Electrical Engineering Curriculum Overview
The Electrical Engineering curriculum at Goel Group of Institutions is designed to provide a balanced blend of theoretical knowledge and practical skills. The program spans eight semesters, with each semester carefully structured to build upon previous learning experiences.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-Requisites |
|---|---|---|---|---|
| 1 | ENGL101 | English Communication Skills | 3-0-0-3 | - |
| 1 | MATH101 | Mathematics I | 4-0-0-4 | - |
| 1 | PHYS101 | Physics | 3-0-0-3 | - |
| 1 | CHEM101 | Chemistry | 3-0-0-3 | - |
| 1 | EC101 | Introduction to Electrical Engineering | 3-0-0-3 | - |
| 1 | EG101 | Engineering Graphics | 2-0-0-2 | - |
| 1 | COMPS101 | Introduction to Programming | 3-0-0-3 | - |
| 1 | L101 | Lab: Introduction to Electrical Engineering | 0-0-3-1 | - |
| 2 | MATH102 | Mathematics II | 4-0-0-4 | MATH101 |
| 2 | CIRCUIT201 | Circuit Analysis | 3-0-0-3 | - |
| 2 | DIGITAL201 | Digital Logic Design | 3-0-0-3 | - |
| 2 | ANALOG201 | Analog Electronics | 3-0-0-3 | - |
| 2 | PHYS201 | Physics Lab | 0-0-3-1 | - |
| 2 | L201 | Lab: Circuit Analysis | 0-0-3-1 | - |
| 3 | MATH201 | Mathematics III | 4-0-0-4 | MATH102 |
| 3 | EMF201 | Electromagnetic Fields | 3-0-0-3 | - |
| 3 | SIGNALS201 | Signals and Systems | 3-0-0-3 | - |
| 3 | CONTROL201 | Control Systems | 3-0-0-3 | - |
| 3 | MACHINES201 | Electrical Machines | 3-0-0-3 | - |
| 3 | L301 | Lab: Electrical Machines | 0-0-3-1 | - |
| 4 | MATH202 | Mathematics IV | 4-0-0-4 | MATH201 |
| 4 | POWER201 | Power Systems | 3-0-0-3 | - |
| 4 | ELECTRO201 | Power Electronics | 3-0-0-3 | - |
| 4 | DRIVES201 | Motor Drives | 3-0-0-3 | - |
| 4 | L401 | Lab: Power Electronics | 0-0-3-1 | - |
| 5 | ELEC501 | Electronics and Instrumentation | 3-0-0-3 | - |
| 5 | COMMUNICATION501 | Communication Systems | 3-0-0-3 | - |
| 5 | EMBEDDED501 | Embedded Systems | 3-0-0-3 | - |
| 5 | L501 | Lab: Embedded Systems | 0-0-3-1 | - |
| 6 | AI501 | Artificial Intelligence | 3-0-0-3 | - |
| 6 | ML501 | Machine Learning | 3-0-0-3 | - |
| 6 | RF501 | Radar and RF Systems | 3-0-0-3 | - |
| 6 | L601 | Lab: AI & ML Projects | 0-0-3-1 | - |
| 7 | RENEWABLE701 | Renewable Energy Systems | 3-0-0-3 | - |
| 7 | GRID701 | Smart Grid Technologies | 3-0-0-3 | - |
| 7 | ENERGY701 | Energy Storage Systems | 3-0-0-3 | |
| 7 | L701 | Lab: Renewable Energy Systems | 0-0-3-1 | - |
| 8 | THESIS801 | Final Year Project/Thesis | 0-0-6-6 | - |
Advanced Departmental Electives
The department offers several advanced elective courses to deepen students' understanding of specialized areas within electrical engineering. These courses are tailored to provide depth in emerging technologies and applications.
Power Electronics and Drives
This course explores the design and implementation of power conversion systems, including DC-DC converters, inverters, and motor drives. Students gain hands-on experience with power electronics circuits and learn to optimize efficiency in energy conversion systems.
Embedded Systems Design
This elective focuses on designing embedded platforms using microcontrollers and FPGAs. Students develop projects involving real-time systems, sensor integration, and communication protocols, preparing them for careers in IoT and automation.
Artificial Intelligence and Machine Learning
This course introduces students to AI algorithms and ML techniques used in signal processing, pattern recognition, and predictive analytics. Practical applications include image classification, natural language processing, and robotics control systems.
Signal Processing Techniques
This advanced elective covers digital signal processing methods for audio, video, and biomedical signals. Students learn to implement filters, perform spectral analysis, and apply DSP techniques in practical engineering scenarios.
Radar and RF Systems
This course explores the principles of radar systems, radio frequency design, and wireless communication technologies. Students study antenna design, propagation models, and modern radar architectures used in defense and civilian applications.
Smart Grid Technologies
This course delves into smart grid concepts, including demand response systems, distributed energy resources, and grid stability management. It prepares students for careers in energy infrastructure development and policy planning.
Renewable Energy Integration
This elective examines renewable energy technologies such as solar, wind, and hydroelectric power. Students learn to integrate these sources into existing power systems and develop strategies for sustainable energy management.
Electromagnetic Compatibility and EMI
This course focuses on electromagnetic interference and compatibility issues in electronic devices. Students explore shielding techniques, filtering methods, and regulatory compliance standards for modern electronics.
Control Systems for Robotics
This elective integrates control theory with robotics applications, teaching students to design controllers for autonomous systems. Topics include PID control, state-space modeling, and motion planning algorithms.
Digital Image Processing
This course introduces digital image processing techniques used in medical imaging, computer vision, and multimedia applications. Students learn to implement filters, enhance images, and extract features using MATLAB and Python.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core component of the curriculum. From first-year mini-projects to final-year capstone projects, students are encouraged to apply theoretical knowledge to real-world problems. Projects are assigned based on student interests and faculty expertise, ensuring relevance and engagement.
Mini-projects span two semesters and involve small teams working on specific engineering challenges. These projects are evaluated through presentations, documentation, and peer reviews, fostering collaboration and communication skills.
The final-year thesis project is a comprehensive endeavor that allows students to explore advanced topics in depth. Students work closely with faculty mentors to select projects aligned with their career goals and research interests. The evaluation process includes a proposal presentation, progress reports, and a final defense, providing a platform for showcasing technical capabilities and innovation.