Curriculum

The curriculum of the Electrical Engineering program at Government Polytechnic Bazpur is meticulously designed to provide a balanced mix of theoretical knowledge and practical application. It spans four years and includes core courses, departmental electives, science electives, and laboratory sessions.

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	EG101	Engineering Mathematics I	3-1-0-4	-
1	PH101	Physics for Engineers	3-1-0-4	-
1	CE101	Basic Electrical Engineering	3-1-0-4	-
1	CS101	Programming in C	3-0-2-4	-
1	EC101	Basic Electronics	3-1-0-4	-
2	EG102	Engineering Mathematics II	3-1-0-4	EG101
2	PH102	Electromagnetic Fields	3-1-0-4	PH101
2	EE101	Circuit Theory	3-1-0-4	CE101
2	CS102	Data Structures and Algorithms	3-0-2-4	CS101
2	EC102	Electronic Devices and Circuits	3-1-0-4	EC101
3	EG201	Engineering Mathematics III	3-1-0-4	EG102
3	EE201	Electrical Machines I	3-1-0-4	EE101
3	EE202	Power Systems Analysis	3-1-0-4	EE101
3	EE203	Control Systems	3-1-0-4	EG102
3	EE204	Digital Electronics	3-1-0-4	EC102
3	EE205	Signal and Systems	3-1-0-4	EG102
4	EG202	Engineering Mathematics IV	3-1-0-4	EG201
4	EE301	Electrical Machines II	3-1-0-4	EE201
4	EE302	Power Electronics and Drives	3-1-0-4	EE201
4	EE303	Industrial Instrumentation	3-1-0-4	EE205
4	EE304	Microprocessor and Microcontroller	3-1-0-4	EC102
4	EE305	Digital Signal Processing	3-1-0-4	EE205
5	EE401	Renewable Energy Sources	3-1-0-4	EE202
5	EE402	Power System Protection	3-1-0-4	EE202
5	EE403	Advanced Control Systems	3-1-0-4	EE203
5	EE404	Embedded Systems Design	3-1-0-4	EE304
5	EE405	VLSI Design	3-1-0-4	EC102
6	EE501	Smart Grid Technologies	3-1-0-4	EE202
6	EE502	Energy Storage Systems	3-1-0-4	EE401
6	EE503	Project Management	3-1-0-4	-
6	EE504	Industrial Robotics	3-1-0-4	EE203
6	EE505	Advanced Digital Signal Processing	3-1-0-4	EE305
7	EE601	Artificial Intelligence and Machine Learning	3-1-0-4	EG202
7	EE602	Wireless Sensor Networks	3-1-0-4	EE305
7	EE603	Image Processing and Computer Vision	3-1-0-4	EE305
7	EE604	Data Science and Analytics	3-1-0-4	EG202
7	EE605	Electromagnetic Field Theory	3-1-0-4	PH102
8	EE701	Capstone Project	3-0-6-9	All previous semesters
8	EE702	Research Thesis	3-0-6-9	All previous semesters

The advanced departmental elective courses offered in the program are designed to provide students with specialized knowledge and skills. Here are detailed descriptions of ten such courses:

1. Artificial Intelligence and Machine Learning: This course introduces students to the fundamental concepts of AI and ML, including neural networks, deep learning algorithms, reinforcement learning, and natural language processing. Students gain hands-on experience in developing intelligent systems using Python and TensorFlow.

2. Digital Signal Processing: This course covers the analysis and manipulation of discrete-time signals and systems. Topics include Fourier transforms, Z-transforms, filter design, and spectral analysis. Students implement algorithms using MATLAB and DSP processors.

3. Embedded Systems Design: Students learn to design and develop embedded systems for various applications. The course covers microcontroller architecture, real-time operating systems, hardware-software co-design, and system integration techniques.

4. VLSI Design: This advanced course focuses on the design and implementation of Very Large Scale Integration circuits. Students study CMOS technology, logic synthesis, layout design, and testing methodologies using industry-standard tools.

5. Renewable Energy Sources: The course explores various renewable energy technologies such as solar, wind, hydroelectric, and geothermal power. Students learn about energy conversion processes, grid integration, and environmental impact assessment.

6. Power System Protection: This course covers protective relaying principles and applications in power systems. Students study fault analysis, protection schemes, circuit breakers, and modern protection technologies.

7. Industrial Instrumentation: The course introduces students to measurement techniques, sensors, transducers, and control instrumentation used in industrial processes. Practical sessions involve working with real-time data acquisition systems.

8. Advanced Control Systems: This course delves into advanced control theory, including state-space methods, optimal control, robust control, and nonlinear control systems. Students apply these concepts to real-world engineering problems.

9. Wireless Sensor Networks: Students explore the design and implementation of wireless sensor networks for monitoring environmental parameters, smart cities, and industrial automation. The course covers network protocols, data fusion, and energy efficiency.

10. Image Processing and Computer Vision: This course provides an overview of image processing techniques and computer vision algorithms. Students learn about image enhancement, feature extraction, object detection, and machine learning-based vision systems.

The department's philosophy on project-based learning emphasizes the importance of practical application in education. Projects are assigned throughout the curriculum to reinforce theoretical concepts and develop problem-solving skills. Mini-projects are undertaken in the second year, followed by a major capstone project in the final year.

Mini-projects typically involve teams of 3-5 students working on a specific engineering challenge under faculty supervision. These projects are evaluated based on design quality, implementation success, presentation skills, and team collaboration. Students present their findings at departmental symposiums and industry meet-ups.

The final-year thesis/capstone project is a comprehensive endeavor that integrates knowledge from all semesters. Students select a research topic or industrial problem relevant to their interests or career goals. They work closely with faculty mentors to develop innovative solutions or conduct meaningful research. The project culminates in a detailed report and an oral defense before a panel of experts.

Students can choose projects based on their academic interests, industry needs, or personal aspirations. Faculty mentors guide students through the selection process, ensuring alignment between student capabilities and project requirements. Regular meetings, progress reviews, and milestone assessments help maintain quality standards and timely completion.