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Fees
₹2,50,000
Placement
92.5%
Avg Package
₹4,20,000
Highest Package
₹8,50,000
Fees
₹2,50,000
Placement
92.5%
Avg Package
₹4,20,000
Highest Package
₹8,50,000
Seats
120
Students
1,200
Seats
120
Students
1,200
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisite |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CSE101 | Introduction to Programming | 2-0-2-4 | None |
| 1 | ECE101 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | MEC101 | Engineering Graphics | 2-0-2-4 | None |
| 1 | ENG102 | English for Engineers | 2-0-0-2 | None |
| 2 | ENG103 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | MAT101 | Mathematics for Engineers | 3-1-0-4 | None |
| 2 | CSE102 | Data Structures and Algorithms | 3-0-2-5 | CSE101 |
| 2 | ECE102 | Network Analysis | 3-1-0-4 | ECE101 |
| 2 | PHY102 | Modern Physics | 3-1-0-4 | PHY101 |
| 2 | MEC102 | Mechanics of Materials | 3-1-0-4 | None |
| 3 | ECE201 | Analog Electronic Circuits | 3-1-0-4 | ECE102 |
| 3 | ECE202 | Digital Electronics | 3-1-0-4 | ECE102 |
| 3 | MAT201 | Probability and Statistics | 3-1-0-4 | ENG103 |
| 3 | ECE203 | Electromagnetic Field Theory | 3-1-0-4 | MAT101 |
| 3 | CSE201 | Object-Oriented Programming | 2-0-2-4 | CSE102 |
| 4 | ECE301 | Power System Analysis | 3-1-0-4 | ECE201 |
| 4 | ECE302 | Control Engineering | 3-1-0-4 | ECE201 |
| 4 | ECE303 | Digital Signal Processing | 3-1-0-4 | ECE202 |
| 4 | CSE301 | Database Management Systems | 3-0-2-5 | CSE201 |
| 4 | MAT301 | Linear Algebra | 3-1-0-4 | MAT201 |
| 5 | ECE401 | Power Electronics | 3-1-0-4 | ECE301 |
| 5 | ECE402 | Motor Drives | 3-1-0-4 | ECE301 |
| 5 | ECE403 | VLSI Design | 3-1-0-4 | ECE202 |
| 5 | CSE401 | Computer Networks | 3-0-2-5 | CSE301 |
| 5 | MAT401 | Numerical Methods | 3-1-0-4 | MAT301 |
| 6 | ECE501 | Renewable Energy Sources | 3-1-0-4 | ECE401 |
| 6 | ECE502 | Smart Grid Technologies | 3-1-0-4 | ECE401 |
| 6 | ECE503 | Robotics and Automation | 3-1-0-4 | ECE302 |
| 6 | CSE501 | Machine Learning | 3-0-2-5 | CSE401 |
| 6 | MAT501 | Optimization Techniques | 3-1-0-4 | MAT401 |
| 7 | ECE601 | Advanced Power Systems | 3-1-0-4 | ECE501 |
| 7 | ECE602 | Signal Integrity Analysis | 3-1-0-4 | ECE303 |
| 7 | ECE603 | EMC Principles | 3-1-0-4 | ECE203 |
| 7 | CSE601 | Distributed Systems | 3-0-2-5 | CSE501 |
| 7 | MAT601 | Advanced Calculus | 3-1-0-4 | MAT501 |
| 8 | ECE701 | Final Year Project | 0-0-6-12 | ECE601 |
| 8 | ECE702 | Mini Project | 0-0-4-8 | ECE501 |
Departmental electives in Electrical Engineering offer students the opportunity to specialize and gain expertise in niche areas that align with their career aspirations. These courses are designed to provide in-depth knowledge and practical skills required for advanced engineering roles.
This elective course focuses on the design, analysis, and application of power electronic converters used in industrial drives and renewable energy systems. Students learn about various topologies such as DC-DC converters, AC-DC rectifiers, and inverters. The course includes hands-on laboratory sessions where students build prototype circuits and test their performance under different load conditions.
This course delves into the architecture of modern microprocessors, focusing on instruction set design, memory management, and peripheral interfacing. Students gain proficiency in assembly language programming and learn how to optimize code for performance and efficiency. Practical labs involve designing embedded systems using microcontrollers like ARM Cortex-M series processors.
Control Systems Design is an advanced course that explores mathematical modeling, stability analysis, and controller design techniques for complex industrial processes. Topics include state-space representation, frequency domain methods, digital control systems, and robust control theory. Students apply these concepts to real-world scenarios through simulation-based projects using MATLAB/Simulink.
Students explore advanced signal processing techniques used in audio, video, and telecommunications applications. The course covers topics such as digital filters, Fast Fourier Transform (FFT), wavelet transforms, and adaptive filtering algorithms. Laboratory sessions involve implementing DSP algorithms using digital signal processors (DSPs) and software tools like MATLAB.
This elective focuses on understanding electromagnetic interference (EMI) and ensuring compliance with international standards. Students learn about EMI sources, propagation mechanisms, shielding techniques, and testing procedures. Practical sessions involve conducting EMI measurements and designing compliant electronic systems using simulation software.
This course addresses the integration of renewable energy sources into power grids, focusing on solar, wind, and hydroelectric technologies. Students study power generation characteristics, grid integration challenges, and control strategies for hybrid renewable systems. The course includes field visits to solar farms and wind parks to observe operational practices.
Students explore modern wireless communication protocols including Wi-Fi, Bluetooth, LTE, and 5G technologies. The course covers modulation techniques, multiple access schemes, error correction codes, and network architectures. Laboratory sessions involve building wireless communication nodes using software-defined radios (SDRs) and performing performance evaluations.
This course provides comprehensive coverage of embedded system design principles, including microcontroller selection, real-time operating systems (RTOS), hardware-software co-design, and power optimization techniques. Students develop embedded applications using ARM Cortex-M based platforms and learn to debug complex system issues.
This interdisciplinary course combines electrical engineering principles with AI and machine learning algorithms. Topics include neural networks, deep learning architectures, reinforcement learning, and their applications in signal processing, control systems, and power electronics. Students implement AI models using TensorFlow and PyTorch frameworks.
This course delves into the complexities of modern power systems, including load forecasting, economic dispatch, voltage regulation, and stability analysis. Students learn to model large-scale power networks and simulate transient behaviors using specialized software tools like PSS/E and ETAP. Case studies from Indian power systems are included to enhance practical understanding.
The department emphasizes project-based learning as a cornerstone of its educational approach. Students begin working on projects from their second year, gradually increasing complexity and independence as they progress through the program.
Mini projects are assigned in the third and fourth semesters, allowing students to apply theoretical concepts in practical settings. These projects are typically completed within 3-4 weeks and involve small teams of 3-5 students. Projects are evaluated based on innovation, technical execution, presentation quality, and peer feedback.
The final year thesis is a significant undertaking that spans six months and requires students to conduct independent research or solve a real-world engineering problem. Students select their projects in consultation with faculty advisors, ensuring alignment with current industry trends and academic interests. The project culminates in a formal presentation and a detailed written report.
Students can choose from a list of pre-approved projects provided by faculty members or propose their own ideas after discussion with advisors. Projects are categorized into three levels: theoretical, experimental, and applied research. Each project is assigned a mentor who guides the student throughout the process.
