Centre for Intelligent Cyber Physical Systems

Indian Institute of Technology Guwahati

Funded by Technology Innovation Hub for Underwater Exploration

PhD

The Ph.D. programme consists mainly of an open-ended research work that is expected to make a contribution to Technologies for Underwater Exploration. Major goal of the centre is to provide students with a broad variety of educational experiences: developing their problem solving skills, challenging them with open-ended problems and design projects, providing opportunity for teamwork, developing their written and verbal communication skills, and making research or independent study experiences available to those students with the desire and capability. Following are the broad areas of research for PhD.


Topics for July 2024 session

1. Internet-of-Things (IoT), Sensors, Embedded Systems

2. Robotics, Control and Sensing:

  • Modelling, Analysis and Design of Robotic Systems
  • Intelligent Sensing and Control for Autonomous Vehicles
  • Control of Swarm Systems
  • Sensor and Actuator Design

3. Artificial Intelligence in Cyber-Physical systems:

  • Machine Learning, Deep Learning, Computer Vision
  • Natural Language Processing
  • Speech and Audio Signal Processing
  • Biomedical Signal & Image Processing
  • Data Science and Analytics

4. Underwater Systems:

  • Underwater Systems Design and Development
  • Underwater Exploration
  • Underwater Structural Analysis
  • Sonar and Underwater Acoustics
  • Compressed Air Energy Storage
  • Power Electronics for Underwater Systems
  • Underwater Concrete Printing

5. Smart and Intelligent Manufacturing:

  • Robotics Manufacturing
  • AI and ML in Manufacturing Processes
  • Digital Twin of Manufacturing Systems and processes

6. Biomechanics and Biomedical Devices


M. Tech in Robotics and Artificial Intelligence

M. Tech in Robotics and AI is a four semesters program with eleven courses in first and second semesters and followed by two project works in fourth and fifth semesters. Total forty seats, in which twenty is for regular category, and rest 20 seats for sponsored category (to be filled by employees from R&D, Academic organizations and Industry). From the 20 regular category seats 10 students will get scholarship from the Institute and other ten students will get scholarship from IIT Guwahati Technology Innovation and Development Foundation (Section 8 Company). Candidates applying in regular category, must have completed (or appearing final semester) B. Tech or equivalent in EE/EEE/CSE/Mech/PE/Civil/IT/Design and with valid GATE or CEED Score in EE/ECE/ME/CSE/CE/PE/Design. Regular candidates having GATE score will be selected through COAP. Candidates having other valid score card (e.g., CEED) will be selected through written test and/or interview. For sponsored category candidates GATE score is not mandatory and for their selection written test and/or interview will be conducted. Industry Sponsored candidates may choose one guide from industry and one guide from IITG in their project phase. Course fee for the candidates of this center are same as those for other Dept. and Center. Following are the course details.


Semester 1

Please click on row of the tables for syllabus

Course No Course Name L-T-P-C
RA501 Fundamentals of Robotics 3-0-0-6

1.Introduction to Robotics: Types and Classification of robots; Science and Technology of Robots
2.Rigid Body Transformation: Overview of Rigid Body Kinematics; Homogeneous Transformation; Link Transformation Matrices
3.Forward and Inverse Kinematics & Dynamics of Robots
4.Planning and Control of Robots
Textbooks
1) Fu. K.S., Gonzalez R.C. and Lee C.S.G., Robotics: Control, Sensing, Vision and Intelligence, Tata McGraw Hill, 2008.
2) Ghosal A. Robotics: Fundamental Concepts and Analysis, Oxford University Press, 2006.
3) Craig J.J., Introduction to Robotics – Mechanics and Control, Pearson Prentice Hall, 2005.
4) Murray, Li and Sastry, A Mathematical Introduction to Robot Manipulation, CRC Press, 1994.
References:
1) Spong M.W., Hutchinson S. and Vidyasagar M., Robot Modeling and Control, John Wiley Sons & Inc., 2005.
2) Saha. S.K., Introduction to Robotics, McGraw Hill Education (India) Private Limited, 2014.
RA502 Artificial Intelligence 2-0-2-6
Introduction to Artificial Intelligence
1.Searching Techniques: uninformed search strategies, informed (heuristic) search strategies, local search algorithms, searching in non-deterministic and partially observable environment, adversarial search.
2.Temporal Probability models and inference in temporal models: filtering, prediction, smoothing, most likely explanation, Dynamic Bayesian Networks, Hidden Markov Model, Kalman Filter, Extended Kalman Filter, Particle Filter, Learning Probabilistic Models.
3.Decision making: Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs).
4.Learning: Introduction to supervised learning, unsupervised learning, and reinforcement learning
Textbooks
1) Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, 3rd Edition, Pearson, 2014. References 1) Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
2) C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
3) R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, 2nd Edition, MIT Press, 2018.
RA503 Robot Design Laboratory 0-0-3-3
1.Demonstrations on Robot Mechanisms and their design.
2.Studies on Existing Robots, Computer-Aided-Design of Robots.
3.Robot Hardware and Control System Design
4.ROS
5.Topics in Machine Elements.
Textbooks
1) Sandor G.N. and Erdman A.G., Advanced Mechanism Design: Analysis and Synthesis, Vol.
2, Prentice Hall, New Jersey, 1984.
3) Zeid, Ibrahim. CAD/CAM theory and practice. McGraw-Hill Higher Education, 1991.
4) Rivin E.I., Mechanical Design of Robots, McGraw Hill, New York, 1988.
5) D.J. Bell, P.A. Cook, N. Munro, Design of Modern Control Systems, IEE Control Engineering Series, Institution of Engineering and Technology, 1982.
References
1) G. Budynas and J. K. Nisbett, Shigley’s Mechanical Engineering Design, 10th Edition, McGraw Hill, 2015.
2) Joseph L., Mastering ROS for Robotics Programming, Packt Publishing, Birmingham, 2015. 3) Nnaji B.O., Computer-aided Design, Selection and Evaluation of Robots, Manufacturing Research & Technology, Elsevier Science Ltd, 1986.
RA504 Programming Laboratory 0-0-3-3
1.Introduction: why Python
2.Ecosystem: installation, workflow, data types, control flow, functions, scripts and modules, input, output, standard library, Numpy arrays, Pandas Basic, Generators ,List Comprehensions, Multiple Function Arguments, Regular Expressions, Exception Handling, Sets, Serialization, Partial functions, Code Introspection, Closures, Decorators, Map, Filter, Reduce,
3.Visualization with Matplotlib, Libraries for AI.
Textbooks/References
1.Python Data Science Handbook,O’REILLY
XXXXX Elective 1 (Choose any one) X-X-X-6
Electives:
1. RA602 - Control Engineering for Robotics (offered by CICPS)
2. CS666 - Mobile Robotics (offered by CSE Dept)
3. CS549 - Computer and Network Security (offered by CSE Dept)
4. CS566 - Speech Processing (offered by CSE Dept)
5. CS601 - Mathematics for ML (offered by CSE Dept)
6. CS590 - Deep Learning (offered by CSE Dept)
7. DD550 - Image Processing with Machine Learning (offered by Design Dept)
8. DD611 - Foundation for Electronic Product Design (offered by Design Dept)
9. EE623 - Speech Signal Processing and Coding (offered by EEE Dept)
10. EE647 - Micro sensors and Nano Sensors (offered by EEE Dept)
11. ME609 - Optimization Methods in Engineering (offered by ME Dept)
12. ME617 - Manufacturing of Polymers and Polymer Composites (offered by ME Dept)
13. ME644 - Modern Control (offered by ME Dept)
14. ME532 - Finite Elements in Engineering (offered by ME Dept)

***Comprehensive details about each subject are available on the designated departmental websites***
XXXXX Elective 2 (Choose any one ) X-X-X-6
Electives:
1. RA602 - Control Engineering for Robotics (offered by CICPS)
2. CS666 - Mobile Robotics (offered by CSE Dept)
3. CS549 - Computer and Network Security (offered by CSE Dept)
4. CS566 - Speech Processing (offered by CSE Dept)
5. CS601 - Mathematics for ML (offered by CSE Dept)
6. CS590 - Deep Learning (offered by CSE Dept)
7. DD550 - Image Processing with Machine Learning (offered by Design Dept)
8. DD611 - Foundation for Electronic Product Design (offered by Design Dept)
9. EE623 - Speech Signal Processing and Coding (offered by EEE Dept)
10. EE647 - Micro sensors and Nano Sensors (offered by EEE Dept)
11. ME609 - Optimization Methods in Engineering (offered by ME Dept)
12. ME617 - Manufacturing of Polymers and Polymer Composites (offered by ME Dept)
13. ME644 - Modern Control (offered by ME Dept)
14. ME532 - Finite Elements in Engineering (offered by ME Dept)

***Comprehensive details about each subject are available on the designated departmental websites***


Semester 2

Please click on row of the tables for syllabus

Course No Course Name L-T-P-C
RA505 Robot Sensing and Vision 3-0-0-6
1. Geometrical Computer Vision: Pinhole camera model, camera calibration, stereo vision;
2. Image Processing: Basic operations, transformations, image features; Motion estimation;Object Detection and Recognition; Object Tracking.
3. Robot sensor: Binary, monochrome and RGB imaging sensors; ultrasound rangefinders; optical rangefinders - LASER scanner, static LED array; structured lighting, dynamic focusing; interfacing of vision sensors; velocity sensors, accelerometers, tactile sensors.
Text Books:
1.D. A. Forsyth and J. Ponce, Computer Vision, A Modern Approach, Pearson Education, 2003.
2.Dahiya, Ravinder S., Valle, Maurizio, Robotic Tactile Sensing, Springer, 2013.
3.S.R. Deb, Sankha Deb, Robotics Technology and Flexible Automation, 2nd edition, McGraw Hill Education, 2017.
References Books:
1.Milan Sonka, Vaclav Hlavac and Roger Boyle, Image Processing, Analysis and Machine Vision, Cengage, Third Edition (2013).
2.H. R. Everett, Sensors for Mobile Robots: Theory and Application, A K Peters/CRC Press, 1995.
RA506 Machine Learning 3-0-0-6

1.Introduction to supervised and unsupervised learning frameworks;
2.Dimensionality reduction: Feature selection; PCA;
3.Supervised learning: Bayesian classification, Perceptrons, Multi-layer perceptron, RBF Networks, Decision Trees, Support Vector Machines, Convolutional Neural Networks, Recurrent Neural Networks;
4.Unsupervised learning: K-Means clustering, DBSCAN, Non-parametric Estimation, Mean-shift clustering; Classification performance analysis; Ensemble methods – Boosting and Bagging; > 5.Applications and Case Studies in Robotics.
Textbooks:
1. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India) 2015.
2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.
3. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2006.
4. S. O. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson Education (India), 2016
5. I. Goodfellow, Y. Bengio , A. Courville, Deep Learning, MIT Press, 2017
XXXXX Elective 3 (Choose any one ) X-X-X-6
Electives:
RA604 - Advanced Engineering Dynamics, RA603 - CAD/CAM, RA605 - Deep Learning for Robotics, RA607 - Underactuated Systems, RA602 - (Control Engineering for Robotics) or equivalent, RA608 - Design, Simulation and Programming Lab, RA609 - Robotics Lab, ME531 - Mechanical Vibration, ME542 - Numerical Analysis, ME628 - Additive manufacturing technologies, ME532 - Finite Element Methods in Engineering, ME629 - Design of Mechatronic Products, ME674 - Soft Computing in Engineering, ME608 - CAD CAM ME615 Rotor Dynamics, ME543 - Computational Fluid Dynamics, ME645 - Mechatronics, ME609 - Optimization Methods in Engineering, ME644 - Modern Control CS551 Wireless Networks, CS590 - Deep Learning CS666 Mobile Robotics, CS571 - Human Computer Interaction, CS530 - Machine Learning using Cloud Computing, CS565 - Intelligent Systems And Interfaces, CS566 - Speech Processing, CS578 - Internet of Things, EE523 - Introduction to Machine Learning, EE535 - Advanced Topics in Machine Learning, EE646 - Optical Measurement Techniques Applications, EE550 - Linear Systems Theory, EE551 - Estimation and Identification, EE659 - Fuzzy Logic and Neural Networks, EE626 - Pattern Recognition and Machine Learning, EE660 - Modeling and Control of Power Electronic Converters, EE656 - Robust Control, EE657 - Intelligent Sensors and Actuator, EE553 - Optimal Control, EE554 - Nonlinear Systems and Control, EE653 - Modeling and Simulation of Dynamic Systems, EE694 - Introduction to Parallel Computing DD533 - Auditory and Voice Interaction Design DD509 - Interaction Design DD516 - Digital Human Modelling and Simulation in Product Design DD518 - Representation Techniques for Animation
XXXXX Elective 4 (Choose any one) X-X-X-6
Electives:
RA604 - Advanced Engineering Dynamics, RA603 - CAD/CAM, RA605 - Deep Learning for Robotics, RA607 - Underactuated Systems, RA602 - (Control Engineering for Robotics) or equivalent, RA608 - Design, Simulation and Programming Lab, RA609 - Robotics Lab, ME531 - Mechanical Vibration, ME542 - Numerical Analysis, ME628 - Additive manufacturing technologies, ME532 - Finite Element Methods in Engineering, ME629 - Design of Mechatronic Products, ME674 - Soft Computing in Engineering, ME608 - CAD CAM ME615 Rotor Dynamics, ME543 - Computational Fluid Dynamics, ME645 - Mechatronics, ME609 - Optimization Methods in Engineering, ME644 - Modern Control CS551 Wireless Networks, CS590 - Deep Learning CS666 Mobile Robotics, CS571 - Human Computer Interaction, CS530 - Machine Learning using Cloud Computing, CS565 - Intelligent Systems And Interfaces, CS566 - Speech Processing, CS578 - Internet of Things, EE523 - Introduction to Machine Learning, EE535 - Advanced Topics in Machine Learning, EE646 - Optical Measurement Techniques Applications, EE550 - Linear Systems Theory, EE551 - Estimation and Identification, EE659 - Fuzzy Logic and Neural Networks, EE626 - Pattern Recognition and Machine Learning, EE660 - Modeling and Control of Power Electronic Converters, EE656 - Robust Control, EE657 - Intelligent Sensors and Actuator, EE553 - Optimal Control, EE554 - Nonlinear Systems and Control, EE653 - Modeling and Simulation of Dynamic Systems, EE694 - Introduction to Parallel Computing DD533 - Auditory and Voice Interaction Design DD509 - Interaction Design DD516 - Digital Human Modelling and Simulation in Product Design DD518 - Representation Techniques for Animation
XXXXX Elective 5 (Choose any one) X-X-X-6
Electives: RA604 - Advanced Engineering Dynamics, RA603 - CAD/CAM, RA605 - Deep Learning for Robotics, RA607 - Underactuated Systems, RA602 - (Control Engineering for Robotics) or equivalent, RA608 - Design, Simulation and Programming Lab, RA609 - Robotics Lab, ME531 - Mechanical Vibration, ME542 - Numerical Analysis, ME628 - Additive manufacturing technologies, ME532 - Finite Element Methods in Engineering, ME629 - Design of Mechatronic Products, ME674 - Soft Computing in Engineering, ME608 - CAD CAM ME615 Rotor Dynamics, ME543 - Computational Fluid Dynamics, ME645 - Mechatronics, ME609 - Optimization Methods in Engineering, ME644 - Modern Control CS551 Wireless Networks, CS590 - Deep Learning CS666 Mobile Robotics, CS571 - Human Computer Interaction, CS530 - Machine Learning using Cloud Computing, CS565 - Intelligent Systems And Interfaces, CS566 - Speech Processing, CS578 - Internet of Things, EE523 - Introduction to Machine Learning, EE535 - Advanced Topics in Machine Learning, EE646 - Optical Measurement Techniques Applications, EE550 - Linear Systems Theory, EE551 - Estimation and Identification, EE659 - Fuzzy Logic and Neural Networks, EE626 - Pattern Recognition and Machine Learning, EE660 - Modeling and Control of Power Electronic Converters, EE656 - Robust Control, EE657 - Intelligent Sensors and Actuator, EE553 - Optimal Control, EE554 - Nonlinear Systems and Control, EE653 - Modeling and Simulation of Dynamic Systems, EE694 - Introduction to Parallel Computing DD533 - Auditory and Voice Interaction Design DD509 - Interaction Design DD516 - Digital Human Modelling and Simulation in Product Design DD518 - Representation Techniques for Animation
Semester 3

Please click on row of the tables for syllabus

Course No Course Name L-T-P-C
RA507 Technical Writing 1-0-2-4
1.Modes of technical communication: Reports, Technical papers, book chapters, Manuals, Posters.
2.Structure of a technical document.
3.Copyright issues in technical writing: existing laws, open sources, permission procedure.
4.How to write a good technical paper?, Proper procedure in citing already published works, Referencing styles.
5.Common mistakes of English in scientific documents.
6.Proper way of writing and citing equations. Proper use of figures and tables.
7.Writing a good review paper.
8.Writing of abstract, synopsis, cover letters, responses, discussion and keywords
References
1. Alred, G. J., Brusaw, C. T., & Oliu, W. E. Handbook of technical writing. 9th edition. Bedford/St. Martin, 2009
2. Parija, S.C. & Kate, V. (Ed.), Writing and publishing a scientific research paper, Springer/Singapore, 2017
RA598 Project Phase - I 0-0-20-20
Semester 4

Please click on row of the tables for syllabus

Course No Course Name L-T-P-C
RA599 Project Phase - II 0-0-24-24

Centre for Intelligent Cyber Physical Systems
Indian Institute of Technology Guwahati, Guwahati-781039, Assam, India.

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