Course Structure Overview
The Electrical Engineering program at Girijananda Chowdhury University Kamrup is structured over 8 semesters, with a blend of foundational courses, core engineering subjects, departmental electives, and lab-based learning experiences.
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CSE101 | Introduction to Programming | 2-1-0-3 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Graphics | 2-1-0-3 | - |
| 1 | ESC101 | English for Engineers | 2-0-0-2 | - |
| 1 | LAB101 | Basic Electronics Lab | 0-0-3-1 | - |
| 2 | ENG103 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ECE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 2 | PHY102 | Modern Physics | 3-1-0-4 | PHY101 |
| 2 | CSE102 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 2 | ENG104 | Workshop Practice | 0-0-2-1 | - |
| 2 | LALB101 | Basic Electrical Lab | 0-0-3-1 | ECE101 |
| 3 | ENG201 | Engineering Mathematics III | 3-1-0-4 | ENG103 |
| 3 | ECE201 | Circuit Analysis | 3-1-0-4 | ECE101 |
| 3 | ECE202 | Signals and Systems | 3-1-0-4 | ENG103 |
| 3 | ECE203 | Electronic Devices | 3-1-0-4 | - |
| 3 | ECE204 | Electromagnetic Fields | 3-1-0-4 | ENG103 |
| 3 | LALB201 | Circuit Analysis Lab | 0-0-3-1 | ECE201 |
| 4 | ENG202 | Engineering Mathematics IV | 3-1-0-4 | ENG201 |
| 4 | ECE301 | Electrical Machines | 3-1-0-4 | ECE201 |
| 4 | ECE302 | Power Electronics | 3-1-0-4 | ECE203 |
| 4 | ECE303 | Control Systems | 3-1-0-4 | ECE202 |
| 4 | ECE304 | Microprocessors and Microcontrollers | 3-1-0-4 | CSE102 |
| 4 | LALB301 | Electrical Machines Lab | 0-0-3-1 | ECE301 |
| 5 | ECE401 | Power System Analysis | 3-1-0-4 | ECE301 |
| 5 | ECE402 | Digital Signal Processing | 3-1-0-4 | ECE202 |
| 5 | ECE403 | Communication Systems | 3-1-0-4 | ECE202 |
| 5 | ECE404 | Embedded Systems | 3-1-0-4 | ECE304 |
| 5 | LALB401 | DSP and Communication Lab | 0-0-3-1 | ECE402, ECE403 |
| 6 | ECE501 | Renewable Energy Systems | 3-1-0-4 | ECE301 |
| 6 | ECE502 | Advanced Power Electronics | 3-1-0-4 | ECE302 |
| 6 | ECE503 | Wireless Communication | 3-1-0-4 | ECE403 |
| 6 | ECE504 | VLSI Design | 3-1-0-4 | ECE203 |
| 6 | LALB501 | VLSI Design Lab | 0-0-3-1 | ECE504 |
| 7 | ECE601 | Robotics and Automation | 3-1-0-4 | ECE303 |
| 7 | ECE602 | Artificial Intelligence in Engineering | 3-1-0-4 | ECE402 |
| 7 | ECE603 | Smart Grid Technologies | 3-1-0-4 | ECE501 |
| 7 | ECE604 | Nanotechnology Applications | 3-1-0-4 | - |
| 7 | LALB601 | Robotics and AI Lab | 0-0-3-1 | ECE601, ECE602 |
| 8 | ECE701 | Capstone Project | 0-0-6-6 | All previous courses |
| 8 | ECE702 | Advanced Topics in Electrical Engineering | 3-1-0-4 | - |
| 8 | ECE703 | Industrial Training | 0-0-0-6 | - |
| 8 | ECE704 | Research Methodology | 2-0-0-2 | - |
Advanced Departmental Electives
Departmental electives offer students a chance to specialize in areas of personal interest and industry relevance. These courses are designed by faculty experts who lead ongoing research projects, ensuring that students receive up-to-date knowledge.
- Power System Protection: This course focuses on protection schemes for power systems, including relay settings, fault analysis, and system stability.
- Digital Image Processing: Students learn about image enhancement, filtering, segmentation, and feature extraction using MATLAB and Python.
- Advanced Control Systems: Covers modern control techniques such as optimal control, robust control, and adaptive control.
- Network Security: Introduces concepts of cybersecurity in networked systems, including encryption, authentication, and intrusion detection.
- Industrial Automation: Explores PLC programming, SCADA systems, and automation technologies used in manufacturing environments.
- Machine Learning for Engineers: Provides a practical approach to applying machine learning algorithms to engineering problems.
- Optical Fiber Communication: Covers principles of fiber optic transmission, including modulation techniques and network design.
- Renewable Energy Integration: Focuses on integrating solar and wind power into existing grids with an emphasis on smart grid technologies.
- Neural Networks in Engineering: Applies neural networks to solve engineering challenges in signal processing and control systems.
- Advanced Microprocessors: Explores the architecture of modern microprocessors and their applications in embedded systems.
Project-Based Learning Philosophy
The department promotes a project-based learning approach where students engage in hands-on experiences from the early semesters. This methodology enhances understanding, develops problem-solving skills, and builds collaborative abilities essential for professional success.
Mini-projects are introduced in the second year, allowing students to apply theoretical concepts to real-world scenarios. These projects are evaluated based on design documentation, implementation quality, and presentation skills.
The final-year capstone project is a comprehensive endeavor that integrates all learned knowledge. Students select projects aligned with their interests or industry needs, working closely with faculty mentors who provide guidance and supervision throughout the process.
Project Selection and Evaluation
Students begin selecting their capstone project in the seventh semester, often collaborating with industry partners or faculty research initiatives. The evaluation criteria include innovation, technical depth, feasibility, and impact on society.