Curriculum Overview
The Electrical Engineering program at Al Karim University Katihar is structured to provide a comprehensive and progressive educational experience that balances theoretical knowledge with practical application. The curriculum spans eight semesters, with each semester building upon the previous one to ensure students develop a deep understanding of electrical engineering principles.
Throughout the program, students are exposed to foundational courses in mathematics, physics, and computer programming during their first year. These subjects lay the groundwork for more advanced topics in later semesters. The second year introduces core electrical engineering concepts such as electrical machines, network analysis, and digital logic design, preparing students for specialized areas of study.
The third and fourth years offer increasing specialization through elective courses that allow students to tailor their education to their interests and career goals. Advanced electives cover topics ranging from power systems to signal processing, communication systems, VLSI design, and artificial intelligence in electrical engineering. This flexibility ensures that graduates are well-prepared for diverse career paths.
Project-based learning is a key component of the curriculum, with students engaging in both mini-projects and capstone projects throughout their academic journey. These projects encourage creativity, problem-solving, and collaboration while reinforcing theoretical concepts through practical implementation.
Course Structure
The following table provides a detailed breakdown of all courses offered across eight semesters:
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH-101 | Mathematics I | 3-1-0-4 | - |
| 1 | MATH-102 | Mathematics II | 3-1-0-4 | MATH-101 |
| 1 | PHYS-101 | Physics I | 3-1-0-4 | - |
| 1 | PHYS-102 | Physics II | 3-1-0-4 | PHYS-101 |
| 1 | CSE-101 | Introduction to Computer Programming | 2-0-2-3 | - |
| 1 | EE-101 | Basic Electrical Circuits | 3-1-0-4 | - |
| 1 | EE-102 | Basic Electronics | 3-1-0-4 | - |
| 1 | ENGL-101 | English for Engineering | 2-0-0-2 | - |
| 2 | MATH-201 | Mathematics III | 3-1-0-4 | MATH-102 |
| 2 | MATH-202 | Mathematics IV | 3-1-0-4 | MATH-201 |
| 2 | PHYS-201 | Chemistry I | 3-1-0-4 | - |
| 2 | PHYS-202 | Chemistry II | 3-1-0-4 | PHYS-201 |
| 2 | EE-201 | Electrical Machines I | 3-1-0-4 | EE-101 |
| 2 | EE-202 | Network Analysis | 3-1-0-4 | EE-101 |
| 2 | EE-203 | Digital Logic Design | 3-1-0-4 | - |
| 2 | EE-204 | Electronics Devices and Circuits | 3-1-0-4 | EE-102 |
| 2 | ENGL-201 | Technical Communication | 2-0-0-2 | - |
| 3 | MATH-301 | Probability and Statistics | 3-1-0-4 | MATH-202 |
| 3 | MATH-302 | Differential Equations | 3-1-0-4 | MATH-202 |
| 3 | EE-301 | Power System Analysis | 3-1-0-4 | EE-201 |
| 3 | EE-302 | Control Systems | 3-1-0-4 | EE-201 |
| 3 | EE-303 | Digital Signal Processing | 3-1-0-4 | EE-203 |
| 3 | EE-304 | Microprocessor and Microcontroller | 3-1-0-4 | EE-204 |
| 3 | EE-305 | Electromagnetic Fields | 3-1-0-4 | MATH-302 |
| 3 | EE-306 | Embedded Systems | 3-1-0-4 | EE-304 |
| 4 | MATH-401 | Linear Algebra | 3-1-0-4 | MATH-301 |
| 4 | MATH-402 | Numerical Methods | 3-1-0-4 | MATH-301 |
| 4 | EE-401 | Power Electronics | 3-1-0-4 | EE-201 |
| 4 | EE-402 | Signal and Systems | 3-1-0-4 | EE-303 |
| 4 | EE-403 | VLSI Design | 3-1-0-4 | EE-306 |
| 4 | EE-404 | Communication Systems | 3-1-0-4 | EE-302 |
| 4 | EE-405 | Renewable Energy Technologies | 3-1-0-4 | EE-301 |
| 4 | EE-406 | Artificial Intelligence in Electrical Engineering | 3-1-0-4 | EE-303 |
| 5 | EE-501 | Advanced Power Systems | 3-1-0-4 | EE-301 |
| 5 | EE-502 | Modern Control Theory | 3-1-0-4 | EE-302 |
| 5 | EE-503 | Image Processing | 3-1-0-4 | EE-303 |
| 5 | EE-504 | Computer Architecture and Organization | 3-1-0-4 | - |
| 5 | EE-505 | Smart Grid Technologies | 3-1-0-4 | EE-501 |
| 5 | EE-506 | Machine Learning in Engineering | 3-1-0-4 | EE-303 |
| 6 | EE-601 | Power System Protection | 3-1-0-4 | EE-501 |
| 6 | EE-602 | Optical Fiber Communications | 3-1-0-4 | EE-404 |
| 6 | EE-603 | Advanced Embedded Systems | 3-1-0-4 | EE-306 |
| 6 | EE-604 | RF and Microwave Engineering | 3-1-0-4 | EE-305 |
| 6 | EE-605 | Renewable Energy Integration | 3-1-0-4 | EE-505 |
| 6 | EE-606 | Neural Networks and Deep Learning | 3-1-0-4 | EE-506 |
| 7 | EE-701 | Research Methodology | 2-0-0-2 | - |
| 7 | EE-702 | Capstone Project I | 3-1-0-4 | - |
| 7 | EE-703 | Advanced Topics in Power Systems | 3-1-0-4 | EE-501 |
| 7 | EE-704 | Advanced Signal Processing | 3-1-0-4 | EE-402 |
| 7 | EE-705 | Project Management in Engineering | 2-0-0-2 | - |
| 8 | EE-801 | Capstone Project II | 3-1-0-4 | EE-702 |
| 8 | EE-802 | Internship | 3-0-0-3 | - |
| 8 | EE-803 | Professional Ethics and Social Responsibility | 2-0-0-2 | - |
| 8 | EE-804 | Advanced VLSI Design | 3-1-0-4 | EE-403 |
Advanced Departmental Electives
The department offers several advanced departmental elective courses that allow students to specialize in areas of interest and gain deeper insights into cutting-edge technologies:
- Advanced Power Systems: This course delves into complex topics such as power system stability analysis, load flow studies, fault analysis, and protection schemes. It prepares students for careers in power utilities and engineering firms involved in power generation and distribution.
- Modern Control Theory: Students learn about state-space representation, controllability, observability, and optimal control techniques. The course includes practical applications using MATLAB/Simulink tools.
- Digital Signal Processing: This course covers digital filtering, transform methods, and spectral analysis. It is crucial for students pursuing careers in telecommunications, audio processing, and biomedical engineering.
- VLSI Design: The course introduces students to the principles of Very Large Scale Integration (VLSI) design, including logic synthesis, layout design, and verification techniques.
- Communication Systems: Students explore analog and digital communication systems, modulation techniques, and error correction codes. This course is essential for careers in wireless communications and network engineering.
- Renewable Energy Technologies: The focus is on solar panels, wind turbines, energy storage systems, and grid integration strategies. It prepares students for roles in the growing renewable energy sector.
- Machine Learning in Engineering: This course teaches students how to apply machine learning algorithms to solve engineering problems. Topics include supervised and unsupervised learning, neural networks, and deep learning.
- Smart Grid Technologies: Students learn about smart grid components, communication protocols, and control strategies. It is designed for those interested in the future of power systems.
- Neural Networks and Deep Learning: This advanced course covers neural network architectures, backpropagation algorithms, and deep learning frameworks such as TensorFlow and PyTorch.
- Optical Fiber Communications: The course explores the principles of optical fiber transmission, components, and systems. It prepares students for careers in telecommunications and data networking.
Project-Based Learning Approach
The department places significant emphasis on project-based learning as a core component of its educational philosophy. Projects are integrated throughout the curriculum to ensure that students apply theoretical concepts in real-world scenarios.
Mini-projects begin in the second year and involve solving real-world problems under faculty supervision. These projects are evaluated based on design quality, functionality, presentation skills, and teamwork abilities. They provide students with early exposure to collaborative work environments and professional expectations.
The final-year thesis or capstone project is a significant undertaking that allows students to explore a specific area of interest in depth. Projects are selected based on student preferences, faculty expertise, and industry relevance. Students work closely with assigned mentors throughout the process, receiving guidance and feedback at regular intervals.
The structure and scope of these projects are carefully defined to ensure they challenge students while remaining achievable within the timeframe. Evaluation criteria include originality, technical depth, clarity of presentation, and overall impact of the solution or innovation.