Curriculum Overview
The curriculum of the Electrical Engineering program at Motherhood University Haridwar is meticulously structured to provide a balanced mix of theoretical knowledge, practical skills, and industry exposure. The following table outlines all courses offered over eight semesters:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EE101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | EE102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | EE103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | EE104 | Basic Electrical Circuits | 3-1-0-4 | - |
| 1 | EE105 | Programming Fundamentals | 3-0-2-4 | - |
| 1 | EE106 | Engineering Graphics and Design | 2-1-0-3 | - |
| 2 | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Analog Electronics I | 3-1-0-4 | EE104 |
| 2 | EE203 | Digital Electronics I | 3-1-0-4 | EE104 |
| 2 | EE204 | Signals and Systems | 3-1-0-4 | EE101 |
| 2 | EE205 | Network Analysis | 3-1-0-4 | EE104 |
| 2 | EE206 | Electromagnetic Fields | 3-1-0-4 | EE102 |
| 3 | EE301 | Power Systems Analysis | 3-1-0-4 | EE205 |
| 3 | EE302 | Control Systems | 3-1-0-4 | EE204 |
| 3 | EE303 | Communication Engineering | 3-1-0-4 | EE204 |
| 3 | EE304 | Microprocessor Architecture | 3-1-0-4 | EE203 |
| 3 | EE305 | Electromagnetic Waves | 3-1-0-4 | EE206 |
| 3 | EE306 | Electronics Lab I | 0-0-3-2 | - |
| 4 | EE401 | Power Electronics | 3-1-0-4 | EE301 |
| 4 | EE402 | Embedded Systems Design | 3-1-0-4 | EE304 |
| 4 | EE403 | Digital Signal Processing | 3-1-0-4 | EE204 |
| 4 | EE404 | Optical Fiber Communication | 3-1-0-4 | EE303 |
| 4 | EE405 | Microwave Engineering | 3-1-0-4 | EE206 |
| 4 | EE406 | Electronics Lab II | 0-0-3-2 | - |
| 5 | EE501 | Renewable Energy Systems | 3-1-0-4 | EE301 |
| 5 | EE502 | VLSI Design | 3-1-0-4 | EE203 |
| 5 | EE503 | Artificial Intelligence & Machine Learning | 3-1-0-4 | EE204 |
| 5 | EE504 | Sensor Networks | 3-1-0-4 | EE204 |
| 5 | EE505 | Power System Protection | 3-1-0-4 | EE301 |
| 5 | EE506 | Advanced Electronics Lab | 0-0-3-2 | - |
| 6 | EE601 | Smart Grid Technologies | 3-1-0-4 | EE501 |
| 6 | EE602 | Signal Processing Lab | 0-0-3-2 | - |
| 6 | EE603 | Robotics & Automation | 3-1-0-4 | EE205 |
| 6 | EE604 | Data Analytics & Visualization | 3-1-0-4 | EE204 |
| 6 | EE605 | Industrial Automation | 3-1-0-4 | EE205 |
| 6 | EE606 | Advanced Control Systems | 3-1-0-4 | EE302 |
| 7 | EE701 | Capstone Project I | 0-0-6-4 | - |
| 7 | EE702 | Research Methodology | 2-1-0-3 | - |
| 7 | EE703 | Project Proposal Writing | 2-1-0-3 | - |
| 8 | EE801 | Capstone Project II | 0-0-6-4 | - |
| 8 | EE802 | Internship | 0-0-0-6 | - |
Advanced departmental elective courses include:
- Renewable Energy Systems (EE501): This course explores solar, wind, hydroelectric, and geothermal energy technologies. It covers design principles, system integration, grid compatibility, and environmental impact assessments. Students engage in hands-on projects involving photovoltaic panel testing, wind turbine simulations, and microgrid design.
- VLSI Design (EE502): Focused on the design and implementation of very large-scale integrated circuits, this course covers CMOS technology, logic synthesis, layout design, and testing techniques. Students work with industry-standard tools like Cadence and Synopsys to develop custom chips.
- Artificial Intelligence & Machine Learning (EE503): This elective introduces students to neural networks, deep learning architectures, natural language processing, computer vision, and reinforcement learning. Projects involve building recommendation systems, image classification models, and chatbots using frameworks like TensorFlow and PyTorch.
- Sensor Networks (EE504): Students learn about wireless sensor networks, data collection protocols, routing algorithms, and energy efficiency in IoT applications. Labs include setting up wireless sensor nodes, monitoring environmental parameters, and analyzing real-time data streams.
- Smart Grid Technologies (EE601): This course focuses on the modernization of power grids with smart meters, demand response systems, and grid stability management. Students explore concepts like distributed generation, energy storage, and microgrids through simulations and field visits.
- Robotics & Automation (EE603): Covering both mechanical and control aspects of robotics, this course teaches students to design robotic systems, implement control algorithms, and integrate sensors and actuators. Labs involve building autonomous robots and programming them for navigation and manipulation tasks.
- Data Analytics & Visualization (EE604): This course combines statistical methods, data mining techniques, and visualization tools to analyze complex datasets. Students use Python, R, and Tableau to extract insights from real-world data and create interactive dashboards.
- Industrial Automation (EE605): Designed for students interested in manufacturing automation, this course covers PLC programming, SCADA systems, industrial communication protocols, and process control. Students work on projects involving conveyor belt automation and robotic assembly lines.
- Advanced Control Systems (EE606): This elective delves into modern control theory, including optimal control, adaptive control, and nonlinear control methods. Students implement control strategies using MATLAB/Simulink and simulate real-world systems such as aircraft stabilization and chemical process control.
- Power System Protection (EE505): This course focuses on protective relaying, fault analysis, and system stability in power networks. Students learn to design protection schemes for transformers, transmission lines, and generators through simulations and case studies.
The department emphasizes project-based learning as a core component of the educational experience. From early semesters, students are introduced to mini-projects that reinforce classroom concepts and encourage teamwork. These projects often involve collaboration with industry partners or faculty-led research initiatives.
The final-year capstone project is a significant milestone in the program. Students select a project topic aligned with their specialization or personal interests, working closely with a faculty advisor. The project involves literature review, experimental design, implementation, testing, and documentation. The final deliverables include a detailed report, a presentation, and a demonstration of the completed system.
Students can choose projects from a list provided by faculty members or propose their own ideas after consultation with advisors. The department facilitates access to advanced lab equipment, software licenses, and funding for project materials. Regular progress meetings ensure that students stay on track and receive timely feedback throughout the process.