Curriculum Overview

The Bachelor of Electronics and Communication program at Gyan Ganga College of Technology is meticulously structured to provide students with a robust foundation in both theoretical and applied aspects of electronics and communication technologies. The curriculum spans eight semesters, integrating core engineering subjects, departmental electives, science electives, and extensive laboratory experiences.

Course Structure Across Eight Semesters

SEMESTER	COURSE CODE	COURSE TITLE	CREDIT STRUCTURE (L-T-P-C)	PRE-REQUISITES
I	EC101	Engineering Mathematics I	3-1-0-4	-
I	EC102	Physics for Electronics	3-1-0-4	-
I	EC103	Chemistry for Engineers	3-1-0-4	-
I	EC104	Introduction to Programming	2-0-2-3	-
I	EC105	Engineering Drawing	1-0-2-2	-
I	EC106	Basic Electronics	3-1-0-4	-
II	EC201	Engineering Mathematics II	3-1-0-4	EC101
II	EC202	Circuit Analysis	3-1-0-4	EC106
II	EC203	Digital Logic Design	3-1-0-4	EC106
II	EC204	Electromagnetic Fields	3-1-0-4	EC102
II	EC205	Computer Programming Lab	0-0-2-2	EC104
II	EC206	Basic Electronics Lab	0-0-2-2	EC106
III	EC301	Signals and Systems	3-1-0-4	EC201
III	EC302	Analog Electronics I	3-1-0-4	EC202
III	EC303	Microprocessor Architecture	3-1-0-4	EC203
III	EC304	Communication Systems	3-1-0-4	EC301
III	EC305	Microprocessor Lab	0-0-2-2	EC303
III	EC306	Analog Electronics Lab	0-0-2-2	EC302
IV	EC401	Probability and Random Processes	3-1-0-4	EC301
IV	EC402	Analog Electronics II	3-1-0-4	EC302
IV	EC403	Digital Signal Processing	3-1-0-4	EC301
IV	EC404	Wireless Communication	3-1-0-4	EC304
IV	EC405	Digital Signal Processing Lab	0-0-2-2	EC403
IV	EC406	Embedded Systems Lab	0-0-2-2	EC303
V	EC501	Control Systems	3-1-0-4	EC301
V	EC502	VLSI Design	3-1-0-4	EC302
V	EC503	Communication Networks	3-1-0-4	EC304
V	EC504	Network Security	3-1-0-4	EC404
V	EC505	VLSI Design Lab	0-0-2-2	EC502
V	EC506	Wireless Communication Lab	0-0-2-2	EC404
VI	EC601	Artificial Intelligence	3-1-0-4	EC401
VI	EC602	Power Electronics	3-1-0-4	EC202
VI	EC603	Robotics and Automation	3-1-0-4	EC501
VI	EC604	Quantitative Finance	3-1-0-4	EC401
VI	EC605	AI and ML Lab	0-0-2-2	EC601
VI	EC606	Power Electronics Lab	0-0-2-2	EC602
VII	EC701	Advanced Topics in Communication	3-1-0-4	EC503
VII	EC702	Electronics System Design	3-1-0-4	EC601
VII	EC703	Research Methodology	2-0-0-3	-
VII	EC704	Mini Project I	0-0-4-2	-
VIII	EC801	Capstone Project	0-0-8-6	EC703
VIII	EC802	Internship	0-0-0-6	-

Advanced Departmental Electives

The department offers a range of advanced elective courses designed to provide students with specialized knowledge and practical skills relevant to emerging technologies. These courses are taught by experienced faculty members who bring industry expertise into the classroom.

1. Artificial Intelligence and Machine Learning

This course introduces students to fundamental concepts in AI, including search algorithms, neural networks, deep learning frameworks, and reinforcement learning. Students explore real-world applications such as natural language processing, computer vision, and robotics, with hands-on experience using tools like TensorFlow, PyTorch, and scikit-learn.

2. Wireless Communication Systems

This course covers the design and implementation of wireless communication systems, including modulation techniques, channel coding, multiple access protocols, and antenna arrays. Students learn to analyze performance metrics and optimize system parameters for efficient data transmission over various wireless channels.

3. Embedded Systems Design

This elective focuses on designing and implementing embedded systems using microcontrollers and real-time operating systems. Topics include hardware-software co-design, memory management, interrupt handling, and interfacing with sensors and actuators to create intelligent devices for industrial and consumer applications.

4. VLSI Design and Testing

This course provides an in-depth understanding of Very Large Scale Integration (VLSI) design principles, including logic synthesis, physical design, testing methodologies, and layout automation tools. Students gain experience using industry-standard CAD tools like Cadence, Synopsys, and Mentor Graphics for designing digital circuits and integrated systems.

5. Signal Processing and Pattern Recognition

This course explores advanced signal processing techniques used in audio, video, biomedical, and image analysis. Students study filtering methods, spectral estimation, feature extraction, and classification algorithms applied to real-world data sets using MATLAB and Python-based platforms.

6. Network Security and Cryptography

This elective delves into the principles of network security, including encryption standards, authentication protocols, intrusion detection systems, and secure communication architectures. Students examine vulnerabilities in modern networks and learn to implement robust defense mechanisms using industry tools like Wireshark, Snort, and Nessus.

7. Power Electronics and Renewable Energy

This course addresses the conversion and control of electrical power using electronic devices, focusing on applications in renewable energy systems such as solar panels, wind turbines, and battery storage systems. Students study power converters, inverters, motor drives, and grid integration strategies for sustainable energy solutions.

8. Robotics and Autonomous Systems

This course integrates mechanical engineering, electronics, control theory, and artificial intelligence to build autonomous robots capable of performing complex tasks in dynamic environments. Students work with ROS (Robot Operating System), sensors, actuators, and machine learning algorithms to develop intelligent robotic systems.

9. Quantitative Finance and Financial Engineering

This course bridges the gap between finance and engineering by applying mathematical models and computational tools to financial markets. Students study derivatives pricing, risk management, algorithmic trading, portfolio optimization, and quantitative modeling techniques using Python, R, and financial databases.

10. Digital Image Processing

This elective covers techniques for analyzing, manipulating, and enhancing digital images using mathematical algorithms and signal processing methods. Students explore image restoration, compression, segmentation, feature extraction, and pattern recognition applied to medical imaging, remote sensing, and computer vision applications.

Project-Based Learning Philosophy

The department emphasizes project-based learning as a cornerstone of engineering education. This approach enables students to apply theoretical knowledge in practical scenarios, fostering creativity, collaboration, and problem-solving skills essential for professional success.

Mini Projects

Mini projects are conducted during the second year, allowing students to work on small-scale implementations that reinforce concepts learned in core courses. These projects typically span 6-8 weeks and involve team-based activities with mentorship from faculty members. Students are expected to present their findings at end-of-project symposiums and submit detailed reports documenting their methodology and outcomes.

Final-Year Thesis/Capstone Project

The final-year thesis/capstone project represents the culmination of students' academic journey, requiring them to conduct independent research or develop a comprehensive solution to a significant engineering challenge. Projects are selected based on student interests, faculty availability, and industry relevance.

Project Selection Process

Students begin selecting projects during the seventh semester, guided by their academic performance, personal interests, and faculty recommendations. They submit project proposals outlining objectives, scope, timeline, and resource requirements for approval by the Department Head and relevant faculty mentors.

Evaluation Criteria

Projects are evaluated based on several criteria including technical depth, innovation, feasibility, documentation quality, presentation skills, and team collaboration. Regular progress reviews ensure timely completion and adherence to academic standards.