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.