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Fees
₹8,50,000
Placement
92.0%
Avg Package
₹7,50,000
Highest Package
₹25,00,000
Fees
₹8,50,000
Placement
92.0%
Avg Package
₹7,50,000
Highest Package
₹25,00,000
Seats
120
Students
1,200
Seats
120
Students
1,200
The Electrical Engineering program at Sandip University Madhubani is meticulously structured to provide students with a balanced mix of theoretical knowledge and practical skills. The curriculum spans eight semesters, with each semester designed to build upon the previous one, ensuring progressive learning and specialization.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | None |
| 1 | BEE101 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | CS101 | Computer Programming using C | 2-0-2-3 | None |
| 1 | ENG102 | Engineering Graphics | 2-0-2-3 | None |
| 1 | ENG103 | Communication Skills | 2-0-0-2 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ECE201 | Circuit Analysis | 3-1-0-4 | BEE101 |
| 2 | ECE202 | Electronic Devices and Circuits | 3-1-0-4 | BEE101 |
| 2 | CS201 | Data Structures and Algorithms | 2-0-2-3 | CS101 |
| 2 | ENG201 | Signals and Systems | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Basic Electronics | 3-1-0-4 | BEE101 |
| 3 | ECE301 | Electrical Machines I | 3-1-0-4 | ECE201 |
| 3 | ECE302 | Power System Analysis | 3-1-0-4 | ECE201 |
| 3 | ECE303 | Digital Electronics | 3-1-0-4 | ECE202 |
| 3 | ECE304 | Control Systems | 3-1-0-4 | ENG201 |
| 3 | ECE305 | Electromagnetic Fields and Waves | 3-1-0-4 | ENG101 |
| 3 | ECE306 | Communication Systems | 3-1-0-4 | ENG201 |
| 4 | ECE401 | Electrical Machines II | 3-1-0-4 | ECE301 |
| 4 | ECE402 | Power Electronics | 3-1-0-4 | ECE301 |
| 4 | ECE403 | Digital Signal Processing | 3-1-0-4 | ENG201 |
| 4 | ECE404 | Microprocessor Architecture | 3-1-0-4 | CS201 |
| 4 | ECE405 | Embedded Systems Design | 3-1-0-4 | ECE303 |
| 4 | ECE406 | Industrial Automation | 3-1-0-4 | ECE304 |
| 5 | ECE501 | Power System Protection | 3-1-0-4 | ECE302 |
| 5 | ECE502 | Renewable Energy Systems | 3-1-0-4 | ECE302 |
| 5 | ECE503 | Advanced Control Systems | 3-1-0-4 | ECE304 |
| 5 | ECE504 | VLSI Design | 3-1-0-4 | ECE303 |
| 5 | ECE505 | Wireless Communication Systems | 3-1-0-4 | ECE306 |
| 5 | ECE506 | Artificial Intelligence and Machine Learning | 3-1-0-4 | CS201 |
| 6 | ECE601 | Smart Grid Technologies | 3-1-0-4 | ECE502 |
| 6 | ECE602 | Advanced Power Electronics | 3-1-0-4 | ECE402 |
| 6 | ECE603 | Signal Processing for Communications | 3-1-0-4 | ECE403 |
| 6 | ECE604 | Industrial Network Technologies | 3-1-0-4 | ECE601 |
| 6 | ECE605 | Internet of Things Applications | 3-1-0-4 | ECE505 |
| 6 | ECE606 | Robotics and Automation | 3-1-0-4 | ECE304 |
| 7 | ECE701 | Capstone Project I | 2-0-4-6 | None |
| 7 | ECE702 | Research Methodology | 2-0-0-2 | None |
| 7 | ECE703 | Project Management | 2-0-0-2 | None |
| 8 | ECE801 | Capstone Project II | 2-0-4-6 | ECE701 |
| 8 | ECE802 | Final Year Thesis | 0-0-6-8 | ECE701 |
The department offers several advanced elective courses that allow students to explore specialized areas within Electrical Engineering. These courses are designed to provide in-depth knowledge and practical skills relevant to current industry trends.
This course delves into the principles and applications of power system protection schemes. Students learn about protective relays, fault analysis, and system stability. The curriculum covers both conventional and modern protection techniques, including digital relaying systems and communication-based protection methods.
The learning objectives include understanding different types of faults in power systems, designing protective schemes for various components, and analyzing the performance of protection systems under different conditions. Students engage in laboratory sessions where they simulate fault conditions and test protective devices.
This course focuses on the design and implementation of renewable energy technologies. Students study solar photovoltaic systems, wind turbines, hydroelectric power generation, and energy storage solutions. The curriculum covers both theoretical aspects and practical applications of renewable energy systems.
Learning outcomes include understanding the principles of renewable energy conversion, designing solar and wind energy systems, and evaluating the economic feasibility of renewable energy projects. Students work on real-world case studies and design projects that address current challenges in sustainable energy.
This course explores advanced topics in control system theory and design. Students learn about state-space analysis, optimal control, robust control, and nonlinear control systems. The curriculum includes both theoretical concepts and practical applications using simulation tools.
The learning objectives encompass understanding modern control techniques, designing controllers for complex systems, and analyzing system stability and performance. Students engage in laboratory experiments that involve designing and implementing control systems using MATLAB/Simulink and real-time hardware.
This course covers the principles of Very Large Scale Integration (VLSI) design and implementation. Students learn about digital circuit design, logic synthesis, and physical design aspects of integrated circuits. The curriculum includes both theoretical foundations and practical design methodologies.
Learning outcomes include understanding VLSI design flow, designing digital circuits using HDLs, and implementing designs on FPGAs and ASICs. Students work on design projects that involve creating custom digital circuits and testing them using industry-standard tools.
This course provides comprehensive coverage of wireless communication technologies and systems. Students study modulation techniques, channel coding, multiple access schemes, and wireless network architectures. The curriculum includes both classical and modern wireless communication concepts.
The learning objectives include understanding wireless channel characteristics, designing communication protocols, and analyzing system performance. Laboratory sessions involve practical implementation of wireless communication systems using software-defined radios and spectrum analyzers.
This course introduces students to the fundamental concepts of AI and ML in electrical engineering applications. Students learn about neural networks, deep learning architectures, data mining techniques, and pattern recognition algorithms.
Learning outcomes include understanding machine learning algorithms, implementing AI solutions for engineering problems, and applying ML techniques to signal processing and control systems. The curriculum includes hands-on projects where students develop AI-based solutions using Python and TensorFlow frameworks.
This course explores the emerging technologies in smart grid systems. Students study grid automation, demand response management, energy storage integration, and grid reliability optimization. The curriculum covers both technical aspects and policy considerations of smart grid implementation.
The learning objectives include understanding smart grid architecture, designing intelligent grid systems, and analyzing grid performance under various conditions. Students work on simulation projects that model smart grid scenarios and evaluate different control strategies.
This course focuses on advanced power electronics converters and applications. Students learn about high-frequency power conversion, resonant converters, and power quality improvement techniques. The curriculum includes both theoretical analysis and practical implementation of power electronic systems.
Learning outcomes include understanding power conversion principles, designing efficient power electronic circuits, and analyzing power system harmonics. Laboratory sessions involve building and testing various power converter topologies using real-time hardware and simulation tools.
This course provides in-depth knowledge of signal processing techniques applied to communication systems. Students study digital filtering, spectral analysis, and advanced modulation schemes. The curriculum includes both classical and modern signal processing methods.
The learning objectives encompass understanding signal processing fundamentals, designing communication filters, and analyzing system performance. Practical sessions involve implementing signal processing algorithms using MATLAB and implementing real-time signal processing applications.
This course explores the practical implementation of IoT technologies in various domains. Students learn about sensor networks, wireless protocols, data analytics, and embedded system design for IoT applications. The curriculum covers both technical aspects and business models of IoT deployment.
Learning outcomes include understanding IoT architecture, designing IoT systems, and evaluating IoT project feasibility. Students work on end-to-end IoT projects that involve hardware design, software development, and cloud integration.
The department's philosophy on project-based learning is rooted in the belief that hands-on experience is essential for developing competent engineers. Projects are designed to simulate real-world challenges and provide students with practical exposure to industry practices.
Mini-projects are integrated throughout the curriculum, starting from the second year. These projects typically span 2-3 months and involve small teams of 3-5 students. They focus on specific technical challenges and require students to apply concepts learned in their coursework.
The evaluation criteria for mini-projects include project design, implementation quality, presentation skills, and peer collaboration. Students must submit detailed project reports and present their work to faculty members and peers. These projects often lead to publications or patent applications.
The final-year capstone project is a comprehensive endeavor that integrates all knowledge and skills acquired during the program. Students select projects from industry partners, faculty research areas, or their own innovative ideas.
Students work closely with faculty mentors to define project scope, develop methodologies, and execute implementation plans. The project culminates in a final presentation and thesis submission. This experience prepares students for graduate studies or professional careers in engineering.
The project selection process is designed to ensure that students work on relevant and challenging problems. Students can propose their own ideas, select from faculty research projects, or choose from industry-sponsored challenges.
Faculty mentors are assigned based on students' interests and project requirements. The selection committee evaluates proposals based on technical feasibility, innovation potential, and alignment with departmental goals. Students also have opportunities to collaborate with other departments on interdisciplinary projects.
