M.Tech in Electrical Engineering with Specializations


M. Tech in Electrical Engineering has the following three specializations that the students choose during the admission:

  1. Microelectronics and VLSI
  2. Communication, Signal Processing and ML (CSPML)
  3. Power electronics and Power systems  

It is a two year programme with one year project work. The curriculum is broad enough to get enough exposure to many areas of electrical engineering while specializing in one of the streams mentioned above. This enables the students to work on interdisciplinary and industry relevant projects. Further, the students are encouraged to do internships in the industry. Admissions will be conducted once per year. At the time of admission, students will be admitted to one of the specializations listed above. In the first year, students will have to take courses and the second year will comprise of MTP I and MTP II. The courses are divided into core theory courses, core lab courses and elective courses. These elective courses are further divided into three baskets or pools corresponding to three specializations mentioned above. A student has to earn at least 131 credits, out of which

  • At least 18 credits should be from core theory courses

  • At least 3 credits from core lab courses

  • 4 credits come from a mandatory seminar course

To successfully complete the M. Tech degree, a student should complete at least 24 credits from one of the three baskets or pools of elective courses provided. In particular, the basket from which a student should choose his/her electives is dependent on the specialization that the student has chosen. For example, a student with the CSPML specialization should choose 24 credits of electives from the basket named Communication, Signal Processing and Machine learning.

  • The 18 credits can be chosen from any basket (in addition to core theory, core lab and seminar credits) or outside the department courses subject to the following requirements

    • A student can take up to two electives (12 credits) from outside the department to meet their elective credits requirements. These electives must be at level 600 or above (PG level courses).

    • A student can take at most 1 UG elective (6 credits), i.e., 300 or above Level course to meet their elective credits requirements.

  • 64 credits from MTP I and MTP II (32 credits each).

List of courses

The core theory courses are (6 credits each)

  1. EE 634 Linear Algebra and its applications

  2. EE 690 Embedded systems Design 

  3. EE 629 Probability models and applications

  4. EE 622 Multivariable Control Systems


The student (regardless of the specialization) has to take at least 3 out of these 4 courses to complete the core theory course requirements.

The core lab courses are (3 credits each)

  1. EE 615 Embedded systems Design lab 
  2. EE 616 VLSI Simulations Lab 

The student (regardless of the specialization)  has to take at least 1 out of these 2 lab courses to complete the core lab course requirements.

Semester wise credits:

Semester 1

Semester 2

Semester 3

Semester 4

18 core theory credits30 credits of electives

MTech Project I

 32 credits

MTech Project II 

32 credits

3 core lab credits
4 credits seminar
12 credits electives
P/NP
Communications skills course

 

Autumn (Odd) Semester 
Basket: VLSI and Microelectronics
  • EE 610 VLSI Design
  • EE 688 Physics of Transistors
  • EE 601 Analog IC design
  • EE 648 Nanoelectronics
 

Basket: Communication, Signal Processing and Machine learning
  • EE 621 Speech Processing
  • EE 613 Speech Processing Lab
 
Basket: Power electronics and power systems
  • EE 691 Design of Photovoltaic Systems
  • EE 607 Power System Dynamics and Control
 

 

Spring (Even) semester
Basket: VLSI and Microelectronics
  • EE 633 Mixed signal VLSI Design
  • EE 626 VLSI Technology
  • EE 701 Power semiconductor devices
  • EE 632 System Design of Electronic Products
  • EE 656 VLSI Testing and testability
Basket: Communication, Signal Processing and Machine learning
  • EE 609 Pattern Recognition and Machine learning (PRML)
  • EE 612 PRML Lab
  • EE Detection and estimation theory
  • EE 624 Optimization theory and algorithms
  • EE 433 Next generation wireless networks
  • EE 608 Wireless communications
  • EE 620 Neural networks and deep learning (NNDL)
  • EE 611 NNDL Lab
Basket: Power electronics and power systems
  • EE 631 Advanced Electric Drives
  • EE 684 Design of power converters
  • EE 642 Microgrid dynamics and control
  • EE 632 System Design of Electronic Products
  • EE 422 Power system protection
  • EE 654 Smart grid
  • EE 643 Power systems operation and control
  • EE 628 Modeling and control of Renewable energy Resources
  • EE 653 Electric Vehicles: Systems and components
  • EE 623 Advanced power electronics and drives
  • EE 422 Power System Protection 
  • EE 617 Power System Simulation Lab