Course Structure Overview

Semester	Course Code	Course Title	Credit (L-T-P-C)	Pre-requisites
I	EE101	Engineering Mathematics I	3-1-0-4	-
I	EE102	Physics for Electronics	3-1-0-4	-
I	EE103	Chemistry for Electronics	3-1-0-4	-
I	EE104	Programming and Problem Solving	3-1-0-4	-
I	EE105	Engineering Drawing and Graphics	2-1-0-3	-
I	EE106	Basic Electrical Engineering	3-1-0-4	-
I	EE107	Electronics Devices and Circuits Lab	0-0-3-1.5	-
I	EE108	Programming Lab	0-0-3-1.5	-
II	EE201	Engineering Mathematics II	3-1-0-4	EE101
II	EE202	Electromagnetic Fields	3-1-0-4	EE102
II	EE203	Electronic Circuits I	3-1-0-4	EE106
II	EE204	Digital Logic Design	3-1-0-4	EE106
II	EE205	Object-Oriented Programming using C++	3-1-0-4	EE104
II	EE206	Basic Electronics Lab	0-0-3-1.5	-
II	EE207	Digital Logic Design Lab	0-0-3-1.5	-
III	EE301	Engineering Mathematics III	3-1-0-4	EE201
III	EE302	Electronics Devices and Circuits II	3-1-0-4	EE203
III	EE303	Signals and Systems	3-1-0-4	EE201
III	EE304	Microprocessor Architecture	3-1-0-4	EE204
III	EE305	Control Systems	3-1-0-4	EE201
III	EE306	Electronics Circuits Lab	0-0-3-1.5	-
III	EE307	Microprocessor Lab	0-0-3-1.5	-
IV	EE401	Engineering Mathematics IV	3-1-0-4	EE301
IV	EE402	Communication Engineering	3-1-0-4	EE303
IV	EE403	Embedded Systems	3-1-0-4	EE304
IV	EE404	Power Electronics	3-1-0-4	EE203
IV	EE405	Wireless Communication	3-1-0-4	EE303
IV	EE406	Embedded Systems Lab	0-0-3-1.5	-
IV	EE407	Power Electronics Lab	0-0-3-1.5	-
V	EE501	Advanced Signal Processing	3-1-0-4	EE303
V	EE502	Microcontroller Based Design	3-1-0-4	EE304
V	EE503	Digital Image Processing	3-1-0-4	EE303
V	EE504	Computer Architecture	3-1-0-4	EE204
V	EE505	VLSI Design Principles	3-1-0-4	EE203
V	EE506	Digital Image Processing Lab	0-0-3-1.5	-
V	EE507	Computer Architecture Lab	0-0-3-1.5	-
VI	EE601	Artificial Intelligence & Machine Learning	3-1-0-4	EE501
VI	EE602	Network Security	3-1-0-4	EE402
VI	EE603	Renewable Energy Systems	3-1-0-4	EE404
VI	EE604	Robotics and Automation	3-1-0-4	EE305
VI	EE605	Medical Electronics	3-1-0-4	EE203
VI	EE606	AI & ML Lab	0-0-3-1.5	-
VI	EE607	Robotics Lab	0-0-3-1.5	-
VII	EE701	Capstone Project I	0-0-6-3	-
VII	EE702	Elective Course I	3-1-0-4	-
VII	EE703	Elective Course II	3-1-0-4	-
VIII	EE801	Capstone Project II	0-0-6-3	-
VIII	EE802	Elective Course III	3-1-0-4	-
VIII	EE803	Elective Course IV	3-1-0-4	-

Advanced Departmental Electives

The department offers a range of advanced elective courses designed to meet the growing demands of specialized fields within electronics. These courses provide students with in-depth knowledge and practical skills necessary for tackling complex real-world challenges.

Advanced Signal Processing: This course delves into advanced techniques for signal analysis, including wavelet transforms, adaptive filtering, and spectral estimation. Students learn to apply these methods to real-world applications such as audio and image processing, biomedical signal analysis, and telecommunications systems. The course includes hands-on labs using MATLAB and Python, where students implement algorithms and analyze their performance under various conditions.

Microcontroller Based Design: This elective introduces students to the design and implementation of embedded systems using microcontrollers such as ARM Cortex-M series, PIC, and Arduino platforms. The course covers topics like real-time operating systems, interrupt handling, peripheral interfacing, and communication protocols. Students build complete projects involving sensors, actuators, and wireless modules, gaining practical experience in designing robust embedded solutions.

Digital Image Processing: This course focuses on the techniques used to process and analyze digital images for applications in computer vision, medical imaging, and remote sensing. Students learn about image enhancement, restoration, segmentation, feature extraction, and object recognition. The course includes lab sessions using OpenCV and MATLAB, where students develop algorithms for tasks like face detection, image compression, and motion tracking.

Computer Architecture: This course explores the design principles and implementation details of modern computer systems. Topics include instruction set architecture, pipeline design, memory hierarchy, cache organization, and virtualization. Students engage in simulation exercises using tools like SPIM and MARS, gaining insights into how processors execute instructions and manage resources efficiently.

VLSI Design Principles: This course provides an introduction to the design and fabrication of integrated circuits. Students learn about CMOS technology, logic synthesis, layout design, and testing methods. The course includes lab sessions using industry-standard EDA tools such as Cadence and Synopsys, where students design and simulate digital circuits and learn about the challenges involved in translating designs into physical chips.

Artificial Intelligence & Machine Learning: This elective covers fundamental concepts in AI and ML, including supervised and unsupervised learning, neural networks, deep learning frameworks, and reinforcement learning. Students implement algorithms using TensorFlow, PyTorch, and scikit-learn, working on real datasets to solve problems in natural language processing, computer vision, and robotics.

Network Security: This course addresses the challenges of securing networked systems against cyber threats. Topics include cryptography, secure protocols, firewall design, intrusion detection, and vulnerability assessment. Students gain practical experience through labs involving packet analysis, penetration testing, and secure coding practices, preparing them for careers in cybersecurity and network administration.

Renewable Energy Systems: This elective focuses on the integration of renewable energy sources into electrical grids. Students study solar photovoltaic systems, wind turbines, battery storage technologies, and smart grid concepts. The course includes simulations and case studies involving actual installations, helping students understand the technical and economic aspects of deploying sustainable energy solutions.

Robotics and Automation: This course combines principles from control theory, sensor integration, and mechanical design to create autonomous robotic systems. Students learn about robot kinematics, path planning, sensor fusion, and control algorithms. The course includes hands-on projects where students build and program robots capable of performing tasks such as object recognition, navigation, and manipulation.

Medical Electronics: This elective explores the application of electronic principles in healthcare technologies. Students study biomedical sensors, patient monitoring systems, medical imaging devices, and diagnostic equipment. The course includes lab sessions involving the design and testing of electronic circuits for medical applications, preparing students for careers in the rapidly growing field of health technology.

Project-Based Learning Philosophy

The department places a strong emphasis on project-based learning as a means to bridge the gap between theory and practice. The curriculum integrates project work throughout all semesters, ensuring that students gain real-world experience while building their technical skills and creativity.

Mini Projects: In the second year, students undertake mini-projects focusing on specific areas of interest. These projects are typically small-scale and designed to reinforce concepts learned in core courses. Students work in teams under faculty supervision, developing prototypes or conducting experiments that demonstrate their understanding of key principles.

Final-Year Thesis/Capstone Project: The final year culminates in a comprehensive capstone project, where students select a topic aligned with their interests and career goals. This project involves extensive research, design, implementation, and presentation. Students collaborate closely with faculty mentors and industry partners to ensure relevance and impact.

The selection process for projects is transparent and fair. Students are encouraged to propose ideas based on their interests or suggestions from faculty members. Faculty mentors guide students through the research phase, helping them refine their approach, select appropriate tools, and develop effective methodologies.

Evaluation criteria include innovation, technical depth, feasibility, documentation quality, and presentation skills. Projects are assessed by a panel of faculty members and industry experts, ensuring that students receive constructive feedback and recognition for their efforts.