Course Structure Overview
The Electronics program at Get Group Of Institution Faculty Of Technology is structured over eight semesters, with a balanced mix of core engineering courses, departmental electives, science electives, and laboratory sessions. This comprehensive structure ensures that students receive both foundational knowledge and specialized expertise required for success in the field.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EE101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | EE102 | Physics for Electronics | 3-1-0-4 | - |
| 1 | EE103 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | EE104 | Introduction to Programming | 2-0-2-3 | - |
| 1 | EE105 | Engineering Graphics & Design | 2-0-2-3 | - |
| 1 | EE106 | Chemistry for Electronics | 3-1-0-4 | - |
| 2 | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Analog Electronics I | 3-1-0-4 | EE103 |
| 2 | EE203 | Digital Logic Design | 3-1-0-4 | - |
| 2 | EE204 | Signals and Systems | 3-1-0-4 | EE101 |
| 2 | EE205 | Electromagnetic Fields | 3-1-0-4 | EE102 |
| 2 | EE206 | Programming Lab | 0-0-2-2 | EE104 |
| 3 | EE301 | Electronic Devices & Circuits | 3-1-0-4 | EE202 |
| 3 | EE302 | Microprocessor Architecture | 3-1-0-4 | EE203 |
| 3 | EE303 | Control Systems | 3-1-0-4 | EE204 |
| 3 | EE304 | Communication Engineering | 3-1-0-4 | EE204 |
| 3 | EE305 | Electromagnetic Wave Propagation | 3-1-0-4 | EE205 |
| 3 | EE306 | Digital Electronics Lab | 0-0-2-2 | EE203 |
| 4 | EE401 | VLSI Design | 3-1-0-4 | EE301 |
| 4 | EE402 | Embedded Systems | 3-1-0-4 | EE302 |
| 4 | EE403 | Wireless Communication | 3-1-0-4 | EE304 |
| 4 | EE404 | Power Electronics | 3-1-0-4 | EE301 |
| 4 | EE405 | Signal Processing | 3-1-0-4 | EE204 |
| 4 | EE406 | Microcontroller Lab | 0-0-2-2 | EE302 |
| 5 | EE501 | Advanced Digital Design | 3-1-0-4 | EE401 |
| 5 | EE502 | Artificial Intelligence & Machine Learning | 3-1-0-4 | EE405 |
| 5 | EE503 | RF & Microwave Engineering | 3-1-0-4 | EE305 |
| 5 | EE504 | Renewable Energy Systems | 3-1-0-4 | EE404 |
| 5 | EE505 | Biomedical Electronics | 3-1-0-4 | EE301 |
| 5 | EE506 | VLSI Design Lab | 0-0-2-2 | EE401 |
| 6 | EE601 | Quantum Computing & Nanotechnology | 3-1-0-4 | EE501 |
| 6 | EE602 | Robotics and Automation | 3-1-0-4 | EE303 |
| 6 | EE603 | Advanced Communication Systems | 3-1-0-4 | EE403 |
| 6 | EE604 | Control Systems Lab | 0-0-2-2 | EE303 |
| 6 | EE605 | Electronics Project I | 0-0-4-4 | - |
| 7 | EE701 | Advanced Signal Processing | 3-1-0-4 | EE502 |
| 7 | EE702 | Smart Grid Technologies | 3-1-0-4 | EE504 |
| 7 | EE703 | Machine Learning Applications | 3-1-0-4 | EE502 |
| 7 | EE704 | Advanced Embedded Systems | 3-1-0-4 | EE402 |
| 7 | EE705 | Electronics Project II | 0-0-4-4 | - |
| 8 | EE801 | Final Year Thesis/Capstone Project | 0-0-6-6 | - |
| 8 | EE802 | Industrial Training | 0-0-2-2 | - |
| 8 | EE803 | Electronics Internship | 0-0-2-2 | - |
Detailed Course Descriptions
The department offers a rich array of advanced departmental electives designed to deepen students' understanding and practical skills in specialized areas. Here are descriptions for several key courses:
- Artificial Intelligence & Machine Learning (EE502): This course explores the fundamentals of machine learning algorithms, neural networks, deep learning architectures, reinforcement learning, and their applications in image recognition, natural language processing, and predictive analytics. Students engage with datasets from real-world domains and develop projects using frameworks like TensorFlow and PyTorch.
- VLSI Design (EE401): Focused on the design and implementation of Very Large Scale Integration circuits, this course covers CMOS technology, logic synthesis, physical design, and verification techniques. Students utilize industry-standard tools like Cadence and Synopsys for circuit design and simulation.
- Wireless Communication (EE403): This subject delves into wireless transmission principles, modulation schemes, channel coding, multiple access techniques, and modern wireless standards such as 5G, LTE, and Wi-Fi. Practical sessions involve the use of spectrum analyzers and software-defined radios.
- Power Electronics (EE404): Students learn about power converters, inverters, rectifiers, and motor drives, with emphasis on efficiency optimization and control strategies. Labs include building prototype circuits for solar inverters, electric vehicle charging systems, and energy storage solutions.
- Signal Processing (EE405): This course introduces digital signal processing concepts including sampling theory, discrete-time systems, Fourier transforms, filtering techniques, and spectral analysis. Applications span audio processing, biomedical signal analysis, and image enhancement.
- Embedded Systems (EE402): A hands-on exploration of microcontroller architectures, real-time operating systems, embedded software development, and hardware-software co-design. Students build functional prototypes using ARM Cortex-M series processors and develop IoT applications.
- Biomedical Electronics (EE505): This interdisciplinary course bridges electronics with healthcare, covering medical instrumentation, biosensors, physiological signal processing, and implantable devices. Projects involve designing electrocardiogram (ECG) monitors, pulse oximeters, and neurostimulation systems.
- Quantum Computing & Nanotechnology (EE601): An introduction to quantum mechanics, quantum algorithms, qubit manipulation, and nanofabrication techniques. Students explore current research trends in quantum computing platforms and their potential impact on future technologies.
- Robotics and Automation (EE602): This course combines mechanical design, sensor integration, control systems, and artificial intelligence to create autonomous robots. Labs involve building mobile robots, manipulator arms, and robotic systems for industrial automation tasks.
- Advanced Communication Systems (EE603): Advanced topics in communication engineering including spread spectrum techniques, MIMO systems, OFDM, and satellite communications. Students conduct simulations using MATLAB and implement communication protocols on FPGA platforms.
Project-Based Learning Philosophy
The department strongly advocates for a project-based learning approach that integrates theoretical knowledge with practical implementation. Mini-projects are assigned during the third and fourth semesters, allowing students to apply concepts learned in class to real-world problems. These projects span across multiple disciplines, encouraging interdisciplinary collaboration.
Final-year capstone projects are undertaken under the guidance of faculty mentors from the department or industry partners. Students select topics based on their interests and career aspirations, with a focus on innovation and societal relevance. The evaluation criteria include technical execution, creativity, presentation quality, and documentation.