Curriculum Overview
The Electrical Engineering program at Mata Gujri University Kishangunj follows a carefully designed curriculum that ensures students receive both theoretical knowledge and practical skills required for success in the industry. The program spans eight semesters, with each semester comprising core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EE101 | Mathematics I | 4-0-0-4 | - |
| 1 | EE102 | Physics for Engineers | 3-0-0-3 | - |
| 1 | EE103 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | EE104 | Introduction to Engineering | 2-0-0-2 | - |
| 1 | EE105 | Engineering Graphics | 2-0-0-2 | - |
| 1 | EE106 | Computer Programming | 3-0-0-3 | - |
| 2 | EE201 | Mathematics II | 4-0-0-4 | EE101 |
| 2 | EE202 | Circuit Analysis | 3-0-0-3 | EE102 |
| 2 | EE203 | Electromagnetic Fields | 3-0-0-3 | EE102 |
| 2 | EE204 | Electronic Devices | 3-0-0-3 | EE103 |
| 2 | EE205 | Engineering Mechanics | 3-0-0-3 | - |
| 2 | EE206 | Programming Lab | 0-0-3-1 | EE106 |
| 3 | EE301 | Mathematics III | 4-0-0-4 | EE201 |
| 3 | EE302 | Signals and Systems | 3-0-0-3 | EE201 |
| 3 | EE303 | Digital Electronics | 3-0-0-3 | EE204 |
| 3 | EE304 | Power Systems | 3-0-0-3 | EE202 |
| 3 | EE305 | Control Systems | 3-0-0-3 | EE301 |
| 3 | EE306 | Microcontroller Lab | 0-0-3-1 | EE204 |
| 4 | EE401 | Mathematics IV | 4-0-0-4 | EE301 |
| 4 | EE402 | Communication Systems | 3-0-0-3 | EE302 |
| 4 | EE403 | Power Electronics | 3-0-0-3 | EE304 |
| 4 | EE404 | Embedded Systems | 3-0-0-3 | EE303 |
| 4 | EE405 | Signal Processing | 3-0-0-3 | EE302 |
| 4 | EE406 | Electronics Lab | 0-0-3-1 | EE304 |
| 5 | EE501 | Advanced Mathematics | 4-0-0-4 | EE401 |
| 5 | EE502 | Renewable Energy Systems | 3-0-0-3 | EE304 |
| 5 | EE503 | Artificial Intelligence | 3-0-0-3 | EE401 |
| 5 | EE504 | Smart Grid Technologies | 3-0-0-3 | EE304 |
| 5 | EE505 | Robotics | 3-0-0-3 | EE305 |
| 5 | EE506 | Research Methodology | 2-0-0-2 | - |
| 6 | EE601 | Electromagnetic Compatibility | 3-0-0-3 | EE203 |
| 6 | EE602 | Energy Storage Systems | 3-0-0-3 | EE304 |
| 6 | EE603 | Network Security | 3-0-0-3 | EE402 |
| 6 | EE604 | Advanced Control Theory | 3-0-0-3 | EE305 |
| 6 | EE605 | Power System Protection | 3-0-0-3 | EE304 |
| 6 | EE606 | Project Lab | 0-0-6-2 | EE501 |
| 7 | EE701 | Special Topics in Electrical Engineering | 3-0-0-3 | EE601 |
| 7 | EE702 | Advanced Signal Processing | 3-0-0-3 | EE405 |
| 7 | EE703 | Wireless Communication | 3-0-0-3 | EE402 |
| 7 | EE704 | Machine Learning Applications | 3-0-0-3 | EE503 |
| 7 | EE705 | Advanced Power Electronics | 3-0-0-3 | EE403 |
| 7 | EE706 | Research Project | 0-0-12-4 | EE606 |
| 8 | EE801 | Capstone Project | 0-0-12-4 | EE706 |
| 8 | EE802 | Industrial Training | 0-0-0-3 | - |
| 8 | EE803 | Final Year Thesis | 0-0-0-6 | EE706 |
Advanced Departmental Electives
The department offers a wide array of advanced departmental electives that allow students to specialize in specific areas based on their interests and career aspirations. These courses are designed to provide in-depth knowledge and hands-on experience with emerging technologies.
Solar Cell Technology
This elective course delves into the science and engineering behind photovoltaic cells, covering topics such as semiconductor physics, solar cell materials, device modeling, and efficiency optimization techniques. Students learn how to design and test solar panels for residential and commercial applications.
Wind Energy Engineering
The course explores the principles of wind energy conversion systems, including aerodynamics, turbine design, power generation, and grid integration. Students gain practical experience in wind farm layout planning and performance analysis using industry-standard software tools.
Wireless Power Transfer
This elective focuses on wireless power transmission technologies, covering electromagnetic coupling, resonant power transfer, and efficiency optimization methods. The course includes laboratory sessions where students build and test wireless charging systems for various applications.
Advanced Control Systems
The course introduces advanced control theory concepts such as state-space representation, optimal control, robust control, and nonlinear control systems. Students learn to design controllers for complex industrial processes and robotic platforms using simulation software.
Neural Networks in Engineering Applications
This course explores the application of artificial neural networks in solving engineering problems, including pattern recognition, system identification, and prediction modeling. Students develop skills in designing and training neural networks using MATLAB and Python.
Smart Grid Integration
The course examines the integration of renewable energy sources into power grids, covering grid stability, demand response systems, and smart meter technologies. Students analyze real-world case studies and propose solutions for improving grid reliability and efficiency.
Power System Protection
This elective focuses on protection schemes for electrical power systems, including relay design, fault analysis, and protective device coordination. The course includes laboratory sessions where students simulate power system faults and implement protection algorithms.
Embedded Systems Design
The course covers the design and implementation of embedded systems using microcontrollers and real-time operating systems. Students learn to program ARM-based processors, interface sensors and actuators, and develop applications for IoT devices.
Signal Processing for Communications
This course delves into signal processing techniques used in communication systems, including modulation schemes, error correction codes, and spectral analysis methods. Students work on projects involving digital signal processing algorithms for wireless communications.
Robot Kinematics and Dynamics
The course explores the mathematical foundations of robot motion and control, covering kinematic modeling, dynamic analysis, and trajectory planning. Students design and simulate robotic manipulators using CAD software and implement control algorithms in MATLAB.
Project-Based Learning Philosophy
The department places a strong emphasis on project-based learning as a core component of the educational experience. This approach encourages students to apply theoretical concepts to real-world engineering challenges, fostering creativity, problem-solving skills, and teamwork abilities.
The structure of project-based learning begins with an orientation phase where students are introduced to various domains and problem statements. They then form teams based on shared interests and complementary skill sets. Faculty mentors guide each team through the process of defining objectives, designing solutions, implementing prototypes, and presenting results.
Mini-projects are conducted during the third and fourth years, requiring students to work collaboratively on specific engineering tasks. These projects are typically completed over a period of six weeks and involve multiple stages including literature review, design, prototyping, testing, and documentation.
The final-year capstone project is a comprehensive endeavor that allows students to integrate all aspects of their learning into a substantial engineering solution. Students select topics aligned with their specializations and work closely with faculty advisors to develop innovative designs or systems that address current industry needs.
Evaluation criteria for projects include technical feasibility, innovation, teamwork, presentation quality, and adherence to deadlines. Students are assessed on their individual contributions as well as their collective performance throughout the project lifecycle. The final presentations are evaluated by a panel of faculty members and industry experts.