Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisite |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ECE102 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | CSE103 | Introduction to Programming (C) | 2-1-0-3 | - |
| 1 | PHY104 | Applied Physics | 3-1-0-4 | - |
| 1 | ECE105 | Engineering Drawing & Workshop Practice | 2-1-0-3 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ECE202 | Electronic Devices and Circuits | 3-1-0-4 | ECE102 |
| 2 | ECE203 | Network Analysis | 3-1-0-4 | ECE102 |
| 2 | CSE204 | Data Structures & Algorithms | 3-1-0-4 | CSE103 |
| 2 | ECE205 | Analog and Digital Electronics | 3-1-0-4 | ECE202 |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ECE302 | Microprocessor Architecture | 3-1-0-4 | ECE205 |
| 3 | ECE303 | Embedded Systems | 3-1-0-4 | ECE205 |
| 3 | ECE304 | Control Systems | 3-1-0-4 | ENG201 |
| 3 | ECE305 | Communication Systems | 3-1-0-4 | ECE205 |
| 4 | ECE401 | VLSI Design | 3-1-0-4 | ECE302 |
| 4 | ECE402 | Power Electronics | 3-1-0-4 | ECE205 |
| 4 | ECE403 | Digital Signal Processing | 3-1-0-4 | ENG201 |
| 4 | ECE404 | Antenna and Microwave Engineering | 3-1-0-4 | ECE305 |
| 4 | ECE405 | Internet of Things (IoT) | 3-1-0-4 | ECE303 |
| 5 | ECE501 | Advanced Embedded Systems | 3-1-0-4 | ECE303 |
| 5 | ECE502 | Machine Learning & AI | 3-1-0-4 | CSE204 |
| 5 | ECE503 | RF and Microwave Engineering | 3-1-0-4 | ECE404 |
| 5 | ECE504 | Renewable Energy Systems | 3-1-0-4 | ECE402 |
| 5 | ECE505 | Signal Processing & Pattern Recognition | 3-1-0-4 | ECE403 |
| 6 | ECE601 | Advanced Digital Design | 3-1-0-4 | ECE401 |
| 6 | ECE602 | Research Methodology | 2-1-0-3 | - |
| 6 | ECE603 | Final Year Project I | 4-0-0-4 | - |
| 7 | ECE701 | Final Year Project II | 4-0-0-4 | ECE603 |
| 7 | ECE702 | Capstone Lab | 2-1-0-3 | ECE603 |
| 7 | ECE703 | Project Presentation & Viva | 2-0-0-2 | ECE701 |
| 8 | ECE801 | Internship | 6-0-0-6 | - |
Detailed Departmental Elective Courses
Machine Learning & AI (ECE502): This course delves into the core concepts of machine learning algorithms, including supervised and unsupervised learning techniques. Students will gain hands-on experience with libraries like TensorFlow, PyTorch, and scikit-learn, enabling them to build predictive models and implement intelligent systems.
Advanced Embedded Systems (ECE501): This elective explores advanced topics in embedded software development, including real-time operating systems, memory management, and low-power design. Students will work on projects involving ARM Cortex-M microcontrollers and IoT platforms.
Renewable Energy Systems (ECE504): Focusing on the integration of renewable energy sources into electrical grids, this course covers photovoltaic systems, wind turbines, and battery storage technologies. Practical sessions involve designing and simulating power systems using MATLAB/Simulink.
RF and Microwave Engineering (ECE503): This course introduces students to the principles of radio frequency and microwave engineering, including transmission lines, waveguides, antennas, and circuit design. Students will design and test RF circuits using simulation tools like CST Studio Suite.
Signal Processing & Pattern Recognition (ECE505): Emphasizing signal processing techniques for pattern recognition, this course covers image processing, audio analysis, and feature extraction methods. Practical sessions involve analyzing biomedical signals and applying machine learning algorithms to classify patterns.
Advanced Digital Design (ECE601): Designed for students interested in VLSI design, this course covers digital logic synthesis, FPGA implementation, and system-on-chip (SoC) architecture. Students will develop custom digital circuits using Verilog HDL and Xilinx Vivado tools.
Research Methodology (ECE602): This foundational course prepares students for conducting independent research by teaching them how to formulate hypotheses, design experiments, analyze data, and present findings effectively. It emphasizes ethical considerations in scientific research and the importance of peer review.
Final Year Project I (ECE603): Students begin their final-year project under faculty supervision, selecting a topic aligned with their interests or industry needs. They develop a detailed proposal, conduct literature review, and initiate preliminary experiments or simulations.
Final Year Project II (ECE701): In this advanced phase, students execute their projects, refine methodologies, collect and analyze data, and document results. They present their work in a formal setting, demonstrating technical proficiency and communication skills.
Capstone Lab (ECE702): This lab component allows students to integrate knowledge from various courses into a cohesive project. It involves designing, building, testing, and documenting a complete system that addresses real-world problems in electronics engineering.
Project Presentation & Viva (ECE703): Students defend their final projects through presentations and viva voce examinations. This process assesses their understanding of the subject matter, ability to communicate complex ideas clearly, and readiness for professional practice.
Internship (ECE801): The internship provides students with real-world experience in a professional setting. They work on actual industry projects, gaining insight into company operations, team dynamics, and practical problem-solving techniques while contributing to meaningful outcomes.
Project-Based Learning Philosophy
Our department strongly believes that project-based learning is the most effective way to develop critical thinking, innovation, and practical application skills. Projects are structured to align with industry standards and real-world challenges, ensuring students gain relevant experience before entering the workforce.
The mandatory mini-projects in early semesters provide foundational exposure to problem-solving and teamwork. These projects are typically small-scale, focused on specific learning outcomes, and serve as building blocks for more complex capstone projects in later years.
For the final-year thesis/capstone project, students can choose from a wide range of topics related to their specializations or industry needs. Faculty mentors guide them through the process, helping them define scope, select appropriate methodologies, and execute their ideas effectively.
Students are encouraged to collaborate with peers from other disciplines, such as computer science and mechanical engineering, fostering interdisciplinary thinking and enhancing project complexity. This collaborative approach mirrors real-world engineering environments where multidisciplinary teams work together to solve complex problems.
Evaluation criteria for projects include innovation, technical depth, clarity of documentation, presentation quality, and adherence to deadlines. Regular feedback from mentors ensures continuous improvement throughout the project lifecycle, preparing students for success in professional settings.