Comprehensive Course Structure
The Electrical Engineering curriculum at Mansarovar Global University Sehore is meticulously designed to provide students with a strong foundation in both fundamental and advanced engineering concepts. The program spans eight semesters, offering a balanced mix of theoretical knowledge, practical skills, and research exposure.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EE101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | EE102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | EE103 | Introduction to Electrical Engineering | 3-0-0-3 | None |
| 1 | EE104 | Basic Electronics | 2-0-2-3 | None |
| 1 | EE105 | Engineering Graphics & Design | 2-0-0-2 | None |
| 1 | EE106 | Programming for Engineers | 2-0-2-3 | None |
| 2 | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Electromagnetic Fields | 3-1-0-4 | EE102 |
| 2 | EE203 | Network Analysis | 3-1-0-4 | EE103, EE104 |
| 2 | EE204 | Electronic Devices and Circuits | 3-1-0-4 | EE104 |
| 2 | EE205 | Electrical Machines I | 3-1-0-4 | EE103 |
| 2 | EE206 | Physics Laboratory | 0-0-2-2 | EE102 |
| 3 | EE301 | Probability and Statistics | 3-1-0-4 | EE201 |
| 3 | EE302 | Signals and Systems | 3-1-0-4 | EE201, EE203 |
| 3 | EE303 | Digital Logic Design | 3-1-0-4 | EE204 |
| 3 | EE304 | Electrical Machines II | 3-1-0-4 | EE205 |
| 3 | EE305 | Control Systems | 3-1-0-4 | EE203, EE302 |
| 3 | EE306 | Digital Electronics Laboratory | 0-0-2-2 | EE303 |
| 4 | EE401 | Power System Analysis | 3-1-0-4 | EE205, EE305 |
| 4 | EE402 | Power Electronics | 3-1-0-4 | EE204, EE304 |
| 4 | EE403 | Communication Systems | 3-1-0-4 | EE302 |
| 4 | EE404 | Microcontroller Applications | 3-1-0-4 | EE303, EE305 |
| 4 | EE405 | Electromagnetic Compatibility | 3-1-0-4 | EE202, EE302 |
| 4 | EE406 | Power Systems Laboratory | 0-0-2-2 | EE401, EE402 |
| 5 | EE501 | Renewable Energy Systems | 3-1-0-4 | EE401, EE402 |
| 5 | EE502 | Advanced Control Systems | 3-1-0-4 | EE305 |
| 5 | EE503 | Digital Signal Processing | 3-1-0-4 | EE302, EE403 |
| 5 | EE504 | Artificial Intelligence and Machine Learning | 3-1-0-4 | EE301, EE302 |
| 5 | EE505 | Optoelectronics and Photonics | 3-1-0-4 | EE202, EE302 |
| 5 | EE506 | Embedded Systems Laboratory | 0-0-2-2 | EE404, EE504 |
| 6 | EE601 | VLSI Design | 3-1-0-4 | EE303, EE402 |
| 6 | EE602 | Advanced Power Electronics | 3-1-0-4 | EE402 |
| 6 | EE603 | Wireless Communication Systems | 3-1-0-4 | EE403 |
| 6 | EE604 | Industrial Automation and Robotics | 3-1-0-4 | EE305, EE502 |
| 6 | EE605 | Smart Grid Technologies | 3-1-0-4 | EE401, EE501 |
| 6 | EE606 | Research Methodology and Project Planning | 2-0-0-2 | None |
| 7 | EE701 | Mini Project I | 0-0-4-2 | EE503, EE504 |
| 7 | EE702 | Mini Project II | 0-0-4-2 | EE503, EE604 |
| 8 | EE801 | Final Year Project / Thesis | 0-0-8-4 | All previous courses |
Detailed Departmental Elective Courses
The department offers several advanced elective courses designed to enhance specialization and research capabilities. These courses are taught by experienced faculty members who bring both academic expertise and industry insights.
- Renewable Energy Systems (EE501): This course explores the principles of solar, wind, hydroelectric, and other renewable energy sources. Students learn about energy conversion techniques, grid integration challenges, and sustainability aspects. The course includes laboratory sessions on photovoltaic cell testing and wind turbine modeling.
- Advanced Control Systems (EE502): Building upon foundational control theory, this course covers modern control design methods, robust control, and nonlinear systems. Students implement controller designs using MATLAB/Simulink and participate in real-time control experiments.
- Digital Signal Processing (EE503): Focusing on digital filter design, spectral analysis, and signal modeling, this course introduces students to DSP applications in audio processing, image enhancement, and biomedical signal analysis. Laboratory work involves programming FIR and IIR filters using MATLAB.
- Artificial Intelligence and Machine Learning (EE504): This interdisciplinary course covers machine learning algorithms, neural networks, deep learning frameworks, and AI applications in engineering. Students work on projects involving computer vision, natural language processing, and predictive analytics.
- Optoelectronics and Photonics (EE505): The course delves into the interaction between light and matter, focusing on photonic devices such as lasers, optical fibers, and detectors. Students engage in experiments involving fiber optic communication systems and laser materials characterization.
- Electromagnetic Compatibility (EE506): This course addresses electromagnetic interference sources, shielding techniques, and regulatory compliance standards. Students learn to analyze EMI issues and design compliant systems for various applications including automotive electronics and consumer devices.
- VLSI Design (EE601): Students study the principles of Very Large Scale Integration, including circuit design, layout techniques, and testing strategies. The course includes hands-on work with CAD tools such as Cadence and Synopsys for designing digital ICs.
- Advanced Power Electronics (EE602): This course focuses on power conversion topologies, switching devices, and control strategies for modern power systems. Students gain experience with high-efficiency converters and energy storage systems in laboratory settings.
- Wireless Communication Systems (EE603): Covering wireless standards, modulation techniques, and network protocols, this course prepares students for careers in telecommunications and networking. Laboratory sessions involve designing wireless communication links using software-defined radios.
- Industrial Automation and Robotics (EE604): The course explores automation technologies including PLCs, SCADA systems, and robotic control. Students work on projects involving industrial process control and autonomous robot navigation.
- Smart Grid Technologies (EE605): This emerging field combines traditional power systems with modern communication and information technologies. Topics include smart meters, demand response systems, and distributed energy resources integration.
- Research Methodology and Project Planning (EE606): A foundational course for final-year students, this course teaches research methodologies, project planning, and proposal writing. Students learn to define research problems, formulate hypotheses, and develop experimental plans.
- Mini Project I (EE701): This mini-project involves designing and implementing a small-scale engineering solution under faculty supervision. Projects are selected based on student interests and industry relevance, typically lasting one semester.
- Mini Project II (EE702): An extension of Mini Project I, this course provides students with opportunities to expand their projects, conduct experiments, and prepare for final-year thesis work.
- Final Year Project / Thesis (EE801): The culmination of the undergraduate program, this capstone project allows students to demonstrate their expertise in a chosen area. Students work closely with faculty mentors on original research or applied engineering projects, culminating in a comprehensive report and presentation.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around the concept of experiential education, where students learn by doing. This approach ensures that theoretical knowledge is applied to real-world problems, fostering critical thinking, creativity, and teamwork skills.
The structure of project-based learning begins with foundational courses that provide students with essential tools and methodologies. As they progress through their academic journey, projects become increasingly complex and interdisciplinary, reflecting the multifaceted nature of modern engineering challenges.
Mini-projects, which begin in the third year, are designed to give students early exposure to hands-on experimentation and collaborative problem-solving. These projects are typically completed in teams of 3-5 students and involve selecting a relevant topic, conducting literature reviews, designing solutions, building prototypes, and presenting results.
The scope of these mini-projects ranges from developing simple embedded systems to analyzing power system stability or implementing basic AI algorithms. Students are encouraged to choose projects that align with their interests and career aspirations, ensuring motivation and engagement throughout the process.
Evaluation criteria for mini-projects include technical feasibility, innovation, presentation quality, peer feedback, and overall contribution to learning outcomes. Faculty mentors provide guidance on project selection, methodology, and resource allocation, while also encouraging independent exploration and experimentation.
The final-year thesis or capstone project represents the most significant component of the program's project-based learning framework. Students select a topic under the supervision of a faculty member, conduct in-depth research or engineering design, and produce a comprehensive report. This project often leads to publication opportunities or patent applications, enhancing students' academic and professional profiles.
Faculty mentors play a crucial role in guiding students through their projects, providing technical expertise, and helping them navigate challenges. Regular meetings, progress reports, and milestone reviews ensure that projects stay on track and meet academic standards.
The department also hosts annual project exhibitions where students showcase their work to faculty, peers, and industry professionals. These events foster a culture of innovation and provide platforms for networking and feedback.
By integrating project-based learning throughout the curriculum, the department ensures that students develop not only technical competencies but also professional skills essential for success in engineering careers. This approach prepares graduates to tackle complex problems, adapt to changing technologies, and contribute meaningfully to their fields of specialization.