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.