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.