Comprehensive Course Listing by Semester
| Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | English for Engineers | 3-0-0-3 | - |
| 1 | MAT101 | Mathematics I | 4-0-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| 1 | CHE101 | Chemistry for Engineers | 3-0-0-3 | - |
| 1 | ECE101 | Introduction to Electrical Engineering | 2-0-0-2 | - |
| 1 | ENG102 | Engineering Graphics | 2-0-0-2 | - |
| 1 | MAT102 | Mathematics II | 4-0-0-4 | MAT101 |
| 1 | ECE102 | Basic Electrical Circuits | 3-0-0-3 | - |
| 2 | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
| 2 | ECE201 | Digital Logic Design | 3-0-0-3 | - |
| 2 | ECE202 | Electromagnetic Fields | 3-0-0-3 | MAT102 |
| 2 | ECE203 | Analog Electronics | 3-0-0-3 | - |
| 2 | PHY201 | Physics Laboratory | 0-0-3-1 | - |
| 2 | ECE204 | Circuit Analysis | 3-0-0-3 | MAT102 |
| 3 | ECE301 | Signals and Systems | 3-0-0-3 | MAT201 |
| 3 | ECE302 | Microprocessors | 3-0-0-3 | - |
| 3 | ECE303 | Power Electronics | 3-0-0-3 | - |
| 3 | ECE304 | Control Systems | 3-0-0-3 | MAT201 |
| 3 | ECE305 | Communication Systems | 3-0-0-3 | - |
| 4 | ECE401 | Power Systems | 3-0-0-3 | ECE201 |
| 4 | ECE402 | Embedded Systems | 3-0-0-3 | - |
| 4 | ECE403 | Renewable Energy | 3-0-0-3 | - |
| 4 | ECE404 | Advanced Microprocessors | 3-0-0-3 | ECE202 |
| 5 | ECE501 | VLSI Design | 3-0-0-3 | ECE302 |
| 5 | ECE502 | Smart Grid Technologies | 3-0-0-3 | - |
| 5 | ECE503 | Robotics and Automation | 3-0-0-3 | - |
| 5 | ECE504 | Energy Storage Systems | 3-0-0-3 | - |
| 6 | ECE601 | Advanced Control Theory | 3-0-0-3 | ECE304 |
| 6 | ECE602 | Machine Learning for Electrical Systems | 3-0-0-3 | - |
| 6 | ECE603 | Signal Processing Applications | 3-0-0-3 | ECE301 |
| 6 | ECE604 | Energy Efficiency in Buildings | 3-0-0-3 | - |
| 7 | ECE701 | Capstone Project I | 2-0-0-2 | - |
| 7 | ECE702 | Research Methodology | 2-0-0-2 | - |
| 8 | ECE801 | Capstone Project II | 3-0-0-3 | ECE701 |
| 8 | ECE802 | Industry Internship | 0-0-6-3 | - |
Detailed Descriptions of Advanced Departmental Electives
The department offers several advanced elective courses that allow students to specialize in niche areas relevant to current industry demands. One such course is 'Machine Learning for Electrical Systems', which introduces students to machine learning algorithms specifically tailored for applications in power systems, signal processing, and control theory. The course emphasizes practical implementation using Python and TensorFlow frameworks.
'Smart Grid Technologies' delves into the integration of renewable energy sources into existing grid infrastructure. Students learn about advanced metering infrastructure, demand response mechanisms, and cybersecurity aspects of smart grids. Real-world case studies from utilities in Europe and North America are used to illustrate key concepts.
Under 'VLSI Design', students study the principles of Very Large Scale Integration, focusing on design automation tools like Cadence and Synopsys. The course covers layout design, timing analysis, and verification techniques essential for semiconductor manufacturing.
'Advanced Control Theory' explores modern control strategies including state-space representation, optimal control, and robust control methods. This course prepares students for roles in aerospace, automotive, and robotics industries where precise system control is crucial.
Students interested in renewable energy can opt for 'Energy Storage Systems', which examines battery technologies, supercapacitors, and grid-scale storage solutions. The course includes laboratory sessions involving lithium-ion batteries, lead-acid batteries, and fuel cells.
'Robotics and Automation' combines theoretical knowledge with hands-on robotics projects using Arduino, Raspberry Pi, and ROS (Robot Operating System). Students build autonomous robots capable of navigation, object detection, and manipulation tasks.
Project-Based Learning Philosophy
Eternal University Sirmour's Electrical Engineering program places significant emphasis on project-based learning as a means to bridge the gap between theory and practice. The curriculum includes mandatory mini-projects in the second and third years, followed by a comprehensive final-year thesis or capstone project.
Mini-projects are designed to be small-scale but conceptually rich, allowing students to apply fundamental principles learned in class to real-world scenarios. These projects typically span 2-3 months and require students to work in teams, presenting their findings through written reports and oral presentations.
The final-year capstone project is an extended research endeavor that spans the entire academic year. Students select a topic aligned with their interests or industry requirements, working closely with faculty mentors who guide them through literature review, experimental design, data collection, and analysis phases.
Project selection is facilitated through a formal process where students submit proposals outlining their intended scope, methodology, and expected outcomes. Faculty members evaluate these proposals based on feasibility, relevance to current trends, and alignment with departmental expertise.
Evaluation criteria for projects include technical depth, innovation, clarity of documentation, presentation quality, and peer feedback. The program encourages interdisciplinary collaboration, enabling students from different branches to work together on multifaceted challenges.