Course Structure Across 8 Semesters
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisite |
|---|---|---|---|---|
| 1 | EC101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | EC102 | Physics for Electronics | 3-1-0-4 | - |
| 1 | EC103 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | EC104 | Introduction to Programming | 2-0-2-3 | - |
| 1 | EC105 | Engineering Drawing & Graphics | 2-0-2-3 | - |
| 1 | EC106 | Workshop Practice | 0-0-2-1 | - |
| 2 | EC201 | Engineering Mathematics II | 3-1-0-4 | EC101 |
| 2 | EC202 | Electromagnetic Field Theory | 3-1-0-4 | EC102 |
| 2 | EC203 | Analog Electronics I | 3-1-0-4 | EC103 |
| 2 | EC204 | Digital Logic Design | 3-1-0-4 | - |
| 2 | EC205 | Signals & Systems | 3-1-0-4 | EC101 |
| 2 | EC206 | Circuit Analysis | 3-1-0-4 | - |
| 3 | EC301 | Engineering Mathematics III | 3-1-0-4 | EC201 |
| 3 | EC302 | Digital Electronics | 3-1-0-4 | EC204 |
| 3 | EC303 | Analog Electronics II | 3-1-0-4 | EC203 |
| 3 | EC304 | Microprocessor & Microcontroller | 3-1-0-4 | EC204 |
| 3 | EC305 | Communication Systems | 3-1-0-4 | EC205 |
| 3 | EC306 | Control Systems | 3-1-0-4 | EC205 |
| 4 | EC401 | Probability & Random Processes | 3-1-0-4 | EC201 |
| 4 | EC402 | Digital Signal Processing | 3-1-0-4 | EC205 |
| 4 | EC403 | VLSI Design Principles | 3-1-0-4 | EC302 |
| 4 | EC404 | Power Electronics | 3-1-0-4 | EC203 |
| 4 | EC405 | Antenna & Wave Propagation | 3-1-0-4 | EC202 |
| 4 | EC406 | Embedded Systems | 3-1-0-4 | EC304 |
| 5 | EC501 | Wireless Communication | 3-1-0-4 | EC305 |
| 5 | EC502 | Optical Fiber Communication | 3-1-0-4 | EC305 |
| 5 | EC503 | Computer Architecture | 3-1-0-4 | EC304 |
| 5 | EC504 | Renewable Energy Systems | 3-1-0-4 | EC404 |
| 5 | EC505 | Biomedical Instrumentation | 3-1-0-4 | - |
| 5 | EC506 | Advanced Digital Electronics | 3-1-0-4 | EC302 |
| 6 | EC601 | Machine Learning for Electronics | 3-1-0-4 | EC402 |
| 6 | EC602 | Smart Sensors & Actuators | 3-1-0-4 | - |
| 6 | EC603 | Internet of Things (IoT) | 3-1-0-4 | EC304 |
| 6 | EC604 | Advanced Control Systems | 3-1-0-4 | EC306 |
| 6 | EC605 | Digital Image Processing | 3-1-0-4 | EC402 |
| 6 | EC606 | Industrial Automation | 3-1-0-4 | EC306 |
| 7 | EC701 | Capstone Project I | 2-0-4-4 | - |
| 7 | EC702 | Research Methodology | 3-1-0-4 | - |
| 7 | EC703 | Project Management | 3-1-0-4 | - |
| 7 | EC704 | Special Topics in Electronics | 3-1-0-4 | - |
| 8 | EC801 | Capstone Project II | 2-0-4-4 | - |
| 8 | EC802 | Internship & Industry Exposure | 0-0-4-4 | - |
| 8 | EC803 | Professional Ethics & Social Responsibility | 2-0-0-2 | - |
Detailed Course Descriptions for Advanced Departmental Electives
Machine Learning for Electronics: This course introduces students to machine learning algorithms and their applications in electronic systems. Topics include supervised and unsupervised learning, neural networks, deep learning architectures, and optimization techniques tailored for hardware implementations. Students will explore real-world case studies involving sensor data processing, predictive maintenance, and autonomous systems.
Smart Sensors & Actuators: Focused on the development of intelligent sensing technologies, this elective covers sensor design principles, signal conditioning circuits, actuator control mechanisms, and integration with embedded platforms. Emphasis is placed on low-power design strategies, wireless communication protocols, and data fusion techniques for smart monitoring systems.
Internet of Things (IoT): This course explores the architecture, protocols, and applications of IoT systems. Students will learn about device connectivity, cloud computing integration, edge computing, security considerations, and privacy issues in connected environments. Practical sessions involve building end-to-end IoT solutions using microcontrollers, sensors, and communication modules.
Advanced Control Systems: Building on foundational control theory, this course delves into modern control techniques such as state-space representation, optimal control, robust control, and adaptive control. Students will apply these concepts to complex systems including robotic arms, power plants, and aerospace vehicles, using simulation tools like MATLAB/Simulink.
Digital Image Processing: This course focuses on algorithms and techniques used in digital image processing and computer vision. It covers image enhancement, filtering, segmentation, feature extraction, object detection, and recognition methods. Practical assignments involve implementing image processing pipelines using Python libraries such as OpenCV and scikit-image.
Industrial Automation: Designed to bridge theory with practice, this course provides an overview of industrial control systems, programmable logic controllers (PLCs), SCADA systems, and automation software. Students will engage in hands-on laboratory experiments involving process control, robot programming, and factory floor simulations.
Capstone Project I: In this initial phase of the capstone experience, students work under faculty supervision to define a research problem or design challenge related to electronics engineering. They develop project proposals, conduct literature reviews, and create implementation plans. Regular progress meetings ensure timely completion of milestones.
Research Methodology: This course equips students with essential skills for conducting independent research in electronics engineering. It covers scientific method, hypothesis testing, experimental design, data analysis, and report writing. Students learn how to structure research papers, present findings at conferences, and seek funding for innovative projects.
Project Management: Focused on managing complex engineering projects effectively, this course teaches project planning, risk assessment, resource allocation, timeline management, and team coordination strategies. Students will gain experience in using project management tools like Gantt charts, critical path method (CPM), and agile methodologies.
Special Topics in Electronics: This elective allows students to explore emerging areas in electronics engineering through specialized lectures and workshops. Past topics have included quantum computing, neuromorphic chips, flexible electronics, and bioelectronics. The course encourages interdisciplinary thinking and fosters creativity in tackling novel challenges.
Project-Based Learning Philosophy
Our department believes that learning by doing is the most effective way to develop technical proficiency and innovation capabilities. Project-based learning is integrated throughout the curriculum, starting from early semesters with mini-projects and culminating in a final-year capstone project.
The structure of our project framework emphasizes iterative development cycles, collaboration between students and faculty mentors, and real-world relevance. Students begin by selecting a topic aligned with their interests and career goals, followed by proposal writing, literature review, design phase, implementation, testing, and documentation.
Evaluation criteria include technical execution, creativity, presentation quality, peer feedback, and adherence to deadlines. Faculty members guide students through each stage, providing mentorship, resources, and constructive criticism to enhance learning outcomes.