Curriculum Overview
The Electrical Engineering curriculum at BAGULA MUKHI COLLEGE OF TECHNOLOGY is structured to provide students with a strong foundation in fundamental principles followed by specialization in advanced areas. The program spans eight semesters, each building upon the previous one to ensure a comprehensive understanding of 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 | CHE101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ECE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | COM101 | Communication Skills | 2-0-0-2 | - |
| 1 | PROG101 | Programming for Engineers | 2-0-2-4 | - |
| 2 | ENG102 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ECE102 | Circuit Analysis | 3-1-0-4 | ECE101 |
| 2 | PHY102 | Electromagnetic Fields | 3-1-0-4 | PHY101 |
| 2 | ECE103 | Electronic Devices | 3-1-0-4 | ECE101 |
| 2 | PROG102 | Data Structures and Algorithms | 2-0-2-4 | PROG101 |
| 3 | ECE201 | Signals and Systems | 3-1-0-4 | ENG102 |
| 3 | ECE202 | Power Electronics | 3-1-0-4 | ECE103 |
| 3 | ECE203 | Control Systems | 3-1-0-4 | ECE102 |
| 3 | ECE204 | Digital Logic Design | 3-1-0-4 | ECE103 |
| 3 | STAT101 | Probability and Statistics | 3-1-0-4 | ENG102 |
| 4 | ECE301 | Microprocessors & Microcontrollers | 3-1-0-4 | ECE204 |
| 4 | ECE302 | Communication Systems | 3-1-0-4 | ECE201 |
| 4 | ECE303 | Digital Signal Processing | 3-1-0-4 | ECE201 |
| 4 | ECE304 | Power Systems Analysis | 3-1-0-4 | ECE202 |
| 4 | PROJ101 | Mini Project I | 0-0-6-3 | - |
| 5 | ECE401 | Embedded Systems | 3-1-0-4 | ECE301 |
| 5 | ECE402 | Antennas and Wave Propagation | 3-1-0-4 | ECE201 |
| 5 | ECE403 | Electromagnetic Compatibility | 3-1-0-4 | PHY102 |
| 5 | ECE404 | Renewable Energy Systems | 3-1-0-4 | ECE204 |
| 5 | PROJ102 | Mini Project II | 0-0-6-3 | - |
| 6 | ECE501 | VLSI Design | 3-1-0-4 | ECE204 |
| 6 | ECE502 | Smart Grid Technologies | 3-1-0-4 | ECE304 |
| 6 | ECE503 | AI and Machine Learning | 3-1-0-4 | ECE201 |
| 6 | ECE504 | Bioelectronics | 3-1-0-4 | ECE203 |
| 6 | PROJ103 | Mini Project III | 0-0-6-3 | - |
| 7 | ECE601 | Advanced Power Converters | 3-1-0-4 | ECE202 |
| 7 | ECE602 | Wireless Networks | 3-1-0-4 | ECE302 |
| 7 | ECE603 | Robotics and Control | 3-1-0-4 | ECE203 |
| 7 | ECE604 | Quantum Computing Fundamentals | 3-1-0-4 | ECE201 |
| 7 | PROJ104 | Mini Project IV | 0-0-6-3 | - |
| 8 | ECE701 | Final Year Project / Thesis | 0-0-12-12 | - |
Advanced Departmental Electives
These advanced elective courses are offered in the latter semesters and allow students to specialize in specific areas of interest:
- Advanced Power Converters: This course delves into high-efficiency power conversion techniques, including DC-DC converters, AC-DC rectifiers, and resonant converters. Students learn how to design and analyze converters for applications in renewable energy systems and electric vehicles.
- Wireless Networks: Focused on modern wireless communication standards such as 5G, Wi-Fi, Bluetooth, and IoT protocols. The course covers network architecture, signal propagation models, and security issues in wireless environments.
- Robotics and Control: Combines principles of control theory with robotics applications. Students design and implement robotic systems using microcontrollers and sensors, focusing on autonomous navigation and manipulation tasks.
- Quantum Computing Fundamentals: Introduces the basics of quantum mechanics and quantum algorithms. Students explore how quantum computers differ from classical ones and gain hands-on experience with quantum simulation tools.
- Smart Grid Technologies: Examines smart grid components such as advanced metering infrastructure, demand response systems, and distributed energy resources. The course includes case studies from global smart grid implementations.
- AI and Machine Learning: Covers supervised and unsupervised learning techniques, neural networks, deep learning architectures, and reinforcement learning. Applications include image recognition, natural language processing, and predictive analytics.
- Bioelectronics: Focuses on electronic devices used in medical applications, such as pacemakers, hearing aids, and brain-machine interfaces. Students learn about biosensors, biocompatibility issues, and regulatory requirements for medical devices.
- VLSI Design: Involves designing integrated circuits using CAD tools and techniques. Topics include logic synthesis, layout design, testing, and optimization of VLSI systems for various applications including processors and memory chips.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a means to bridge the gap between theory and practice. Students are encouraged to work on real-world projects throughout their academic journey, starting from mini-projects in early semesters to final-year capstone projects.
The structure of these projects is carefully designed to ensure maximum impact:
- Mini Projects: In the second year, students work in small teams on a fixed topic under faculty supervision. These projects last for 3-4 months and involve planning, execution, documentation, and presentation.
- Capstone Project: In the final year, students select a research-oriented project that aligns with their specialization. They work closely with a faculty mentor to define objectives, conduct literature review, develop methodology, carry out experiments, and write a comprehensive thesis.
Evaluation criteria include:
- Technical competence
- Problem-solving ability
- Team collaboration skills
- Quality of documentation and presentation
- Innovation and creativity
Students are supported through regular feedback sessions, access to research databases, and opportunities to present their work at national conferences and symposiums. The goal is to produce graduates who are not only technically skilled but also capable of leading innovation in their chosen fields.