Course Structure Overview
The Electronics program at Government Polytechnic Bans is structured over three years, with a total of six semesters. Each semester includes core subjects, departmental electives, science electives, and practical laboratory sessions. The curriculum is designed to provide students with both theoretical knowledge and practical skills required in the industry.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1st | ELN101 | Basic Electronics | 3-1-2-5 | - |
| 1st | ELN102 | Mathematics I | 4-0-0-4 | - |
| 1st | ELN103 | Physics I | 3-0-0-3 | - |
| 1st | ELN104 | English Communication | 2-0-0-2 | - |
| 1st | ELN105 | Computer Fundamentals | 2-0-2-4 | - |
| 1st | ELN106 | Engineering Drawing | 1-0-3-4 | - |
| 2nd | ELN201 | Analog Electronics | 3-1-2-5 | ELN101 |
| 2nd | ELN202 | Digital Electronics | 3-1-2-5 | ELN101 |
| 2nd | ELN203 | Mathematics II | 4-0-0-4 | ELN102 |
| 2nd | ELN204 | Physics II | 3-0-0-3 | ELN103 |
| 2nd | ELN205 | Programming in C | 2-0-2-4 | ELN105 |
| 2nd | ELN206 | Electrical Circuits and Machines | 3-1-2-5 | - |
| 3rd | ELN301 | Signals and Systems | 3-1-2-5 | ELN203 |
| 3rd | ELN302 | Microprocessors and Microcontrollers | 3-1-2-5 | ELN202 |
| 3rd | ELN303 | Control Systems | 3-1-2-5 | ELN301 |
| 3rd | ELN304 | VLSI Design | 3-1-2-5 | ELN202 |
| 3rd | ELN305 | Electromagnetic Field Theory | 3-1-2-5 | ELN204 |
| 3rd | ELN306 | Mathematics III | 4-0-0-4 | ELN203 |
| 4th | ELN401 | Embedded Systems | 3-1-2-5 | ELN302 |
| 4th | ELN402 | Communication Systems | 3-1-2-5 | ELN301 |
| 4th | ELN403 | Power Electronics | 3-1-2-5 | ELN206 |
| 4th | ELN404 | Robotics and Automation | 3-1-2-5 | ELN303 |
| 4th | ELN405 | Electronics Lab II | 0-0-6-6 | - |
| 4th | ELN406 | Project Work I | 0-0-8-8 | - |
| 5th | ELN501 | Advanced Embedded Systems | 3-1-2-5 | ELN401 |
| 5th | ELN502 | Signal Processing | 3-1-2-5 | ELN301 |
| 5th | ELN503 | Wireless Communication | 3-1-2-5 | ELN402 |
| 5th | ELN504 | Renewable Energy Systems | 3-1-2-5 | ELN303 |
| 5th | ELN505 | Electronics Lab III | 0-0-6-6 | - |
| 5th | ELN506 | Project Work II | 0-0-8-8 | - |
| 6th | ELN601 | Capstone Project | 0-0-12-12 | - |
| 6th | ELN602 | Internship | 0-0-8-8 | - |
| 6th | ELN603 | Electronics Lab IV | 0-0-6-6 | - |
| 6th | ELN604 | Electronics Elective I | 3-1-2-5 | - |
| 6th | ELN605 | Electronics Elective II | 3-1-2-5 | - |
Advanced Departmental Electives
The department offers several advanced elective courses that allow students to explore specialized areas of interest. These courses are designed to align with current industry demands and technological trends.
1. Advanced Embedded Systems
This course delves into the design and implementation of complex embedded systems, focusing on real-time operating systems, memory management, and device drivers. Students gain hands-on experience in developing applications for ARM-based processors and IoT platforms.
2. Signal Processing
Students learn advanced techniques in signal processing, including digital filter design, spectral analysis, and statistical signal processing. The course emphasizes practical implementation using MATLAB and Simulink tools.
3. Wireless Communication
This elective explores the principles of wireless communication systems, covering topics such as modulation schemes, error correction codes, and network protocols. Students implement communication algorithms on software-defined radios (SDRs).
4. Renewable Energy Systems
The course focuses on integrating renewable energy sources into electrical grids. Students study photovoltaic systems, wind turbines, and energy storage technologies while designing small-scale renewable energy installations.
5. VLSI Design with Verilog
This course provides in-depth knowledge of VLSI design using Verilog HDL. Students learn about logic synthesis, layout design, and testing strategies for digital circuits and systems-on-chip (SoCs).
6. Digital Image Processing
Students explore techniques for image enhancement, compression, segmentation, and recognition using MATLAB and Python libraries. The course includes practical projects involving medical imaging and computer vision applications.
7. Machine Learning for Electronics
This elective introduces machine learning algorithms applied to electronic systems, including neural networks, deep learning models, and their implementation in embedded platforms. Students build predictive models for sensor data analysis.
8. Internet of Things (IoT) and Smart Devices
The course covers IoT architecture, protocols, and application development for smart devices. Students design and deploy IoT solutions using microcontrollers, sensors, and cloud services.
9. Power Electronics and Drives
This course examines the design and control of power electronic converters used in motor drives, renewable energy systems, and industrial applications. Students work with simulation tools to optimize power conversion efficiency.
10. Robotics and Automation
Students learn the fundamentals of robotics, including kinematics, control algorithms, sensor integration, and autonomous navigation. Practical sessions involve building and programming robots using Arduino and Raspberry Pi platforms.
Project-Based Learning Philosophy
The department believes in experiential learning through project-based education. Students are encouraged to apply theoretical concepts to real-world problems, fostering innovation and problem-solving skills.
Mini Projects
Mini projects are assigned during the second year and focus on specific areas of electronics such as microcontroller programming, circuit design, or software integration. These projects are evaluated based on creativity, technical execution, and documentation quality.
Final-Year Thesis/Capstone Project
The final-year capstone project allows students to work on an industry-relevant problem under the supervision of a faculty mentor. Projects typically involve research, design, prototyping, and presentation of results. Students are encouraged to collaborate with external organizations or startups.
Project Selection Process
Students can choose from a list of available projects proposed by faculty members or submit their own ideas. The selection process considers academic performance, interest alignment, and feasibility of execution.
Evaluation Criteria
Projects are assessed based on multiple criteria including innovation, technical depth, documentation, presentation skills, and peer feedback. A formal review panel evaluates each project at different milestones to ensure quality and progress.