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.