Comprehensive Course Structure

The Bachelor of Electrical Engineering program at Iasscom Fortune Institute of Technology is meticulously structured to ensure a seamless progression from foundational concepts to advanced engineering skills. The curriculum spans eight semesters, each designed with careful attention to academic rigor and practical relevance.

Semester-wise Course Breakdown

Year	Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
Year 1	Semester 1	MA101	Calculus I	3-1-0-4	-
		PH101	Physics for Engineers	3-1-0-4	-
		CH101	Chemistry for Engineers	3-1-0-4	-
		ME101	Introduction to Engineering	2-0-0-2	-
		CS101	Programming Fundamentals	2-0-2-4	-
		EG101	Engineering Graphics	2-0-2-4	-
		EC101	Basic Electrical Engineering	3-1-0-4	-
Year 1	Semester 2	MA102	Calculus II	3-1-0-4	MA101
		PH102	Modern Physics	3-1-0-4	PH101
		EC102	Circuit Analysis	3-1-0-4	EC101
		CS102	Data Structures and Algorithms	2-0-2-4	CS101
		ES101	Environmental Science	3-0-0-3	-
		EC103	Electromagnetic Fields	3-1-0-4	PH101
		ME102	Engineering Mechanics	3-1-0-4	-
Year 2	Semester 3	MA201	Linear Algebra and Differential Equations	3-1-0-4	MA102
		EC201	Signals and Systems	3-1-0-4	EC102
		EC202	Electronic Devices and Circuits	3-1-0-4	EC103
		EC203	Network Analysis	3-1-0-4	EC102
		CS201	Object-Oriented Programming	2-0-2-4	CS102
		EC204	Microprocessor and Microcontroller Applications	3-1-2-6	EC202
		EC205	Electrical Machines I	3-1-0-4	EC102
Year 2	Semester 4	MA202	Probability and Statistics	3-1-0-4	MA201
		EC206	Power Electronics	3-1-0-4	EC202
		EC207	Control Systems	3-1-0-4	EC201
		EC208	Electromagnetic Waves and Transmission Lines	3-1-0-4	EC103
		EC209	Communication Systems	3-1-0-4	EC201
		EC210	Digital Logic Design	3-1-2-6	EC202
		EC211	Electrical Machines II	3-1-0-4	EC205
Year 3	Semester 5	EC301	Digital Signal Processing	3-1-0-4	EC201
		EC302	Power Generation and Distribution	3-1-0-4	EC205
		EC303	Embedded Systems	3-1-2-6	EC204
		EC304	Control System Design	3-1-0-4	EC207
		EC305	Antenna Theory	3-1-0-4	EC208
		EC306	Optoelectronics	3-1-0-4	EC202
		EC307	Industrial Automation	3-1-0-4	EC207
Year 3	Semester 6	EC308	Artificial Intelligence and Machine Learning	3-1-0-4	EC201
		EC309	Renewable Energy Systems	3-1-0-4	EC202
		EC310	Wireless Communication	3-1-0-4	EC209
		EC311	VLSI Design	3-1-2-6	EC202
		EC312	Smart Grid Technologies	3-1-0-4	EC302
		EC313	Image Processing	3-1-0-4	EC301
		EC314	Robotics and Automation	3-1-2-6	EC207
Year 4	Semester 7	EC401	Advanced Power Systems	3-1-0-4	EC302
		EC402	Research Methodology	2-0-0-2	-
		EC403	Mini Project I	2-0-2-4	-
		EC404	Capstone Project I	2-0-2-4	-
		EC405	Elective I (AI/ML)	3-1-0-4	EC308
		EC406	Elective II (Embedded Systems)	3-1-0-4	EC303
		EC407	Elective III (Control Systems)	3-1-0-4	EC304
Year 4	Semester 8	EC408	Advanced Capstone Project II	2-0-2-4	EC404
		EC409	Professional Ethics and Sustainability	2-0-0-2	-
		EC410	Mini Project II	2-0-2-4	-
		EC411	Elective IV (Power Electronics)	3-1-0-4	EC206
		EC412	Elective V (Robotics)	3-1-0-4	EC314
		EC413	Elective VI (Signal Processing)	3-1-0-4	EC301
		EC414	Elective VII (VLSI Design)	3-1-0-4	EC311

Detailed Departmental Elective Courses

• Digital Signal Processing: This course covers discrete-time signal processing techniques, including filtering, transform methods, and digital filter design. Students learn to implement these concepts using MATLAB and Python for applications in audio/video systems, biomedical engineering, and telecommunications.
• Power Generation and Distribution: This course explores the principles of power generation from various sources, including fossil fuels, nuclear, hydroelectric, and renewable energy. It also covers the design and operation of transmission and distribution systems to ensure reliable electricity supply.
• Embedded Systems: Students study embedded system architecture, real-time operating systems, microcontroller programming, and hardware-software co-design. Projects include designing smart home automation systems and IoT devices.
• Control System Design: This course introduces classical and modern control techniques for designing stable and efficient feedback control systems. Topics include state-space representation, PID controllers, root locus analysis, and frequency response methods.
• Antenna Theory: Students explore the fundamentals of electromagnetic wave propagation and antenna design. The course covers radiation patterns, impedance matching, array configurations, and practical applications in wireless communication systems.
• Optoelectronics: This elective delves into optoelectronic devices such as lasers, photodiodes, LEDs, and fiber optic components. Applications include optical communication networks, sensors, and medical imaging technologies.
• Industrial Automation: The course focuses on automation technologies used in manufacturing environments, including PLC programming, SCADA systems, sensor integration, and robotic control. Students work on real-world projects in collaboration with industry partners.
• Artificial Intelligence and Machine Learning: This advanced elective introduces students to AI algorithms, neural networks, deep learning frameworks, and their applications in electrical engineering domains like predictive maintenance, smart grids, and robotics.
• Renewable Energy Systems: Students examine solar, wind, hydro, and geothermal energy systems. The course covers system design, energy storage solutions, grid integration challenges, and environmental impact assessments.
• Wireless Communication: This course covers modulation techniques, multiple access methods, channel coding, and wireless network protocols. Practical sessions involve setting up and testing wireless communication systems using software-defined radios (SDRs).
• VLSI Design: Students learn the design process of very large-scale integration circuits, covering CMOS technology, logic synthesis, layout design, and testing methodologies. Projects include designing custom ICs for specific applications.
• Smart Grid Technologies: This course explores smart grid components such as smart meters, demand response systems, and energy management platforms. Students study how these technologies enhance grid reliability, efficiency, and integration of renewable sources.
• Image Processing: The course teaches digital image processing techniques for enhancement, compression, segmentation, and feature extraction. Applications include medical imaging, surveillance systems, and computer vision in robotics.
• Robotics and Automation: This elective covers robot kinematics, dynamics, control systems, and sensor integration. Students build and program robots for various tasks, from industrial automation to search-and-rescue missions.

Project-Based Learning Philosophy

The Bachelor of Electrical Engineering program at Iasscom Fortune Institute of Technology places significant emphasis on project-based learning (PBL). PBL is integrated throughout the curriculum as a pedagogical approach to foster creativity, innovation, and practical problem-solving skills.

Mini Projects

Mini projects are conducted during the first two years of the program. Each student works on a small-scale project related to core subjects, such as designing a simple electronic circuit or simulating power systems using software tools. These projects help students apply theoretical knowledge in real-world scenarios.

Final-Year Thesis/Capstone Project

The final-year capstone project is the most significant component of the program. Students work on an advanced, industry-relevant problem under the supervision of a faculty mentor. The project involves extensive research, experimentation, and documentation. Students present their findings at an annual showcase event open to faculty, industry partners, and fellow students.

Project Selection and Mentorship

Students can choose their projects based on interest and career goals, or they may be assigned projects aligned with ongoing research initiatives led by faculty members. Each student is paired with a mentor who guides them through the research process, provides feedback, and ensures timely completion of the project.

Evaluation Criteria

Projects are evaluated based on several criteria including innovation, technical depth, presentation quality, documentation, teamwork, and real-world applicability. A panel of faculty members and industry experts assesses each project, providing constructive feedback for improvement.