Comprehensive Curriculum Overview
The Electrical Engineering program at Mahaveer University Meerut is meticulously structured to provide students with a balanced blend of theoretical knowledge and practical application. The curriculum spans eight semesters, each designed to build upon the previous one, ensuring a progressive and comprehensive understanding of electrical systems and technologies.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | MA101 | Calculus I | 3-1-0-4 | - |
| I | PH101 | Physics for Engineers | 3-1-0-4 | - |
| I | EE101 | Introduction to Electrical Engineering | 3-1-0-4 | - |
| I | CS101 | Programming for Engineers | 2-1-0-3 | - |
| I | ME101 | Engineering Graphics and Design | 2-1-0-3 | - |
| I | HS101 | English for Communication | 2-0-0-2 | - |
| I | CE101 | Basic Civil Engineering | 2-0-0-2 | - |
| I | EE102 | Basic Circuit Analysis | 3-1-0-4 | MA101, PH101 |
| I | EE103 | Digital Logic Design | 3-1-0-4 | - |
| I | EE104 | Electronics Devices and Circuits | 3-1-0-4 | - |
| I | EE105 | Signals and Systems | 3-1-0-4 | MA101 |
| I | EE106 | Electromagnetic Fields | 3-1-0-4 | PH101 |
| I | EE107 | Engineering Workshop | 2-0-2-3 | - |
| I | EE108 | Project Orientation | 1-0-0-1 | - |
| II | MA201 | Calculus II | 3-1-0-4 | MA101 |
| II | PH201 | Modern Physics | 3-1-0-4 | PH101 |
| II | EE201 | Network Analysis | 3-1-0-4 | EE102 |
| II | EE202 | Electrical Machines I | 3-1-0-4 | - |
| II | EE203 | Control Systems I | 3-1-0-4 | - |
| II | EE204 | Electronics Circuits I | 3-1-0-4 | EE104 |
| II | EE205 | Digital Electronics | 3-1-0-4 | EE103 |
| II | EE206 | Microprocessor and Microcontroller | 3-1-0-4 | - |
| II | EE207 | Electrical Measurements and Instrumentation | 3-1-0-4 | - |
| II | EE208 | Engineering Economics and Management | 2-0-0-2 | - |
| II | EE209 | Computer Aided Design and Drafting | 2-1-0-3 | CS101 |
| II | EE210 | Mini Project I | 1-0-2-2 | - |
| III | MA301 | Probability and Statistics | 3-1-0-4 | MA201 |
| III | EE301 | Electrical Machines II | 3-1-0-4 | EE202 |
| III | EE302 | Power Electronics | 3-1-0-4 | - |
| III | EE303 | Control Systems II | 3-1-0-4 | EE203 |
| III | EE304 | Electronics Circuits II | 3-1-0-4 | EE204 |
| III | EE305 | Digital Signal Processing | 3-1-0-4 | EE205 |
| III | EE306 | Communication Systems | 3-1-0-4 | EE205 |
| III | EE307 | Power Systems Analysis | 3-1-0-4 | - |
| III | EE308 | Electromagnetic Compatibility | 3-1-0-4 | - |
| III | EE309 | Embedded Systems | 3-1-0-4 | - |
| III | EE310 | Mini Project II | 1-0-2-2 | - |
| IV | EE401 | Renewable Energy Systems | 3-1-0-4 | - |
| IV | EE402 | Power System Protection | 3-1-0-4 | - |
| IV | EE403 | Industrial Drives and Automation | 3-1-0-4 | - |
| IV | EE404 | Advanced Control Systems | 3-1-0-4 | EE303 |
| IV | EE405 | Signal and Image Processing | 3-1-0-4 | EE305 |
| IV | EE406 | Antennas and Wave Propagation | 3-1-0-4 | - |
| IV | EE407 | Smart Grid Technologies | 3-1-0-4 | - |
| IV | EE408 | Biomedical Instrumentation | 3-1-0-4 | - |
| IV | EE409 | Artificial Intelligence and Machine Learning | 3-1-0-4 | - |
| IV | EE410 | Mini Project III | 1-0-2-2 | - |
| V | EE501 | Power System Operation and Control | 3-1-0-4 | - |
| V | EE502 | Electrical Power Distribution | 3-1-0-4 | - |
| V | EE503 | Electromagnetic Fields and Waves | 3-1-0-4 | - |
| V | EE504 | Optimization Techniques in Engineering | 3-1-0-4 | - |
| V | EE505 | Advanced Digital Signal Processing | 3-1-0-4 | - |
| V | EE506 | Wireless Communication Systems | 3-1-0-4 | - |
| V | EE507 | Nuclear Power Engineering | 3-1-0-4 | - |
| V | EE508 | Advanced Embedded Systems | 3-1-0-4 | - |
| V | EE509 | Research Methodology and Ethics | 2-0-0-2 | - |
| V | EE510 | Mini Project IV | 1-0-2-2 | - |
| VI | EE601 | Power Quality Analysis and Control | 3-1-0-4 | - |
| VI | EE602 | Advanced Power Electronics | 3-1-0-4 | - |
| VI | EE603 | Industrial Automation and PLC Programming | 3-1-0-4 | - |
| VI | EE604 | Renewable Energy Integration | 3-1-0-4 | - |
| VI | EE605 | Advanced Control Systems Design | 3-1-0-4 | - |
| VI | EE606 | Signal Processing Applications | 3-1-0-4 | - |
| VI | EE607 | Optical Fiber Communication | 3-1-0-4 | - |
| VI | EE608 | Energy Storage Systems | 3-1-0-4 | - |
| VI | EE609 | Advanced Machine Learning Applications | 3-1-0-4 | - |
| VI | EE610 | Mini Project V | 1-0-2-2 | - |
| VII | EE701 | Smart Grid Integration | 3-1-0-4 | - |
| VII | EE702 | Advanced Power Systems Analysis | 3-1-0-4 | - |
| VII | EE703 | Advanced Control Techniques | 3-1-0-4 | - |
| VII | EE704 | Biomedical Signal Processing | 3-1-0-4 | - |
| VII | EE705 | Wireless Sensor Networks | 3-1-0-4 | - |
| VII | EE706 | Advanced Digital Signal Processing | 3-1-0-4 | - |
| VII | EE707 | Energy Economics and Policy | 3-1-0-4 | - |
| VII | EE708 | Project Management in Engineering | 3-1-0-4 | - |
| VII | EE709 | Research Thesis | 6-0-0-6 | - |
| VII | EE710 | Capstone Project | 3-0-0-3 | - |
| VIII | EE801 | Advanced Topics in Power Systems | 3-1-0-4 | - |
| VIII | EE802 | Power System Stability Analysis | 3-1-0-4 | - |
| VIII | EE803 | Smart Grid Technologies | 3-1-0-4 | - |
| VIII | EE804 | Advanced Control Systems | 3-1-0-4 | - |
| VIII | EE805 | Advanced Signal Processing | 3-1-0-4 | - |
| VIII | EE806 | Communication Networks | 3-1-0-4 | - |
| VIII | EE807 | Energy Conversion Systems | 3-1-0-4 | - |
| VIII | EE808 | Emerging Trends in Electrical Engineering | 3-1-0-4 | - |
| VIII | EE809 | Research Thesis | 6-0-0-6 | - |
| VIII | EE810 | Capstone Project | 3-0-0-3 | - |
Detailed Course Descriptions for Advanced Departmental Electives
Advanced departmental electives play a crucial role in tailoring the educational experience to meet individual interests and career goals. These courses are designed to provide in-depth knowledge and specialized skills that align with current industry trends and technological advancements.
Renewable Energy Systems
This elective explores the principles and applications of renewable energy technologies, including solar, wind, hydroelectric, and geothermal power generation. Students study photovoltaic cell design, wind turbine aerodynamics, and grid integration strategies for sustainable energy systems. The course emphasizes hands-on projects involving system modeling, simulation, and real-world implementation.
Power Electronics and Drives
This course focuses on the design and application of power electronic converters and drives used in industrial and commercial settings. Topics include DC-DC converters, AC-DC rectifiers, inverters, and motor drive systems. Students gain practical experience through laboratory experiments and project-based learning to optimize efficiency and performance.
Embedded Systems
The embedded systems elective introduces students to the design and development of computer systems integrated into larger mechanical or electrical systems. Emphasis is placed on microcontroller programming, real-time operating systems, sensor integration, and hardware-software co-design techniques for IoT applications.
Control Systems
This course covers modern control theory and its practical applications in various domains such as robotics, aerospace, and manufacturing. Students learn about feedback control, system modeling, stability analysis, and optimal control strategies using mathematical tools and simulation software.
Signal Processing
Signal processing fundamentals are explored through digital signal processing techniques, including filtering, spectral analysis, and transform methods. The course integrates practical applications in audio processing, image enhancement, and biomedical signal analysis to demonstrate real-world relevance.
Communication Systems
This elective delves into modern communication technologies including wireless networks, satellite communications, and data transmission protocols. Students study modulation schemes, error correction methods, network design principles, and emerging trends in telecommunications infrastructure.
Biomedical Engineering
Bridging electrical engineering with medical sciences, this course explores healthcare technologies such as medical imaging systems, bio-sensors, and therapeutic devices. Students engage in interdisciplinary projects combining engineering principles with clinical applications to develop innovative solutions for patient care.
Smart Grid Technologies
This course addresses the evolution of power grids into smart networks capable of integrating distributed energy resources, managing demand response, and ensuring reliability through advanced monitoring and control systems. Topics include grid modernization, cyber security, and energy storage integration.
Artificial Intelligence and Machine Learning
Students are introduced to AI concepts and machine learning algorithms applied in electrical engineering contexts. The course covers neural networks, deep learning architectures, data analytics for system optimization, and practical implementation using modern software tools and frameworks.
Electromagnetic Compatibility
This elective focuses on the study of electromagnetic interference and compatibility issues in electronic systems. Students learn about shielding techniques, grounding methods, and regulatory compliance requirements to ensure reliable operation of electrical equipment in various environments.
Project-Based Learning Philosophy
The department emphasizes a project-based learning approach that integrates theory with practical application throughout the curriculum. This philosophy is embedded in the program structure through mandatory mini-projects and a comprehensive final-year thesis/capstone project.
Mini-Projects Structure
Mini-projects are assigned at different stages of the program to reinforce learning outcomes and develop practical skills. These projects typically span one to two semesters and involve teams of 3-5 students working under faculty supervision. Each project is designed to address real-world challenges and incorporate elements of innovation, research, and problem-solving.
Final-Year Thesis/Capstone Project
The capstone project represents the culmination of the student's academic journey, requiring them to apply all acquired knowledge to solve a complex engineering problem. Students select their projects in consultation with faculty mentors based on their interests and career aspirations. The process involves literature review, system design, implementation, testing, and documentation.
Project Selection and Mentorship
Students are encouraged to propose project ideas aligned with their specialization tracks or personal interests. Faculty mentors guide students through the selection process, ensuring projects align with departmental resources, industry relevance, and academic standards. Regular progress meetings and milestone reviews ensure successful completion within specified timelines.