Comprehensive Course Listing Across 8 Semesters
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | CSE101 | Computer Programming | 2-0-2-3 | - |
| 1 | ELE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Graphics and Design | 2-1-0-3 | - |
| 1 | ENG103 | Communication Skills | 2-0-0-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ELE201 | Circuit Analysis | 3-1-0-4 | ELE101 |
| 2 | ELE202 | Electromagnetic Fields | 3-1-0-4 | PHY101 |
| 2 | ELE203 | Digital Logic Design | 3-1-0-4 | CSE101 |
| 2 | ELE204 | Signals and Systems | 3-1-0-4 | ENG201 |
| 2 | ELE205 | Electrical Machines I | 3-1-0-4 | ELE101 |
| 2 | ENG202 | Workshop Practice | 0-0-2-1 | - |
| 3 | ELE301 | Power System Analysis | 3-1-0-4 | ELE201 |
| 3 | ELE302 | Control Systems | 3-1-0-4 | ELE204 |
| 3 | ELE303 | Microprocessors and Microcontrollers | 3-1-0-4 | CSE101 |
| 3 | ELE304 | Electromagnetic Compatibility | 3-1-0-4 | ELE202 |
| 3 | ELE305 | Analog Electronics | 3-1-0-4 | ELE201 |
| 3 | ELE306 | Communication Systems | 3-1-0-4 | ELE204 |
| 4 | ELE401 | Power Electronics and Drives | 3-1-0-4 | ELE305 |
| 4 | ELE402 | Renewable Energy Systems | 3-1-0-4 | ELE301 |
| 4 | ELE403 | Embedded Systems | 3-1-0-4 | ELE303 |
| 4 | ELE404 | Advanced Control Systems | 3-1-0-4 | ELE302 |
| 4 | ELE405 | Digital Signal Processing | 3-1-0-4 | ELE204 |
| 4 | ELE406 | Artificial Intelligence in Electrical Engineering | 3-1-0-4 | ELE303 |
| 5 | ELE501 | Smart Grid Technologies | 3-1-0-4 | ELE301 |
| 5 | ELE502 | RF Engineering and Antenna Design | 3-1-0-4 | ELE202 |
| 5 | ELE503 | Nanoelectronics and VLSI Design | 3-1-0-4 | ELE305 |
| 5 | ELE504 | Wireless Communication Systems | 3-1-0-4 | ELE306 |
| 5 | ELE505 | Power System Protection | 3-1-0-4 | ELE301 |
| 5 | ELE506 | Industrial Automation | 3-1-0-4 | ELE302 |
| 6 | ELE601 | Energy Storage Systems | 3-1-0-4 | ELE402 |
| 6 | ELE602 | Advanced Microprocessors | 3-1-0-4 | ELE303 |
| 6 | ELE603 | Motion Control Systems | 3-1-0-4 | ELE302 |
| 6 | ELE604 | Wireless Sensor Networks | 3-1-0-4 | ELE306 |
| 6 | ELE605 | Machine Learning for Electrical Systems | 3-1-0-4 | ELE406 |
| 7 | ELE701 | Research Methodology | 2-0-0-2 | - |
| 7 | ELE702 | Capstone Project I | 0-0-4-4 | ELE605 |
| 8 | ELE801 | Capstone Project II | 0-0-6-6 | ELE702 |
Detailed Descriptions of Advanced Departmental Electives
Power Electronics and Drives: This course explores the principles and applications of power electronics converters, inverters, rectifiers, and motor drives. Students study the design and implementation of switching circuits used in electric vehicles, renewable energy systems, and industrial automation.
Renewable Energy Systems: Focused on solar, wind, hydroelectric, and geothermal energy sources, this course covers system design, integration challenges, grid compatibility, and economic analysis. Students work on projects involving solar panel arrays, wind turbine modeling, and microgrid configurations.
Embedded Systems: This elective teaches students how to design embedded software systems using microcontrollers, real-time operating systems, and hardware-software co-design techniques. Projects include developing smart sensors, home automation systems, and mobile robotics platforms.
Advanced Control Systems: Building upon introductory control theory, this course delves into nonlinear control, adaptive control, robust control, and optimal control methods. Students learn to model complex dynamic systems and design controllers for aerospace, automotive, and industrial applications.
Digital Signal Processing: This course covers discrete-time signal processing techniques including Fourier transforms, filter design, and spectral analysis. Applications include audio processing, biomedical signal analysis, radar systems, and image enhancement algorithms.
Artificial Intelligence in Electrical Engineering: Integrating AI tools with electrical engineering problems, this course focuses on neural networks, deep learning, reinforcement learning, and their applications in power system optimization, fault diagnosis, and smart grid management.
Smart Grid Technologies: This subject explores modern grid infrastructure including smart meters, demand response systems, energy storage integration, and grid stability enhancement techniques. Students examine real-world case studies from utilities like Tata Power and BHEL.
RF Engineering and Antenna Design: Designed for students interested in wireless communications, this course covers electromagnetic wave propagation, antenna radiation patterns, transmission lines, and microwave circuit design. Practical lab sessions involve designing and testing antennas for different applications.
Nanoelectronics and VLSI Design: This advanced topic introduces semiconductor device physics, CMOS technology, digital integrated circuit design, and FPGA-based systems. Students gain experience in CAD tools like Cadence and Synopsys while building custom chips for specific functions.
Wireless Communication Systems: Covering modulation schemes, channel coding, multiple access techniques, and wireless network protocols, this course provides theoretical understanding and practical insights into modern communication standards such as 5G and Wi-Fi.
Project-Based Learning Philosophy
Our department places significant emphasis on project-based learning to ensure students acquire both technical competence and real-world problem-solving skills. The approach integrates classroom knowledge with hands-on experience, encouraging creativity and innovation in engineering design.
The mandatory Mini-Projects are undertaken during the third and fourth years of study. These projects typically last 6–8 weeks and involve teams of 3–5 students working under faculty supervision. Topics range from developing a prototype for an energy-efficient lighting system to designing a low-cost water quality monitoring device.
The final-year Capstone Project is the most comprehensive component of our curriculum. Students select a research topic aligned with their specialization and work closely with a faculty mentor for 12–14 weeks. The project must demonstrate originality, technical depth, and practical relevance. Evaluation includes progress reports, oral presentations, and a final written thesis.
Faculty mentors are selected based on expertise in the relevant domain. Each student is assigned one primary supervisor and may collaborate with secondary experts from other departments or external institutions. Regular meetings and milestone reviews ensure project alignment with academic standards and industry expectations.