R Final ME InstControl Merged organized 1 Syllabus Mumbai University by munotes
Page 2
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AC – 11 July, 2022
Item No. – 6.36 (R)
Revised Syllabus for
M.E. ( Instrumentation and Control)
(Sem. - I to IV)
(Choice Based Credit System)
(With effect from the academic year 2022 -23)
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Preamble
To meet the challenge of ensuring excellence in engineering education, the issue of quality needs to
be addressed, debated and taken forward in a systematic manner. Accreditation is the principal
means of quality assurance in higher education. The major emphasis of accreditation process is to
measure the outcomes of the program that is being accredited. In line with this Faculty of Science
and Technology (in particular Engineering) of University of Mumbai has taken a lead in
incorporating philosoph y of outcome -based education in the process of curriculum development.
Faculty resolved that course objectives and course outcomes are to be clearly defined for each
course, so that all faculty members in affiliated institutes understand the depth and appr oach of
course to be taught, which will enhance learner’s learning process. Choice based Credit and grading
system enables a much -required shift in focus from teacher -centric to learner -centric education since
the workload estimated is based on the investm ent of time in learning and not in teaching.
Italsofocusesoncontinuousevaluationwhichwillenhancethequalityofeducation. Credit assignment
for courses is based on 15 weeks teaching learning process, however content of courses is to be
taught in 12 -13 weeks a nd remaining 2 -3 weeks to be utilized for revision, guest lectures, coverage
of content beyond syllabus etc.
There was a concern that the earlier revised curriculum more focused on providing information and
knowledge across various domains of the said prog ram, which led to heavily loading of students in
terms of direct contact hours. In this regard, faculty of science and technology resolved that to minimize
the burden of contact hours, total credits of entire program will be of 170, wherein focus is not on ly on
providing knowledge but also on building skills, attitude and self -learning. Therefore, in the present
curriculum skill -based laboratories and mini projects are made mandatory across all disciplines of engineering
in second and third year of programs , which will definitely facilitate self -learning of students. The overall
credits and approach of curriculum proposed in the present revision is in line with AICTE model
curriculum.
The present curriculum will be implemented for M.E in Instrumentation and Control Engineering
from the academic year 202 2-2023.
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Incorporation and implementation of Online Contents from NPTEL/ Swayam Platform
The curriculum revision is mainly focused on knowledge component, skill -based activities and project -
based activities. Self -learning opportunities are provided to learners. In the revision process this time in
particular Revised syllabus of ‘C’ scheme wher ever possible additional resource links of platforms such as
NPTEL, Swayam are appropriately provided. In an earlier revision of curriculum in the year 2012 and 2016
in Revised scheme ‘A' and ‘B' respectively, efforts were made to use online contents more appropriately as
additional learning materials to enhance learning of students.
In the current revision based on the recommendation of AICTE model curriculum overall credits are
reduced to 171, to provide opportunity of self -learning to learner. Learners are now getting sufficient time
for self -learning either through online courses or additional projects for enhancing their knowledge and
skill sets.
The Principals/ HoD’s/ Faculties of all the institute are required to motivate and encourage learners to us e
additional online resources available on platforms such as NPTEL/ Swayam. Learners can be advised to
take up online courses, on successful completion they are required to submit certification for the same. This
will definitely help learners to facilitate their enhanced learning based on their interest.
Dr. S. K. Ukarande
Associate Dean
Faculty of Science and Technology
Member, Academic Council, RRC in Engineering
University of Mumbai
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From Chairman’s Desk
The overall technical education in our country is changing rapidly in manifolds. Now it is very much
challenging to maintain the quality of education with its rate of expansion. To meet present requirement a
systematic approach is necessary to build the strong technical base wi th the quality. Accreditation will
provide the quality assurance in higher education and to achieve recognition of the institution or program
meeting certain specified standards. The main -focus of an accreditation process is to measure the program
outcomes , essentially a range of skills and knowledge that a student will have at the time of graduation
from the program that is being accredited. Faculty of Science &Technology of University of Mumbai has
taken a lead in incorporating philosophy of outcome -based education in the process of curriculum
development.
I, as a Chairman, Board of Studies in Instrumentation Engineering of University of Mumbai, am happy to
state here that, Program Objectives (POs) were finalized for PG engineering program in Instrumentati on
Engineering, more than ten senior faculty members from the different institutes affiliated to University of
Mumbai were actively participated in this process. NBA has defined the following three POs for a graduate
of PG Engineering Program:
PO1: An ability to independently carry out research /investigation and development work to
solve practical problems.
PO2: An ability to write and present a substantial technical report/document.
PO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the program. The mastery should be at a level higher than the requirements
in the appropriate bachelor program
Dr. Alice N. Cheeran
Chairman,
Board of Studies in Instrumentation Engineering,
Member - Academic C ouncil, University of Mumbai
Dr. Mukesh D.Patil -Member BoS
Dr.Sharad P.Jadhav -Member BoS
Dr. Dipak D Gawali -Member BoS
Dr.M. J Lengare -Member BoS
Dr.Harish K. Pillai -Member BoS
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Semester I
Course
Code Course Name Teaching Scheme (Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INC101 Advanced signal
processing for
Sensors 3 -- -- 3 -- -- 3
INC102 Higher
Mathematics for
Control
Engineering/Robu
st control 3 -- 3 -- 3
INPE101 Program Elective 1 3 -- -- 3 -- -- 3
INPE102 Program Elective 2 3 -- -- 3 -- -- 3
INIE101 Institute Elective 1 3 -- -- 3 -- -- 3
INL101 Program Lab -I -- 2 -- -- 1 -- 1
INSBL101 Skill Based Lab -I -- 4$ -- -- 2 -- 2
Total 15 06 -- 15 03 -- 18
Course
Code Course Name Examination Scheme
Theory Term
Work Pract
/
Oral Total
Internal Assessment End
Sem.
Exam Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INC101 Advanced signal
processing for
Sensors 20 20 20 80 3 -- -- 100
INC102 Higher
Mathematics for
Control
Engineering 20 20 20 80 3 -- -- 100
INPE101 Program Elective 1 20 20 20 80 3 -- -- 100
INPE102 Program Elective 2 20 20 20 80 3 -- -- 100
INIE101 Institute Elective 1 20 20 20 80 3 -- -- 100
INL101 Program Lab -I -- -- -- -- -- 25 25 50
INSBL101 Skill Based Lab -I
(MATLAB) -- -- -- -- -- 50 50 100
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Total -- -- 100 400 -- 75 75 650
Semester II
Course
Code Course Name Teaching Scheme(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INC201 State Estimation
and
Stochastic
Processes 3 -- -- 3 -- -- 3
INC202
Advanced Process
Control and
Automation 3 -- 3 -- 3
INPE201 Program Elective 3 3 -- -- 3 -- -- 3
INPE202 Program Elective 4 3 -- -- 3 -- -- 3
INIE201 Institute Elective 2 3 -- -- 3 -- -- 3
INL201 Program Lab -II -- 2 -- -- 1 -- 1
INSBL201 Skill Based Lab -II
(Python) -- 4$ -- -- 2 -- 2
Total 15 06 -- 15 03 -- 18
Course
Code Course Name Examination Scheme
Theory Term
Work Pract
/
Oral Total
Internal Assessment End
Sem.
Exam Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INC201 State Estimation
and
Stochastic
Processes 20 20 20 80 3 -- -- 100
INC202
Advanced Process
Control and
Automation 20 20 20 80 3 -- -- 100
INPE201 Program Elective 3 20 20 20 80 3 -- -- 100
INPE202 Program Elective 4 20 20 20 80 3 -- -- 100
INIE201 Institute Elective 2 20 20 20 80 3 -- -- 100
INL201 Program Lab -II -- -- -- -- -- 25 25 50
INSBL201 Skill Based Lab -II -- -- -- -- -- 50 50 100
Total -- -- 100 400 -- 75 75 650
Note 1: Skill Based Lab - I and II are focused on learning through experience. SBL shall facilitate the learner to
Page 10
acquire the fundamentals of practical engineering in his or her specialization in a project -oriented
environment . The learning through skill-based labs can be useful in facilitating their research work and
hence useful in early completion of their dissertation work.
Semester III
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INMP301 Major Project:
Dissertation -I -- 20 -- -- 10 -- 10
Total 00 20 00 00 10 -- 10
Course
Code Course Name Examination Scheme
Theory Term
Work Pract/
Oral Total
Internal Assessment End
Sem
.
Exa
m Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INMP301 Major Project:
Dissertation -I -- -- -- -- -- 100 -- 100
Total -- -- -- -- -- 100 -- 100
Online Credit Courses
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
XXOCC301 Online Credit Course - I -- -- -- -- -- -- 3
XXOCC301 Online Credit Course - II -- -- -- -- -- -- 3
Total -- -- -- 00 00 00 06
Note 2: It is mandatory to complete the Online Credit Courses (OCC) available on NPTEL / Swayam /MOOC or
similar platform approved by UoM . These two courses shall be completed in any semester I or II or III, but
not later end of the Semester III. University shall make a provision that credits earned with OCC - I and
OCC -II shall be accounted in the third semester grade -sheet with actual name s of courses. The learner shall
be allowed to take up these courses from his or her institute or organization / industry where his / her major
project is carried out. The students shall complete the courses and shall qualify the exam conducted by the
respec tive authorities/ instructor from the platform. The fees for any such courses and the corresponding
examination shall be borne by the learner.
Online Credit Course – I
The learner shall opt for the course in the domain of Research Methodology or Research & Publication
Ethics or IPR. The opted course shall be of 3 credits of equivalent number of weeks.
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Online Credit Course –II
The learner shall opt for the course recommended by Faculty Advisor/ Project Supervisor from the
institute. The opted co urse shall be of 3 credits of equivalent number of weeks.
Semester IV
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INMP401 Major Project:
Dissertation -II -- 32 -- -- 16 -- 16
Total -- 32 -- -- 16 -- 16
Course
Code Course Name Examination Scheme
Theory Term
Work Pract/
Oral Total
Internal Assessment End
Sem
.
Exa
m Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INMP401 Major Project :
Dissertation -II -- -- -- -- -- 100 100 200
Total -- -- -- -- -- 100 100 200
Total Credits: 68
Note 3: The Dissertation -II submission shall not be permitted till the learner completes all the requirements
ME course.
Note 4: The contact hours for the calculation of load of the teacher for Major Project are as follows:
Major Project Dissertation I and II - 02 Hour / week / student
Guidelines for Dissertation -I
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 solution to the problem by analy tical/simulation/experimental methods. The solution to be validated
with proper justification and compile the report in standard format. Guidelines for Assessment of
Dissertation -I.
Dissertation -I should be assessed based on following points
∙ Quality of Literature survey and Novelty in the problem
∙ Clarity of Problem definition and Feasibility of problem solution
∙ Relevance to the specialization
∙ Clarity of objective and scope Dissertation -I should be assessed through a presentation by a panel of Internal
examiners and external examiner appointed by the Head of the Department/Institute of respective
Programme .
Guidelines for Assessment of Disserta tion II
Dissertation II should be assessed based on following points:
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∙ 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
∙ Quality of work attempted or learner contribution
∙ Validation of results
∙ Quality of Written and Oral Presentation
Students should publish at least one paper based on the work in the referred National/ International
conference /Journal of repute.
Dissertation II should be assessed by internal and External Examiners appointed by the University of
Mumbai .
Subject
Code Program Elective I Subject
Code Program Elective II
INPE1011 Advanced Biomedical Instrumentation INPE1021 Robust Control
INPE1012 Advanced Measurement Techniques
INPE1022 Expert System
INPE1013 Advanced analytical instrumentation INPE1023 Robotics and control
Subject
Code Program Elective III Subject
Code Program Elective IV
INPE2011 Electronics System Design INPE2021 Advanced Nuclear
Instrumentation
INPE2012 Advanced Fiber Optics and laser
Instrumentation INPE2022 Machine learning and Deep
learning
INPE201 3 Rehabilitation Engineering INPE202 3 MEMS and Nanotechnology
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Subject Code Subject Name Credits
ISC101 Advanced Signal Processing for Sensors 03
Course Objectives:
· To give students knowledge in the field of advanced signal processing systems
required for processing the signals from various sensors.
· To give knowledge regarding applications of various types of sensors used for high
resolution measurement of various parameters.
Course Outcomes:
· The students will be able to understand the methodology and design of electronic
circuits utilized for processing the signals for various sensors.
Module Detailed content Hours
Prerequisite: Knowledge in the field of transducers and sensors, Basic
concepts in electronic signal processing
1 Classification of sensors and transducers: Input and output characteristics
of various transducers, variable resistance transducer and its equivalent
circuit, potentiometers, their construction and performance, variable
inductance and variable capacitance transducers, their construction and
performance, Piezoelectric transducer. 06
2 Design techniques for sensor signal conditioning: Sensor and signal
conditioning for strain, force, pressure, flow and temperature measurement,
Bridge configurations, Amplifying and linearizing bridge outputs, Driving
bridge circuits. Ratio metric techniques.
08
3 High impedance sensors: Photodiodes and high impedance charge output
sensors, Signal conditioning of high impedance sensors, Chemical and
Biosensors.
06
4 Positioning, motion and temperature sensors: LVDT, Hall effect magnetic
sensors, optical encoder Accelerometer, RTDs, thermistors, thermocouples,
semiconductors temperature sensors and their signal conditioning.
08
5 Micro -sensors and smart sensors: Construction, characteristics, and
applications. 04
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6 Radioactivity detectors and Counting systems:
Gas filled, Scintillation and Semiconductor detectors, preamplifiers,
Shaping amplifiers, Single Channel analyzer, Multi -channel analyzer.
07
Assessment:
Internal: Assessment consists of two tests out of which; one should be compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions to be
set each of 20 marks, out of these any four questions to be attempted by students. Minimum 80% syllabus
should be covered in question papers of end semester examination.
References:
1. H.K.P Neubert ―Instrument Transducers Oxford Herman University Press Eighth Impression 2008.
2. Ramon Pallas -Arenyand Johan G. Webster ―Sensor and Signal Conditioning‖ John Wiley,
New York 1991.
3. Dan Sheingold -Editior ―Transducer Interfacing Handbook‖, Analog Devices Inc 1980
4. ―High Speed Design Technique‖ Analog Device Inc 1996
5. Jacoba Fraden ―Handbook of Modern Sensors ―2nd Edition, Springer -Verlag.New York 1996
6. Jerald G.Graeme ―Photodiode Amp lifiers And Op -Amp Solution‖, Mc Graw Hill 1995
7. Harry L. Trietly, ―Transducers in Mechanical and Electronic Design‖, Marcel Dekker Inc 1986
8. Dan Shiengold, ―Non Linear Circuits Handbook‖, Analog Device Inc
9. Walt Kester -Editior, ―System Application G uide‖, Analog Devices Inc 1993
10. IMEGA, ―Temperature Measurement Handbook‖, Omega Instruments Inc
11. Henry Ott, ―Noise Reduction Technique In Electronic Systems‖, N.Y.John Wiley And Sons 1988
12. Ralph Morrison,‖Grounding And Shielding Technique‖, Fourt h Edition,John Wiley,1998
13. G.F.Knoll ,―Radiation detection and measurement‖, John Wiely and Sons, 2nd edition, 1998.
Page 15
Subject Code Subject Name Credits
ISC102 Higher Mathematics for Control Engineering 3
Course Objectives:
· To introduce different methods of solving systems of linear equations
· Introduce concept of Linear Vector Spaces
· To present the concept of Orthogonality and Quadratics Forms
Course Outcomes:
· Demonstrate ability to solve systems of linear equations
· Demonstrate ability to work with Vector Spaces
· Demonstrate ability to get least square solutions to systems
· Demonstrate ability to effect linear transformation
Module Detailed content Hours
Prerequisite: Knowledge about Matrices, Matrix,
ElementaryOperations, Determinants and Matrix Inverse
1 Linear Equations in Linear Algebra: Systems of Linear
Equations,Gaussian Elimination, Row Reduction , Echelon Forms , LU
Factorization.
06
2 Euclidean Vector Spaces: Euclidean n -Space, Linear transformation
from Rn to Rm, Properties of Linear Transformations from Rn to
Rm,Linear Transformation.
06
3 General Vector Spaces: Real Vector Spaces, Subspaces,
LinearIndependence, Basis and Dimension, Row Space, Column Space
and Nullspace, Rank, Nullity and Change of basis.
07
4 Eigenvalues and Eigenvectors: Eigenvectors and Eigenvalues, The
Characteristic Equation, Diagonalization, Eigenvectors and
LinearTransformations, Complex Eigenvalues, Discrete Dynamical
Systems.
07
5 Orthogonality and Least Squares: Inner Product, length and
Orthogonality, Orthogonal Sets, Orthogonal Projections, The Gram -
Schmidt Process, Least –Square Problems, Applications to Linear
Models, Inner Product Spaces, Applications of Inner Product Spaces.
07
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6 Symmetric Matrices and Quadratic Forms: Diagonalization of
Symmetric Matrices, Quadratic Forms, Constrained Optimization, The
Singular Value Decomposition, Application to Image Processing and
Statistics.
06
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the q uestion papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be covered in question papers of end semester examination.
References:
1. Anthanasios Papoulis, ―Probability, random Variable & Stochastic Processes‖ 3rd Edn,
McGraw Hill, Inc 1995
2. Gantmacher, Feliks R.‖the theory of Matrices Vol.I and II‖ Chelsia Publishing Co.1959
3. Gantmacher F.R. ―Application of Theory of Matrices‖
4. Hoffman K. & R. Kunez, ―Linear Algebra‖ 2nd Edn, Printice Hall 1971
5. Howard Anton, ―Elementary Linear Algebra‖ - Wiley Student End, 2011
Page 17
Program Elective -I
Subject Code Subject Name Credits
INPE1011 Advanced Biomedical Instrumentation 03
Course Objectives:
· To introduce concepts of advanced biomedical instruments used in hospitals.
· To study the design considerations of various signal conditioning systems for
measurement of Bio -signals like ECG, EEG and EMG.
· To study the concept behind various Advanced Medical imaging techniques. Course Outcomes:
· The students should be able to understand the principle and working of various
advanced biomedical instruments.
· The students should be able to design signal conditioning systems for bio -signal
measurements.
· The students should be able to apply concepts of biomedical techniques for various
applications.
· The students should be able to understand the concept and working of various
advanced medical image acquisition and reconstruction techniques.
Module Detailed content Hours
Prerequisite: Knowledge of Anatomy and Physiology of Human Systems,
Knowledge of various Bio -signals and their basic Measurement techniques,
Knowledge of basic principle of Medical Imaging Techniques
1 Instrumentation for Bio -Potential Recording: Sensors, Biopotential
Amplifiers like Chopper Amplifiers, Isolation Amplifiers and Advanced
Instrumentation Amplifiers, Signal Conditioning Circuit designing for ECG,
EEG and EMG, Multi -Channel Data Acquisition System .
10
2 Diathermy in Medicine: Electro Surgical Diathermy, Short Wave
Diathermy, Microwave Diathermy and Ultrasound
Diathermy, Lithotripsy.
05
3 Cardiac and Neuro -Assist Devices: Cardiac Pace
Makers -constructional details and design, Internal and External
Defibrillators with Design, Stimulation Electronics – Nerve and Muscle
Stimulators .
06
4 Telemetry and Telemedicine: Introduction to Telemetry System, Types of
Wireless, Power and Data Transmission System, Receiver and Transmitter
specifications, Telemedicine.
06
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5 Advanced Medical Imaging Systems: CT Scanning Systems – tube design,
types of Gantries, Image Reconstruction Techniques in Tomography.
MRI – Image Acquisition and Reconstruction Techniques.
Nuclear Imaging – Scanners, Gamma Camera, Positron Emission
Tomography (PET), Single Photon Emission Computed Tomography
(SPECT).
08
6 Laser Application in Medicine: Types of Lasers, Properties of Lasers and
Interaction of Lasers with tissues, Basic Endoscope System and its
characteristics.
04
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimu m 80% syllabus should be covered in question papers of end semester
examination.
References:
1. Jacobsons and Webster, ―Medicine and Clinical Engineering‖, PHI, 1981.
2. Carr and Brown, ―Introduction of Biomedical Equipment Technology‖, PHI, 1981.
3. Jacob Kline, ―Handbook of BioMedical Engineering‖, Academic Press, 1988.
4. J B Gupta, ―A course in Electronic and Electrical Measurement and
Instrumentation‖, S K Kataria and Sons, 1999.
5. Steve Webb, ―The Physics of Medical Imaging‖, Taylor & Francis , New York, 1988.
6. Norris, A.C., ―Essential of Telemedicine and Telecare‖, Wiley, 2002.
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Subject Code Subject Name Credits
INPE1012 Advanced Measurement Techniques 03
Course Objectives:
To provide knowledge to the students regarding various methods used for high resolution
measurement of various parameters like voltage, current, resistance, inductance, capacitance,
time, frequency and phase difference.
Course Outcomes:
· Understand principles and methods used for measurement of various parameters.
· Make use of proper methods of measurement depending upon requirement of resolution,
accuracy and speed of measurement.
Module Detailed content Hours
Prerequisite: Basic knowledge of electronic measurements,
analog and digital circuits.
1 High resolution measurement for electrical components:
Analog and digital techniques for high resolution measurement
of Resistance, Inductance, Capacitance. Various bridge circuits
and auto balancing methods. Polar and Cartesian type
impedance meters. Tan delta measurement.
10
2 High resolution time measurement: Philosophy of digital and
microprocessor/ microcontroller -based instruments.; Time
measurement techniques: Time standards; Measurement of time
interval between events, order of events, Vernier technique,
very low time, period, phase, time constant measurements
06
3 Frequency measurement techniques: Frequency, ratio and
product, high and low frequency measurements; Deviation
meter and tachometer, Peak/valley recorder.; 06
4 Programmable circuits: Programmable resistors, amplifiers,
filters; Programmable amplifiers as DACs
04
5 Applications of ADCs and DACs: Application of various
types of ADCs and DACs in measurement techniques; DVM
and its design; Voltage and current ratio measurements.
08
6 Sampling theory and applications: Modulation
index meter, Sampling theory and its application in
05
Page 20
current, voltage, power and energy measurements.
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be cover ed in question papers of end semester examination.
References:
1. T. S. Rathore, ―Digital Measurement Techniques‖, Narosa Publishing House, 1996.
2. B. S. Sonde, ―Monographs on System Design using Integrated Circuits‖, Tata Mc -Graw
Hill, 1974.
3. D. J. DeFatta, J. G. Lucas, ―Digital Signal Processing‖, J Wiley and Sons, 1988.
Page 21
Subject Code Subject Name Credits
INPE1013 Advanced Analytical Instrumentation 03
Module Detailed content Hours
1 Spectrophotometric /Gas analyzers
IR/NIR/UV/VIS analyzers – Cells, Detectors, Signal Processing, Calibration,
Minimization of Interference and Comparative Analysis of Analyzers &
Gaseous Components Detected
Hydrocarbon analyzers - Flame Ionization Detectors, Principle of Operation
Oxygen and NO/NO2 analyzers - Signal Processing, Calibration, Minimization
of Interference, Applications
Sampling Systems – Desirable Features, Filters, Flow and Pressure regulators,
Coolers, Condensers, Vacuum Pumps, Blowback Cleaning System, Exhaust
Practices. 07
2 Electrochemical/Liquid Analyzers pH Analyzers – Measurement and
Compensation, Pre-amplifiers, Transmitters, Measurement cells.
Conductivity Analyzer – Cells and Cell Constant, Effect of temperature on
measurement calibration, acid and alkali titration meas urement. Redox
Analyzer - Principle of operation, components of analyzers and applications.
Trace Oxygen and Residual Chlorine Analyzer - Principle of operation,
components of analyzers and applications. 08
3 Compositional Process Analyzer Gas and Liquid Chromatography –
columns, gas and liquid detectors, data processing, process chromatography,
calibration and application.
Mass Spectrometry – Components, different types, sampling systems,
calibration and applications. 07
4 Biomedical Spectroscopy -Types of Biomolecules, different spectroscopic
analysis techniques, principle of operation, components, data processing and
applications, Blood gas analyzers 06
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5 Environmental Analy zers: Waste Water Environmental Analysis, analy zer’s
principle of operations & components, calibration and applications. 04
6 Nuclear Magnetic Resonance Spectroscopy - Principle of operation,
components, sensitivity enhancement techniques and different types of NMR
Spectrometers with applications. 06
REFERENCES
1. B. G. Liptak, “Instrument Engineers’ Handbook: Process Measurement and Analysis”,
Butterworth Hieneman, Boston, 1995.
2. D.M. Considine, “Process Instruments and Control Handbook”, 4th edition, McGraw Hill
New York, 1993.
3. K. J. Clevett, “Process Analyzer Technology”, John Wiley & Sons, 1986, New York.
4. Gas Analysis – Book 14 Fisher Rosemount Educational Services.
5. G. K. Macmillan, “pH Measurement and Control”, ISA 1994.
6. pH and Conductivity – Book 13 Fisher Rosemount Educational Services.
7. R.E. Sherman, “Analytical Instrumentation”, TWI Press, Indiana, 1996.
8. Meyers, “Encyclopedia of Analytical Chemistry”.
9. Instruction Manuals at http:// www.frco.com/proanalytic/library/publicmanuals.html.
Page 23
Program Elective II
Subject Code Subject Name Credits
ISE1021 Robus t Control 03
Course Objectives:
● To study the effect of disturbance, parametric uncertainties and model errors on the
● stability of the system.
● To study the robust control techniques such as a control based on Kharitonov theorem,
● internal model control and introduction to Quantitative feedback technique for t he system
● with parametric uncertainties and external disturbances.
● To study the sliding mode control for asymptotic stability in presence of disturbances.
Course Outcomes:
● The students should be able to understand the robustness properties of the system ag ainst
uncertainties.
● Students should be able to design robust control that overcomes parametric uncertainties.
● Students should be able to design the internal model control for uncertain systems.
● Students should be able to understand the concept of Quantitative feedback techniques.
● Students should be able to design the sliding mode control for uncertain systems.
Module Detailed content Hours
Prerequisite: Regulators and Servo Mechanism, Concepts in State -
space analysis, Controllability and Observability.
1 Introduction to Sliding Mode Control : Main Concepts of Sliding
Mode Control, Chattering Avoidance: Attenuation and Elimination,
Concept of Equivalent Control, Sliding Mode Equations, The Matching
Condition and Insensitivity Properties, Conventional Sliding Mode
Controller Design 06
2 Conventional Sliding Modes: Introduction, Filippov Solution, Concept
of Equivalent Control, State -Feedback Sliding Surface Design, Regular
Form, Eigenvalue Placement, Quadratic Minimization, State -Feedback
Relay, Control Law Design, Single -Input Nominal Systems, Single -
Input Perturbed Systems, Relay Contr ol for Multi -Input Systems. 08
3 Interval Polynomials: Kharitonov's Theorem: Kharitonov's Theorem
for Real Polynomials, Kharitonov's Theorem for Complex
Polynomials, Robust State Feedback Stabilization 06
Page 24
4 Internal Model Control (IMC): Introduction to Model -Based Control,
Practical Open -Loop Controller Design, Generalization of the Open -
Loop Control Design Procedure, Model Uncertainty and Disturbances,
Development of the IMC Structure, IMC Background, The IMC
Structure, The IMC Design Procedure, Effect of Model Uncertainty and
Disturbances, Improving Disturbance Rejection Design 07
5 The IMC -Based PID Control : Background, The Equivalent Feedback
Form to IMC, IMC -Based Feedback Design for Delay -Free Processes,
IMC -Based Feedback Design for Processes with a Time Delay,
Summary of IMC -Based PID Controller Design for Stable Processes,
IMC -Based PID Controller Design for Unstable Processes 08
6 Introduction to Quantitative Feedback Theory: Quantitative
Feedback Theory (QFT), Why Feedback, QFT Overview, QFT Design
Objective, Structured Parametric Uncertainty, Control System
Performance Specifications, QFT Design Overview, QFT Basics, QFT
Design, Insight to the QFT Technique, Open -Loop Plant, Closed -Loop
Formulation, Benefits of QFT. 04
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students. Minimum 80%
syllabus should be covered in question papers of end semester examination.
Referenc es:
1. S. P. Bhattacharyya, H. Chapellat, and L. H. Keel. ―Robust control: the parametric approach," Upper
Saddle River (1995).
2. Manfred Morari and Evanghelos Za_riou, ―Robust process control," Vol. 488. Englewood Cliffs, NJ,
Prentice hall, 1989.
3. B. W ayne Bequette, ―Process Control: Modeling, Design and Simulation," Prentice Hall Professional,
2003.
4. Constantine H. Houpis, Steven J. Rasmussen and Mario Garcia -Sanz, ―Quantitative feedback theory:
fundamentals and applications," CRC Press, 2005.
5. Ode d Yaniv, ―Quantitative feedback design of linear and nonlinear control systems”, Vol.509.
Springer Science & Business Media, 2013.
6. Yuri Shtessel, Christopher Edwards, Leonid Fridman and Arie Levant, ―Sliding mode control
and observation," New York, USA: Birkhuser, 2014.
7. Christopher Edwards and Sarah Spurgeon, ―Sliding mode control: theory and applications, CRC
Press, 1998.
8. Dorf, Richard C., and Robert H. Bishop, ―Modern control systems," Prentice Hall, 2011
Page 25
Subject Code Subject Name Credits
INEP1022 Expert Systems 03
Course Objectives:
● To give knowledge to the students regarding Neural Networks and their applications in
control engineering.
● To familiarize the students with concepts in Fuzzy Logic and their applications in
control engineering .
Course Outcomes:
● Students will be able to understand concepts in Neural Networks and their applications
in control engineering.
● Students will be able to understand concepts in Fuzzy logic and their applications in
control engineering.
● Students will be able to understand concepts in Artificial intelligent systems such as
perceptrons.
Module Detailed content Hours
Prerequisite: Basic knowledge in computer scienc e and Control
Engineering
1 Introduction to Neural Networks: Artificial Neural Networks: Basic
properties of Neurons; Neuron Models; Feedforward networks -
Perceptrons; Widrow -Hoff LMS algorithm; Multiplayer networks - Exact
and approximate representation; Back propagation algorithm; variants of
Back propagation; Un supervised and Reinforcement learning; Symmetric
Hopfield networks and Associative memory; Competitive learning and
self-organizing networks, Hybrid Learning; Computational complexity of
ANNs.
08
2 Neural Networks Based Control: ANN based control: Introduction:
Representation and identification; modeling the plant, control structures -
supervised control, Model reference control, Internal model control,
Predictive control: Examples - Inferential estimation of viscosity a
chemical process; Auto - tuning feedback control; industrial distillation
tower.
06
3 Introduction to Fuzzy Logic: Fuzzy Controllers: Preliminaries -Fuzzy
sets and Basic notions - Fuzzy relation calculations - Fuzzy members -
Indices of Fuzziness - comparison of Fuzzy quantities -Methods of
determination of membership functions.
06
4 Fuzzy Logic Based Control: Fuzzy Controllers: Basic construction of
fuzzy controller - Analysis of static properties of fuzzy controller - Analysis
of dynamic properties of fuzzy controller - simulation studies - case studies
- fuzzy control for smart cars.
08
Page 26
5 Neuro - Fuzzy and Fuzzy: Neural Controllers: Neuro - fuzzy systems; A
unified approximate reasonin g approach - Construction of role bases by
self-learning : System structure and learning algorithm - A hybrid neural
network based Fuzzy controller wit h self-learning teacher. Fuzzified
CMAC and RBF network based self -learning controll ers.
06
6 Artificial Neural Networks: Supervised Learning: Introduction and how
brain works, Neuron as a simple computing element, The perceptron,
Backpropagation networks - architecture, multilayer perceptron 05
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be covered in question papers of end semester
examination.
References:
1. Bose & Liang, ― Artificial Neural Networks ―, Tata Mcgraw Hill, 1996
2. Kosco B, ― Neural Networks and Fuzzy Systems : A Dynamic Approach to Machine
Intelligence, Prentice Hall of India, New Delhi, 1992.
3. Klir G.J. and Folger T.A., Fuzzy sets, ― Uncertainty and Information ― , Prentice
Hall of India, New Delhi, 1994.
4. Simon Haykin - ― Neural Networks ―, ISA, Research Triangle Park, 1995
Page 27
Subject Code Subject Name Credits
INPE1023 Robotics and Control 03
Course Objectives:
· To introduce robot terminologies and robotic sensors
· To educate on direct and inverse kinematics
· To introduce robot control techniques
Course Outcomes:
● Students would be able to understand the concepts behind various robotic sensors and
manipulators.
● Students would be able to understand the kinematics and control strategies behind robot
movement.
● Students would be able to apply robots for various applications.
Module Detailed content Hours
Prerequisite: Knowledge of basic control strategies, Knowledge of working of
basic controllers, Knowledge of basic programming languages like C, C++
1 Robot Organization: Coordinate transformation, kinematics and inverse
kinematics, Trajectory planning and remote manipulation. 06
2 Robot Hardware: Robot sensors, Proximity sensors, Range sensors, Visual
sensors, Auditory sensors, Robot manipulators, Manipulator dynamics,
Manipulator control, Wrists, End efforts, Robot grippers.
09
3 Robot and Artificial Intelligence: Principles of AI, Basics of learning, Planning
movement, Basics of knowledge representations, Robot programming languages.
08
4 Robot Vision System: Principles of edge detection, determining optical flow
and shape, Image segmentation, Pattern recognition, Model directed scene
analysis.
06
5 Robot Control System: Linear control schemes, joint actuators, decentralized
PID control, Computed torque control, force control, hybrid position force
control, Robot control using voice and infrared.
06
6 Robot Application: Overview of robot applications. Prosthetic devices. Robots
in material handling, processing assembly and storage.
04
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Page 28
Minimum 80% syllabus should be covered in question papers of end semester
examination.
References:
1. Koren, ―Robotics for Engineers‖, McGraw Hill International Company, Tokyo, 1995.
2. Vokopravotic, ―Introduction to Robotics‖, Springer, 1988.
3. Rathmill. K., ―Robot Technology and Application‖, Springer, 1985.
4. Charniak and McDarmott, ―Introducation to Artificial Intelligence‖, McGraw Hill, 1986.
5. K. S. Fu, R. C. Gonzally, C.S. G. Lee, ―Robotics Control, Sensing, Vision and
Intelligence‖, McGraw Hill Book Company, 1997.
6. Barru Leatham, Jones, ―Elements of Industrial Robotics‖, Pittmann Publishing, 1987.
7. Mikell P. Groover, Mitchell Weiss, Roger. N. Nagel, Nicholas G. Odrey, ―Industrial
Robotic Technology Programming and Applications‖, McGraw Hill B ook Company,
1986.
Page 29
Subject Code Subject Name Credits
INL101 Program Lab -I 01
E Title
1 Linearizing circuit for ―single element‖ varying bridge.
2 Kelvin sensing system to drive remote bridges.
3 Active low pass, band pass and high pass filters for transducer signal processing.
4 Use of high-resolution ADC for transducer signal processing.
5 Simulation of boiler start -up process control using PLC
6 Simulation of paint manufacturing process using PLC
7 Study of SCADA (HMI) software
NOTE: Perform any six experiments from above list and two experiments from Department
Elective Course.
Term work: Term work consists of performing 08 practicals mentioned as above. Final
certification and acceptance of the term work ensures satisfactory performa nce of laboratory
work
Assessment:
End Semester Examination: Practical/Oral examination is to be conducted by pair of
internal and external examiners.
Page 30
Subject Code Subject Name Credits
INSBL101 Skill Based Lab -I 01
Expt no. Title
1 Experiments in MATLAB/Scilab for Computation of Eigenvalues, Eigen vectors,
different types of norms etc.
2 QR Decomposition
3 LQ Decomposition
4 Gram Schmidt Orthogonalization
5 Design the sliding mode control for SISO systems
6 Design the IMC controller for the —
a) First order delay system
b) First order NMP system
7 Design PID controller based on IMC controller
8 Design state feedback control for interval systems.
NOTE: Perform any six experiments from above list and two experiments from Department
Elective Course.
Term work: Term work consists of performing 08 practical mentioned as above. Final
certification and acceptance of the term work ensures satisfactory performa nce of laboratory
work
Assessment:
End Semester Examination: Practical/Oral examination is to be conducted by pair of
internal and external examiners.
Page 31
Sem-II
Subject Code Subject Name Credits
INC201 State Estimation and Stochastic Processes 03
Course Objectives:
· To study the concept of Stochastic Processes, Monte Carlo Simulation and fractional calculus
· To study the concept of Kalman filtering
Course Outcomes:
· The students should be able to understand the Stochastic Properties of random variables in
terms of pdf.
· Students should be able to understand the concept of stochastic processes
· Students should be able to understand concept of least square estimation
· Students should be able to realize the significance of Kalman filter and its applications
to linear and nonlinear systems.
Module Detailed content Hours
Prerequisite: Knowledge about concept of probability and Random Variable,
Knowledge about concept of state and state space models of systems
1 Random Variables: Introduction to Random Variables, Probability
Distribution Function, Probability Density Function, Exponential Distribution,
Gaussian Distribution, Binomial Distribution, Poisson Distribution, Two
Dimensional Random Variables, Joint Probability, Marginal Density Function,
Conditional Probability and Independence,
Correlation, Covariance, Introduction to n -dimensional Random Variables.
10
2 Stochastic Processes: Definition, Statistics of Stochastic Processes, Types of
Stochastic Processes, Random Walk, Markov Process, Brownian Motion,
Poisson Process, Concept of Monte Carlo Simulation, Monte Carlo Simulation
of Stochastic Processes such as Random Walk. Correlatio n functions, Power
Spectrum, White Noise,
Linear Systems with Stochastic input.
08
Parameter Estimation: Point Estimation, Optimal Estimates, Acceptable
Estimates, Least Squares Estimation: The deterministic point of view (Gauss),
Sequential Bayes Theorem, Linear Minimum
Page 32
3 Mean -square -error Estimation: Vector case sequential MMSE Estimation. 08
4 The Discrete -time Kalman Filter: Propagation of states
and covariances, Derivation of the discrete -time Kalman filter,
Kalman filter properties, Divergence issues
05
5 Nonlinear Kalman Filtering: The extended Kalman Filter, The Unscented
Kalman Filter, General Unscented transformations, The Simplex unscented
transformation, The spherical unscented transformation, Introduction to
Particle filtering
05
6 Fractional Calculus: Introduction to Fractional Calculus, Functions for the
Fractional Calculus, Riemann -Liouvelli fractional derivative(Left Hand
Definition), Caputo definition of fractional derivative (Right Hand Definition),
Fractional random walk, Application of fractional calculus to engineering
systems 03
Assessment:
Internal: Asse ssment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project .
End Semester Examination: Some guidelines for setting the ques tion papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be covered in question papers of end semester examination.
References:
1. Starks and Woods, ―Probability and Random Processes with applications to Signal
Processing, Phi, 2002.
2. Simon Haykins, ―Adaptive filter theory‖, Pearson 2012
3. W.C.Van Etten, ― Introduction to Random signals and noise‖, Wiley 2009
4. G.N. Saridis, ― Stochastic Processes, Estimation a nd Control‖, Wiley 1995
5. Meditch. J., ― Stochastic Linear Estimation and Control‖, Tata Mcgraw Hills, 1969
6. Paupolis, ―Probability , Random Variables and Stochastic Processes, Mc -Grawhill, 1995
7. Shantanu Das, ―Functional Fractional Calculus‖ 2nd Edn, Springer Verlag, Germany, 2012
8. Dan Simon, ―Optimal State Estimation‖ – Wiery 200 6
Page 33
Subject Code Subject Name Credits
INC202 Advanced Process Control and Automation 03
Course Objectives:
· To study the concepts of process modeling
· To study the effect of constraints and interaction between different loops
· To study the sizing of PLC and DCS.
· To study the knowledge about safety Instrumented Systems and advances in intrinsic safety.
Course Outcomes:
· The students should be able to design the pr ocess and behavioral model of the process.
· The students should be able to select appropriate control configuration to minimize
interaction between different loops
· The students should be able to design PLC and DCS based systems.
· The students should be able to calculate Safety Integrity Level for a given process.
Module Detailed content Hours
Prerequisite: Basic knowledge of Process control and automation tools
such as PLC, DCS and SCADA
1 Process Dynamics and Control: Fundamentals of process modeling,
Design for process modeling and behavioral model, Linearisation of
model equations - Level process, evaporation and chemical reactor
model. Dynamics of CSTR, Heat exchanger and evaporator.
06
2 Multivariable control: Constraint Control, SISO constraint control,Signal
selectors, Relative gain analysis, steady state decoupling, dynamic
decoupling.
05
3 Integrated Automation: Process and factory automation, PLC, DCS and
SCADA - programming, selection and sizing, PLC networking, PLC -
HMI interfacing, Installation and troubleshooting.
10
4 Buses and Networks: Introduction to networks in Industrial Automation,
PLC Proprietary and open networks, hardware selection for Fieldbus
systems, Fieldbus advantages and disadvantages, Limitations of open
networks. Design and installation of Field Bus ori ented Industrial
Communication Networks - Foundation Fieldbus, Profibus PA,
Devicenet, As -i segments in Hazardous and Non -Hazardous area.
08
Page 34
5 Safety Instrumented System: Life cycle model of Safety Instrumented
System, technologies, SIL calculation methods, SIL -calculation of PFD,
RRF etc., Phases of SIS overall implementation and reliability.
06
6 Advanced intrinsic safety: Entity concept, FISCO, High power trunk,
Dynamic arc recognition and termination technology with advantages
and disadvantages.
04
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions to be
set each of 20 marks, out of these any four questions to be attempted by students. Minimum 80% syllabus
should be covered in question papers of end semest er examination.
References:
1. Myke King, ―Process control -A practical approach‖, John Wiley, !st edition,2011.
2. Bela G Liptak, ― Instrument Enginner‘s Handbook -Process software and digital networks‖,
CRC press, ISA,3rd edition,2002.
3. Bela G Liptak, ―Optimisation of Unit operaion‖, ISA.
4. Bela G Liptak, ― Instrument Enginner‘s Handbook -Process Control‖, Chilton Book
Company, 3rd edition.
5. Gary Dunning, ―Introduction to Programmable Logic controller‖, Thomas Learning,
edition, 2001.
6. Thomas Hughes, ―Programmable Logic Controller‖, ISA Publication.
7. Stuart A. Boyer, ―SCADA supervisory control and data acquisition‖, ISA Publication.
8. George Stephanopoulos, ―Chemical process control‖, PHI -1999
9. Paul Gruhn, Harry L cheddie, ― Safety Instrumented S ystem: Design, Analysis and
justification‖, ISA, 2nd edition, 2006.
10. Ian Verhappen, Augsto Periria, ―Foundation fieldbus‖, ISA,2006
Page 35
Program Elective - III
Subject Code Subject Name Credits
INPE 2011 Electronics Systems Design 03
Course Objectives:
· To provide students with knowledge to design basic electronic systems.
· To make students aware of practical design considerations like noise reduction,
grounding techniques, shielding and isolation which are required to design high
performan ce electronic instrumentation systems.
Course Outcomes:
· Students will be able to understand practical design considerations such as Noise
reduction, Shielding and grounding techniques, Isolation and Power management
associated with design of electronic systems.
· Students will be able to design Analog, Digital and Mixed signal processing circuits
required for electronic systems.
Module Detailed content Hours
Prerequisite: Basic knowledge of analog and digital electronic circuits.
1 Design of linear integrated circuits and their applications: Linear and
log amplifiers, peak detect and milli volt rectifier circuits, analog
switches and multiplexers, current and voltages references and their
stability
06
2 Instrumentation and special operational amplifiers: Advanced
instrumentation amplifier and various designs to improve dynamic range
and reduce power dissipation. High speed OP -amps CMOS OP -amps
Micro power amplifiers low noise and chopper stabilized OP -amps
07
3 Nonlinear integrated circuits: Comparators, voltage to frequency and
frequency to voltage converters switched capacitor circuit ‘ s filters.
Analog filters, Sample and hold circuits.
06
4 Converters: D.C to D.C converters. Mixed signal processing. High
speed and high-resolution DACs and A/D converters. Various techniques
of A/D conversion. flash, successive approximation, multi slope ADC.
Delta sigma ADC.
08
Page 36
5 Noise reduction techniques: Design of mixed signal processing circuits,
grounding and isolation techniques R.F shielding, Power supply noise
reduction and filtering, Over voltage and ESD protection.
08
6 Power Management : Power management issues in low power portable
systems, Linear and switch mode regulators. 04
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be cov ered in question papers of end semester examination
References:
1. E.Allen Douglas R.Holberg, ―CMOS Analog Circuit Design‖, Philip Oxford ,
University Press 2004
2. Kevin M.Daugherty, ―Analog To Digital Converter‖, Tata McGraw Hill Inc 1995
3. Manual: Hig h Speed Design Technique - Analog Devices Inc 1996
4. Dan Shiengold, ―Non-Linear Integrated Circuits Handbook‖, Analog Devices.
5. Ralph Morrison,‖Grounding And Shielding Technique‖, Fourth Edition,John Wiley,1998
Page 37
Subject Code Subject Name Credits
INPE2012 Advanced Fiber Optics and LASER Instrumentation 03
Course Objectives:
· To expose the students to the concepts of instrumentation based on optical
fibers and lasers along with their properties.
· To provide sufficient knowledge about the extensive utilization of optical
fibers and lasers in Industries.
Course Outcomes:
· Understand the principle of optical fibers, its losses, sources and detectors and their
importance.
· Understand the operation of lasers in detail.
· Master the various principles of optical fiber used for different parameter
measurement.
· Perceive the significance of the intensive use of laser and optical fiber in
Industrial applications.
Module Detail content Hours
Prerequisite: Awareness of light theory, Basics of fiber optics, Basics of
Physics of Laser, Basics of measurement in Instrumentation.
1 Optical Fibers and their properties: Ray theory, Principle of light
propagation through a fiber, different types of fibers and their properties,
Transmission characteristics of optical fiber, Absorption losses, Scattering
losses, Dispersion losses, Non -linear phenomena. 07
2 Optical sources and Detectors: LED, LD, PIN,
APD their characteristics, modulation circuits, optical detection
principle, LED coupling to fiber
05
3 Fiber Optic Sensors: Principle of fiber optic sensors, classification, principle
of intensity modulated sensors, phase modulated sensors, wavelength
modulated sensors, distributed optical fiber sensing
06
4 Optical Fiber Measurement: Measurement of numerical aperture, refractive
index profile, OTDR. concepts of temperature, flow, pressure and level
measurement.
08
Page 38
5 Laser Fundamentals: Fundamental characteristics of lasers,3 and 4 level
lasers, its properties, modes, resonator configuration, Q switching and mode
locking. Types of lasers: solid, liquid and gas.
06
6 Industrial & Biomedical Application of Lasers: Laser for measurement of
distance, length velocity, acceleration, Material processing, Laser heating,
welding, melting and trimming of materials. Laser instruments for surgery,
Application of Laser for removal of tumors, brain surgery, oncology, plastic
surgery.
07
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live
problems or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be covered in question papers of end semester examination.
References:
1. Gerd Keiser, ―Optical Fiber Communication‖, McGraw Hill
2. John M Senior, ―Optical Fiber Communications Principles and Practice‖,3rd edition, Pearson
3. D.A.Krohn, ―Fiber Optic Sensors - fundamentals and applications ‖3rd edition, ISA
4. I. John and Harry, ―Industrial lasers and their applications‖, McGraw Hill
5. John Crisp, ―Introduction to Fibre Optics‖, an imprint of Elseveir Science,1996
6. John F Ready, ―Industrial applications of Lasers, Academic Press,1978
Page 39
Subject Code Subject Name Credits
INPE2013 Rehabilitation Engineering 04
Course Objectives:
● To develop an understanding of the principle and working of various rehabilitation aids.
● To give information about the application of various recent rehabilitation aids.
● To give information about rehabilitation medicine and Advocacy.
Course Outcomes:
● The students will be able to understand the principle and working of various
rehabilitation aids.
● The students will be able to understand the design considerations of various
rehabilitation aids.
● The students would be able to select which rehabilitation aid to apply for challenged
people based on their medical conditions.
● The student would be aware of the various legal considerations while selecting a
rehabilitation aid.
Module Detailed content Hours
Prerequisite: Knowledge of Anatomy and Physiology of Human Systems,
Knowledge of various basic stimulation techniques, Knowledge of basic
concept of human -assist devices.
1 Prosthetic and orthotic devices: Hand and arm replacement, different types of
models for externally powered limb prosthetics, feedback in orthotic system,
material for prosthetic and orthotic devices, mobility aids.
08
2 Auditory and speech assist devices: Types of deafness, hearing aids,
application of DSP in hearing aids, cochlear implants 05
3 Visual aids: Retinal Implants, Types of retinal implants – Epi-retinal and
subretinal, design and working, applications of retinal implants. Ultrasonic
and laser canes, Intraocular lens, Text voice converter, screen readers.
08
4 Medical stimulator: Muscle and nerve stimulator, Location for
Stimulation,Functional Electrical Stimulation, Sensory Assist Devices. 08
5 Rehabilitation medicine: Physiological aspects of Function
recovery, psychological aspects of Rehabilitation therapy. 06
Page 40
6 Advocacy: Legal aspect available in choosing the device and provision
available in education, job and in day -to-day life. 04
Assessment:
Internal: Assessment consists of two tests out of which; one should be compulsory class test (on
minimum 02 Modules) and the other is either a class test or assignment on live problems or course
project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions to be
set each of 20 marks, out of these any four q uestions to be attempted by students. Minimum 80% syllabus
should be covered in question papers of end semester examination.
References:
1. Rory A Cooper, ―An Introduction to Rehabilitation Engineering‖, CRC press, 2006.
2. Joseph D.Bronzino, ―The Biomedic al Engineering Handbook‖,Third Edition,CRC
Press,2006
3. Levine.S.N.Editor, ―Advances in Bio Medical Engineering and Medical Physics‖,
Inter University Publication, New York 1968.
4. Albert M.Cook and Webster J.G, ―Therapeutic Medical devices‖, Prentice Ha ll Inc.,
New Jersey, 1982.
5. Reswick.J, ―What is Rehabilitation Engineering, Annual review of Rehabilitation -
volume2‖, Springer -Verlag, New York 1982.
Page 41
Program Elective -IV
Subject Code Subject Name Credits
INPE2021 Advanced Nuclear Instrumentation 03
Course Objectives:
· To give students knowledge in the field of nuclear instrumentation, which is used for
various hi -tech applications including field of nuclear research, nuclear reactors,
accelerators and nuclear medical instruments
Course Outcomes:
· The students should be able to understand the design and working of
advanced nuclear instruments used in nuclear research, nuclear reactors
and other related nuclear fields.
· Students will be able to apply the concepts for basic design of nucl ear instruments.
Module Detailed content Hours
Prerequisite: Basic concepts of Radioactivity, Measurement
of Radioactivity.
1 Nuclear instrumentation for research: Radiation detectors for high
resolution nuclear pulse spectroscopy, HPGE, Ge(Li), Si(Li) detectors, high
resolution Multi Channel Analyzers, Nuclear ADCs, Wilkinson, Gatti‘s
sliding scale technique, various modes of Multi -Channel Analyzer, portable
spectro scopy systems and their design. Timing spectroscopy, Time Pick -off
circuits, TDCs, TACs, spectrum stabilization.
12
2 Instrumentation for reactors: Log and linear amplifiers, in core and out of
core instrumentation, Neutron detector, BF3 detector, Fission counters,
nuclear instrumentation for pressurized water reactors, boiling water
reactors, self -powered detectors, fast Neutron detection and spectroscopy.
08
3 Detection of very low radio -activity: Liquid scintillation counting
systems, noise reduction by coincidence detection. Counting interferences in
LSC, Methods of quench corrections.
03
4 Instrumentation for accelerators: Various types of accelerators,
detectors and electronics used. 03
Page 42
5 Nuclear medical instrumentation: Functional imaging, design and
construction of imaging systems gamma camera, PET SPET. Calibrations
and testing of various nuclear instruments and systems.
10
6 Instrumentation for astrophysics experiments: Detection of cosmic
events, detector arrays and trigger systems 03
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions
to be set each of 20 marks, out of these any four questions to be attempted by students.
Minimum 80% syllabus should be covered in question papers of end semest er examination.
References:
1. G.F.Knoll ,―R adiation detection and measurement‖, John Wiely and Sons, 4th edition, 2010.
2. P.W. Nicolson, ―Nuclear electronics‖, John Wiely,1998.
3. Gerald. J.Hine, James A Sorenson, ―Instrumentation in nuclear Medicine‖, Vol II,
Academic press,1974
4. Ramesh Chandra, ―Nuclear Medicine Physics‖, Williams and Wilkins,1998.
5. Irving Kaplan ―Nuclear Physics.‖, Narosa Publishing House.1992
Page 43
Subject Code Subject Name Credits
INPE2022 Machine Learning and Deep Learning 03
Course Objectives:
To give students knowledge in the field of Machine learning and Deep learning, which
find extensive applications in various fields, ranging from predictive analytics,
medical diagnosis, image processing, control engineering.
Course Outcomes: Students would be able
● To explain the basic concepts and techniques of Machine Learning.
● To have a thorough understanding of the Supervised and Unsupervised learning techniques
● To study the various probability -based learning technique s
● To understand the concepts of deep learning
Module Detailed content Hours
Prerequisite: Basic concepts of Expert systems and artificial intelligence
1 Introduction: Types of Machine Learning, Supervised Learning: concept of
working of brain and the neuron, Perspectives and Issues in Machine Learning,
Linear Discriminants – Perceptron – Linear Separability – Linear Regression
08
2 Linear models: Multi -layer Perceptron, Propagating Forwards, Propagating
Backwards: Back Propagation Error – Multi -layer Perceptron in Practice –
Examples of using the MLP – Overview – Deriving Back -Propagation – Radial
Basis Functions and Splines, Concepts of RBF Network.
06
3 Tree and Random Forest model: Decision Trees, Constructing Decision
Trees, Classification and Regression Trees, Ensemble Learning, Boosting,
Bagging, Different ways to Combine Classifiers, Random Forest algorithm
06
4 Probabilistic Model: Probability and Learning, Basic Statistics, Gaussian
Mixture Models, Nearest Neighbor Methods (kNN), Unsupervised Learning –
Clustering, K means Algorithm, Vector Quantization. 06
5 Dimensionality Reduction: Linear Discriminant Analysis, Principal
Component Analysis, Factor Analysis, Independent Component Analysis, 06
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Locally Linear Embedding, Least Squares Optimization, Genetic algorithms
6 Deep learning: Basic concept of Deep learning, Optimization in deep
learning, non -convex optimization for deep networks, Stocha stic Optimization,
Generalization in neural networks, Recurrent networks, LSTM, Recurrent
Neural Network Language Models 07
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions to be
set each of 20 marks, out of these any four questions to be attemp ted by students. Minimum 80% syllabus
should be covered in question papers of end semester examination.
References:
1. Peter Flach, “Machine Learning: The Art and Science of Algorithms that Make Sense of Data”,
First Edition, Cambridge University Press, 2012
2. Ethem Alpaydin, “Introduction to Machine Learning 3e (Adaptive Computation and Machine
Learning Series)”, Third Edition, MIT Press, 2014
3. Jason Bell, “Machine learning – Hands on for Developers and Technical Professionals”, First
Edition, Wiley, 2014.
4. Tom M Mitchell, “Machine Learning”, First Edition, McGraw Hill Education, 2013.
5. Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, Second Edition, Chapman
and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
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Subject Code Subject Name Credits
INPE2023 MEMS and Nanotechnology 03
Course Objectives:
● To give students adequate knowledge regarding quantum mechanics to understand
principles utilized in Nanotechnology and MEMS.
● To familiarize the students with advanced technologies used in fabrication of nano
materials and MEMS.
Course Outcomes:
● Students will be able to understand concepts in quantum mechanics used in nanotechnology
● Students will be able to understand technologie s used in fabrication of nano materials
and MEMS.
Module Detailed content Hours
Prerequisite: Basic knowledge in quantum mechanics and material science.
1 Introduction: Introduction to nanotechnology and Nanomaterials, How It All
Began: Synthesis of carbon buckyballs, List of stable carbon allotropes
extended, fullerenes, metallofullerenes, solid C 60, bucky onions, nanotubes,
nanocones. 04
2 Quantum Mechanics : Review of classical mechanics, de Broglie's hypothesis,
Heisenberg uncertainty principle Pauli Exclusion Principle, Schrödinger's
equation, Properties of the wave function, Application: quantum well, wire, dot,
quantum cryptography Solid State Physics and Nanodevices -Structure and
bonding, Application: carbon nanotube, Electronic band structure Electron
statistics, Application: Optical transitions in solids, Semiconductor quantum
dots, photonic crystals. 10
3 Nanomaterials - Fabrication, MEMS and NEMS nanotubes synthesis:
Bottom -up vs. top -down approach, Epitaxial growth, Self -assembly, Modeling
and Applications Production Techniques of Nanotubes Carbon arc bulk
synthesis in presence and absence of catalysts Hi gh-purity material (Bucky
paper) production using Pulsed Laser Vaporization (PLV) of pure and doped
graphite High -pressure CO conversion (HIPCO) nanotube synthesis based on
Boudoir Reaction Chemical Vapor Deposition (CVD). 06
4 Nanomaterials: Characterization and commercial processes of synthesis of
nonmaterial, Nanoclay, Nanoinroganic materials, Nanocarbon Tubes CNT,
Applications of nanomaterials in water treatment, polymers,atalysis etc 07
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Structural, XRD, TEM, SEM, STM, AFM.
5 MEMS Technology: Introduction to Microelectromechanical Systems
(MEMS), Microsensors and Micro -actuators, Micromachining, System
modeling and Simulation, different types of MEMS sensors and actuators. 06
6 Micro Electromechanical Systems: MEMS: Micro -transducers Analysis,
Design and Fabrication, Microprocessor -Based Controllers and
Microelectronics , Micro -switches, Micro -actuators for Electromechanical
systems. 06
Assessment:
Internal: Assessment consists of two tests out of which; one should be a compulsory class
test (on minimum 02 Modules) and the other is either a class test or assignment on live problems
or course project.
End Semester Examination: Some guidelines for setting the question papers are as, six questions to be
set each of 20 ma rks, out of these any four questions to be attempted by students. Minimum 80% syllabus
should be covered in question papers of end semester examination.
References:
1. K. Eric Drexler, ―Nanosystems: Molecular Machinery, Manufacturing, and
Computation‖, 1992 .
2. Mark Ratner & Daniel Ratner, ―Nanotechnology: A Gentle Introduction to the Next Big
Idea‖, November 2002 Read reviews .
3. Nitaigour Premchand Mahalik, ―MEMS ‖, Tata McGraw Hill, New Delhi, 2007.
4. K. K. Appukuttan, ―Introduction to Mechatronics‖, Oxford Higher Education, 2003.
5. Nitaigour Premchand Mahalik, ―Mechatronics‖, Tata McGraw -Hill, 2003
Page 47
Subject Code Subject Name Credits
INL201 Program Lab -II 01
Expt No. Title
1 Simulation of batch reactor control using PLC with GUI
2 Study of Ethernet network communication
3 Study of Modbus communication
4 Simulation of furnace control using PLC with GUI
5 Simulation of Heat exchanger feedback control scheme using DCS
6 Simulation of cascade control scheme using DCS
7 Simulation of feedforward control scheme using DCS
8 Simulation of boiler level control using DCS
NOTE: Perform any six experiments from above list and two experiments from Department Elective
Course.
Term work: Term work consists of performing 08 practical mentioned above. Final certification and
acceptance of the term work ensures satisfactory performance of laboratory work
Assessment:
End Semester E xamination: Practical/Oral examination is to be conducted by a pair of internal and
external examiners.
Page 48
Subject Code Subject Name Credits
INSBL201 Skill Based Lab -II 01
Expt. Title
1 Implement Linear Regression with example
2 Implement logistic regression with example
3 Implement Principal component analysis for dimensionality reduction
4 Implement Support Vector Machine with example
5 Implement Decision tree classification techniques
6 Implement Random Forest algorithm for an example
7 Implement clustering techniques with example
8 Implement any one deep learning algorithm for an example
NOTE: Perform any six experiments from above list and two experiments from Department Elective
Course.
Term work: Term work consists of performing 08 practicals mentioned above. Final certification and
acceptance of the term work ensures satisfactory performance of laboratory work
Assessment:
End Semester Examination: Practical/Oral examination is to be conducted by a pair of internal and
external examiners.
Page 49
Semester III
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INMP301 Major Project:
Dissertation -I -- 20 -- -- 10 -- 10
Total 00 20 00 00 10 -- 10
Course
Code Course Name Examination Scheme
Theory Term
Work Pract/
Oral Total
Internal Assessment End
Sem
.
Exa
m Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INMP301 Major Project:
Dissertation -I -- -- -- -- -- 100 -- 100
Total -- -- -- -- -- 100 -- 100
Guidelines for Dissertation -I
Students should do a literature survey and identify the problem for Dissertation and finalize in consultation
with the Guide/Supervisor. Students should use multiple literatures and understand the problem. Students
should attempt to provide solutions to th e 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.
Dissertation -I should be assessed based on following po ints
∙ Quality of Literature survey and Novelty in the problem
∙ Clarity of Problem definition and Feasibility of problem solution
∙ Relevance to the specialization
∙ Clarity of objective and scope Dissertation -I should be assessed through a present ation by a panel of Internal
examiners and external examiner appointed by the Head of the Department/Institute of respective
Programme.
Page 50
Online Credit Courses
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
XXOCC301 Online Credit Course - I -- -- -- -- -- -- 3
XXOCC301 Online Credit Course - II -- -- -- -- -- -- 3
Total -- -- -- 00 00 00 06
Note 2:
It is mandatory to complete the Online Credit Courses (OCC) available on NPTEL / Swayam
/MOOC or similar platform approved by UoM. These two courses shall be completed in any
semester I or II or III, but not later end of the Semester III. University shall make a provision that
credits earned with OCC - I and OCC -II shall be accounted in the third semester grade -sheet with
actual names of courses. The learner shall be allowed to take up these courses from his or her
institute or organisation/ industry where h is / her major project is carried out. The students shall
complete the courses and shall qualify the exam conducted by the respective authorities/ instructor
from the platform. The fees for any such courses and the corresponding examination shall be borne
by the learner.
Online Credit Course – I
The learner shall opt for the course in the domain of Research Methodology or Research & Publication
Ethics or IPR. The opted course shall be of 3 credits of equivalent number of weeks.
Online Credit Course –II
The learner shall opt for the course recommended by Faculty Advisor/ Project Supervisor from the
institute. The opted course shall be of 3 credits of equivalent number of weeks.
Page 51
Semester IV
Course
Code Course Name Teaching Scheme
(Contact Hours) Credits Assigned
Theory Pract. Tut. Theory Pract. Tut. Total
INMP401 Major Project:
Dissertation -II -- 32 -- -- 16 -- 16
Total -- 32 -- -- 16 -- 16
Course
Code Course Name Examination Scheme
Theory Term
Work Pract/
Oral Total
Internal Assessment End
Sem
.
Exa
m Exam.
Durati
on (in
Hrs) Test-1 Test-2 Avg
INMP401 Major Project:
Dissertation -II -- -- -- -- -- 100 100 200
Total -- -- -- -- -- 100 100 200
Guidelines for Assessment of Dissertation II
Dissertation II should be assessed based on following points:
∙ 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
∙ Quality of work attempted or learner contribution
∙ Validation of results
∙ Quality of Written and Oral Presentation
Students should publish at least one paper based on the work in the referred National/ International
conference/Journal of repute.
Dissertation II should be assessed by internal and External Examiners appointed by the University of
Mumbai.