Basic

relationships between pixels, Distance Measures. Basic operations on images-image addition, subtraction,

logical operations, scaling, translation, rotation. Image Histogram. Color fundamentals & models – RGB,

HSI YIQ.

06 CO1

2.

Image Enhancement and Restoration: Spatial domain enhancement: Point Operations-Log

transformation, Power-law transformation, Piecewise linear transformations, Histogram equalization.

Filtering operations- Image smoothing, Image sharpening. Frequency domain enhancement: 2D DFT,

Smoothing and Sharpening in frequency domain.Homomorphic filtering. Restoration: Noise models,

Restoration using Inverse filtering and Wiener filtering.

08 CO2

3.

Image Compression: Types of redundancy, Fidelity criteria, Lossless compression – Runlength coding,

Huffman coding, Bit-plane coding, Arithmetic coding. Introduction to DCT, Wavelet transform. Lossy

compression – DCT based compression, Wavelet based compression. Image and Video Compression

Standards – JPEG, MPEG.

06 CO3



24

4.

Image Segmentation and Morphological Operations: Image Segmentation: Point Detections, Line

detection, Edge Detection-First order derivative – Prewitt and Sobel. Second order derivative – LoG, DoG,

Canny. Edge linking, Hough Transform, Thresholding – Global, Adaptive. Otsu’s Method. Region

Growing, Region Splitting and Merging. Morphological Operations: Dilation, Erosion, Opening, Closing,

Hit-or-Miss transform, Boundary Detection, Thinning, Thickening, Skeleton.

07 CO4

5.

Representation and Description: Representation – Chain codes, Polygonal approximation, Signatures.

Boundary Descriptors – Shape numbers, Fourier Descriptors, Statistical moments. Regional Descriptors –

Topological, Texture. Principal Components for Description.

06 CO5

6.

Object Recognition and Applications: Feature extraction, Patterns and Pattern Classes, Representation of

Pattern classes, Types of classification algorithms, Minimum distance classifier, Correlation based

classifier, Bayes classifier. Applications: Biometric Authentication, Character Recognition, Content based

Image Retrieval, Remote Sensing, Medical application of Image processing

06 CO6

Text Books

1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Pearson Education, 2007.

2. S Sridhar, “Digital Image Processing”, Oxford University Press, 2016.

Reference Books

1. Rafael C. Gonzalez, Richard E. Woods, and Steven L. Eddins, “Digital Image Processing Using MATLAB”, Tata McGraw Hill Publication,

2009.

2. S Jayaraman, S Esakkirajan, T Veerakumar, “Digital Image Processing”, Tata McGraw Hill, 2019.

Evaluation Scheme:




25









26

Subject Name: Design of Embedded Systems

Course Code

Course Name

Teaching Scheme



MT-INDLO1013 Design of Embedded Systems

3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1013 Design of Embedded Systems

20 20 20 20 60 2 - - - 100

Course Description:

To prepare students for the design of practical digital hardware systems using real time operating systems. This course will cover the OS based

embedded system design, RISC architectures, ARM programming and DSP processors. They will be able to develop applications of embedded

systems in instrumentation.

Course Objectives

To teach students to the modern embedded systems program such systems using a concrete platform.

To emphasizes high-level tools and hardware/software trade-offs, rather than low-level assembly-language programming and logic design.

Expose them with trends and challenges, the design and use of single-purpose processors ("hardware") and general-purpose processors ("software"),

describes memories and buses, and illustrates hardware/software tradeoffs, chip technologies and modern design tools.

Develop skill to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social,

political, ethical, health and safety, manufacturability, and sustainability.



27

Course Outcomes:

1. Ability to differentiate between the general computing system and the embedded system, also recognize the classification of embedded systems.

2. Able to design ARM based and DSP based embedded systems.

3. Ability to expose the concept of Raspberry Pi hardware and software.

4. Demonstrate the concept of operating system and real time embedded systems using the concepts of RTOS.

5. Analyze various examples of embedded systems based on ARM processor and familiar with advanced processor.

6. Design embedded systems in automobile, smart card & mobile phone software for key inputs.

Module Detailed Content Hours CO Mapping

1.

Introduction to Embedded systems: Embedded processor into a system, embedded hardware units and

devices in a system, embedded software in a system, examples of embedded systems, Design process in

embedded system, formalization of system design, classification of embedded systems, skills required for an

embedded system designer.

08 CO1

2.

PIC Microcontrollers: PIC 16 Series family: Overview, An architecture overview of the 16F84A, Status

register, 16F84A memory, Some issues of timing, Power-up and Reset, PIC 16F84A parallel ports, 16F84A

clock oscillator,16F84A operating conditions, 16F84A interrupt structure.

07 CO2

3. Raspberry Pi Basics: Introduction to Raspberry Pi (RPi), Raspberry Pi Hardware, Raspberry Pi Accessories

Raspberry Pi Software, Communicating with the RPi, Configuring the Rpi. 06 CO3

4. Programming with Python: Python Basics, Loops, Strings, Lists, and Dictionaries, Functions, Modules,

Classes, and Methods 06 CO4

5. Real Time Operating System: Basic design using RTOS, Micro/OS-II and Vx works, windows CE, OSEK,

real-time Linux functions, 06 CO5



28

6. Case studies: Digital camera hardware and software architecture, embedded systems in automobile,

embedded system for a smart card, mobile phone software for key inputs. 06 CO6

Text books:

1. Raj Kamal, “Embedded Systems Architecture Programming and Design”,Tata McGraw-Hill., 2008.

2. TimWilmshurst, “Designing Embedded Systems with PIC Microcontrollers: principles and applications”, Elsevier2009.

3. Derek Molloy, “Exploring Raspberry Pi, Interfacing to the Real World with Embedded Linux”,Wiley, 2016.

4. Dr. Simon Monk, “Programming the Raspberry Pi, Getting Started with Python”,McGraw Hill, 2013.

Reference Books:

1. Steve Heath, “Embedded Systems Design” II edition, Newnes publications

2. Tammy Noergaard, “Embedded Systems Architecture: A Comprehensive Guide for Engineers andProgrammers”,Elsevier.

Evaluation Scheme:










29

Subject Name: Communication Protocol for Instrumentation

Course Code

Course Name

Teaching Scheme



MT-INDLO1014 Communication Protocol

for Instrumentation 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg

MT-INDLO1014 Communication Protocol

for Instrumentation 20 20 20 20 60 2 - - - 100

Course Description:

This course introduces the applications of communication systems. The course covers basic OSI model and their applications. The main aspect of

this course is that students should be able to compare between different networks at different layers. The course also focuses on the design,

calibration and commissioning of these communication protocols. Wireless technologies are also introduced in this course.

Course Objectives

Understand the use of communication model for recent industry networks and protocols

To explore the knowledge on communication protocols.

To learn about the different network architectures.

Understand the Foundation Fieldbus in detail.



30

To explore HART protocol and its applications

Enrich expertise on the planning and commissioning of industrial network systems.

Course Outcomes: The students should be able to

1. Explain the concept of communication model, OSI reference model and Recent

industry networks

2. Outline the design parameters for specific network.

3. Differentiate the network Architecture and understand the concepts of industrial protocols.

4. To understand Foundation Fieldbus and its trends.

5. To explore HART protocol with its applications.

6. To calibrate Profibus and fieldbus of any industrial application.


1

An Introduction to Networks in process automation: Information flow requirements, Hierarchical

communication model, Data Communication basics, OSI reference model, Industry Network,

Recent networks.

06 CO1

2 Introduction to Communication Protocols: Communication basics, Network Classification, Device

Networks, Control Networks, Enterprise Networking, Network selection. 06 CO2

3

Proprietary and open networks: Network Architectures, Building blocks, Industry open protocols

(RS-232C, RS- 422, RS-485), Ethernet, Modbus, Modbus Plus, Data Highway Plus, Advantages

and Limitations of Open networks.

08 CO3

4 Fieldbus: Fieldbus Trends, Hardware selection, Fieldbus design, Installation, Documentation,

Fieldbus advantages and limitations. 06 CO4



31

5 HART: Introduction, Design, Installation, calibration, commissioning, Application in Hazardous

and Non-Hazardous area. 06 CO5

6

Planning and commissioning: Introduction, Design, Calibration, Commissioning, Application in

Hazardous and Non-Hazardous area.

Introduction to wireless Protocols: WPAN, Wi-Fi, Bluetooth, ZigBee, Z-wave.

07 CO6

References:

1. B.G. Liptak, ‘Process Software and Digital Networks:, (CRC Press ISA- The Instrumentation, Systems, and Automation Society).

2. Romilly Bowden , ‘HART Communications Protocol’, (Fisher-Rosemount).

3. User Manuals of Foundation Fieldbus, Profibus, Modbus, Ethernet, Devicenet, Controlnet.

Evaluation Scheme:










32

Subject Name: Soft computing Techniques

Course Code

Course Name

Teaching Scheme



MT-INDLO1021 Soft Computing

Techniques 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration

(in Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1021 Soft Computing

Techniques 20 20 20 20 60 2 - - - 100

Course Description:

The course enhances the ideas to use adequate knowledge about the FLC and NN toolbox. The Course focuses innovate many real world control

problems.

Course Objectives:

To familiarize with soft computing concepts.

To teach about the concept of fuzziness involved in various systems.

To expose the ideas about genetic algorithm.

To provide adequate knowledge about of FLC and NN toolbox.

To innovate many real world control problems.



33

Course Outcomes:

1. Ability to apply the knowledge of basic neural networks paradigms.

2. Demonstrate the basic concepts of training in neural networks.

3. Able to apply the techniques for identification and fuzzy logic.

4. Able to inculcate adequate knowledge about Genetic algorithm.

5. Able to apply ANN in real time application

6. Able to apply fuzzy logic and GA for different application


1

Neural System: Artificial Neuron Model, Operations of Artificial Neuron, Types of Neuron

Activation Function, ANN Architectures, Classification Taxonomy of ANN - Connectivity, Neural

Dynamics (Activation and Synaptic), Learning Strategy (Supervised, Unsupervised, and

Reinforcement), Learning Rules, and Multilayer feed forward Neural Networks.

06 CO1

2

Artificial Neural Networks: Counter propagation network- architecture- functioning &

characteristics of counter Propagation network-Hopfield/ Recurrent network- configuration-

stability constraints associative memory- and characteristics- limitations and applications- Hopfield

v/s Boltzman machine- Adaptive Resonance Theory- Architecture- classifications Implementation

and training-Associative Memory.

08 CO2

3

Fuzzy logic systems: Introduction to crisp sets and fuzzy sets- basic fuzzy set operation and

approximate reasoning. crisp relations, fuzzy relations. Introduction to fuzzy logic modeling and

control- Fuzzification, inferencing and defuzzification-Fuzzy knowledge and rule bases-Fuzzy

modeling and control schemes for nonlinear systems. Self-organizing fuzzy logic control- Fuzzy

logic control for nonlinear time delay system..

07 CO3

4

Genetic Algorithm: Basic concept of Genetic algorithm and detail algorithmic steps-adjustment of

free Parameters- Solution of typical control problems using genetic algorithm- Concept on some

other search techniques like tabu search and ant colony search techniques for solving optimization

problems

06 CO4



34

5 Applications: Case Studies of Neural network : Pattern recognition, control and Process Monitoring,

fault diagnosis and load forecasting. 06 CO5

6

Applications:. Case studies of Fuzzy logic : Greg viot's fuzzy cruise controller, Air conditioner

controller.

System identification, design and implementation of linear and nonlinear control systems using

computing tools such GA

06 CO6

References:

1. Devendra K. Chaturvedi, ―Soft Computing Techniques and its Applications in Electrical Engineering,‖ Vol. 103, Studies in Computational

Intelligence, Springer, 2008.

2. Hagan, Demuth, Beale- Thomas Learning , ―Neural Network Design,‖ Vikas Publishing House.

3. Jacek M. Zuarda, ― Introduction to Artificial Neural Systems,‖ Jaico Publishing House, 1997.

4. Rajasekharan and Rai , ―Neural Networks, Fuzzy logic, Genetic algorithms: synthesis and applications,‖ PHI Publication.

5. S.N. Sivanandan and S.N. Deepa, ―Principles of Soft Computing,‖ Wiley India, 2007.

6. N. Yadaiah and S. BapiRaju, ―Neural and Fuzzy Systems: Foundation, Architectures and Applications,‖ Pearson Education.

7. James A Freeman and Davis Skapura, ―Neural Networks,‖ Pearson, 2002.

8. J.S.R.Jang, C.T.Sun and E.Mizutani, ―Neuro-Fuzzy and Soft Computing,‖ Pearson Education, PHI, 2004.

9. S. Rajasekaran and G.A.V.Pai, ―Neural Networks, Fuzzy Logic and Genetic Algorithms,‖ PHI, 2003

Evaluation Scheme:










35

Subject Name: Multi Sensor Data Fusion

Course Code

Course Name

Teaching Scheme



MT-INDLO1022 Multi Sensor Data Fusion

3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1022 Multi Sensor Data Fusion

20 20 20 20 60 2 - - - 100

Course Description:

This course highlights elementary applications and techniques for data fusion in military and civilian systems. This course also focuses on

Inference; Classification; Multisensor classification; Tracking; and Multisensor registration.

Course Objectives:

To introduce the basic concepts of sensor systems, the need for multi-sensor data fusion and various approaches to data fusion.

To provide representations of data and data fusion architectures, derives various algorithmic approaches to data fusion.

To explains the importance of sensor management and data association in the context of sensor fusion.

To demonstrate the practical applications and implications of using sensor fusion throughout using case studies.



36

Course Outcomes:

Students will be able to

1. Understand the importance of using data fusion in multi-sensor systems.

2. Understanding the data fusion dynamic model.

3. Apply the Taxonomy of algorithms.

4. Apply the concept of estimation to data fusion problems.

5. Explain the details of optimal sensor fusion.

6. Interpret high performance data structures.

Module No. Detailed Content Hours CO

Mapping

1.

Introduction to Multi-sensor data fusion: Sensors and sensor data, Use of multiple sensors, Fusion

applications, The inference hierarchy: output data. Data fusion model. Architectural concepts and issues.

Benefits of data fusion,

04 CO1

2.

Data Fusion Process Model: Mathematical tools used: Algorithms, Co-ordinate transformations, Rigid body

motion, Alternative Data Fusion Process Models- Dasarathy's Functional Model, Boyd's Decision Loop,

Bedworth and O'Brien's Omnibus Process Model, TRIP Model.

07 CO2

3. Taxonomy of algorithms: Data association, Identity declaration, Ancillary Support Functions,

Dependability and Markov chains, Meta - heuristics. 08 CO3

4. Estimation: Kalman filtering, Practical aspects of Kalman filtering, Extended Kalman filters, Decision level

identify fusion, Knowledge based approaches. 06 CO4



37

5. Optimal sensor fusion: Data information filter, Extended information filter, Decentralized and scalable

decentralized estimation, Sensor fusion and approximate agreement, Optimal sensor fusion using range trees

recursively, Distributed dynamic sensor fusion.

08 CO5

6. High performance data structures: Tessellated, trees, graphs and function. Representing ranges and

uncertainty in data structures. Designing optimal sensor systems within dependability bounds. Implementing

data fusion system.

06 CO6

Text Books:

1. David L. Hall, “Mathematical techniques in Multisensor data fusion”, Artech House, Boston, 1992.

2. R.R. Brooks and S.S.Iyengar, “Multisensor Fusion: Fundamentals and Applications with Software”, Prentice Hall Inc., New Jersey, 1998.

Reference Books:

1. Arthur Gelb, “Applied Optimal Estimation”, M.I.T. Press, 1982.

2. James V. Candy, “Signal Processing: The Model Based Approach”, McGraw –Hill Book Company, 1987.

Evaluation Scheme:










38

Subject Name: Advanced Biomedical Instrumentation

Course Code

Course Name

Teaching Scheme



MT-INDLO1023

Advanced Biomedical

Instrumentation 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration

(in Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1023

Advanced Biomedical

Instrumentation 20 20 20 20 60 2 - - - 100

Course Description:

This subject builds the strong foundation in medical instrumentation of students needed for the field of Instrumentation Engineering. Able to

determine the origin of bio-potential, different types of electrodes used in bio potential recording. This will able to measure various physiological

parameters and helps to design biomedical sensors, ability to apply knowledge about physiotherapy and electrotherapy equipment, instruments

dealing with kidney and bones, Inculcate knowledge of instruments used for sensory measurements, imaging equipment’s and diagnostics

measurements, Demonstrate special medical assistive and therapeutic equipment’s and learn how to use that equipment’s and servicing.

Course Objectives:

• To introduce about various instruments functionality and advanced techniques in biomedical instrumentation.



39

• To build adequate knowledge about measurement of various physiological parameters and to understand the fundamental principle and working of

the biomedical instruments involved in the measurement.

• To gain basic knowledge about Bio-potentials, bioelectrodes and bioamplifiers and to give a complete exposure of various recording mechanism.

• To provide latest knowledge of special medical assistive and therapeutic equipment‘s and learn how to use that equipment‘s and servicing.

• To explain the fundamentals of different types of laser, its operations and applications in medical field

Course Outcomes:

1. Able to determine the origin of bio-potential, different types of electrodes used in biopotential recording.

2. Able to measure various physiological parameters and helps to design biomedical sensors.

3. Ability to apply knowledge about physiotherapy and electrotherapy equipment, instruments dealing with kidney and bones.

4. Inculcate knowledge of instruments used for sensory measurements, imaging equipment’s and diagnostics measurements.

5. Demonstrate special medical assistive and therapeutic equipments and learn how to use that equipment’s and servicing.

6. Inculcate knowledge of different types of laser, its operations and applications in medical field.


1.

Introduction to Biomedical measurements: Recording of membrane potential & Action potential,

Contraction & excitation of smooth muscle, Electrocardiographic interpretation of cardiac muscle & cardiac

arrhythmias, Physical characteristics of circulation, measurement of fluid volumes.

04 CO1

2.

Instrumentation for Diagnosis & Monitoring: Electrocardiograph system, principles of vectoral analysis

of vectocardiogram, Advancement in EEG signal processing, computerised patient monitoring system, Bio-

potential electrodes- types and characteristics, Origin, recording schemes & analysis of biomedical using

ECG, EEG,

10 CO2



40

3.

Biotherauptic Instruments and Electrical safety aspects:

Defibrillator system, Nerve & Muscle Stimulator, Electrosurgical unit for cutting & coagulation of muscles,

Shortwave diathermy, Hemodialysis machine, Ventilator, Anesthesia machine, Automated drug delivery

systems, Human and Equipment safety, Physiological effects of electricity, Micro and macro shocks, thermal

effects.

08 CO3

4.

Bio-Imaging Modality: Advanced medical imaging techniques & modalities- Imaging with X-ray,

principle and production of soft X rays & hard X-rays, screen film & image intensifier system, Computed

tomography imaging, CT image reconstruction, CT angiography, Optical coherence tomography,

Radionuclide imaging, PET and SPECT systems, Infrared imaging, Clinical applications of thermography,

Image acquisition in MRI, spin echo technique & spin relaxation technique.

07 CO4

5.

Biomedical Implants & Microsystems: Implantable medical devices, Artificial valves, Cochlear implants,

Cardiac pacemaker, Bio-prosthetic devices, Micro fabrication technologies for biomedical microsystems,

micro-sensors for clinical applications, Biomedical micro-fluid systems.

04 CO5

6.

Bio-photonics: Laser physics, fundamental of laser, types of laser, properties of lasers, Interaction of laser

with tissue, Overview of laser propagation in tissue, medical applications of laser ,Laser hazards & safety

aspects, biological effects of laser, radiations safety exposure limits.

06 CO6

References:

1. Guyton, A.C. ―Medical Physiology‖, 12thEdition, Saunders Publisher,Philadelphia, 2010.

2. Thyagarajan K, Ajoy K, Ghatak A, ―Lasers Fundamentals and Applications, Second edition, Springer 2010.

3. Markolf H. Niemz, ―Laser-Tissue Interactions: Fundamentals and Applications, Third

edition, Springer 2007.

4. Subhas Chandra Mukhopadhyay and Aimé Lay-Ekuakille (Eds.), Advances

in biomedical Sensing, Measurements, Instrumentation and Systems, Springer, 2010.

5. John G.Webster (editor), ―Bioinstrumentation, John Wiley & Sons, 2004.

6. Shayne Cox Gad, ―Safety Evaluation of Medical Devices, Marcel Deckle Inc, 2002.



41

7. Cromwell l., ―Biomedical Instrumentation and Measurements, Prentice Hall of India.

8. Rangaraj M. Rangayan, ―Biomedical signal analysis, John Wiley & Sons (ASIA) Pvt. Ltd.

Evaluation Scheme:










42

Subject Name: Advanced Process Instrumentation

Course Code

Course Name

Teaching Scheme



MT-INDLO1024 Advanced Process


Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO1024 Advanced Process


Prerequisite:

Overview of different transducers, introduction to process control, feedback control system, various unit operations and instrumentation, PID

controller etc.

Course Description:

The course enhances the ideas to use modern process instrumentation devices. The Course focuses on innovating many industrial applications

where accurate instrumentation is required so as to get desired output.

Course Objectives

Introduce basic fundamentals of Instrumentation Engineering.



43

To elaborate different concepts of Instrumentation engineering like different

transducers, their dynamic and static characteristics, errors in measurement.

Analysis and investigations of the basic control schemes starting from discontinuous

controller to continuous controller and other process control design basics.

To make students familiar about different pneumatic controllers, hydraulic controllers

and electronic controllers.

To introduce the students regarding different unit operations and their instrumentation

and control.

Course Outcomes

1. Know the application of different transducers, calculation of errors in measurement,

2. Experimental determination of transfer functions of the sensors or systems.

3. Be conversant with application of different controllers and their applications to suitable process.

4. Know the constructional details, principle of operation, and performance of different unit operations and their Instrumentation.

5. Able to understand the concepts of different pneumatic controllers, hydraulic controllers and electronic controllers.

6. Apply the different complex control modes for process industries.


1 Introduction to performance characteristics of different transducers and systems, Dynamic analysis of

measurement systems, errors in instrumentation systems. 04 CO1

2 Introduction to process control, representative process control problems, classification of process control

strategies, Major steps in control system developments. 06 CO2



44

3

Introduction to Unit Operations and theoretical modeling, concept of Unit and Unit Operation, Material

Balance and Energy Balance, Introduction to Evaporation, Distillation, Crystallization processes and

associated Instrumentation and control, Introduction to process equipments like Continuous Stirred Tank

Reactor (CSTR), Heat Exchanger, liquid storage systems and their modeling, dynamic behavior of first and

second order processes, dynamic response of the processes, development of empirical models for process

data.

09 CO3

4 Overview of process control system design: introduction, degree of freedom for process control, selection

of controlled, manipulated and measured variable, process safety and process control. 06 CO4

5

Control system instrumentation, introduction, basic control modes, on-off controller, features of PID

controller, PID controller design, tuning and trouble shootings, digital version of PID controller,

electronic/pneumatic/hydraulic controller, optimum control settings, transducers, transmitters, transmission

lines, final control elements and their calculations and selection.

10 CO5

6 Feed forward and ratio control, cascade control: introduction to Feed forward and ratio control, cascade

control and their design consideration, tuning. 04 CO6

References:

1. Process dynamics and control by Dale E. Seborg, Thoman F. Edgar, Dyncan A. Mellichamp, IInd Edition, Willey publication

2. Instrument Engineers Handbook by B. G. Liptak Vol. I and II, Third Edition, Chilton and Book Company, 1990.

3. Process control by Peter Harriot Tata McGraw hill

4. Automatic process control by D. Ekman, Wiley Eastern Ltd

5. Process control system Application, Design and tunning by F.G. Shinsky McGraw hill

6. Unit operation and chemical engineering by Mc Cabe McGraw hill Publication

7. Chemical process industries by Shreve McGraw hill Publication

Evaluation Scheme:




45









46

Subject Name: Optimization Techniques in Problem Solving

Course Code

Course Name

Teaching Scheme



MTILO1011 Optimization Techniques in

Problem Solving 03 -- -- 03 -- - 03

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MTILO1011 Optimization Techniques in

Problem Solving 20 20 20 20 60 -- -- -- - 100

Prerequisites:

Basic Matrix manipulation and probability theory.

Microsoft Excel

C Programming

Course Description:

The courses introduce the concepts of decision making and problem solving. It delves into optimization techniques used to solve real life problems in

engineering, manufacturing and management. It looks at LPP problems, their formulation and solutions. It looks at concepts of sensitivity analysis

using algorithms like Simplex Method and Dual Simplex Method. It delves into aspects that are relevant in solving problems using algorithms. Classes



47

of real life problems like transportation, assignment, job sequencing, replacement and inventory management. It introduces to concepts of game theory,

queuing and Monte Carlo Simulation techniques.

Course Objectives:

To familiarize the students with optimization techniques

To understand and be able to apply deterministic and probabilistic techniques for solving real world decision making problems

Course Outcomes:

At the end of the course learner will able to

1. Understand and apply optimization, decision making and problem solving for real life

2. Formulate and solve linear programming problems

3. Develop algorithms and solve transportation and assignment problems

4. Analyze Queuing Systems

5. Analyze and solve 2 person games

6. Use probabilistic formulation for Monte-Carlo simulations


1. Introduction to Optimization: Optimization techniques and Decision making. Applications. Limitations.

Fundamental Matrix Manipulations. Fundamental Probability techniques 5 CO1

2.

Linear Programming: Introduction, Linear Programming Problem,

Requirements of LPP, Mathematical Formulation of LPP, Graphical method,

Simplex Method, Special cases in Simplex, Penalty Cost Method or Big M-method, Two Phase Method,

Duality, Primal – Dual construction, Symmetric and Asymmetric Dual, Weak Duality Theorem,

Complimentary Slackness Theorem, Main Duality Theorem, Dual Simplex Method, Sensitivity Analysis,

Post-Optimal Analysis. Introduction to Integer Programming. Introduction to Revised Simplex Method.

10 CO2

3. LPP Applications: 6 CO3



48

Transportation Problem: Formulation, solution, unbalanced Transportation problem. Finding basic

feasible solutions – Northwest corner rule, least cost method and Vogel’s approximation method.

Optimizing using 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, Travelling Salesman Problem

4. Queuing Models: Formulation of Queing Model. Types of Queues, MM1:∞∞ queue model and

calculation of probabilities. Analysis of the queuing system and calculation of parameters 6 CO4

5.

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, 2Xn and mX2 games,

6 CO5

6.

Simulation: Introduction, Methodology of Simulation,

Simulation Procedure, Application of Simulation Monte-Carlo Method: Introduction, Monte-Carlo

Simulation, Applications of Simulation, Advantages of Simulation, Limitations of Simulation

6 CO6

Text Books:

1. Taha, H.A. "Operations Research - An Introduction", Prentice Hall, (7th Edition), 2002.

2. Hiller, F. S. and Liebermann, G. J. "Introduction to Operations Research", Tata McGraw Hill, 2002.

Reference Books:

1. Operations Research, S. D. Sharma, KedarNath Ram Nath-Meerut.

2. Operations Research, KantiSwarup, P. K. Gupta and Man Mohan, Sultan Chand & Sons.

Evaluation Scheme:







49






50

Subject Name: Cyber and Data Laws

Course Code Course Name Theory

Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/ Oral

Credit

Tutorial

Credits

Total

Credits

MTILO1012 Cyber and Data Laws 03 -- -- 03 -- -- 03


Examination Scheme

Theory Marks Term Work Practical Oral Total

In-Sem Evaluations

End Sem

Exam

A1 A2 Avg. of

2 As

Mid

Sem

Exam

MTILO1012 Cyber and Data Laws 20 20 20 20 60 -- -- -- 100

Course Description:

This Course gives Introduction to the Cyber World and Cyber Law, Regulatory Framework such as International Legal Regime, International legal

regime relating to E-Commerce and Domestic Legal Regime. What is Cybercrime, Kinds of cybercrimes and relevant provisions under Information

Technology Act 2000 Indian Penal Code? Also addresses the Important Issues in Global E-commerce, Issues relating to Access, Trust, Privacy and

Security, Consumer Protection and so on.

Course Objectives:

To understand and identify different types cybercrime and cyber law



51

To recognized Indian IT Act 2008 and its latest amendments

To learn various types of security standards compliances

Course Outcomes: At the end of the course learner will 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. Apply various tools used for cyber crime for investigation

4. Distinguish different aspects of cyber law & its compliance

5. Apply and analysed IT Acts for current cyber crimes

6. Apply Information Security Standards compliance during software design and development


1.

Introduction to Cybercrime:

Cybercrime definition and origins of the world, Cybercrime and information security,

Classifications of cybercrime, Cybercrime and the Indian ITA 2000, A global Perspective on

cybercrimes.

06 CO1

2.

Cyber offenses & Cybercrime :

Social egg attacks, Cyber stalking, Botnets, Attack vector, Credit Card Frauds in Mobile and

Wireless Computing Era, Security Challenges for wireless Devices, Authentication Service

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

Organizations, Organizational Measures for Handling Mobile, Devices-Related Security Issues,

Organizational Security Policies and Measures in Mobile Computing Era, Laptops

06 CO2

3. Tools for Cyberline : 06 CO3



52

Methods , Phishing, Password Cracking, Key loggers and Spywares, Virus and Worms,

Steganography, Covert channels, storage and timing covert channels, counter measures for covert

communication, DoS and DDoS Attacks, SQL Injection, Buffer Over Flow, Attacks on Wireless

Networks, Identity Theft -ID Theft

4.

The Law of IT Compliance:

How to conduct investigations: Cooperation with investigations, Numerous Examples of Fraud

(PostMordems), Securities Fraud, Federal Sentencing Guidelines, Codes of Ethics, Hotlines,

Reporting, Whistleblowing, Employee Monitoring, Entrapment, Raids & Seizures Electronic

Banking , The Need for an Indian Cyber Law

05 CO4

5.

Indian IT Act:

Cyber Crime and Criminal Justice: Penalties, Adjudication and Appeals Under the IT Act, 2000,

IT Act. 2008 and its Amendments

08 CO5

6. Information Security Standard compliances:

Information Security Standard compliances SOX, GLBA, HIPAA, ISO, FISMA, NERC, PCI. 08 CO6

Text books:

1. Sood,“Cyber Laws Simplified”, Mc Graw Hill

2. Anthony Reyes, “Cyber Crime Investigations: Bridging the Gaps Between Security Professionals, Law Enforcement, and Prosecutors”

3. Nina Godbole, Sunit Belapure, Cyber Security, Wiley India, New Delhi

Reference books:

1. The Indian Cyber Law by Suresh T. Vishwanathan; Bharat Law House New Delhi

2. The Information technology Act, 2000; Bare Act- Professional Book Publishers, New Delhi.

3. Cyber Law & Cyber Crimes By Advocate Prashant Mali; Snow White Publications, Mumbai

4. Nina Godbole, Information Systems Security, Wiley India, New Delhi

5. Kennetch J. Knapp, Cyber Security &Global Information Assurance Information Science Publishing.



53

Evaluation Scheme:










54

Subject Name: Product Lifecycle Management


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/ Oral

Credit

Tutorial

Credits

Total

Credits

MTILO1013 Product Lifecycle Management 03 -- -- 03 -- - 03


Examination Scheme

Theory Marks Term Work Practical Oral Total

In-Sem Evaluations End

Sem

Exam

A1 A2 Avg. Mid

Sem

Exam

MTILO1013 Product Lifecycle Management 20 20 20 20 60 -- -- -- 100

Course Description:

The purpose of the course of to provide an overview of how IT tools and systems are used to create and manage product information and knowledge,

from identification of customers’ need to product retirement. In particular, the course focuses on the advanced use of computer models, information,

and knowledge in the product realisation process. This course aims to develop these skills. In particular, the course is aimed towards students from

the master programs Product Development, Product Engineering, Industrial Engineering

Course Objectives

To familiarize the students with the need, benefits and components of PLM.

To acquaint students with Product Data Management & PLM strategies.



55

To give insights into new product development program and guidelines for designing and developing a product.

To familiarize the students with Virtual Product Development

Course Outcomes

At the end of the course learner will 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 plan.

5.Integrate of environmental aspects in product design.

6.Carry out Life Cycle Assessment and Life Cycle Cost Analysis.


1

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.

09 CO1

2

Product Design: 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

08 CO2



56

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.

3

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.

04 CO3

4

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.

06 CO4

5

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.

06 CO5

6

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.

06 CO6

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, ImmonenAnselmie, ―Product Life Cycle Management‖, Springer, Dreamtech, ISBN: 3540257314.

4. Michael Grieve, ―Product Lifecycle Management: Driving the next generation of lean thinking‖, Tata McGraw Hill, 2006, ISBN: 0070636265.

Evaluation Scheme:






57







58

Subject Name: Entrepreneurship Development and Management


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Or

al

Credit

Tutorial

Credits

Total

Credits

MTILO1014

Entrepreneurship

Development and

Management 03 -- - 03 -- - 03


Examination Scheme

Theory Marks Term

Work Practical Oral Total

In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg.

Mid

Sem

Exam

MTILO1014

Entrepreneurship

Development and

Management 20 20 20 20 60 -- -- -- 100

Course Description:

To familiarize the participants with the concept and overview of entrepreneurship with a view to enhance entrepreneurial talent. To impart

knowledge on the basics of entrepreneurial skills and competencies to provide the participants with necessary inputs for creation of new ventures.

To explore new vistas of entrepreneurship in 21st century environment to generate innovative business ideas.



59

Course Objectives:

1. To motivate the students to become and/or work for entrepreneurship and start-ups

2. To understand and be able to apply processes, problem-solving techniques and legal clauses for success in venture


1. Understand the ideation process and the importance of innovative thinking

2. Develop Business plans, financial analysis and perform market analysis for their venture

3. Familiarize with Legal matters

4. Understanding government schemes and support

5. Prepare proper pitch for funding

6. Use tools and techniques for solving problems


1.

Ideation:

Idea Identification and Assessment. Idea Validation. Documentation of Ideas and Feasibility Analysis.

Introduction to Entrepreneurship and Business Startups

06 CO1

2.

Business Planning and Financial Basics: Learn how to build an effective Business Plan for your venture.

Understand the fundamentals of finance & accounting comprising of financial statements. Break-even

analysis. Risk Assesment. Marketing Plans, Management and Personnel, Start-up Costs and Financing as

well as Projected Financial Statements. Capital and its Importance to the Entrepreneur Starting a New

Business, Buying an Existing Business, New Product Development, Business Growth.

09 CO2

3.

Legal Foundations:

Define the right legal foundation for your company and explore fundamentals like Company Registration,

Compliances and Patents. Entrepreneur Law and its Relevance to Business Operations. 06 CO3



60

4.

Government support: Indian Environment for Entrepreneurship: key regulations and legal aspects ,

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., Public private partnerships, National Skill development Mission, Credit Guarantee Fund,

PMEGP, discussions, group exercises etc

06 CO4

5.

Fund Raising: Company valuation, Fundraising, Equity Management. Role of Money and Capital Markets

in Entrepreneurial Development:Contribution of Government Agencies in Sourcing information for

Entrepreneurship

06 CO5

6.

Small Business:

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 rectangular games without saddle

point – mixed strategy for 2 X 2 games, 2Xn and mX2 games,

06 CO6

Text Books:

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

Reference Books:

1. Law and Practice relating to Micro, Small and Medium enterprises, Taxmann Publication Ltd.

2. Kurakto, Entrepreneurship- Principles and Practices, Thomson Publication



61

Evaluation Scheme:










62

Subject Name: Renewable Energy Systems


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Or

al

Credit

Tutorial

Credits

Total

Credits

MTILO1015 Renewable Energy

Systems 03 -- -- 03 -- - 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg.

Mid

Sem

Exam

MTILO1015 Renewable Energy

Systems 20 20 20 20 60 -- -- -- 100

Pre-requisite:

Environmental Study

Fundamentals of Energy

Course Description:

This course gives the importance of Renewable energy systems or sources, wind energy, solar pv and thermal systems, solar pv and thermal systems,

biomass energy, other energy sources. It also includes the tidal energy: energy from the tides, barage and non barage tidal power systems. The wave

energy: energy from waves, wave power devices. With this course knowledge one can innovate new system that can generate energy from natural

sources.



63

Course Objectives:

To impart knowledge on the various topics namely,

Recognize current and possible future role of renewable energy sources.

Adequate inputs on a variety of issues in harnessing renewable Energy.

Awareness’ about renewable Energy Sources and technologies.

Course Outcome:

Upon the completion of this course, learners will be able to:

1. Understand the renewable energy sources

2. Analyze wind power plants

3. Understand and analyze Solar Photo Voltaic (SPV) systems

4. Explore the use of biomass energy as a renewable energy source

5. Apply Knowledge of working principle of geothermal energy systems

6. Evaluate and explore the capability of tidal, hydrogen and hybrid energy systems


1.

Global and National Energy Scenario: Overview of conventional & renewable energy sources, need, potential &development of renewable energy sources, types of renewable energy systems, Future of Energy Use, Global and Indian Energy scenario, Energy for sustainable development, renewable electricity and key elements, Global climate change, CO2 reduction potential of renewable energy- concept of Hybrid systems.

08 CO1

2.

Wind Energy

Power in the Wind, Nature of the wind, Wind Energy Conversion, Wind Data and Energy Estimation, Site

selection, Types of wind turbines, Wind farms, Wind Generation and Control, classification of wind,

characteristics, offshore wind energy, Types of Wind Power Plants (WPPs), Components of WPPs,

08 CO2



64

Textbooks:

1. Solar Energy: Principles of Thermal Collection and Storage, S. P. Sukhatme and J. K. Nayak, TMH, New Delhi, 3rd Edition.

2. Renewable Energy Resources, John Twidell and Tony Weir, Taylor and Francis -second edition, 2013.

3. Non-Conventional Energy Sources /G.D. Rai, Khanna Publishers

Reference Books:

1. Renewable Energy- Edited by Godfrey Boyle-oxford University, press, 3rd edition, 2013.

Working of WPPs, Grid integration issues of WPPs, Hybrid systems, wind energy potential and installation

in India.

3.

Solar Energy

Solar energy system, Solar Radiation, Availability, Measurement and Estimation, Solar Thermal

Conversion Devices and Storage, Solar Electrical Power Generation, general Solar Photo Voltaic (SPV)

system components and their characteristics, Stand alone and Grid Connected SPV systems.

Solar Photovoltaic systems: Basic Principle of solar photovoltaic conversion, types of PV Systems, Types

of Solar Cells, Photovoltaic cell concepts: Cell, module, array, PV Module I-V Characteristics, Efficiency

& Quality of the Cell, series and parallel connections, maximum power point tracking, applications.

08 CO3

4.

Biomass Energy

Introduction, Biomass resources, Principles of Bio-Conversion, Biomass Cogeneration, Environmental

Benefits, Anaerobic/aerobic digestion, types of Bio-gas digesters, gas yield, combustion characteristics of

bio-gas, utilization for cooking.

05 CO4

5. Geothermal Energy: Resources, types of wells, methods of harnessing the energy, potential in India. 03 CO5

6.

Other Energy Sources

Tidal Energy: Energy from the tides, Types of Tidal power systems, wave power devices, Ocean Thermal

Energy Conversion (OTEC).

Hydrogen Production and Storage: Principle of working, various types, construction and applications,

Energy Storage System

Hybrid Energy Systems.

07 CO6



65

2. Handbook of renewable technology Ahmed and Zobaa, Ramesh C Bansal, World scientific, Singapore.

3. Renewable Energy Technologies /Ramesh & Kumar /Narosa.

4. Renewable energy technologies – A practical guide for beginners – Chetong Singh Solanki, PHI

5. Non-conventional energy source –B.H. Khan- TMH-2nd edition.

6. Integrated energy systems modeling Karlsson, Kenneth Bernard; Skytte, Klaus Morthorst;Publishedin:DTU International Energy Report 2015.

Evaluation Scheme:










66

Subject Name: Design of Experiments


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Or

al

Credit

Tutorial

Credits

Total

Credits

MTILO1016 Design of Experiments 03 -- -- 03 -- - 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg.

Mid

Sem

Exam

MTILO1016 Design of Experiments 20 20 20 20 60 -- -- -- 100

Prerequisite:

Probability Statistics

Course Description:

This course objective is to learn how to plan, design and conduct experiments efficiently and effectively, and analyze the resulting data to obtain

objective conclusions. Both design and statistical analysis issues are discussed. Opportunities to use the principles taught in the course arise in all

aspects of today’s industrial and business environment. Applications from various fields will be illustrated throughout the course. All experiments

are designed experiments; some of them are poorly designed, and others are well-designed. Well-designed experiments allow you to obtain reliable,



67

valid results faster, easier, and with fewer resources than with poorly-designed experiments. You will learn how to plan, conduct and analyze

experiments efficiently in this course.

Course Objectives:

To impart students a holistic view of the fundamentals of experimental designs, analysis tools and techniques, interpretation and applications.

Learn how to plan, conduct and analyze experiments efficiently.

Course Outcome:

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

1. Understand the fundamentals of experiments and its uses,

2. Analyze experimental designs such as randomized block design, Latin square, factorial and fractional factorial designs,

3. Understand and explore multifactor experiments

4. Analyze special experimental designs

5. Analyze response surface methodology

6. Apply Taguchi method in analyzing experimental data


1

Experimental Design Fundamentals Importance of experiments, experimental strategies, basic principles of design, terminology, ANOVA, steps in experimentation, sample size, normal probability plot, linear regression model

08 CO1

2

Single Factor Experiments

Completely randomized design, Randomized block design, Latin square design Statistical analysis, estimation

of model parameters, model adequacy checking, and pair wise comparison tests. 08 CO2



68

Textbooks:

1. Montgomer, D.C., /Design and analysis experiments/ John Wiley and sons 2003.

2. Nicolo Belavendram / Quality by design; Taguchi techniques for industrial perimentation /prentice hall, 1995.

Reference Books:

1. Phillip J. Rose/ Taguchi techniques for quality engineering/McGraw Hill, 1996

Evaluation Scheme:








3 Multi Factor Experiments

Two and three factor full factorial experiments, 2K factorial Experiments, Confounding and blocking designs. 08 CO3

4 Special Experimental Design

Special experimental design, Fractional factorial design, nested designs, Split plot design, 04 CO4

5 Introduction to Response Surface Methodology:

Experiments with random factors, rules for expected mean squares, approximate F- tests. 04 CO5

6

Taguchi Method

Steps in experimentation, design using Orthogonal arrays, data analysis, Robust design control and noise factors,

S/N ratios, parameter design, and case studies. 07 CO6



69

Subject Name: Lab Practice-I

Course Code

Course Name

Teaching Scheme



MT-INL101 Laboratory -I 2 - 1 1

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INL101 Lab practice -I - - - - - - 25 - 25 50

Course Description:

It provides adequate knowledge of mathematical computation in engineering applications; attain hands-on experience on different computational

tools and software. This lab improves skill of student in engineering mathematics.

Courses Objectives:

1. To provide adequate knowledge of mathematical computation in engineering applications.

2. To attain hands-on experience on different computational tools and software.

3. To improve skill of student in engineering mathematics.

Courses Outcomes:

1. Ability to use the computational tool and software like MATLAB / SCI Lab effectively.



70

2. Ability to select the method, analysis and optimize the given problem by mathematical approach.

3. Ability to define, illustrate and apply the concepts of random variables, probability distributions.

4. Apply programming skills and use mathematical software as a discovery tool and to solve a real-world problem.

List of Experiments:

Sr. No. Detailed Content

1 Computation of Eigen values, Eigen vectors, norms and their mathematical operations using MATLAB /SCI lab.

2 LQ and QR decomposition methods for system performance using computational tools.

3 Gram – Schmidt orthonormalization using computational tools.

4 Write a C-program to find solution of algebraic equation using Gauss-Jordan method, Gauss-Seidal method.

5 Write a MATLAB program to find solution of algebraic equation using Gauss elimination method, Newton-Raphson method.

6 Case study on Ritz and Kantorovich method for higher order systems.

7 Engineering application based case study on Probability.

8 Multidisciplinary application of integral transforms.

NOTE: Perform any six experiments from above list and two experiments from electives.



71

Subject Name: Lab Practice-II

Course Code

Course Name

Teaching Scheme



MT-INL102 Lab Practice-II 2 - 1 1

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INL102 Lab Practice -II - - - - - - 25 - 25 50

Course Description:

This Lab provides adequate knowledge of design and implementation of control systems also hands-on experience to apply their practical knowledge in

designing, testing and simulation of any instrumentation and process control system. This provides practical experience to the students in simulation

software and real time interfacing with process control applications.

Course Objectives:

1. To provide adequate knowledge of design and implementation of control systems.

2. To provide students with hands-on experience to apply their practical knowledge in designing, testing and simulation of any instrumentation and

process control system.

3. To provide practical experience to the students in simulation software and real time interfacing with process control applications.



72

Course Outcomes:

1. Ability to design signal conditioning circuit for any given sensor.

2. Ability to simulate process control loop using simulation tools and software like LabVIEW / MATLAB.

3. Ability to interface hardware and software tools for analysis and optimization.

4. Ability to find out mathematical model of any given real time system.



1 Microcontroller based temperature/ strain/ vibration measurement system.

2 PC based temperature/ level/ flow/ measurement system.

3 Hardware-In- Loop (HIL) simulation of PC based data acquisition and control.

4 LabVIEW based data logger and analysis.

5 Speed and Position control of servo motor using MATLAB / LabVIEW.

6 Modeling of simple process using system identification technique.

7 Tank level control simulation in LabVIEW.

8 Real time testing performance of different tuning methods of PID.

NOTE: Perform any six experiments from above list and two from electives.



73

Subject Name: Modern Control Engineering

Course Code

Course Name

Teaching Scheme



MT-INC201 Modern Control Engineering 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INC201 Modern Control Engineering 20 20 20 20 60 2 - - - 100

Course Description:

This course develops the skills needed to represent the system in state space form, impart knowledge required to design state feedback controllers

and state estimator, impart knowledge and skills needed to classify singular points and construct phase trajectory using delta and isocline methods,

understand the concepts of stability and introduce techniques to assess the stability of certain class of nonlinear systems and understand the various

non-linear behaviors such as Limit cycles, input multiplicity and output multiplicity.

Course Objectives

1. To develop the skills needed to represent the system in state space form.

2. To impart knowledge required to design state feedback controller and state estimator.

3. To impart knowledge and skills needed to classify singular points and construct phase trajectory using delta and isocline methods.



74

4. To make the students understand the concepts of stability and introduce techniques to assess the stability of certain class of non-linear system.

5. To make the students understand the various non-linear behaviors such as Limit cycles, input multiplicity and output multiplicity.

Course Outcomes

1. Ability to represent the time-invariant systems in state space form as well as to analyze, whether the system is stabilizable, controllable, observable

and detectable.

2. Ability to design state feedback controller and state estimator.

3. Ability to classify singular points and construct phase trajectory using delta and isocline methods.

4. Ability to use the techniques such as describing function, Lyapunov Stability, Popov’s Stability Criterion and Circle Criterion to assess the stability

of certain class of non-linear system.

5. Ability to describe non-linear behaviors such as Limit cycles, input multiplicity and output multiplicity.

6. Able to understand the various non-linear behaviors such as Limit cycles, input multiplicity and output multiplicity


1.

State Space Approach

Review of state model for systems, Non uniqueness of state model, Role of Eigen values and Eigen vectors,

State transition matrix and its properties, free and forced responses, State Diagrams, minimal realization,

balanced realization.

06 CO1

2.

Controllability and Observability

Analysis and Design of Control System in State Space Controllability, observability properties, Canonical

Realizations, Duality, Design of State Feedback Control through Pole placement. Full order observer and

reduced order observer

08 CO2

3.

State space analysis of discrete time multivariable systems

Discretization of State equations for dynamic systems, State equations using phase, physical and canonical

variables, realization of transfer matrices, Solution of state equation, stability.

07 CO3



75

4.

Non-Linear Systems

Types of Non-Linearity, Typical Examples:–Singular Points, Phase plane analysis (analytical and

graphical methods), Limit cycles, Equivalent Linearization, Describing Function , Analysis, Derivation

of describing functions for different non-linear elements.

07 CO4

5.

Stability of Non-Linear Systems

Stability concepts, Stability Analysis by DF method, Lyapunov Stability Criteria, Krasovskil’ method,

Variable Gradient Method, Popov’s Stability Criterion.

07 CO5

6.

Non Linear Control Analysis

Mathematical preliminaries from point set toplogy, Euler-Lagrange equations of motion, Equilibrium

points, Linearization, State-space formulation, Second-order systems, Phase-portrait, Limit cycle,

Lyapunov stability.

04 CO6

References:

1. M. Gopal "Modern Control System Theory",New Age International Publishers, 2nd edition, 1996

2. D.E.Kirk, “Optimal Control Theory”, Prentice-Hall. 1970.

3. M. Vidyasagar, “Nonlinear Systems Analysis”. 2nd Edition.Prentice Hall, 1993.

4. K. Ogata "Modern Control Engineering", Prentice Hall of India, 3rd edition, 1998

5. I.J. Nagarath and M.Gopal“Control Systems Engineering”, , New Age International (P)

6. M. Gopal,"Control and State Variable Methods", Tata McGraw-Hill Companies, 1997.

7. “Systems and Control” by Stainslaw H. Zak , Oxford Press, 2003

8. K.Ogata, "Modern Control Engineering", Prentice Hall, Fifth Edition, 2012.

9. M.Gopal, "Digital Control and State Variable Methods: Conventional and Intelligent Control Systems", Third Edition, Tata Mc-Graw Hill, 2009.

10. Ogata, K., "Discrete-Time Control Systems", 2/e, Prentice Hall of India.

Evaluation Scheme:







76






77

Subject Name: Industrial Automation

Course Code

Course Name

Teaching Scheme



MT-INC202 Industrial Automation 3 - -

3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term

Work Pract. Oral Total Internal Assessment Mid Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INC202 Industrial Automation 20 20 20 20 60 2 - - - 100

Course Description:

The student will able to impart knowledge about the fundamentals of automation and various automation systems used in industry, architecture,

working and applications of PLC, DCS and SCADA, Safety Instrumented System (SIS).

Course Objectives:

• To impart knowledge about the fundamentals of automation and various automation systems used in industry.

• To impart the knowledge about the architecture, working and applications of PLC, DCS and SCADA

• To make the students understand the requirements of Safety Instrumented System (SIS).



78

Course Outcomes:

The students will be able to

1. Describe automation, need, importance and applications in industry.

2. Identify components of PLC, and develop PLC ladder using instructions of PLC and design PLC based application by proper selection and sizing

criteria

3. Explain evolution and architecture of DCS, hierarchical control in DCS, programming DCS through Function Block Diagram (FBD) method.

4. Describe SCADA architecture, communication in SCADA and develop any application based on SCADA along with GUI using SCADA software.

5. Explain database and alarm management system

6. Recognize the need of SIS and describe risk reduction methods.


Mapping

1 Introduction: Fundamentals of Industrial Automation, Need and role of Automation, Evolution of

Automation, Elements of process control loop, Current Trends, Automation Strategy evolution, Control system

audit, Automation Tools and strategies and their location in plant.

04 CO1

2

Programmable Logic Controller:

PLC Hardware: Types of Processes and Specifications. PLC Memory and Power supply calculation with

redundancy consideration. Interface to Hydraulic/Pneumatic circuits. Choosing PLC for application.

PLC Programming: Development of Relay Logic Ladder Diagram, Introduction to PLC Programming,

Programming devices and languages as per IEC 61131-3 like IL, ST, FBD, CFC, SFC, PLC Timers and

Counters. Advanced PLC instructions, PID Control using PLC, safety PLC.

08 CO2

3

Distributed control system: DCS Introduction, Location of DCS in Plant, functions, advantages and

limitations, Comparison of DCS withPLC, DCS components/ block diagram, Architecture, Functional

requirements at each level, Database management. Layout of DCS, Controller Details, Redundancy, I/O Card

Details, Junction Box and Marshalling Cabinets, Operator Interface, Workstation Layout, different types of

control panels, types of Operating Station, Programming as per IEC 61131-3, Advantages, Overview of

08 CO3



79

Programming Languages, Device Signal Tags, Configuration, Programming for Live Process, Selection of

DCS, DCS plant layout.

4

SCADA and HMI: SCADA Concept of SCADA systems, Programming techniques for : Creation of pages,

Sequencing of pages,Creating graphics & animation, Dynamos programming with variables, Trending,

Historical data storage & Reporting, Alarm management, reporting of events and parameters. HMI types,

Interfacing with PLC. Wireless SCADA System.

07 CO4

5 Communication Protocols: Open and proprietary protocol-advantages and disadvantages, wireless HART,

Fieldbus-functions, hardware selection, Segment design and checking for safe and hazardous area, advantages

and disadvantages, installation, documentation and economics. Wireless communication protocol.

08 CO5

6

Design of Safe Plant: Advanced Intrinsic safety - Entity concept, FISCO, High power trunk, Dynamic arc

recognition and termination technology with advantages and disadvantages. Safety Instrumented System-

Components, technologies, SIL calculation methods, SIL calculation of PFD, RRF etc. Phases of SIS overall

implementation and reliability.

04 CO6

Text Books:

1. Terry Borden, Richard A. Cox, “Technician's Guide to Programmable Controllers”, Delmar Cengage Learning, 27-Jan-2012.

2. Gary A. Dunning, “Introduction to Programmable Logic Controllers”, Third Edition, 2005.

Reference Books:

1. B.G. Liptak, “Instrument Engineer‘s Handbook – Process Software and Digital Network”, CRC Press, 2005.

2. Dobrivojie Popovic, Vijay Bhatkar, “Distributed Computer Control for Industrial

Automation”, CRC Press, 1990.

3. Luis A. Bryan, E. A. Bryan, “Programmable Controllers: Theory and Implementation”, Second Edition, American Technical Publishers,

Incorporated, 2002.

4. John W. Webb, Ronald A. Reis, “Programmable Logic Controllers: Principles and

Applications”, Fifth Edition, Prentice Hall, 2003.



80

Evaluation Scheme:










81

Subject Name: Department Elective Course III

Modelling and Simulation

Course Code

Course Name

Teaching Scheme



MT-INDLO2011 Modelling and Simulation 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2011 Modelling and Simulation

20 20 20 20 60 2 - - - 100

Course Description:

This course covers the fundamentals and techniques for designing and using simulation, modeling, and optimization algorithms with applications in

system performance modeling. The course is focused on developing the practical skills and knowledge needed to solve problems requiring

applications of modeling and simulation.

Course Objectives:

• To provide an understanding of methods, techniques and tools for modelling, simulation

• To familiarize with various simulation models and give practical examples for each category.

• Students are expected to analyse output data produced by a model and test validity of the model.



82

Course Outcomes:

The students shall be able to :

1. Understand basics the process modelling and simulation, tools and approaches are used for simulation.

2. Describe the need of model and their classification

3. Illustrate the modelling of various heat transfer equipment and develop the mathematical model for one of the equipment.

4. Explain the simulation techniques and modelling of mobile robot and control

5. Analyse the numerical methods for systems as well optimization techniques

6. Classify various numerical methods and their application to chemical engineering systems models.

Module Detailed Content Hours CO

Mapping

1 Basic Modelling

Introduction to process modelling and simulation, tools of simulation, approaches of simulation, planning of

calculation in a plant simulation. Parameter estimation techniques in theoretical as well as numerical models.

04 CO1

2 Model Discrimination

Models, need of models and their classification, models based on transport phenomena principles, alternate

classification of models, population balance, stochastic, and empirical models, unit models.

08 CO2

3

Models of Heat Transfer Equipment:

Modelling of exchangers, Absorption columns, Extractors, Distillation columns, Membrane processes,

Development of detailed mathematical models of evaporators, use of Newton Raphson method for solving

evaporator problems.

08 CO3

4

Computer Simulation

Numeric integration, state space simulation techniques, simulation of discrete-time systems, digital simulation

languages. Robotic Systems and Automation – Modelling of robot, control of robots, modelling of mobile

robots and control, applications

07 CO4



83

5 Optimization Techniques

Function, Analysis and numerical methods for single variable and multivariable system, constrained

optimization problems.

06 CO5

6

Numerical Methods

Classification of partial differential equations (PDE's), solution of PDEs by Finite difference techniques,

method of weighted residuals. Orthogonal collocation to solve PDEs with their application to chemical

engineering systems models.

06 CO6

Text Books:

1. B. Wayne Bequette, “Process Control: Modeling, Design and Simulation”, PHI.

2. Lubeyn W.L. “Process Modeling, Simulation and Control Engineering ", McGraw Hill Book Co., New York

3. Edgar T. F., Himmelblau D. M. and Ladson, “Optimization of Chemical Processes”, 2nd Ed., McGraw Hill, New York.

4. Aris R.,“Mathematical Modeling,Vol. 1:A Chemical Engineering Perspective (Process System Engineering)”, Academic Press, .

Reference Books:

1. Naim A Kheir, “System Modeling and Computer Simulation”, Marcel Dekker Inc, 1996

2. Sailendra Jain, “Modeling & Simulation Using Matlab”, Wiley

3. Louis Birta, “Gilbert Arbez, Modeling and Simulation”, Springer

4. Donald Boyo, “System Analysis and Modelling”, Academic Press Frank L.Severance, “System Modelling and Simulation”, Wiley

Evaluation Scheme:








84





85

Subject Name: IoT and Applications

Course Code

Course Name

Teaching Scheme



MT-INDLO2012 IOT and Applications 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2012 IOT and Applications 20 20 20 20 60 2 - - - 100

Course Description:

Internet of Things (IoT) is presently a hot technology worldwide. Government, academia, and industry are involved in different aspects of research,

implementation, and business with IoT. IoT cuts across different application domain verticals ranging from civilian to defence sectors. These domains

include agriculture, space, healthcare, manufacturing, construction, water, and mining, which are presently transitioning their legacy infrastructure

to support IoT. IoT-based applications such as innovative shopping system, infrastructure management in both urban and rural areas, remote health

monitoring and emergency notification systems, and transportation systems, are gradually relying on IoT based systems. Therefore, it is very

important to learn the fundamentals of this emerging technology.

Course Objectives:

• To recognize the factors that contributed to the emergence of IoT

• To emphasize on designing and programming IoT devices and use real IoT protocols for communication



86

• To secure the elements of an IoT device

• To transfer IoT data to the cloud and in between cloud providers and define the infrastructure for supporting IoT deployments

Course Outcomes:

After learning the course, the student will be able:

1. Understand the vision of IoT from a global context.

2. Determine the Market perspective of IoT.

3. Use of Devices, Gateways and Data Management in IoT.

4. Building state of the art architecture in IoT.

5. Application of IoT in Industrial and Commercial Building Automation and Real World Design Constraints.

6. Able to Develop sensor based application through embedded system platform, implementing IoT concepts with python.


Mapping

1 Introduction to IoT: Defining IoT, Characteristics of IoT, Physical design of IoT, Logical design of IoT,

Functional blocks of IoT, Communication models & APIs. 06 CO1

2 IoT& M2M: Machine to Machine, Difference between IoT and M2M, Software define Network. 05 CO2

3 Network & Communication aspects: Wireless medium access issues, MAC protocol survey, Survey routing

protocols, Sensor deployment & Node discovery, Data aggregation & dissemination. 09 CO3

4 Challenges in IoT: Design challenges, Development challenges, Security challenges, other challenges. 06 CO4

5 Domain specific applications of IoT: Home automation, Industry applications, Surveillance applications, Other

IoT applications. 06 CO5

6

Developing IoTs: Introduction to Python, Introduction to different IoT tools, Developing applications through

IoT tools, Developing sensor based application through embedded system platform, Implementing IoT concepts

with python.

07 CO6



87

Reference Books:

1. Vijay Madisetti, Arshdeep Bahga, “Internet of Things: A Hands-On Approach”

Universities Press, 2015.

2.WaltenegusDargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks:

Theory and Practice”, Wiley, 2010.

Evaluation Scheme:










88

Subject Name: Robotics and Automation

Course Code

Course Name

Teaching Scheme



MT-INDLO2013 Robotics and Automation 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2013 Robotics and Automation 20 20 20 20 60 2 - - - 100

Course Description:

This course focuses hands-on approach to introduce the basic concepts of robotics, focusing on the construction and programming of autonomous

mobile robots. Course information will be tied to lab experiments; students will be highlighted to build and test increasingly more complex mobile

robots, culminating in an end-of-semester robot contest.

Course Objectives:

• To provide the concepts of robotics and vision and image system

• To equip students to write programs for automatic functioning of a robot

• To familiarise various robot sensors and their perception principles that enable a robot

• To select appropriate hardware for efficient robots.



89

Course Outcomes:

1. Become familiar with the history, concept, development and key components of robotics technologies

2. Learn the operation of robotic drive system and their types

3. Understand the functioning of gripping instruments and selection criteria

4. Learn principle of robot sensors and machine vision devices

5. Learn kinematics and to characterize the robots based on the configuration and work volume

6. Learn robotic programming and able to solve the problems related to robot design and control


1 Definition – Co-ordinate Systems, Work Envelope, types and classification – Specifications – Pitch, Yaw, Roll,

Joint Notations, Speed of Motion, Pay Load – Basic robot motions - Point to point control, Continuous path

control. Robot Parts and Their Functions – Need for Robots Different Applications.

06 CO1

2

Robot drive systems: Pneumatic Drives – Hydraulic Drives –Mechanical Drives – Electrical Drives – D.C.

Servo Mot ors, Stepper Motor, A.C. Servo Motors – Salient Features, Applications, and Comparison of all

these Drives.

08 CO2

3 End Effectors – Grippers – Mechanical Grippers, Pneumatic and Hydraulic Grippers, Magnetic Grippers,

Vacuum Grippers; Two Fingered and Three Fingered Grippers; Internal Grippers and External Grippers;

Selection and Design Considerations

06 CO3

4 Sensors and machine vision: Requirements of a sensor, Principles and Applications of the following types of

sensors – Position of sensors (Piezo Electric Sensor, LVDT, Resolvers, Optical Encoders), Range Sensors

(Triangulation Principle, Structured, Lighting Approach, Laser Range Meters).

06 CO4

5 Robot kinematics and robot programming: Forward Kinematics, Inverse Kinematics and Differences; Forward

Kinematics and Reverse Kinematics of Manipulators with Two Degrees of Freedom (In 2 Dimensional) –

Deviations and Problems.

07 CO5

6 Teach Pendant Programming, Lead through programming, Robot programming Languages –VAL

Programming – Motion Commands, Sensor Commands, End effecter commands, and Simple programs.

Industrial Applications: Application of robots in machining, welding, assembly, and material handling.

06 CO6



90

Text Book:

1. Industrial Robots, Yu.Kozyrev, Mir Publishers

2. Janakiraman.P.A., Robotics and Image Processing, Tata McGraw-Hill, 1995

3. M.P.Groover, Industrial Robotics – Technology, Programming and Applications, McGraw-Hill, 2001

4. Yoram Koren, Robotics for Engineers, McGraw-Hill Book Co., 1992

References:

1. Fu.K.S. Gonzalz.R.C., and Lee C.S.G., Robotics Control, Sensing, Vision and Intelligence, McGraw-Hill Book Co., 1987

2. K.S.Fu., R.C.Gonalez, C.S.G.Lee, Robotics Control sensing, Vision andIntelligence, McGraw Hill International Edition, 1987

3. Richard D. Klafter, Thomas A. Chmielewski and Michael Negin, Robotic engineering- An Integrated Approach , Prentice Hall Inc, 1989.

Evaluation Scheme:










91

Subject Name: Advanced Power Electronics

Course Code

Course Name

Teaching Scheme



MT-INDLO2014 Advanced Power

Electronics 3 - - 3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2014 Advanced Power

Electronics 20 20 20 20 60 2 - - - 100

Course Description:

The course enhances the ideas for more complex power electronic systems along analytical methods in power electronic circuits. The course

focuses on various applications of power electronics in electronic equipment, drives and non-conventional energy systems.

Course Objectives

1. To enhance the ideas of students for more complex power electronic system.

2. To teach the analytical methods in power electronic systems.

3. To expose the students to various applications of power electronics in electronics

4. equipment, drives and non-conventional energy systems.



92

Course Outcomes

1. Thoroughly understand the modern methods of analysis and control of power electronic

Systems.

2. Carry out the theoretical analysis of the power electronic systems

3. Appreciate the ubiquity of power electronic systems in engineering fields.

4. Simulate and analyse power electronic systems.

5. Able to understand the various Power Electronic Applications in AC Drives.

6. Carry out the various applications of power electronics for Induction heating, Dielectric heating, Solid state relays.


Mapping

1

Three-phase Rectifiers

3-phase half-wave and full-wave controlled rectifiers with R and RL load, Effect of source inductance,

Distortion in line current, calculation of performance parameters.

10 CO1

2

Three-phase inverters and control

Three phase bridge inverters ( and conduction mode) with R and RL load

PWM for 3-phase voltage source inverters, Space Vector Modulation

(SVM) technique for 3-phase voltage source inverters, hysteresis control.

08 CO2

3

DC-DC Converters

Average model, linearized and transfer function models, state-space average

models of basic buck, boost and buck-boost converters.

04 CO3

4

Power Electronic Applications in DC Drives

Introduction to DC motors, speed control of DC motor, drives with semi converters, full converters and dual

converters. Chopper-based drive.

05 CO4

5 Power Electronic Applications in AC Drives 08 CO5



93

Introduction to three-phase induction motor, speed control methods for three-phase induction motor :

i) Stator voltage

ii) Variable frequency

iii) Rotor resistance

iv) V/f control

v) Slip power recovery schemes

6 Power Electronic Applications

Induction heating, Dielectric heating, Solid state relays 04 CO6

References:

1. M. Rashid, Power Electronics: Circuits, Devices, and Applications, PHI, 3rd Edition.

2. R. W. Erickson, D. Maksimovic, Fundamentals of Power Electronics, Springer, 2nd Edition.

3. Mohan, Undeland and Robbins, Power Electronics: Converters, Applicationsand Design, Wiley (Student Edition), 2nd Edition.

4. P. S. Bimbhra, Power Electronics, Khanna Publishers, 2012.

5. M. D. Singh, K. B. Khanchandani, Power Electronics, Tata McGraw Hill, 2nd Edition.

6. J. P. Agrawal, Power Electronics Systems: Theory and Design, Pearson Education, 200

7.

Evaluation Scheme:










94

Subject Name: Department Elective Course IV

Machine Learning and Deep Learning

Course Code

Course Name

Teaching Scheme



MT-INDLO2021 Machine Learning and

Deep Learning 3 - -

3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2021 Machine Learning and

Deep Learning 20 20 20 20 60 2 - - - 100

Course Description:

The course enhances the ideas to use adequate knowledge about the ANN with application. Course enhances the ideas about SVM. The Course

focuses innovate many real world deep learning applications.

Course Objectives:

• To introduce students to the basic concepts and techniques of Machine Learning and Deep Learning.

• To have a thorough understanding of the Supervised and Unsupervised learning techniques

• To study the various probability based learning techniques

• To understand graphical models of machine learning algorithms



95

Course Outcomes:

Upon completion of the course, students will be able to

1. Understand basics machine learning, its perceptive and issues.

2. Distinguish between, supervised, unsupervised and semi-supervised learning.

3. Apply the apt machine learning strategy for any given problem

4. Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem.

5. Modify existing machine learning algorithms to improve classification efficiency.

6. Apply models of machine learning to real world applications.


Mapping

1.

Introduction to Machine Learning:

Introduction: Types of Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement,

Design a Learning System: training data, concept representation, function approximation Perspectives and

Issues in Machine Learning.

06 CO1

2.

Supervised Learning:

Linear Regression (with one variable and multiple variables), Gradient Descent, Classification (Logistic

Regression, Over fitting, Regularization).

08 CO2

3.

Unsupervised Learning: K-means and Hierarchical Clustering, Gaussian Mixture Models, Expectation

Maximization (EM) algorithm, Model Selection, Dimensionality Reduction: Feature selection, Principal

Component Analysis (PCA) and kernel PCA, Scaling.

06 CO3

4.

Artificial Neural Networks:

The Neurons and the Brain, Neural Networks and Representation: Perceptrons, Multilayer perceptrons,

nonlinear regression, back-propagation, Initialization, Training & Validation, decision trees for classification

and regression, basic decision tree algorithm, issues in decision tree learning.

08 CO4



96

5.

Support Vector Machines:

Functional and geometric margins, optimum margin classifier, constrained optimization, primal/dual problems,

KKT conditions, dual of the optimum margin classifier, soft margins, kernels, quadratic programming, SMO

algorithm.

06 CO5

6.

Applying Machine Learning: Machine Learning System Design, Error Analysis, Error Metrics for Skewed

Classes, Trading Off Precision and Recall.

Machine Learning Applications: Spam detection, Anomaly Detection, Recommender Systems.

05 CO6

Text Books:

1. Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Foundations of Machine Learning (FOML), MIT Press, 2012

2. David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2007.

3. Tom Mitchell. Machine Learning, McGraw Hill

Reference Books:

1. Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning (DL), MIT Pess, 2016 (individual chapters freely available online)

2. Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms (UML), Cambridge University Press,

2014.

Evaluation Scheme:








https://mitpress.mit.edu/books/foundations-machine-learning

https://www.deeplearningbook.org/

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf



97

Subject Name: Safety in Engineering Industry

Course Description:

This course provides an overview of process industry safety engineering from the point of view of the Risk Analyst, Process Safety Coordinator,

and Control Systems Design Engineer. It delivers a complete overview of the functional safety lifecycle. The course reviews Process Hazard Analysis

(PHA), Consequence Analysis, Layer of Protection Analysis (LOPA), Safety Integrity Level (SIL) Target Selection, Safety Requirements

Specification (SRS) generation, failure rates, device and system reliability, SIF verification, SIF detailed design and Operations requirements.

Course Objectives:

• To make students able to indentify causes of hazards and risk.

• Students are expected to learn concept of safety instrumented systems.

• Students should integrate international standards in safety design Practices.

• To analyze the safety integrity level and formulate the hazards analysis.

Course Code

Course Name

Teaching Scheme



MT-INDLO2022 Safety In Engineering

Industry 3 - -

3 - - 3

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2022 Safety In Engineering

Industry 20 20 20 20 60 2 - - - 100



98

Course Outcomes:

Students will be able to

1. Learn accident terminology, risk and concept of intrinsic safety

2. Understand the causes of accidents and explosions, their detection and prevention.

3. Do safety analysis in compliance with process industry standards and regulations.

4. Formulate the safety models and implement the analysis methods.

5. Select appropriate SIL levels and solve safety engineering problems.

6. Understand safety responsibilities, justification of suitable life cycle methods.

Module Detailed Content Hours CO Mapping

1

Introduction to hazards and risks:

Risk reduction , Fatal accident rate (FAR), Overview of safety systems engineering (SSE) Introduction to

safety functions, Functional safety

06 CO1

2

Basic hazards of process:

Some causes of explosions, fire and toxic release, Logic diagram for an explosion, Fires: causes and

preventative measures, Toxic material release, Failures of equipment

07 CO2

3

Hazard studies and the IEC model:

Basics of hazard studies, Alignment with the IEC phases, Concept, Scope definition, Hazard and risk analysis,

Process control versus safety control: Functional differences, Specials: integrated safety and control systems

07 CO3

4 Basic reliability analysis: Design objectives, Design process, Failure modes, Overt failure mode, Covert

failure mode, Reliability formulae, Analysis models and methods 06 CO4

5 Engineering the safety system: Project engineering, Project problems, IEC requirements, Functional safety

assessment, Project engineering responsibilities, Developing SIL for each application, SIS detailed design 06 CO5

6 Justification for a safety instrumented system: 07 CO6



99

Text Book:

1. Dave Macdonald BSc (Eng) - Practical industrial safety, risk assessment and shutdown systems for industry-Newnes (2004)

2. J. S. Arendt, D K. Lorenzo - Evaluating Process Safety in the Chemical Industry_ A User's Guide to Quantitative Risk Analysis -Wiley-AIChE

(2000)

3. Speegle, Michael - Safety, Health, and Environmental Concepts for the Process Industry-Cengage Learning, Inc. (2013)

4. ISA--The Instrumentation, Systems, and Automation Society._ American National Standards Institute - Functional safety _ safety instrumented

systems for the process industry sector-ISA Part 1 and Part 2.

Evaluation Scheme:








Justification issues, Impact of safety system failures, Justification Responsibilities, Life cycle cost method,

Costing example, PFD comparisons, Nuisance trip comparisons, Cost comparisons



100

Subject Name: Cyber Physical System

Course Code

Course Name

Teaching Scheme



MT-INDLO2023 Cyber Physical

System 3 - - 3 - - 3


Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2023 Cyber Physical

System 20 20 20 20 60 2 - - - 100

Course Description:

Cyber-physical systems, which consist of physical systems tightly integrated and/or controlled by software, are ubiquitous in many safety critical

domains, including automotive, avionics, railways, healthcare, atomic energy, power, and industrial automation. The principles of design and

implementation of cyber-physical systems are remarkably different from that of other embedded systems because of the tight integration of real

valued and dense time real time systems with software based discrete automated control. The objective of this course is to develop an exposition of

the challenges in implementing a cyber-physical system from a computational perspective, but based equally on the principles of automated control.

The course aims to expose the student to real world problems in this domain and provide a walk through the design and validation problems for such

systems.

Course Objectives:



101

1. The program aims to produce competent engineers who can design engineered systems that are built from seamless integration of physical system

and cyber systems.

2. The program will enable engineers with required competency to design software-hardware architecture.

3. The program will produce professionals who can design predictable and secure Cyber Physical Systems (CPS).

Course Outcomes:

1. Ability to analyse overall specifications of CPS and translate it to the different sub-systems design requirements.

2. Adequate competency to model overall CPS using Hybrid system and other approaches and validate the model.

3. Capability to co-design hardware-software architecture in distributed environment.

4. Knowledge of Machine Learning algorithms and Distributed Control algorithms.

5. Methods to embedded security in overall design of CPS.

6. Ability to understand applications like smart grid, vehicular networks and different systems for smart city applications and critical thinking and

scientific problem solving.


1 Cyber-Physical Systems (CPS) in the real world Basic principles of design and validation of CPS

Industry 4.0, Auto SAR, IIOT implications Building Automation, Medical CPS 06 CO1

2 CPS HW platforms - Processors, Sensors, Actuators, CPS Network – Wireless HART, CAN,

Automotive Ethernet, Scheduling Real Time CPS tasks 07 CO2

3

Principles of Dynamical Systems

Dynamical Systems and Stability, Controller Design Techniques Performance under Packet drop and

Noise

06 CO3

4

CPS implementation issues

From features to automotive software components, Mapping software components to ECUs CPS

Performance Analysis - effect of scheduling, bus latency, sense and actuation faults on control

performance, network congestion Building real-time networks for CPS

08 CO4



102

5 Intelligent CPS

Gaussian Process Learning: Smart Grid Demand Response, Building Automation 06 CO5

6

Secure Deployment of CPS

State estimation for attack detection: Automotive Case study: Vehicle ABS hacking, Power Distribution

Case study : Attacks on SmartGrids

06 CO6

References Books:

1. E. A. Lee and S. A. Seshia, “Introduction to Embedded Systems: A Cyber-Physical Systems Approach”, 2011.

2. R. Alur, “Principles of Cyber-Physical Systems,” MIT Press, 2015.

3. T. D. Lewis “Network Science: Theory and Applications”, Wiley, 2009.

4. P. Tabuada, “Verification and control of hybrid systems: a symbolic approach”, Springer-Verlag 2009.

5. C. Cassandras, S. Lafortune, “Introduction to Discrete Event Systems”, Springer 2007.

6. Constance Heitmeyer and Dino Mandrioli, “Formal methods for real-time computing”, Wiley publisher, 1996.

Evaluation Scheme:










103

Subject Name: Applied Industrial Instrumentation

Course Code

Course Name

Teaching Scheme



MT-INDLO2024 Applied Industrial


Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INDLO2024 Applied Industrial


Course Description:

The student will able to adapt the knowledge of industrial unit operations, design and develop instrumentation and control schemes for industrial

processes, hazardous areas and their classification.

Course Objectives:

• To educate students with instrumentation involved in all the industrial processes.

• To familiarize the students with the various unit operations and be able to apply control schemes to these processes to get the output with desired

specifications.

• To expose students with hazards in industry and designing of safe plant.



104

Course Outcomes:

1. Able to build complete strategies for process control application.

2. Ability to get acquainted with all the industrial processes and demonstrate their knowledge

in designing the control loops for these processes.

3. Able to inculcate the safety related terms such as classification of hazards in the industry

and design hazard free plant.

4. Ability to understand the applications like food processing and paper manufacturing industries.

5. Know how the instrumentation applied for various industries like automobile and power industries.

6. Able to know how the instrumentation is needed for batch processes.


Mapping

1

Introduction: Industry and Instrumentation: Developments and Trends,

Classifications and use of Instruments Control of Process. Control defined, Response of detecting element,

Measuring Method, Controllers, Final control Element, Other Element, Indicators, Recorders, Transducers or

Convertors, Alarm and shutdown functions.

04 CO1

2

Petroleum Industry: The petroleum industry and instrumentation, Topping unit instrumentation, Off-site

instrumentation.

Iron- and Steel-Industry: Overview of instrumentation in the iron and steelmaking process, Blast furnace

instrumentation, Continuous casting equipment instrumentation, Instrumentation for an electrolytic galvanizing

line.

08 CO2



105

3

Waterworks Instrumentation Applications: Overview of waterworks

facilities, Water treatment-related detectors, Filtration equipment instrumentation, Chemical injection equipment

instrumentation, Instrumentation for water-supply and distribution facilities, An integrated control system for

large-scale, wide-area waterworks facilities, Water distribution information management system, Wastewater

treatment instrumentation.

08 CO3

4

Food Processing Industry: Overview Food Processing Industry, Whiskey distillery instrumentation, Sugar

refinery instrumentation.

Paper Manufacturing Industry: Overview of an integrated paper mill, Pulp plant instrumentation,

Instrumentation applied to the papermaking process.

07 CO4

5

Automobile Industry: Overview of automobile industry instrumentation, Production management at a painting

factory, Storage control.

Power Industry: Overview Power Industry, Thermal power plants, Power plant system control, Nuclear power

plant overview, Pressurized water reactor control system.

08 CO5

6 Product Control in Batch Processing

Batch process recipe management, Batch process control, Recipe management and operation methods. 04 CO6

Text books:

5. Uttam Ray Chaudhuri, “Fundamentals of Petroleum and PetrochemicalEngineering”, 2010.

6. A. Datta, “Mathematical Methods in Science and Engineering”, 2012

7. B.S. Grewal, “Higher Engineering Mathematics”, Khanna Publication, 2014.

Reference Books:

3. Tasuku Senbon, Futoshi Hanabuchi, “Instrumentation Systems: Fundamentals and

Applications”, 1stEdition, Springer Verlag, Berlin Heidelberg GmbH, 1991.

4. William G. Andrew, H. B. Williams, “Applied Instrumentation in the Process Industries: Engineering Data and Resource Material”, Volume III,

Second Edition, Gulf Publishing Company, 1993.

5. Bela G. Liptak, “Instrumentation in the processing industries”, 1thedition, 1973.



106

Subject Name: Research Methodology

Course Code

Course Name

Teaching Scheme



MTILO2021 Research Methodology 03 --- -- 03 -- -- 03

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MTILO2021 Research Methodology 20 20 20 20 60 -- -- -- - 100

Course Description:

The objective of this course is to understand the Research Process and Methodology. It is aimed to enable students to identify research problems and

develop approaches for solving the research problems. It also introduces techniques for data collection, analysis and interpretation. In this way this

course enables the students to perform research.

Course 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

4. To understand and apply different techniques for formulating research problem



107

Course Outcomes: -

At the end of the course learner will able to

1. Understand basics of research concepts including objectives, issues and problems

2. Summarize and compare different types of research

3. Prepare a preliminary research design for projects in their subject matter areas

4. Accurately collect, analyze and report data

5. Present complex data or situations clearly

6. Review and analyze research findings

.


Mapping

1. 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

07

CO1, CO2

2. 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

CO1, CO2,

CO3

3. 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

3.3 Stages in Sample Design Sampling methods/techniques Sampling Errors

07

CO1, CO2,

CO3, CO4

4. Research Methodology 08 CO1, CO2,



108

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

CO3, CO4,

CO5

5. Formulating Research Problem

5.1 Considerations: Relevance, Interest, Data Availability, Choice of data,

5.2 Analysis of data, Generalization and Interpretation of analysis

05

CO2, CO3,

CO4, CO5,

CO6

6. Outcome of Research

6.1 Preparation of the report on conclusion reached

6.2 Validity Testing & Ethical Issues

6.3 Suggestions and Recommendation

05

CO3, CO4,

CO5, CO6

Text and Reference Books: -

1 Dawson, Catherine, 2002, Practical Research Methods, New Delhi, UBS Publishers Distributors.

2. Kothari, C.R.,1990, 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

4. Best and Kahn, Research Methodology, PHI Limited.

5. Garg, B.L., Karadia, R., Agarwal, F. and Agarwal, U.K., 2002. An introduction to Research Methodology, RBSA Publishers.

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



109

Evaluation Scheme:










110

Subject Name: Probability and Random Variables

Course Code

Course Name

Teaching Scheme



MTILO2022 Probability and Random Variables 03 -- -- 03 -- - 03

Course Code

Course Name

Evaluation Scheme

Theory Exam

Duration

(in Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MTILO2022 Probability and Random Variables 20 20 20 20 60 -- -- -- - 100

Prerequisites:

Basic probability theory.

Basics of Numerical Analysis

Course Description:

Concepts in probability and random variables/ processes play a fundamental role in understanding various aspects of in different domains and

problems. Characterizing of several real-life events and problems requires knowledge of principles of random variables and processes. This course is

designed to serve as a basic course towards introducing the students to various aspects of probability from the perspective of problem solving. Thus,

it will focus on basic concepts in probability, distribution, random variables and random processes.



111

Course Objectives:

To familiarize the students with statistical techniques for rela life problems.

To understand and be able to apply probabilistic techniques for solving real world decision making problems.


1) Test the correlation between the given databases

2) To prepare and set up the regression models for the datasets

3) Analyze the time series and build the models for time series.

4) Understand the concepts of probability

5) Analyze and understand the discrete and continuous distributions

6) Apply the knowledge of testing on data and samples.


1

Correlation:

1.1 Definition, meaning and uses of Correlation

1.2 Scatter Diagram, Merits and limitations, Graphic method

1.3 Covariance, properties of covariance, limitations

1.4 Difference between variance and co-variance

1.5 Karl Pearson’s Coefficient of correlation (r), properties and examples

1.6 Spearman’s Rank correlation coefficient (R) (with repeated and non-repeated ranks) properties and

examples

06 CO1

2

Regression Analysis:

2.1 Definition, meaning and uses of regression analysis

2.2 Difference between Correlation and regression

2.3 Regression Models (lines of regression)

2.4 Fitting of line of regression of Y on X

2.5 Fitting of line of regression of X on Y

07 CO2



112

3

Analysis of Time Series:

3.1 Meaning, essential requirements and main objective of Time series

3.2 Components of time series

3.3 Additive and Multiplicative models of Time series

3.4 Fitting of straight line (first degree) trend by least square methods

3.5 Fitting of parabolic (second degree) trend by least square methods

3.6 Fitting of exponential (𝑦 = 𝑎𝑏𝑥) trend by least square methods

07 CO3

4

Probability:

4.1 Definition and basics of probability, conditional probability,

4.2 Total Probability Theorem and Bayes’ theorem (Inverse Probability Theorem)

4.3 Discrete and continuous random variable with probability distribution and probability density function.

4.4 Expectation of random variables with mean, variance and standard deviation, moment generating

function up to four moments.

4.5 Skewness and Kurtosis of distribution (data)

06 CO4

5

Probability Distribution:

5.1 Binomial Distribution: Meaning, examples, properties with applications

5.2 Poisson Distribution: Meaning, examples, properties with applications

5.3 Normal Distribution: Meaning, examples, properties with applications

06 CO5

6

Sampling Theory:

6.1 Sampling distribution,

6.2 Test of Hypothesis, Level of Significance, Critical region, One-tailed, and two-tailed test, Degree of

freedom.

6.3 Students’ t-distribution (Small sample). Test the significance of mean and Difference between the

means of two samples. Chi-Square Test: Test of goodness of fit and independence of attributes,

Contingency table.

07 CO6

Text Books:

1. An Introduction to Statistical Methods, C. B. Gupta and Vijay Gupta, Vikas Publication 23rd Edition.

2. Business Statistics, Dr. P. C. Tulsian and Bharat Jhunjhnuwala, S. Chand, 2nd Edition.



113

Reference Books:

1. Probability, Statistics and Random Processes, T. Veerarajan, McGraw-Hill education.

2. Probability and Statistics, Murray Spiegel, Schaum’s Outline Series, McGraw-Hill education

3. Probability and Statistics for Engineering and the Sciences, Jay Devore, Cengage Learning

4. Regression Models, Methods and Applications, Ludwig Fahrmeir, Springer Verlag

5. Mathematical Statistics Basic Ideas and Selected Topics Vol-I, Peter Bikel and Doksum, Prentice Hall.

Evaluation Scheme:










114

Subject Name: Project Management


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Oral

Credit

Tutorial

Credits

Total

Credits

MTILO2023 Project Management 03 -- - 03 -- - 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg. of

2 IAs

Mid

Sem

Exam

MTILO2023 Project Management 20 20 20

20 60 -- -- -- 100

Course Description:

This course develops a foundation of concepts and solutions that supports the planning, scheduling, controlling, resource allocation, and

performance measurement, closing activities required for successful completion of a project. It explains organizational structures in which projects

are executed and the importance of qualitative and quantitative analysis tools. Financial aspects required for project like procurement, bidding,

budgeting and techniques related with those are introduced.

Course Objectives:

1. To familiarize the students with the use of a Project Management techniques and tools used in various industry sectors for achieving success in

projects

2. To understand and be able to apply processes and techniques throughout the life cycle of a project from initiation to closure



115


1. Understand what are projects and what is the importance of management and project manager

2. Analyse and initiate projects based on numeric and non-numeric criteria. Design a project proposal and build project teams

3. Analyze the effect of different organizational structures on the execution of projects. Perform Project estimation and budgeting. Understand role of

project management office

4. Perform project planning activities including risk planning, scheduling, team building and resource allocation

5. Perform project control activities like monitoring & control, auditing, reporting, tracking, risk mitigation & control

6. Perform proper closure of different types of projects. Apply the techniques learned in the course in the execution of real life projects


1.

Project Management Fundamentals:

Definition of a project, Necessity of project management, Triple constraints, Project life cycles, Project

phases, Qualities of project manager, Role of project manager. Project management in various organization

structures.

06 CO1

2.

Initiation:

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

06 CO2

3.

Project Budgeting and Estimation:

Project Plan, Work Breakdown structure (WBS) and linear responsibility chart, Interface Co-ordination,

Project cost estimation and budgeting, Top down and bottoms up budgeting.

06 CO3

4.

Project Planning and Management:

Networking and Scheduling techniques. PERT, CPM, GANTT chart. 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

09 CO4



116

and risk register. Qualitative and quantitative risk assessment, Project procurement management. Change

Management

5.

Project Monitoring and Control:

Planning monitoring and controlling cycle. Information needs and reporting, engaging with all

stakeholders of the projects. Team management, communication and project meetings. Earned Value

management techniques for measuring value of work completed; Using milestones for measurement;

change requests and scope creep. Project audits.

06 CO5

6.

Project Closure:

Customer acceptance; Reasons of project termination, Various types of project Terminations, Process of

project termination, completing a final report; doing a lessons learned analysis; acknowledging successes

and failures; Project management templates and other resources. Case studies of successful and failed

projects

06 CO6

Text Books:

1. Jack Meredith & Samuel Mantel, Project Management: A managerial approach, Wiley India, 10th Ed.

2. A Guide to the Project Management Body of Knowledge (PMBOK® Guide), 6th Ed, Project

Management Institute PA, USA

Reference Books:

1.Project Management: A Systems Approach to Planning, Scheduling, and Controlling:Harold Kerzner, , 12th Ed, Wiley

Evaluation Scheme:







117






118

Subject Name: Disaster Management and Mitigation Measures


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Oral

Credit

Tutorial

Credits

Total

Credits

MTILO2024 Disaster Management

and Mitigation

Measures

03 -- -- 03 -- - 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg. of

2 IAs

Mid

Sem

Exam

MTILO2024

Disaster Management

and Mitigation

Measures

20 20 20 20 60 -- -- -- 100

Course Description:

This course aims for disaster managers eventually to work themselves out of their job. The ultimate success of disaster management would be the

elimination of the underlying causes of disasters which would contribute to disaster prevention. Obviously, total prevention will not be feasible, but

minimizing the people's vulnerability to disaster and responding to emergencies in positive ways will make an enormous impact on the current deadly

state of disaster events. This enhance awareness of Disaster Risk Management institutional processes in India and builds skills to respond to disasters.



119

Course Objectives

To understand the various types of disaster occurring around the world.

To identify extent and damaging capacity of a disaster.

To study and understand the means of losses and methods to overcome /minimize it.

To understand role of individual and various organization during and after disaster.

To know warning systems, their implementation and based on this to initiate training to a laymen.

To understand application of GIS in the field of disaster management.

To understand the emergency government response structures before, during and after disaster

Course Outcomes

At the end of the course learner will able to,

1. Acquire fundamentals of disaters, long term effects global warming, perspective and concepts of human life.

2. Understand natural as well as manmade disaster and their extent and possible effects on the economy.

3. Planning of national importance structures based upon the previous history.

4. Understand government policies, acts and various organizational structure associated with an emergency.

5. Know the simple do‘s and don‘ts in such extreme events and act accordingly.

6. Know the preventive and mitigation disaster measures.


1

Introduction: 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.

04 CO1



120

2

Natural Disaster and Manmade disasters: 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.

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.

08 CO2

3

Disaster Management, Policy and Administration: Disaster management: meaning, concept,

importance, objective of disaster management policy, disaster risks in India, Paradigm shift in disaster

management. 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.

06 CO3

4

Institutional Framework for Disaster Management in India: 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. Use of Internet and

softwares for effective disaster management. Applications of GIS, Remote sensing and GPS in this regard.

07 CO4

5

Financing Relief Measures: 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. International relief aid agencies and their role in extreme events.

07 CO5

6

Preventive and Mitigation Measures: Pre-disaster, during disaster and post-disaster measures in some

events in general, Structural mapping: Risk mapping, assessment and analysis, sea walls and

embankments, Bio shield, shelters, early warning and communication. Non Structural Mitigation:

Community based disaster preparedness, risk transfer and risk financing, capacity development and

training, awareness and education, contingency plans. Do‘s and don‘ts in case of disasters and effective

implementation of relief aids.

07 CO6



121

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

Evaluation Scheme:










122

Subject Name: Financial Management


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Oral

Credit

Tutorial

Credits

Total

Credits

MTILO2025 Financial

Management 03 -- -- 03 -- -- 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg. Mid Sem

Exam

MTILO2025 Financial

Management 20 20 20 20 60 -- -- -- 100

Course Description:

Overall Objective of this course is to develop awareness among you about the importance and need of learning techniques for managing finances

either on a project, in an organization or in a profit making unit. The course goes into details of financial planning, measurement, analysis and

eventually controlling the finances. Varies techniques and tools useful for managing the finances and budgeting will be explored in this course.



123

Course Objectives:

1. To understand the operational nuances of a Indian financial system, instruments and market

2. To study concepts of value of money, returns and risks, corporate finance, working capital and its management

3. To study technique of making decisions related to finance function


1. Understand Indian finance system and corporate finance

2. Understand Present and future value of money.

3. Evaluate Risk and return on investment

4. Under the importance of working capital and portfolios

5. Possess the techniques of managing finance in an organization

6. Take investment, finance as well as dividend decisions.


1.

The Financial Systems:

Functions of the Financial System, Financial Assets, Financial Markets Financial Market Returns,

Financial Intermediaries, Regulatory Infrastructure, Growth and Trends in the Indian Financial System

Overview of Financial Statements— Balance Sheet, Profit and Loss Account, and Cash Flow

Statement; Purpose of Financial Ratio Analysis; Liquidity Ratios; Efficiency or Activity Ratios;

Profitability Ratios; Capital Structure Ratios; Stock Market Ratios; Limitations of Ratio Analysis

06 CO1

2.

Time value of Money:

Time Lines and Notation, Future Value of a Single Amount, Present Value of a Single Amount, Future

Value of an Annuity, Present Value of an Annuity, Present Value of a Perpetuity, Intra-Year

Compounding and Discounting.

08 CO2

3. Risk and Return:

Historical Returns and Risk, Expected Return and Risk of a Single Security and a Two-security 06 CO3



124

Portfolio, Risk and Return of a Single Security and a Two-security Portfolio, Measurement of Market

Risk , Determinants of Beta , Relationship Between Risk and Return.

4.

Techniques of Capital Budgeting:

Capital Budgeting Process, Project Classification, Investment Criteria, Net Present Value, Benefit-

Cost Ratio, Internal Rate of Return, Modified Internal Rate of Returns (MIRR), Payback Period,

Accounting Rate of Return, Investment Appraisal in Practice.

Concepts of Meaning Working Capital; Importance and Factors Affecting an Entity‘s Working Capital

Needs; Management of Inventories; Management of Receivables; and Management of Cash and

Marketable Securities.

04 CO4

5.

The Cost of Capital:

Cost of Debt and Preference, Cost of Equity, Determining the Proportions, Weighted Average Cost

of Capital, Weighted Marginal Cost of Capital, Determining the Optimal Capital Budget, Divisional

and Project Cost of Capital, Floatation Cost and the Cost of Capital, Factors Affecting the Weighed

Average Cost of Capital

09 CO5

6.

Capital Structure and Firm Value:

Assumptions and Definitions, Net Income Approach, Net Operating Income Approach, Traditional

Position, Modigliani and Miller Position, Taxation and Capital Structure, Tradeoff Theory, Signaling

Theory, Dividend Policy and Firm Value, Miller and Modigliani Position

06 CO6

Text Books:

1. Financial Management Theory & Practice by Prasanna Chandra, Publisher: TMH, New Delhi 2004

2. Fundamentals of Financial Management, 13th Edition (2015) by Eugene F. Brigham and Joel F. Houston; Publisher: Cengage Publications,

New Delhi.

3. Fundamentals of Financial Management by Van Horne, Publisher: Prentice Hall of India.



125

Reference Books:

1. Financial Management, 11th Edition (2015) by I. M. Pandey; Publisher: S. Chand (G/L) & Company Limited, New Delhi

2. Indian Financial System, 9th Edition (2015) by M. Y. Khan; Publisher: McGraw Hill Education, New Delhi..

3. Advanced Accounting by Gupta R.L. and Radha Swamy M., Publisher: Sultan Chand & Sons, New Delhi.

Evaluation Scheme:










126

Subject Name: IPR and Patenting


Hrs

Practical

Hrs

Tutorial

Hrs

Theory

Credit

Practical/Oral

Credit

Tutorial

Credits

Total

Credits

MTILO2026 IPR and Patenting 03 -- -- 03 -- -- 03


Examination Scheme

Theory Marks Term


In-Sem Evaluations

End Sem

Exam

IA1 IA2 Avg. Mid Sem

Exam

MTILO2026 IPR and Patenting 20 20 20 20 60 -- -- -- 100

Course Description:

This course helps to understand intellectual property rights protection system and to promote the knowledge of Intellectual Property Laws of India as

well as International treaty procedures. To get acquaintance with Patent search and patent filing procedure and applications. IPR internalisation process

to help the researchers to set targeted objectives in their research project and also to design and implement their research to clearly differentiate their

work vis-a-vis the existing state of knowledge/ prior art.



127

Course 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

4. To get familiarize with different patent databases

Course Outcomes: - At the end of the course learner will able to

1. Understand Intellectual Property Rights and importance.

2. Demonstrate enforcement of intellectual property rights.

3. Work for development, promotion, protection, compliance, and enforcement of Intellectual Property and Patenting.

4. Inspect emerging issues in IPR.

5. Assist individuals and organizations in capacity building.

6. Understand national and international patent filing procedure.


Mapping

1

1.1 Introduction to Intellectual Property Rights (IPR): Meaning of IPR,

Different category of IPR instruments - Patents, Trademarks, Copyrights,

Industrial Designs, Plant variety protection, Geographical indications, Transfer of technology etc.

1.2 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

06 CO1, CO2,

2

2.1 Enforcement of Intellectual Property Rights: Introduction, Magnitude of problem, Factors that create

and sustain counterfeiting/piracy, International agreements, International organizations (e.g. WIPO, WTO)

active in IPR enforcement

2.2 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 CO1, CO2,

CO3

3 Emerging Issues in IPR: Challenges for IP in digital economy, e-commerce, human genome, biodiversity

and traditional knowledge etc. 05

CO2, CO3,

CO4, CO5

4 Basics of Patents: Definition of Patents, Conditions of patentability, Patentable and non-patentable inventions, 07 CO2, CO3,



128

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 of getting a patent

CO4, CO5

5

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.)

07 CO3, CO4,

CO5, CO6

6

6.1 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 Publication etc,

Time frame and cost, Patent Licensing, Patent Infringement

6.2 Patent databases: Important websites, Searching international databases

07

CO2, CO3,

CO4, CO5,

CO6

Text and 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

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 Intellectual Property Rights,

12. Mathew Y Maa, 2009, Fundamentals of Patenting and Licensing for Scientists and Engineers, World Scientific Publishing Company

13. Indian Patent Acts and Rules (Online – Indian patent office)



129

Evaluation Scheme:










130

Subject Name: Lab Practice-III

Course Code

Course Name

Teaching Scheme



MT-INL201 Lab practice -III - 2 - - 1 - 1


Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INL201 Lab practice -III - - - - - - 25 25 50

Course Description:

This Lab develops the skills needed to represent the system in state space form, impart knowledge required to design state feedback controller and

state estimator also to impart knowledge and skills needed to classify singular points and construct phase trajectory using delta and isocline methods.

This provides students to understand the various non-linear behaviors.

Course Objective:

1. To develop the skills needed to represent the system in state space form.

2. To impart knowledge required to design state feedback controller and state estimator.

3. To impart knowledge and skills needed to classify singular points and construct phase trajectory using delta and isocline methods.

4. To make the students understand the concepts of stability and introduce techniques to assess the stability of certain class of non-linear system.



131

5. To make the students understand the various non-linear behaviors.

Course Outcome:

1. Know the constructional details, principle of operation, and performance of different unit operations and their Instrumentation.

2. Analyze dynamics of a linear system by solving system model/ equation or applying domain transformation.

3. Realize the structure of a discrete time system and model its action mathematically.

4. Examine a system for its stability, controllability and observability.

5. Apply knowledge of control theory for practical implementations in engineering and network analysis.



1. Computation of controllability and observability.

2. Designing pole placement controller for solar tracking system.

Design of full state observer for SISO/MIMO system.

3. Determine the stability of the system and construct the Lyapunov function for wireless networks.

4. Stability determination using Variable Gradient method.

5. Extrimise andminimize for determining the Equilibrium points using EulersLagranges formula.

6. Case study: Application of modern control theory in power electronics/ systems/ power converter.



132

7. Construction of phase trajectory using delta and isocline methods.

8. Estimate the response of SISO / MIMO system using MATLAB.

• Perform any six experiments from above list and two from electives



133

Subject Name: Lab Practice-IV

Course Code

Course Name

Teaching Scheme



MT-INL202 Lab Practice -IV - 2 - - 1 - 1


Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term


Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INL202 Lab Practice -IV - - - - - - 25 25 50

Course Description:

The concept of industrial automation inculcated in this lab. The study of different industrial applications using PLC and DCS It also provides

adequate knowledge about PLC, SCADA and DCS, industrial communication.

Course Objective

1. To get the concept of industrial automation.

2. To study different industrial applications using PLC and DCS.

3. To get adequate knowledge about PLC, SCADA and DCS.



134

4. Get knowledge of industrial communication.

Course Outcomes

1. Able to understand concept of Programmable logic controller (PLC) and develop programming knowledge of PLC.

2. Able to acquire knowledge of Distributed Control System (DCS), detail Engineering, configuration and programming of DCS.

3. Able to Interface PLC to Supervisory Control and Data Acquisition Systems (SCADA).

4. Able to interface LabVIEW software with hardware for different applications.

5. Acquainted with knowledge on basic industrial instrumentation.

6. Able to carry out study of any Process Control plant.


Sr. No. Detailed Contents

1 Simulation of batch reactor control using PLC with GUI.

2 Simulation of paint manufacturing process using PLC with GUI.

3 Interfacing of PLC to any SCADA through Modbus protocol and/or OPC.

4 Simulation of Heat exchanger feedback control scheme using DCS.

5 Real time cascade control scheme using DCS.

6 Creating an analog –open loop and digital loop using DCS.

7 Configure and implement different alarms in DCS system.

8 Simulation of boilerdrum level control using DCS.



135

9 On-line monitoring and control using DCS.

10 Development of HMI using any SCADA package.

11 Case study on Industrial networking.

12 LabVIEW based remote triggered control of process loop.

Note: Perform any six experiments from above list.



136

Subject Name: Seminar

Course Code

Course Name

Teaching Scheme



MT-INS301 Seminar -- 06 - - 03 - 03


Evaluation Scheme

Theory Exam

Duration (in

Hrs.)

Term

Work

Pract/

Oral Total Internal Assessment Mid Sem.

Exam

End Sem

Exam IA1 IA2 Avg.

MT-INS301 Seminar - - - - - - 50 50 100

Course Description:

Seminar is based on thrust areas in Instrumentation Engineering. Students should undergo literature survey and identify the topic of seminar. Students

should use multiple literatures and understand the topic and compile the report. Seminar will be assessed on quality of literature survey and novelty

in the topic, relevance to the specialization & quality of presentation.

Guidelines for Seminar

1. Seminar should be based on thrust areas in Instrumentation Engineering.

2. Students should undergo literature survey and identify the topic of seminar and finalize in consultation with Guide/Supervisor. Students should

use multiple literatures and understand the topic and compile the report in standard format and present in front of Panel of Examiners appointed

by the Head of the Department/Institute of respective Program.

3. Seminar assessment should be based on following points:



137

ƒ Quality of Literature survey and Novelty in the topic.

ƒ Relevance to the specialization

ƒ Understanding of the topic

ƒ Quality of Written and Oral Presentation

Important Note

1. Assessment of Seminar will be carried out by a pair of Internal and External examiner. The external examiner should be selected from approved

panel of examiners for Seminar by University of Mumbai, OR faculty from Premier Educational Institutions

/Research Organizations such as IIT, NIT, BARC, TIFR, DRDO, etc. OR a person having minimum Post-Graduate qualification with at least five

years’ experience in Industries.

2. Literature survey in case of seminar is based on the broader area of interest in recent developments and for dissertation it should be focused mainly

on identified problem.

3. At least 4-5 hours of course on Research Methodology should be conducted which includes Literature Survey, Problems Identification, Analysis

and Interpretation of Results and Technical Paper Writing in the beginning of 3rd

Semester.



138

Subject Name: Dissertation – I and II

Subject Code

Subject Name


Credits Assigned


MT-IND302 Dissertation – I -- 24 -- -- 12 -- 12

MT-IND401 Dissertation - II -- 30 -- -- 15 -- 15

Subject Code

Subject Name

Examination Scheme

Theory

Term

Work

Pract.

/ Oral

Total

Internal Assessment End Sem. Exam.

IA1 IA2 Avg.

MT-IND302 Dissertation – I -- -- -- -- 100 -- 100

MT-IND401 Dissertation - II -- -- -- -- 100 100 200

Course Description:

Students should do literature survey and identify the problem for Dissertation. Students should use multiple literatures and understand the problem.

Students should attempt the solution to the problem by analytical/simulation / experimental methods. The solution to be validated with proper

justification and compile the report. The following points will be assessed quality of Literature survey and novelty in the problem, clarity of problem

definition and feasibility of problem solution, relevance to the specialization or current research / industrial trends & clarity of objective and scope.



139

Guidelines for Dissertation

o Students should do literature survey and identify the problem for Dissertation and finalize in consultation with Guide/Supervisor. Students should

use multiple literatures and understand the problem. Students should attempt the solution to the problem by analytical/simulation/ experimental

methods. The solution to be validated with proper justification and compile the report in standard format.

Guidelines for Assessment of Dissertation I o Dissertation I should be assessed based on following points

ƒ Quality of Literature survey and Novelty in the problem

ƒ Clarity of Problem definition and Feasibility of problem solution

ƒ Relevance to the specialization

ƒ Clarity of objective and scope

o Dissertation I should be assessed through a presentation by a panel of internal examiners appointed by the Head of the Department/Institute of

respective Program.

Guidelines for Assessment of Dissertation II o Dissertation II should be assessed based on following points

ƒ Quality of Literature survey and Novelty in the problem

ƒ Clarity of Problem definition and Feasibility of problem solution

ƒ Relevance to the specialization or current Research / Industrial trends

ƒ Clarity of objective and scope

ƒ Quality of work attempted

ƒ Validation of results

ƒ Quality of Written and Oral Presentation

o Dissertation II should be assessed through a presentation jointly by Internal and External Examiners appointed by the University of Mumbai.

o Students should publish at least one paper based on the work in reputed International / National Conference (desirably in Refereed Journal).

m. tech. in instrumentation engineering detailed syllabus · 2020. 11. 20. · m. tech. in...

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