Curriculum Overview

The Electrical Engineering curriculum at Aryavart University Sehore is designed to provide a comprehensive understanding of core electrical principles while offering flexibility through specialized electives. The program spans eight semesters, with each semester containing a mix of core subjects, departmental electives, science electives, and laboratory sessions.

Course Listing by Semester

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Pre-requisites
I	EE101	Mathematics I	3-1-0-4	None
I	EE102	Physics I	3-1-0-4	None
I	EE103	Engineering Graphics	2-0-0-2	None
I	EE104	Computer Programming	3-0-0-3	None
I	EE105	Basic Electrical Circuits	3-1-0-4	None
I	EE106	Engineering Workshop	2-0-0-2	None
II	EE201	Mathematics II	3-1-0-4	EE101
II	EE202	Physics II	3-1-0-4	EE102
II	EE203	Circuit Analysis	3-1-0-4	EE105
II	EE204	Electronic Devices	3-1-0-4	EE105
II	EE205	Signals and Systems	3-1-0-4	EE101
II	EE206	Digital Logic Design	3-1-0-4	EE105
III	EE301	Power Electronics	3-1-0-4	EE204
III	EE302	Control Systems	3-1-0-4	EE205
III	EE303	Communication Systems	3-1-0-4	EE205
III	EE304	Microprocessors	3-1-0-4	EE206
III	EE305	Electrical Machines	3-1-0-4	EE203
III	EE306	Electromagnetic Fields	3-1-0-4	EE202
IV	EE401	Power System Analysis	3-1-0-4	EE305
IV	EE402	Renewable Energy Sources	3-1-0-4	EE301
IV	EE403	Advanced Control Systems	3-1-0-4	EE302
IV	EE404	Smart Grid Technologies	3-1-0-4	EE401
IV	EE405	Embedded Systems	3-1-0-4	EE304
IV	EE406	Digital Signal Processing	3-1-0-4	EE205
V	EE501	VLSI Design	3-1-0-4	EE204
V	EE502	Artificial Intelligence	3-1-0-4	EE205
V	EE503	Internet of Things	3-1-0-4	EE405
V	EE504	Advanced Power Electronics	3-1-0-4	EE301
V	EE505	Robotics and Automation	3-1-0-4	EE302
V	EE506	Energy Storage Systems	3-1-0-4	EE402
VI	EE601	Capstone Project I	3-0-0-3	EE501, EE502
VI	EE602	Advanced Microelectronics	3-1-0-4	EE501
VI	EE603	Machine Learning for Engineers	3-1-0-4	EE502
VI	EE604	Power System Protection	3-1-0-4	EE401
VI	EE605	Research Methodology	3-1-0-4	None
VI	EE606	Project Lab	2-0-0-2	EE601
VII	EE701	Capstone Project II	3-0-0-3	EE601
VII	EE702	Advanced Control Theory	3-1-0-4	EE302
VII	EE703	Neural Networks and Deep Learning	3-1-0-4	EE502
VII	EE704	Energy Conversion Systems	3-1-0-4	EE402
VII	EE705	Renewable Energy Integration	3-1-0-4	EE402
VII	EE706	Project Management	3-1-0-4	None
VIII	EE801	Final Year Thesis	6-0-0-6	EE701
VIII	EE802	Industry Internship	3-0-0-3	EE701

Advanced Departmental Elective Courses

The advanced departmental electives in Electrical Engineering are designed to give students deeper insights into specialized areas and prepare them for cutting-edge research and industry roles. These courses are offered based on student demand and faculty expertise, ensuring that the curriculum remains relevant and up-to-date with current trends.

VLSI Design

This course provides a comprehensive understanding of Very Large Scale Integration (VLSI) design principles and techniques. Students learn about logic synthesis, layout design, and testing methods for integrated circuits. The course includes hands-on lab sessions using industry-standard tools like Cadence and Synopsys.

Artificial Intelligence

This elective introduces students to fundamental concepts in artificial intelligence, including machine learning algorithms, neural networks, and natural language processing. Students explore real-world applications of AI in electrical engineering domains such as autonomous systems and predictive analytics.

Internet of Things (IoT)

The IoT course covers the architecture, protocols, and security aspects of interconnected devices. Students design and implement IoT solutions using platforms like Arduino and Raspberry Pi, gaining practical experience in sensor integration and cloud computing.

Advanced Power Electronics

This advanced course focuses on high-efficiency power conversion techniques used in renewable energy systems and electric vehicle applications. Topics include resonant converters, wide-bandgap semiconductors, and power quality improvement methods.

Robotics and Automation

This course combines principles of control theory, mechanical engineering, and computer science to build autonomous robots. Students work on projects involving mobile robotics, industrial automation, and human-robot interaction systems.

Energy Storage Systems

Students explore various technologies for storing electrical energy, including batteries, supercapacitors, and pumped hydro storage. The course includes laboratory experiments on battery management systems and grid-scale energy storage solutions.

Neural Networks and Deep Learning

This course delves into the mathematical foundations of neural networks and deep learning architectures. Students implement models for image recognition, speech processing, and other applications using frameworks like TensorFlow and PyTorch.

Smart Grid Technologies

Smart grid technologies are transforming how electricity is generated, distributed, and consumed. This course explores concepts such as demand response, energy management systems, and smart metering technologies, providing students with insights into future power systems.

Advanced Control Theory

This course builds upon basic control systems theory to cover modern techniques such as state-space methods, optimal control, and robust control. Students gain experience in designing controllers for complex dynamic systems using MATLAB/Simulink tools.

Power System Protection

Students learn about protective relays, fault analysis, and system stability in power systems. The course includes case studies of real-world incidents and hands-on simulations to understand protection strategies used in modern power networks.

Project-Based Learning Philosophy

Our program emphasizes project-based learning as a core component of education. This approach encourages students to apply theoretical knowledge in practical scenarios, fostering innovation and problem-solving skills. Projects are structured across multiple semesters, starting with mini-projects in early years and culminating in a final-year thesis or capstone project.

Mini-Projects

Mini-projects are introduced in the second year to give students early exposure to hands-on engineering. These projects typically last one semester and involve small teams working on real-world problems under faculty supervision. Examples include designing a simple embedded system, building an RC car, or creating a basic power supply unit.

Final-Year Thesis/Capstone Project

The final-year thesis is a major undertaking that allows students to explore advanced topics and conduct independent research. Students select a project topic in consultation with faculty mentors and work on it for the entire semester. The project involves literature review, experimental design, implementation, testing, and documentation.

Project Selection Process

Students can choose from a list of proposed projects or propose their own. Faculty mentors are assigned based on the student's interest and the availability of resources. Regular progress meetings ensure that students stay on track with their project timelines.

Evaluation Criteria

Projects are evaluated based on several criteria including technical depth, innovation, presentation quality, and team collaboration. A comprehensive report is required at the end of each project, detailing methodology, results, and future scope.