Course Structure Overview

The Electrical Engineering program at Al Falah University Faridabad is structured over eight semesters, offering a balanced mix of theoretical foundations, practical exposure, and specialization opportunities. Each semester includes core courses, departmental electives, science electives, and laboratory components designed to build comprehensive technical skills.

Semester-wise Course List

Semester	Course Code	Course Title	Credit (L-T-P-C)	Prerequisites
1	ENG101	English for Engineers	3-0-0-3	-
1	MAT101	Calculus I	4-0-0-4	-
1	MAT102	Linear Algebra and Differential Equations	3-0-0-3	-
1	PHY101	Physics I	3-0-0-3	-
1	CHM101	Chemistry for Engineers	3-0-0-3	-
1	CSE101	Introduction to Programming	2-0-2-3	-
1	ECO101	Engineering Economics	3-0-0-3	-
2	MAT201	Calculus II	4-0-0-4	MAT101
2	PHY201	Physics II	3-0-0-3	PHY101
2	CSE201	Data Structures and Algorithms	3-0-0-3	CSE101
2	ECO201	Business Communication	3-0-0-3	-
2	EE201	Circuit Analysis	3-0-0-3	MAT101, PHY101
2	EE202	Electromagnetic Fields	3-0-0-3	MAT101, PHY101
3	EE301	Signals and Systems	3-0-0-3	EE201, MAT201
3	EE302	Digital Logic Design	3-0-0-3	EE201
3	EE303	Electronics Devices	3-0-0-3	PHY201, EE202
3	EE304	Microprocessors and Microcontrollers	2-0-2-3	CSE201
4	EE401	Control Systems	3-0-0-3	EE301, MAT201
4	EE402	Power Electronics	3-0-0-3	EE303
4	EE403	Communication Systems	3-0-0-3	EE301
4	EE404	Electrical Machines	3-0-0-3	EE202
5	EE501	Power Systems Analysis	3-0-0-3	EE404
5	EE502	Advanced Signal Processing	3-0-0-3	EE301
5	EE503	VLSI Design	3-0-0-3	EE303
5	EE504	Renewable Energy Systems	3-0-0-3	EE401
6	EE601	Machine Learning for Engineers	3-0-0-3	EE301, CSE201
6	EE602	Biomedical Instrumentation	3-0-0-3	EE301
6	EE603	Wireless Communication Networks	3-0-0-3	EE403
6	EE604	Embedded Systems	2-0-2-3	EE404, CSE201
7	EE701	Capstone Project I	2-0-2-3	EE501, EE601
7	EE702	Advanced Control Systems	3-0-0-3	EE401
7	EE703	Research Methodology	2-0-0-2	-
8	EE801	Capstone Project II	4-0-0-4	EE701

Advanced Departmental Electives

• Advanced Machine Learning Algorithms: This course explores deep learning architectures, reinforcement learning, and neural network optimization techniques. Students learn to implement complex models using TensorFlow and PyTorch frameworks.
• Smart Grid Technologies: The course covers grid modernization, demand response systems, and energy storage integration in smart grids. Real-world case studies from countries like Germany and the USA are analyzed.
• Robotics and Automation: Integrates control theory with mechanical design to build autonomous robots. Students work on projects involving sensor fusion, path planning, and robot navigation using ROS (Robot Operating System).
• Wireless Sensor Networks: Explores network topology, protocols, and applications in environmental monitoring and smart cities. Focuses on low-power communication standards like Zigbee and LoRaWAN.
• Renewable Energy Integration: Analyzes solar and wind energy systems within existing power grids. Students design hybrid renewable energy systems for remote areas.
• Biomedical Signal Processing: Applies signal processing techniques to medical imaging and physiological data analysis. Includes hands-on experience with ECG, EEG, and MRI data.
• Internet of Things (IoT) Applications: Covers device-level programming, cloud connectivity, and application development for IoT ecosystems. Uses platforms like AWS IoT Core and Azure IoT Hub.
• Quantum Computing Fundamentals: Introduces quantum algorithms, qubits, and applications in cryptography and optimization. Students simulate quantum circuits using Qiskit and Cirq.
• Advanced Power Electronics: Examines high-efficiency converters, motor drives, and power factor correction circuits. Includes practical sessions on switching devices like IGBTs and MOSFETs.
• Digital Image Processing: Studies image enhancement, segmentation, feature extraction, and computer vision algorithms. Practical labs involve OpenCV and MATLAB-based implementations.

Project-Based Learning Philosophy

The department strongly believes in project-based learning as a means to bridge the gap between theory and practice. Projects are designed to simulate real-world engineering challenges and encourage innovation, teamwork, and creativity.

Mini-Projects (Year 2)

Mini-projects in the second year provide students with early exposure to hands-on experimentation and design thinking. These projects are typically three-month-long and involve team-based work under faculty supervision. Examples include:

• Designing a simple microcontroller-based traffic light controller
• Building an analog filter for audio signal processing
• Developing a basic IoT weather station using sensors and cloud connectivity

Each project is evaluated based on design documentation, implementation quality, presentation skills, and peer reviews. Students receive feedback from both faculty and industry mentors.

Final-Year Thesis/Capstone Project (Year 4)

The final-year capstone project is a comprehensive endeavor that allows students to apply all learned concepts in solving a significant engineering problem. Projects are selected based on student interests, faculty expertise, and industry relevance.

• Project Selection Process: Students submit proposals outlining their ideas, objectives, and feasibility. Faculty advisors guide the selection process and ensure alignment with departmental resources.
• Mentorship: Each student is assigned a faculty mentor who provides continuous guidance throughout the project lifecycle. Regular meetings, milestone reviews, and progress reports are part of this structure.
• Evaluation Criteria: Projects are assessed based on innovation, technical depth, documentation quality, presentation skills, and final demonstration. A public exhibition event showcases student achievements.