Course Structure and Curriculum Overview
The curriculum for the Electrical Engineering program at BHABHA ENGINEERING RESEARCH INSTITUTE is designed to provide a comprehensive foundation in both theoretical and applied aspects of electrical engineering, with an emphasis on innovation, problem-solving, and practical application. The program spans eight semesters and includes core courses, departmental electives, science electives, and laboratory components.
Semester-wise Course Breakdown
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Mathematics I | 3-1-0-4 | None |
| 1 | MATH102 | Mathematics II | 3-1-0-4 | MATH101 |
| 1 | PHYS101 | Physics I | 3-1-0-4 | None |
| 1 | PHYS102 | Physics II | 3-1-0-4 | PHYS101 |
| 1 | EC101 | Introduction to Electrical Engineering | 3-0-0-3 | None |
| 1 | CSE101 | Introduction to Computer Science | 2-0-0-2 | None |
| 1 | ENG101 | English for Engineers | 2-0-0-2 | None |
| 1 | LAW101 | Legal and Ethical Aspects of Engineering | 2-0-0-2 | None |
| 2 | MATH201 | Mathematics III | 3-1-0-4 | MATH102 |
| 2 | MATH202 | Mathematics IV | 3-1-0-4 | MATH201 |
| 2 | CHEM101 | Chemistry I | 3-1-0-4 | None |
| 2 | CHEM102 | Chemistry II | 3-1-0-4 | CHEM101 |
| 2 | EC201 | Circuit Analysis and Design | 3-1-0-4 | EC101 |
| 2 | EC202 | Electromagnetic Fields | 3-1-0-4 | PHYS102 |
| 2 | EC203 | Analog Electronics | 3-1-0-4 | EC201 |
| 2 | EC204 | Digital Electronics | 3-1-0-4 | EC201 |
| 3 | EC301 | Signals and Systems | 3-1-0-4 | MATH202 |
| 3 | EC302 | Control Systems | 3-1-0-4 | EC201 |
| 3 | EC303 | Power Electronics | 3-1-0-4 | EC203 |
| 3 | EC304 | Communications Engineering | 3-1-0-4 | EC301 |
| 3 | EC305 | Electrical Machines | 3-1-0-4 | EC201 |
| 4 | EC401 | Microprocessors and Microcontrollers | 3-1-0-4 | EC204 |
| 4 | EC402 | Power System Analysis | 3-1-0-4 | EC305 |
| 4 | EC403 | Electrical Drives and Automation | 3-1-0-4 | EC302 |
| 4 | EC404 | Antennas and Wave Propagation | 3-1-0-4 | EC202 |
| 4 | EC405 | Instrumentation and Measurement | 3-1-0-4 | EC201 |
| 5 | EC501 | Renewable Energy Systems | 3-1-0-4 | EC305 |
| 5 | EC502 | Digital Signal Processing | 3-1-0-4 | EC301 |
| 5 | EC503 | Robotics and Automation | 3-1-0-4 | EC302 |
| 5 | EC504 | VLSI Design | 3-1-0-4 | EC204 |
| 5 | EC505 | Embedded Systems | 3-1-0-4 | EC401 |
| 6 | EC601 | Advanced Power Electronics | 3-1-0-4 | EC303 |
| 6 | EC602 | Smart Grid Technologies | 3-1-0-4 | EC402 |
| 6 | EC603 | Wireless Communication Systems | 3-1-0-4 | EC304 |
| 6 | EC604 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 6 | EC605 | Machine Learning for Electrical Engineers | 3-1-0-4 | EC301 |
| 7 | EC701 | Project Management in Engineering | 2-0-0-2 | None |
| 7 | EC702 | Research Methodology | 2-0-0-2 | None |
| 7 | EC703 | Capstone Project I | 4-0-0-4 | None |
| 8 | EC801 | Capstone Project II | 6-0-0-6 | EC703 |
Advanced Departmental Electives
The advanced departmental elective courses offered in the Electrical Engineering program are designed to provide students with in-depth knowledge and practical skills in specialized areas. These courses are taught by leading experts in their respective fields and include both theoretical concepts and real-world applications.
Renewable Energy Systems
This course explores the principles, technologies, and applications of renewable energy sources such as solar, wind, hydroelectric, and geothermal power. Students learn about grid integration, energy storage systems, and environmental impact assessments. The course includes hands-on projects involving solar panel installation and wind turbine design.
Digital Signal Processing
This course covers advanced topics in digital signal processing including filter design, spectral analysis, and fast Fourier transform algorithms. Students gain proficiency in MATLAB and Python-based tools for signal manipulation and visualization. The course includes practical assignments on audio and image processing.
Robotics and Automation
This course introduces students to robotics fundamentals, including kinematics, dynamics, control systems, and sensor integration. Students build and program robots using ROS (Robot Operating System) and work on autonomous navigation tasks. The course culminates in a robotics competition.
VLSI Design
This course delves into the design and implementation of very large-scale integrated circuits (VLSI). Students learn about logic synthesis, layout design, and circuit simulation using industry-standard tools like Cadence and Synopsys. The course includes a project on designing a microprocessor core.
Embedded Systems
This course focuses on the design and development of embedded systems for real-time applications. Students learn about microcontrollers, operating systems, and hardware-software co-design. The course includes projects involving IoT devices and smart home automation systems.
Advanced Power Electronics
This course covers advanced topics in power electronics including switching converters, inverters, and motor drives. Students study high-efficiency power conversion techniques and work on projects related to electric vehicle charging infrastructure.
Smart Grid Technologies
This course explores the integration of smart technologies in electrical power grids. Topics include demand response, energy management systems, and communication protocols for grid automation. Students analyze real-world case studies and develop solutions for grid optimization.
Wireless Communication Systems
This course covers modern wireless communication standards such as 5G, Wi-Fi, and Bluetooth. Students study propagation models, modulation techniques, and network protocols. The course includes lab sessions on signal transmission and reception using software-defined radios.
Data Structures and Algorithms
This course emphasizes algorithmic thinking and data structure implementation in electrical engineering contexts. Students learn about complexity analysis, sorting algorithms, and graph traversal methods. Practical assignments involve developing efficient solutions for engineering problems.
Machine Learning for Electrical Engineers
This course introduces machine learning techniques tailored for electrical engineering applications. Students study neural networks, deep learning architectures, and supervised/unsupervised learning algorithms. Projects include predictive modeling for power systems and image recognition in medical devices.
Project-Based Learning Philosophy
The Electrical Engineering program at BHABHA ENGINEERING RESEARCH INSTITUTE places a strong emphasis on project-based learning to bridge the gap between academic theory and industry practice. The program includes both mandatory mini-projects in early semesters and a comprehensive capstone project in the final year.
Mini-Projects
Mini-projects are assigned starting from the second semester and continue through the sixth semester. Each project is designed to reinforce learning outcomes of core courses and develop practical skills such as design thinking, problem-solving, teamwork, and presentation abilities. Projects are typically completed in groups of 3-5 students under faculty supervision.
Final-Year Thesis/Capstone Project
The capstone project is the culminating experience of the program, undertaken during the seventh and eighth semesters. Students select a topic aligned with their interests or industry needs, conduct research, and develop a prototype or solution that addresses real-world challenges. The project is supervised by faculty mentors and evaluated based on innovation, technical depth, documentation quality, and presentation skills.
Project Selection Process
Students are encouraged to explore various research areas during their early semesters through internships, lab work, and elective courses. Faculty members guide students in selecting projects that align with their strengths and career goals. The department maintains a database of ongoing research projects, industry collaborations, and alumni-led initiatives to provide diverse project options.
Evaluation Criteria
Projects are evaluated using a combination of peer reviews, faculty assessments, and external evaluations. Grading criteria include design quality, feasibility, innovation, technical execution, documentation standards, and oral presentations. Successful projects may lead to publication opportunities, patent applications, or commercialization.