Comprehensive Course Listing Across 8 Semesters
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| I | ENG101 | English for Engineers | 3-0-0-3 | - |
| I | MAT101 | Calculus and Differential Equations | 4-0-0-4 | - |
| I | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| I | CHE101 | Chemistry for Engineers | 3-0-0-3 | - |
| I | ECE101 | Introduction to Electrical Engineering | 2-0-0-2 | - |
| I | ENG102 | Engineering Graphics and Design | 1-0-3-2 | - |
| II | MAT201 | Linear Algebra and Numerical Methods | 3-0-0-3 | MAT101 |
| II | ECE201 | Circuit Analysis | 4-0-0-4 | MAT101, PHY101 |
| II | ECE202 | Electromagnetic Fields | 3-0-0-3 | MAT101, PHY101 |
| II | ECE203 | Digital Electronics | 3-0-0-3 | ECE101 |
| II | ECE204 | Signals and Systems | 3-0-0-3 | MAT101, MAT201 |
| III | ECE301 | Power System Analysis | 4-0-0-4 | ECE201, ECE202 |
| III | ECE302 | Control Systems | 4-0-0-4 | ECE204 |
| III | ECE303 | Analog Electronic Circuits | 3-0-0-3 | ECE203, ECE201 |
| III | ECE304 | Microprocessors and Microcontrollers | 3-0-0-3 | ECE203 |
| IV | ECE401 | Digital Signal Processing | 4-0-0-4 | ECE204, ECE303 |
| IV | ECE402 | Communication Systems | 4-0-0-4 | ECE204 |
| IV | ECE403 | Power Electronics | 4-0-0-4 | ECE303 |
| IV | ECE404 | Embedded Systems Design | 4-0-0-4 | ECE304, ECE201 |
| V | ECE501 | Renewable Energy Systems | 4-0-0-4 | ECE301 |
| V | ECE502 | Smart Grid Technologies | 4-0-0-4 | ECE301 |
| V | ECE503 | Advanced Control Systems | 4-0-0-4 | ECE302 |
| V | ECE504 | AI and Machine Learning in EE | 4-0-0-4 | ECE401 |
| VI | ECE601 | Advanced Digital Systems | 4-0-0-4 | ECE404 |
| VI | ECE602 | VLSI Design | 4-0-0-4 | ECE303 |
| VI | ECE603 | Wireless Communication | 4-0-0-4 | ECE402 |
| VI | ECE604 | Electrical Machines and Drives | 4-0-0-4 | ECE301 |
| VII | ECE701 | Capstone Project | 8-0-0-8 | All previous semesters |
| VIII | ECE801 | Research Methodology | 2-0-0-2 | ECE701 |
Advanced Departmental Elective Courses
The department offers several advanced elective courses that allow students to specialize in specific areas of interest. These courses are designed to bridge the gap between theory and application, providing students with hands-on experience and exposure to current trends in the field.
Power Electronics and Drives: This course delves into the principles and applications of power electronic converters, motor drives, and variable speed control systems. Students learn about rectifiers, inverters, choppers, and their applications in industrial automation and renewable energy systems. The course includes laboratory sessions where students design and test various power electronic circuits using simulation software like MATLAB/Simulink and hardware prototyping tools.
Smart Grid Technologies: Focused on modernizing traditional power grids with digital technologies, this course covers topics such as grid automation, demand response systems, and energy storage integration. Students explore how smart meters, sensors, and communication networks contribute to improving efficiency, reliability, and sustainability in power distribution.
Artificial Intelligence and Machine Learning in Electrical Engineering: This interdisciplinary course combines knowledge of AI/ML techniques with electrical engineering fundamentals. Students learn how neural networks, deep learning models, and optimization algorithms can be applied to solve problems in signal processing, control systems, and power management. The course includes practical assignments involving real-world datasets and implementation using Python-based libraries like TensorFlow and PyTorch.
VLSI Design: This course introduces students to the design and verification of very large-scale integrated circuits. Topics include logic synthesis, layout design, timing analysis, and testing methodologies. Students gain experience with industry-standard tools such as Cadence Virtuoso and Synopsys Design Compiler while working on projects involving digital circuit implementation.
Embedded Systems Design: Designed for students interested in developing intelligent systems, this course covers microcontroller architectures, real-time operating systems, and embedded software development. Practical labs involve programming ARM-based processors, interfacing sensors and actuators, and designing embedded applications for IoT devices.
Renewable Energy Integration: This course explores the technical challenges associated with integrating renewable energy sources into existing power systems. Students study solar photovoltaic systems, wind turbines, hydroelectric generators, and battery storage technologies. The course emphasizes grid stability, power quality issues, and policy frameworks supporting clean energy transitions.
Advanced Control Systems: Building upon foundational control theory, this course covers advanced topics such as robust control, adaptive control, and optimal control. Students learn to model complex systems, design controllers using state-space methods, and analyze system performance under uncertainty. The course includes case studies involving aerospace, automotive, and manufacturing applications.
Digital Signal Processing: This course provides an in-depth understanding of digital signal processing techniques used in audio, video, and communication systems. Topics include discrete-time signals and systems, Z-transforms, FFT algorithms, filter design, and spectral analysis. Students implement DSP algorithms using MATLAB, Python, and hardware platforms like ARM Cortex-M processors.
Wireless Communication: Covering modern wireless technologies including cellular networks, Wi-Fi, Bluetooth, and satellite communications, this course explores modulation schemes, multiple access techniques, and error correction methods. Students engage in laboratory experiments involving RF signal generation, spectrum analysis, and wireless network simulation using tools like GNU Radio and MATLAB.
Electric Vehicles and Transportation Systems: This course examines the technical aspects of electric vehicle design, including battery management systems, motor controllers, charging infrastructure, and vehicle-to-grid integration. Students study propulsion systems, energy efficiency optimization, and regulatory standards for electric mobility solutions.
Project-Based Learning Philosophy
The department's approach to project-based learning is centered on fostering creativity, critical thinking, and real-world problem-solving skills among students. Projects are structured to mirror actual industry challenges and provide opportunities for interdisciplinary collaboration.
Mini-Projects: Beginning in the second year, students undertake small-scale projects that reinforce classroom learning. These projects typically span one semester and involve designing and implementing solutions to well-defined problems. Mini-projects are evaluated based on technical execution, innovation, presentation quality, and teamwork.
Final-Year Thesis/Capstone Project: The capstone project is a comprehensive endeavor that allows students to integrate knowledge from all previous semesters. Students select projects in consultation with faculty mentors, ensuring alignment with current research trends or industrial needs. The project involves literature review, design, implementation, testing, documentation, and final presentation.
Project Selection Process: Students begin selecting projects in their third year through a formal proposal process. They submit project ideas to faculty advisors, who assess feasibility, novelty, and relevance to departmental strengths. Projects are often aligned with ongoing research initiatives or sponsored by industry partners.
Evaluation Criteria: Projects are assessed using a rubric that includes technical depth, creativity, adherence to deadlines, effective documentation, and presentation skills. Regular progress reviews ensure timely completion and provide feedback for improvement.