Comprehensive Course Listing by Semester
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | MATH101 | Calculus and Analytical Geometry | 3-1-0-4 | None |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | CS101 | Introduction to Programming | 2-1-2-5 | None |
| 1 | EE101 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | ME101 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | LAB101 | Basic Electrical Engineering Lab | 0-0-2-2 | EE101 |
| 2 | MATH201 | Differential Equations | 3-1-0-4 | MATH101 |
| 2 | PHYS201 | Electromagnetic Fields and Waves | 3-1-0-4 | PHYS101 |
| 2 | CS201 | Data Structures and Algorithms | 2-1-2-5 | CS101 |
| 2 | EE201 | Circuit Analysis | 3-1-0-4 | EE101 |
| 2 | ME201 | Thermodynamics and Heat Transfer | 3-1-0-4 | ME101 |
| 2 | LAB201 | Circuit Analysis Lab | 0-0-2-2 | EE201 |
| 3 | MATH301 | Probability and Statistics | 3-1-0-4 | MATH201 |
| 3 | PHYS301 | Optics and Lasers | 3-1-0-4 | PHYS201 |
| 3 | CS301 | Database Management Systems | 2-1-2-5 | CS201 |
| 3 | EE301 | Electronic Devices and Circuits | 3-1-0-4 | EE201 |
| 3 | ME301 | Mechanics of Materials | 3-1-0-4 | ME201 |
| 3 | LAB301 | Electronic Devices Lab | 0-0-2-2 | EE301 |
| 4 | MATH401 | Linear Algebra and Numerical Methods | 3-1-0-4 | MATH301 |
| 4 | PHYS401 | Quantum Physics and Applications | 3-1-0-4 | PHYS301 |
| 4 | CS401 | Operating Systems | 2-1-2-5 | CS301 |
| 4 | EE401 | Signals and Systems | 3-1-0-4 | EE301 |
| 4 | ME401 | Manufacturing Processes | 3-1-0-4 | ME301 |
| 4 | LAB401 | Signals and Systems Lab | 0-0-2-2 | EE401 |
| 5 | MATH501 | Advanced Calculus | 3-1-0-4 | MATH401 |
| 5 | PHYS501 | Electromagnetic Theory | 3-1-0-4 | PHYS401 |
| 5 | CS501 | Computer Networks | 2-1-2-5 | CS401 |
| 5 | EE501 | Communication Systems | 3-1-0-4 | EE401 |
| 5 | ME501 | Fluid Mechanics and Hydraulic Machines | 3-1-0-4 | ME401 |
| 5 | LAB501 | Communication Systems Lab | 0-0-2-2 | EE501 |
| 6 | MATH601 | Mathematical Modeling | 3-1-0-4 | MATH501 |
| 6 | PHYS601 | Optical Communication | 3-1-0-4 | PHYS501 |
| 6 | CS601 | Software Engineering | 2-1-2-5 | CS501 |
| 6 | EE601 | Digital Signal Processing | 3-1-0-4 | EE501 |
| 6 | ME601 | Mechatronics Systems | 3-1-0-4 | ME501 |
| 6 | LAB601 | Digital Signal Processing Lab | 0-0-2-2 | EE601 |
| 7 | MATH701 | Control Systems Theory | 3-1-0-4 | MATH601 |
| 7 | PHYS701 | Electronics and Photonics | 3-1-0-4 | PHYS601 |
| 7 | CS701 | Machine Learning | 2-1-2-5 | CS601 |
| 7 | EE701 | VLSI Design | 3-1-0-4 | EE601 |
| 7 | ME701 | Automation and Robotics | 3-1-0-4 | ME601 |
| 7 | LAB701 | VLSI Design Lab | 0-0-2-2 | EE701 |
| 8 | MATH801 | Advanced Mathematical Methods | 3-1-0-4 | MATH701 |
| 8 | PHYS801 | Emerging Technologies in ECE | 3-1-0-4 | PHYS701 |
| 8 | CS801 | Internet of Things (IoT) | 2-1-2-5 | CS701 |
| 8 | EE801 | Embedded Systems | 3-1-0-4 | EE701 |
| 8 | ME801 | Advanced Manufacturing | 3-1-0-4 | ME701 |
| 8 | LAB801 | Embedded Systems Lab | 0-0-2-2 | EE801 |
Detailed Descriptions of Advanced Departmental Electives
The department offers a range of advanced elective courses designed to deepen students' expertise in specialized areas. These courses are taught by leading faculty members with extensive industry experience and research background.
One such course is 'Digital Signal Processing', which explores the mathematical foundations of digital signal processing, including Fourier transforms, filter design, and spectral analysis. Students gain hands-on experience using MATLAB and DSP processors to implement real-time signal processing algorithms. The course includes a project component where students work on audio enhancement or image compression projects.
Another advanced elective is 'Wireless Communication Systems', which delves into the principles of modern wireless communication techniques, including OFDM, MIMO systems, and cellular networks. The course covers both theoretical aspects and practical implementation using software-defined radios and simulation tools.
The 'VLSI Design' course focuses on integrated circuit design fundamentals, covering CMOS technology, logic synthesis, and physical design. Students learn to use industry-standard EDA tools such as Cadence and Synopsys to design complex digital circuits and verify their functionality.
'Control Systems Theory' introduces students to modern control theory including state-space methods, stability analysis, and feedback control design. The course emphasizes practical applications through simulations and laboratory experiments using MATLAB/Simulink and real-time control systems.
'Embedded Systems Design' provides an in-depth look at designing embedded systems for various applications, including microcontrollers, real-time operating systems, and hardware-software co-design. Students build complete embedded systems from scratch, integrating software components with hardware platforms like ARM Cortex-M processors.
'Artificial Intelligence and Machine Learning' covers the fundamentals of AI techniques, including neural networks, deep learning architectures, reinforcement learning, and natural language processing. The course includes hands-on projects using TensorFlow and PyTorch frameworks to develop intelligent systems for image recognition, speech synthesis, or autonomous navigation.
'Optical Communication Systems' explores the principles of fiber optic communication, including optical sources, detectors, amplifiers, and wavelength division multiplexing techniques. Students conduct experiments in a lab setting using actual fiber optic equipment to understand signal transmission characteristics and impairments.
'Power Electronics and Drives' introduces students to power conversion circuits, motor drives, and renewable energy systems. The course includes practical sessions involving switching power supplies, inverters, and variable frequency drives used in industrial applications.
'Microwave Engineering' focuses on the analysis and design of microwave components and systems, including transmission lines, waveguides, antennas, and filters. Students use electromagnetic simulation software to model and optimize high-frequency circuits for communication and radar systems.
'Digital Image Processing' covers techniques for image enhancement, restoration, segmentation, and feature extraction using digital algorithms. The course includes practical sessions using Python libraries like OpenCV and scikit-image to process real-world images and develop computer vision applications.
Project-Based Learning Philosophy
Our department places a strong emphasis on project-based learning as a core pedagogical strategy. Students engage in mini-projects from the second year onwards, progressing to major capstone projects in their final year. These projects are designed to bridge theory with practice, encouraging innovation and teamwork.
The structure of these projects involves defining a problem statement, conducting literature review, designing solutions, prototyping, testing, and presenting results. Evaluation criteria include technical depth, creativity, documentation quality, presentation skills, and team collaboration.
Mini-projects typically span 3-4 months and involve teams of 3-5 students working under faculty supervision. Topics are selected from current industry challenges or research areas identified by faculty members.
The final-year thesis/capstone project is a significant undertaking that requires students to independently conduct original research or develop an innovative engineering solution. Students must select a topic aligned with their interests and career goals, often in collaboration with industry partners or research institutions.
Faculty mentors are assigned based on expertise alignment and availability. Students can propose their own topics, provided they meet academic rigor standards set by the department.