Comprehensive Course Structure

The Electrical Engineering curriculum at Mahatama Gandhi University Ri Bhoi is meticulously designed to provide a comprehensive understanding of core principles while allowing flexibility for specialization. The program spans eight semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory components.

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Pre-requisites
1st Semester	PH101	Physics for Electrical Engineering	3-1-0-4	None
	CH101	Chemistry for Engineering Students	3-1-0-4	None
	MA101	Mathematics I	3-1-0-4	None
	EC101	Introduction to Electrical Engineering	2-0-0-2	None
	ES101	Engineering Graphics and Design	2-1-0-3	None
	ME101	Introduction to Mechanics of Materials	3-1-0-4	None
	CP101	Computer Programming	2-1-0-3	None
	PE101	Physical Education & Sports	0-0-1-1	None
2nd Semester	PH102	Physics II: Waves and Optics	3-1-0-4	PH101
	CH102	Chemistry II: Organic Chemistry	3-1-0-4	CH101
	MA102	Mathematics II: Calculus and Differential Equations	3-1-0-4	MA101
	EC102	Basic Circuit Analysis	3-1-0-4	EC101
	EC103	Electronic Devices and Circuits	3-1-0-4	EC102
	EC104	Digital Logic Design	2-1-0-3	EC102
	CP102	Data Structures and Algorithms	3-1-0-4	CP101
	PE102	Physical Education & Sports	0-0-1-1	PE101
3rd Semester	MA201	Mathematics III: Linear Algebra and Complex Variables	3-1-0-4	MA102
	EC201	Electromagnetic Fields and Waves	3-1-0-4	PH102
	EC202	Network Analysis and Synthesis	3-1-0-4	EC102
	EC203	Analog Electronics	3-1-0-4	EC103
	EC204	Digital Systems and Microprocessors	3-1-0-4	EC104
	EC205	Signals and Systems	3-1-0-4	MA102
	EC206	Probability and Random Processes	3-1-0-4	MA102
	PE201	Physical Education & Sports	0-0-1-1	PE102
4th Semester	MA202	Mathematics IV: Numerical Methods	3-1-0-4	MA201
	EC301	Electrical Machines I	3-1-0-4	EC202
	EC302	Power Electronics	3-1-0-4	EC203
	EC303	Control Systems I	3-1-0-4	EC205
	EC304	Communication Systems	3-1-0-4	EC205
	EC305	Microcontroller Applications	2-1-0-3	EC204
	EC306	Measurement and Instrumentation	3-1-0-4	EC202
	PE202	Physical Education & Sports	0-0-1-1	PE201
5th Semester	EC401	Electrical Machines II	3-1-0-4	EC301
	EC402	Power Systems Analysis	3-1-0-4	EC301
	EC403	Control Systems II	3-1-0-4	EC303
	EC404	Digital Signal Processing	3-1-0-4	EC205
	EC405	Computer Networks	3-1-0-4	EC304
	EC406	Embedded Systems Design	3-1-0-4	EC204
	EC407	Renewable Energy Sources	3-1-0-4	EC302
	PE301	Physical Education & Sports	0-0-1-1	PE202
6th Semester	EC501	Power System Protection	3-1-0-4	EC402
	EC502	Smart Grid Technologies	3-1-0-4	EC402
	EC503	Advanced Control Systems	3-1-0-4	EC403
	EC504	Pattern Recognition and Machine Learning	3-1-0-4	EC206
	EC505	Optimization Techniques	3-1-0-4	MA202
	EC506	Wireless Communication Systems	3-1-0-4	EC404
	EC507	Advanced Embedded Systems	3-1-0-4	EC406
	PE302	Physical Education & Sports	0-0-1-1	PE301
7th Semester	EC601	Industrial Training	0-0-2-2	None
	EC602	Project Work I	3-0-0-3	EC401, EC402, EC403, EC404
	EC603	Specialized Elective I	3-1-0-4	EC501 or EC502 or EC503 or EC504
	EC604	Specialized Elective II	3-1-0-4	EC501 or EC502 or EC503 or EC504
	EC605	Specialized Elective III	3-1-0-4	EC501 or EC502 or EC503 or EC504
	EC606	Specialized Elective IV	3-1-0-4	EC501 or EC502 or EC503 or EC504
	EC607	Elective Lab	0-0-3-2	EC603 or EC604 or EC605 or EC606
	PE401	Physical Education & Sports	0-0-1-1	PE302
8th Semester	EC701	Final Year Project/Thesis	6-0-0-6	EC602, EC603, EC604, EC605, EC606
	EC702	Advanced Elective I	3-1-0-4	EC602 or EC603 or EC604 or EC605 or EC606
	EC703	Advanced Elective II	3-1-0-4	EC602 or EC603 or EC604 or EC605 or EC606
	EC704	Advanced Elective III	3-1-0-4	EC602 or EC603 or EC604 or EC605 or EC606
	EC705	Advanced Elective IV	3-1-0-4	EC602 or EC603 or EC604 or EC605 or EC606
	EC706	Research Methodology	2-0-0-2	None
	EC707	Capstone Presentation	0-0-3-2	EC701
	PE402	Physical Education & Sports	0-0-1-1	PE401

Advanced Departmental Electives

Departmental electives are designed to give students a deeper understanding of specialized areas within Electrical Engineering. These courses are offered in the later semesters and allow students to tailor their education based on career interests and research aspirations.

• Pattern Recognition and Machine Learning: This course introduces students to machine learning algorithms, neural networks, and pattern recognition techniques. Students learn how to apply these tools to solve complex problems in signal processing, image analysis, and data classification. The course includes practical sessions using Python and TensorFlow libraries.
• Optimization Techniques: Focused on mathematical optimization methods, this course covers linear programming, nonlinear programming, integer programming, and dynamic programming. Students learn how to formulate and solve optimization problems in engineering contexts, particularly in power systems and manufacturing processes.
• Wireless Communication Systems: This course explores the principles of wireless communication including modulation schemes, multiple access techniques, and network protocols. Students gain hands-on experience with simulation tools like MATLAB and Simulink to model and analyze wireless systems.
• Advanced Embedded Systems: Building upon earlier embedded systems courses, this class covers advanced topics such as real-time operating systems, microcontroller architectures, FPGA programming, and IoT integration. Students develop projects involving sensor networks and smart device applications.
• Smart Grid Technologies: As the energy sector evolves towards decentralization and digitization, this course focuses on smart grid concepts including demand response, energy storage, and grid automation. It includes case studies from global implementations and practical simulations of smart grid systems.
• Power System Protection: This elective covers protective relaying, fault analysis, and system stability in power systems. Students learn about various protection schemes for transformers, transmission lines, and generators, and how to design and implement these systems effectively.
• Advanced Control Systems: Extending the fundamentals of control theory, this course delves into robust control, adaptive control, and nonlinear control systems. It includes practical applications in robotics, aerospace engineering, and industrial automation.
• Digital Signal Processing: This course provides an in-depth exploration of digital signal processing techniques including filtering, transforms, and spectral analysis. Students work with real-world signals and learn to design DSP algorithms for audio processing, biomedical applications, and telecommunications.
• Computer Networks: Covering both wired and wireless communication networks, this course explores protocols, architectures, and security issues in modern networking environments. Students gain practical experience through network simulation tools like NS-3 and Wireshark.
• Renewable Energy Sources: This course examines solar photovoltaic systems, wind turbines, hydroelectric power generation, and other sustainable energy technologies. Students learn about grid integration, energy storage solutions, and policy frameworks supporting renewable energy adoption.

Project-Based Learning Philosophy

The department places a strong emphasis on project-based learning as a core component of the educational experience. This approach integrates theoretical knowledge with practical application, encouraging students to think critically, innovate, and collaborate effectively.

Mini-projects are assigned starting from the third semester, allowing students to apply concepts learned in class to real-world scenarios. These projects typically last 6-8 weeks and involve small teams working under faculty supervision. The projects are evaluated based on technical merit, creativity, presentation skills, and teamwork.

The final-year thesis or capstone project represents the culmination of the student's learning journey. Students select a research topic aligned with their specialization and work closely with a faculty advisor throughout the process. The project must demonstrate originality, depth of understanding, and practical relevance.

Project selection involves a formal proposal submission process where students present their ideas to a committee of faculty members. The committee evaluates proposals based on feasibility, novelty, alignment with departmental strengths, and resource availability.

Faculty mentors are assigned based on expertise matching and project requirements. Regular progress meetings ensure that projects stay on track and receive timely feedback. Students are encouraged to present their work at conferences and publish papers in reputable journals.