The curriculum for Electrical Engineering at Shri Kallaji Vedic Vishvavidyalaya Chittorgarh is designed to provide a comprehensive and progressive learning experience. The program is structured over eight semesters, with a blend of core engineering subjects, departmental electives, science electives, and laboratory sessions. The curriculum is aligned with industry needs and incorporates the latest advancements in the field.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ECO101 | Introduction to Engineering | 2-0-0-2 | - |
| 1 | ENG102 | English for Engineers | 2-0-0-2 | - |
| 1 | LAB101 | Basic Electrical Lab | 0-0-3-1 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | PHY201 | Electromagnetic Fields | 3-1-0-4 | PHY101 |
| 2 | ECE201 | Basic Electronics | 3-1-0-4 | - |
| 2 | ECO201 | Engineering Mechanics | 3-1-0-4 | - |
| 2 | LAB201 | Electronics Lab | 0-0-3-1 | - |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ECE301 | Electrical Circuits | 3-1-0-4 | ECE201 |
| 3 | ECO301 | Signals and Systems | 3-1-0-4 | - |
| 3 | ECO302 | Control Systems | 3-1-0-4 | - |
| 3 | LAB301 | Control Systems Lab | 0-0-3-1 | - |
| 4 | ENG401 | Engineering Mathematics IV | 3-1-0-4 | ENG301 |
| 4 | ECE401 | Power Systems | 3-1-0-4 | ECE301 |
| 4 | ECO401 | Communication Systems | 3-1-0-4 | ECO301 |
| 4 | ECO402 | Digital Signal Processing | 3-1-0-4 | - |
| 4 | LAB401 | Power Systems Lab | 0-0-3-1 | - |
| 5 | ECE501 | Power Electronics | 3-1-0-4 | ECE401 |
| 5 | ECO501 | Microprocessors | 3-1-0-4 | - |
| 5 | ECO502 | Embedded Systems | 3-1-0-4 | - |
| 5 | ECO503 | Renewable Energy Systems | 3-1-0-4 | - |
| 5 | LAB501 | Power Electronics Lab | 0-0-3-1 | - |
| 6 | ECE601 | Advanced Power Systems | 3-1-0-4 | ECE501 |
| 6 | ECO601 | Artificial Intelligence | 3-1-0-4 | - |
| 6 | ECO602 | Machine Learning | 3-1-0-4 | - |
| 6 | ECO603 | Smart Grid Technologies | 3-1-0-4 | - |
| 6 | LAB601 | AI and ML Lab | 0-0-3-1 | - |
| 7 | ECE701 | Research Methodology | 2-0-0-2 | - |
| 7 | ECO701 | Capstone Project | 0-0-6-6 | - |
| 7 | ECO702 | Internship | 0-0-0-6 | - |
| 8 | ECE801 | Final Year Thesis | 0-0-6-6 | - |
| 8 | ECO801 | Elective Course 1 | 3-1-0-4 | - |
| 8 | ECO802 | Elective Course 2 | 3-1-0-4 | - |
| 8 | ECO803 | Elective Course 3 | 3-1-0-4 | - |
| 8 | LAB801 | Final Project Lab | 0-0-3-1 | - |
Advanced departmental elective courses play a crucial role in the program, offering students the opportunity to specialize in areas of interest. These courses are designed to provide in-depth knowledge and practical skills in specific domains of electrical engineering.
Power Electronics and Drives: This course delves into the design and analysis of power electronic converters and drives. Students learn about power semiconductor devices, converters, and motor drives. The course includes hands-on lab sessions where students design and test power electronic circuits.
Smart Grid Technologies: This course explores the integration of renewable energy sources into the power grid. Students study grid stability, energy storage systems, and smart grid communication protocols. The course includes case studies of real-world smart grid implementations.
Artificial Intelligence in Electrical Engineering: This course introduces students to AI techniques and their applications in electrical engineering. Topics include neural networks, machine learning algorithms, and their use in signal processing and control systems.
Renewable Energy Systems: This course covers the design and operation of renewable energy systems, including solar, wind, and hydroelectric power. Students learn about energy conversion, system integration, and environmental impact assessments.
Embedded Systems Design: This course focuses on the design and development of embedded systems using microcontrollers and processors. Students learn about real-time operating systems, hardware-software co-design, and system integration.
Signal Processing for Communications: This course covers advanced signal processing techniques used in communication systems. Students study modulation techniques, digital signal processing, and communication protocols.
Control Systems for Robotics: This course explores the application of control systems in robotics. Students learn about robot kinematics, dynamics, and control algorithms. The course includes lab sessions where students build and test robotic systems.
Digital Signal Processing: This course provides a comprehensive overview of digital signal processing techniques. Students study discrete-time signals and systems, Fourier transforms, and filter design.
Power System Protection: This course focuses on the protection of power systems from faults and disturbances. Students learn about protective relays, fault analysis, and system stability.
Advanced Microprocessors: This course covers advanced topics in microprocessor architecture and design. Students study microprocessor instruction sets, memory management, and system design.
Internet of Things (IoT) in Smart Systems: This course explores the integration of IoT in smart systems. Students study sensor networks, wireless communication, and data analytics for IoT applications.
Electromagnetic Compatibility: This course covers the principles of electromagnetic compatibility and interference. Students learn about EMI/EMC design, testing, and compliance.
Advanced Control Systems: This course delves into advanced control theory and design. Students study state-space methods, optimal control, and robust control.
Energy Storage Systems: This course focuses on energy storage technologies and their applications. Students study battery technologies, supercapacitors, and grid-scale storage systems.
Electrical Machine Design: This course covers the design and analysis of electrical machines. Students study transformers, motors, and generators, including their operation and control.
The department's philosophy on project-based learning is centered on the idea that students learn best by doing. The program includes mandatory mini-projects in the second and third years, where students work in teams to solve real-world engineering problems. These projects are designed to enhance problem-solving skills and foster collaboration.
The final-year thesis/capstone project is a significant component of the program. Students select a research topic under the guidance of a faculty mentor and work on it for the entire year. The project involves literature review, experimental design, data analysis, and presentation. Students are evaluated based on their technical competence, innovation, and presentation skills.
Project selection is done through a process where students submit proposals, and faculty members guide them based on their interests and expertise. The department provides resources and support to ensure that students can successfully complete their projects.