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Fees
₹8,00,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Fees
₹8,00,000
Placement
94.5%
Avg Package
₹6,50,000
Highest Package
₹12,00,000
Seats
200
Students
1,200
Seats
200
Students
1,200
The curriculum of the Electrical Engineering program at Isbm University Gariyaband is meticulously structured to provide a comprehensive and progressive educational experience. It spans over eight semesters, integrating foundational science courses with advanced engineering principles and specialized electives tailored to individual career aspirations.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHE101 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | MAT101 | Mathematics for Engineers | 3-1-0-4 | - |
| 1 | ECE101 | Introduction to Electrical Engineering | 3-1-0-4 | - |
| 2 | ENG102 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | PHY102 | Physics Lab | 0-0-3-1 | PHY101 |
| 2 | ECE102 | Circuit Analysis Techniques | 3-1-0-4 | ECE101 |
| 2 | MAT102 | Probability and Statistics | 3-1-0-4 | MAT101 |
| 2 | ECE103 | Electromagnetic Fields | 3-1-0-4 | ENG102 |
| 3 | ECE201 | Analog Electronics I | 3-1-0-4 | ECE102 |
| 3 | ECE202 | Digital Electronics | 3-1-0-4 | ECE102 |
| 3 | ECE203 | Network Analysis | 3-1-0-4 | ECE102 |
| 3 | ECE204 | Electromagnetic Field Theory | 3-1-0-4 | ECE103 |
| 3 | ECE205 | Electrical Machines I | 3-1-0-4 | ECE201 |
| 4 | ECE301 | Analog Electronics II | 3-1-0-4 | ECE201 |
| 4 | ECE302 | Digital Signal Processing | 3-1-0-4 | ECE202 |
| 4 | ECE303 | Control Systems | 3-1-0-4 | ECE203 |
| 4 | ECE304 | Electrical Machines II | 3-1-0-4 | ECE205 |
| 4 | ECE305 | Power System Analysis | 3-1-0-4 | ECE205 |
| 5 | ECE401 | Microprocessors and Microcontrollers | 3-1-0-4 | ECE302 |
| 5 | ECE402 | Embedded Systems Design | 3-1-0-4 | ECE401 |
| 5 | ECE403 | Power Electronics | 3-1-0-4 | ECE304 |
| 5 | ECE404 | Renewable Energy Sources | 3-1-0-4 | ECE305 |
| 5 | ECE405 | Advanced Control Systems | 3-1-0-4 | ECE303 |
| 6 | ECE501 | VLSI Design | 3-1-0-4 | ECE402 |
| 6 | ECE502 | Antenna and Microwave Engineering | 3-1-0-4 | ECE404 |
| 6 | ECE503 | Power System Protection | 3-1-0-4 | ECE405 |
| 6 | ECE504 | Wireless Communication Systems | 3-1-0-4 | ECE302 |
| 6 | ECE505 | Robotics and Automation | 3-1-0-4 | ECE403 |
| 7 | ECE601 | Artificial Intelligence in Engineering | 3-1-0-4 | ECE504 |
| 7 | ECE602 | Signal Processing Applications | 3-1-0-4 | ECE501 |
| 7 | ECE603 | Smart Grid Technologies | 3-1-0-4 | ECE503 |
| 7 | ECE604 | Advanced Power Electronics | 3-1-0-4 | ECE503 |
| 7 | ECE605 | Network Security | 3-1-0-4 | ECE504 |
| 8 | ECE701 | Final Year Project | 0-0-6-12 | All previous courses |
The program includes a rich selection of advanced departmental electives that allow students to explore specialized areas in depth. These courses are designed to meet the evolving demands of industry and research, offering both theoretical knowledge and practical applications.
This course builds upon the foundation laid in Analog Electronics I, focusing on advanced topics such as operational amplifier design, active filters, and analog IC fabrication techniques. Students learn to model complex circuits using SPICE simulation tools and understand the behavior of integrated circuits under varying conditions.
Students are introduced to discrete-time signal processing concepts, including convolution, Fourier transforms, and z-transforms. The course emphasizes practical implementation using MATLAB and DSP processors, preparing students for careers in audio/video processing, telecommunications, and biomedical engineering.
Building on earlier coursework, this advanced course covers modern control theory including state-space representation, stability analysis, and PID controller design. Students engage with real-time systems and learn to implement control algorithms for complex processes using software tools like MATLAB/Simulink.
This course explores the architecture and programming of microprocessors and microcontrollers used in embedded systems. Topics include instruction sets, memory organization, I/O interfaces, and real-time operating systems, providing students with the skills needed for hardware-software co-design.
Students study power conversion circuits such as rectifiers, inverters, and DC-DC converters. The course covers both theoretical analysis and practical design considerations, emphasizing applications in electric vehicles, renewable energy systems, and industrial automation.
This elective focuses on sustainable energy technologies including solar photovoltaic systems, wind turbines, hydroelectric power generation, and energy storage solutions. Students learn about grid integration challenges and environmental impacts of renewable technologies.
Students are exposed to the design and implementation of Very Large Scale Integration circuits using CAD tools such as Cadence and Synopsys. The course covers layout design, timing analysis, and testing methodologies for digital systems.
This course deals with electromagnetic wave propagation, antenna design principles, and microwave components. Students learn to design antennas for specific applications and analyze their performance using simulation software.
Students study protection schemes for power systems including relaying principles, fault analysis, and coordination of protective devices. The course integrates theoretical concepts with practical case studies from real power networks.
This elective introduces wireless communication technologies such as cellular networks, satellite communications, and wireless sensor networks. Students explore modulation techniques, channel coding, and multiple access protocols used in modern wireless systems.
The course covers robotic kinematics, dynamics, control algorithms, and sensor integration. Students work on projects involving autonomous robots, industrial automation, and human-robot interaction.
This interdisciplinary course applies machine learning techniques to engineering problems, covering neural networks, deep learning, reinforcement learning, and optimization methods. Students develop AI models for applications in signal processing, control systems, and data analysis.
Students explore real-world applications of digital signal processing including speech recognition, image processing, biomedical signal analysis, and audio compression. The course includes hands-on labs using MATLAB and FPGA-based platforms.
This advanced topic covers smart grid architectures, demand response management, energy trading platforms, and integration of distributed generation sources into existing power networks. Students learn about regulatory frameworks and smart meter technologies.
The course delves into high-efficiency power conversion topologies, wide bandgap semiconductors, and advanced control strategies for power electronics systems. Applications in electric vehicles, renewable energy systems, and industrial drives are emphasized.
This course addresses cybersecurity challenges in networked systems, including encryption techniques, authentication protocols, intrusion detection, and secure communication architectures. Students learn to design secure networks for critical infrastructure and enterprise applications.
The department strongly advocates for project-based learning as a cornerstone of its educational approach. Projects are integrated throughout the curriculum, starting with mini-projects in early semesters and culminating in comprehensive capstone projects in the final year.
Mini-projects are assigned in semesters 3 and 5 to help students apply theoretical knowledge to practical problems. These projects typically involve small teams working on short-term objectives with clear deliverables. Examples include designing a simple power supply, implementing a basic control system, or analyzing signal characteristics using MATLAB.
The final year project is the capstone experience where students undertake an independent research or development task guided by a faculty mentor. Students select projects based on their interests and career goals, often collaborating with industry partners or research labs. The project involves extensive literature review, experimental design, data collection, analysis, and presentation of findings.
Students begin selecting their final year project during semester 7 through a formal proposal submission process. Faculty mentors are assigned based on availability and expertise matching. Projects are categorized into theoretical research, experimental design, software development, and product prototyping.
Projects are evaluated based on multiple criteria including technical correctness, innovation, presentation quality, teamwork, adherence to deadlines, and documentation standards. A final oral defense is conducted by a panel of faculty members and industry experts.
