Master’s program in Electrical and Electronics Engineering
*Core Courses*
(Common for All Students)
1. *Advanced Mathematics for Engineers*
- Linear algebra, optimization, numerical methods, stochastic processes.
- Applications in circuit design, signal processing, and control systems.
2. *Advanced Electrical Circuits & Systems*
- Nonlinear circuits, network synthesis, distributed parameter systems.
3. *Power Electronics & Drives*
- Converters (AC/DC, DC/DC), inverters, motor drives, renewable energy integration.
4. *Digital Signal & Image Processing*
- Discrete-time systems, wavelet transforms, computer vision, real-time DSP applications.
5. *VLSI Design & Embedded Systems*
- CMOS design, FPGA programming, System-on-Chip (SoC), ARM-based embedded systems.
6. *Control Systems & Robotics*
- State-space modelling, adaptive control, robotic kinematics, industrial automation.
7. *Electromagnetic Fields & Wave Propagation*
- Maxwell’s equations, antennas, RF/microwave engineering, optical communication.
8. *Semiconductor Devices & Nanoelectronics*
- MOSFETs, MEMS, optoelectronics, quantum devices, fabrication techniques.
9. *Research Methodology & Technical Communication*
- Thesis writing, grant proposals, data analysis tools (MATLAB, Python).
*Specialization Tracks*
(Choose 1–2 Focus Areas)
*1. Power Systems & Renewable Energy*
- Smart grid technologies
- Energy storage systems (batteries, supercapacitors)
- High-voltage engineering
- Wind/solar energy systems
- Power system cybersecurity
*2. Electronics & Embedded Systems*
- Analog/digital VLSI design
- IoT hardware and sensor networks
- Real-time operating systems (RTOS)
- MEMS/NEMS devices
- Low-power circuit design
*3. Communications & Networking*
- 5G/6G wireless networks
- Optical fibre communication
- Satellite and radar systems
- Error-correcting codes
- Network security for IoT
*4. Control, Automation & Robotics*
- Industrial robotics (PLC, SCADA)
- Autonomous vehicles and drones
- AI/ML for predictive control
- Swarm robotics
- Human-machine interfaces
*5. Signal Processing & AI*
- Biomedical signal analysis
- Deep learning for computer vision
- Speech and audio processing
- Radar signal processing
- Reinforcement learning in control systems
*6. Microelectronics & Photonics*
- Nanoelectronics and spintronics
- Photonic integrated circuits
- Semiconductor fabrication labs
- Quantum computing basics
- Advanced semiconductor modelling
*Lab/Project Work*
- *Advanced Labs*:
- Power electronics simulation (PSIM, PLECS)
- VLSI design (Cadence, Synopsys)
- Embedded systems (Arduino, Raspberry Pi, FPGA)
- RF/antenna design (ANSYS HFSS, CST)
- *Capstone Project/Thesis*:
- Industry-sponsored projects (e.g., EV charging systems, smart grid prototypes).
- Original research (e.g., AI-driven power management, nano-sensor design).
*Sample Semester-wise Structure*
(2-Year Program Example)
| *Year 1* | *Year 2* |
|-------------------------------------|-------------------------------------|
| *Semester 1* | *Semester 3* |
| - Advanced Mathematics | - Elective 3 |
| - Power Electronics | - Elective 4 |
| - VLSI Design | - Thesis/Project Phase I |
| *Semester 2* | *Semester 4* |
| - Digital Signal Processing | - Thesis/Project Phase II |
| - Control Systems | - Elective 5 (if required) |
| - Elective 1 | - Industry Internship (optional) |
*Emerging Topics (Electives)*
- Electric Vehicle Technology
- AI/ML for Power Systems Optimization
- Biomedical Instrumentation
- Cybersecurity for Critical Infrastructure
- Quantum Sensors and Devices
- Edge Computing for IoT
*Credit System*
- *Total Credits*: 60–80 ECTS (varies by country).
- *Breakdown*:
- Core Courses: 25–30 credits
- Electives: 20–25 credits
- Thesis/Project: 15–20 credits
*Key Skills Developed*
- Circuit design and simulation (PSpice, LTspice).
- Embedded programming (C/C++, Verilog/VHDL).
- Power system analysis (ETAP, MATLAB/Simulink).
- Research and innovation in IoT, robotics, and smart grids.