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Fees
₹12,00,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹18,00,000
Fees
₹12,00,000
Placement
92.5%
Avg Package
₹6,50,000
Highest Package
₹18,00,000
Seats
120
Students
300
Seats
120
Students
300
The curriculum at Gyan Ganga College of Technology for the Bachelor of Electrical Engineering program is meticulously designed to provide a robust foundation in electrical engineering principles while exposing students to contemporary technologies and industry trends. The program spans eight semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MAT101 | Mathematics I | 3-1-0-4 | - |
| 1 | MAT102 | Mathematics II | 3-1-0-4 | MAT101 |
| 1 | PHY101 | Physics I | 3-1-0-4 | - |
| 1 | PHY102 | Physics II | 3-1-0-4 | PHY101 |
| 1 | CHM101 | Chemistry I | 3-1-0-4 | - |
| 1 | CHM102 | Chemistry II | 3-1-0-4 | CHM101 |
| 1 | ENG101 | English Communication | 2-0-0-2 | - |
| 1 | ECE101 | Introduction to Electrical Engineering | 3-0-0-3 | - |
| 1 | ECE102 | Basic Electrical Circuits | 3-1-0-4 | - |
| 1 | LAB101 | Basic Electrical Lab | 0-0-3-1 | - |
| 2 | MAT201 | Mathematics III | 3-1-0-4 | MAT102 |
| 2 | MAT202 | Mathematics IV | 3-1-0-4 | MAT201 |
| 2 | PHY201 | Physics III | 3-1-0-4 | PHY102 |
| 2 | PHY202 | Physics IV | 3-1-0-4 | PHY201 |
| 2 | ECE201 | Electrical Circuits and Networks | 3-1-0-4 | ECE102 |
| 2 | ECE202 | Electronic Devices and Circuits | 3-1-0-4 | ECE102 |
| 2 | ECE203 | Signals and Systems | 3-1-0-4 | MAT202 |
| 2 | LAB201 | Circuit Analysis Lab | 0-0-3-1 | ECE201 |
| 2 | LAB202 | Electronic Circuits Lab | 0-0-3-1 | ECE202 |
| 3 | MAT301 | Mathematics V | 3-1-0-4 | MAT202 |
| 3 | ECE301 | Electromagnetic Fields | 3-1-0-4 | PHY202 |
| 3 | ECE302 | Power Systems I | 3-1-0-4 | ECE201 |
| 3 | ECE303 | Control Systems | 3-1-0-4 | ECE203 |
| 3 | ECE304 | Digital Electronics | 3-1-0-4 | ECE202 |
| 3 | ECE305 | Microprocessors and Microcontrollers | 3-1-0-4 | ECE304 |
| 3 | LAB301 | Electromagnetic Fields Lab | 0-0-3-1 | ECE301 |
| 3 | LAB302 | Control Systems Lab | 0-0-3-1 | ECE303 |
| 4 | ECE401 | Power Systems II | 3-1-0-4 | ECE302 |
| 4 | ECE402 | Communication Systems | 3-1-0-4 | ECE303 |
| 4 | ECE403 | Power Electronics | 3-1-0-4 | ECE302 |
| 4 | ECE404 | Digital Signal Processing | 3-1-0-4 | ECE303 |
| 4 | ECE405 | Embedded Systems | 3-1-0-4 | ECE305 |
| 4 | LAB401 | Power Electronics Lab | 0-0-3-1 | ECE403 |
| 4 | LAB402 | Signal Processing Lab | 0-0-3-1 | ECE404 |
| 5 | ECE501 | Renewable Energy Systems | 3-1-0-4 | ECE401 |
| 5 | ECE502 | Smart Grid Technologies | 3-1-0-4 | ECE401 |
| 5 | ECE503 | Data Analytics | 3-1-0-4 | ECE404 |
| 5 | ECE504 | VLSI Design | 3-1-0-4 | ECE403 |
| 5 | ECE505 | Industrial Automation | 3-1-0-4 | ECE403 |
| 5 | LAB501 | Renewable Energy Lab | 0-0-3-1 | ECE501 |
| 5 | LAB502 | VLSI Design Lab | 0-0-3-1 | ECE504 |
| 6 | ECE601 | Advanced Control Systems | 3-1-0-4 | ECE303 |
| 6 | ECE602 | Telecommunications | 3-1-0-4 | ECE402 |
| 6 | ECE603 | Machine Learning | 3-1-0-4 | ECE503 |
| 6 | ECE604 | Advanced Embedded Systems | 3-1-0-4 | ECE505 |
| 6 | ECE605 | Research Methodology | 2-0-0-2 | - |
| 6 | LAB601 | Advanced Control Systems Lab | 0-0-3-1 | ECE601 |
| 6 | LAB602 | Telecom Lab | 0-0-3-1 | ECE602 |
| 7 | ECE701 | Final Year Project I | 3-0-0-3 | ECE605 |
| 7 | ECE702 | Final Year Project II | 3-0-0-3 | ECE701 |
| 7 | ECE703 | Project Management | 2-0-0-2 | - |
| 7 | ECE704 | Professional Ethics | 1-0-0-1 | - |
| 7 | ECE705 | Elective I | 3-1-0-4 | ECE601 |
| 7 | ECE706 | Elective II | 3-1-0-4 | ECE602 |
| 8 | ECE801 | Final Year Project III | 3-0-0-3 | ECE702 |
| 8 | ECE802 | Final Year Project IV | 3-0-0-3 | ECE801 |
| 8 | ECE803 | Internship Report | 2-0-0-2 | - |
| 8 | ECE804 | Elective III | 3-1-0-4 | ECE603 |
| 8 | ECE805 | Elective IV | 3-1-0-4 | ECE604 |
The department offers a range of advanced departmental electives that allow students to explore specialized areas in electrical engineering. These courses are designed to align with current industry trends and research advancements.
This elective course delves into the principles and applications of renewable energy technologies such as solar photovoltaics, wind turbines, hydroelectric systems, and geothermal energy conversion. Students learn about energy storage solutions, grid integration techniques, and policy frameworks supporting clean energy adoption. The course combines theoretical knowledge with practical simulations using tools like MATLAB/Simulink and PSCAD/EMTDC.
Smart grids represent the next evolution in power distribution systems, integrating digital communication technologies with traditional electrical infrastructure. This course explores topics such as grid stability, demand response management, energy trading platforms, and cybersecurity in smart grid environments. Students engage in hands-on projects involving simulation of smart grid scenarios and development of control algorithms for distributed energy resources.
Data analytics plays a crucial role in modern engineering applications, particularly in predictive maintenance, system optimization, and performance monitoring. This course introduces students to statistical methods, machine learning algorithms, and data visualization techniques relevant to electrical engineering problems. Through practical assignments, students learn to apply these tools to analyze power consumption patterns, fault detection, and asset management.
Very Large Scale Integration (VLSI) design involves creating integrated circuits using millions of transistors on a single chip. This course covers CMOS technology, circuit simulation, logic synthesis, and physical design flow for digital systems. Students gain experience with industry-standard tools such as Cadence and Synopsys, developing skills in designing complex digital blocks and verifying their functionality through simulation.
Industrial automation aims to improve manufacturing efficiency through the use of control systems, sensors, and actuators. This course explores programmable logic controllers (PLCs), SCADA systems, industrial communication protocols, and robotics in automated environments. Students work on real-world automation challenges involving process optimization, predictive maintenance, and fault diagnosis.
Building upon foundational control theory, this advanced elective covers modern control design techniques such as state-space methods, optimal control, robust control, and nonlinear control systems. The course emphasizes practical implementation using MATLAB/Simulink and includes case studies from aerospace, automotive, and process industries.
Telecommunications encompasses the transmission of information over various media including wired and wireless networks. This course covers analog and digital modulation techniques, network protocols, fiber optic communications, satellite systems, and mobile communication standards. Students analyze real-world communication systems and develop simulation models for evaluating system performance.
Machine learning algorithms are increasingly being applied in electrical engineering applications such as signal processing, pattern recognition, and predictive modeling. This course introduces students to supervised and unsupervised learning methods, neural networks, deep learning architectures, and reinforcement learning. Practical sessions involve implementing machine learning models using Python libraries like scikit-learn and TensorFlow.
Advanced embedded systems combine hardware and software components to create specialized computing solutions for specific applications. This course explores real-time operating systems, embedded C programming, hardware-software co-design, and sensor integration. Students develop complete embedded projects involving microcontrollers, wireless communication modules, and data acquisition systems.
Power electronics deals with the conversion and control of electrical power using semiconductor devices. This course covers topics such as rectifiers, inverters, DC-DC converters, and motor drives. Students learn to design power electronic circuits and evaluate their performance under different load conditions using simulation tools.
The department strongly believes in project-based learning as a means to foster critical thinking, problem-solving, and innovation among students. This approach integrates theoretical knowledge with practical implementation, preparing students for real-world engineering challenges.
Mini projects are introduced starting from the third semester, allowing students to apply their classroom knowledge to practical scenarios. Each project is assigned a faculty mentor who guides students through the design process, technical execution, and documentation phases. Mini projects typically span 8-10 weeks and involve individual or small group work.
The final year thesis/capstone project represents the culmination of the undergraduate program, integrating all learned concepts into a comprehensive engineering solution. Students select topics based on their interests and career aspirations, often in collaboration with industry partners or research groups. The project involves extensive literature review, experimental design, prototype development, testing, and presentation.
Students are encouraged to propose project ideas aligned with current technological trends or industry needs. Faculty mentors are selected based on their expertise and availability, ensuring that each student receives adequate guidance throughout the project lifecycle. Regular progress meetings, milestone reviews, and feedback sessions help maintain quality standards.
