Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | EE101 | Mathematics I | 3-1-0-4 | - |
| 1 | EE102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | EE103 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | EE104 | Engineering Graphics & Design | 2-0-0-2 | - |
| 1 | EE105 | Programming & Problem Solving | 2-0-2-3 | - |
| 1 | EE106 | Workshop Practice | 0-0-2-1 | - |
| 2 | EE201 | Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Chemistry for Engineers | 3-1-0-4 | - |
| 2 | EE203 | Circuit Analysis | 3-1-0-4 | EE103 |
| 2 | EE204 | Electromagnetic Fields | 3-1-0-4 | EE102 |
| 2 | EE205 | Signals & Systems | 3-1-0-4 | EE201 |
| 2 | EE206 | Digital Logic Design | 3-1-0-4 | - |
| 3 | EE301 | Mathematics III | 3-1-0-4 | EE201 |
| 3 | EE302 | Electronics Devices & Circuits | 3-1-0-4 | EE203 |
| 3 | EE303 | Power System Analysis | 3-1-0-4 | EE203 |
| 3 | EE304 | Control Systems | 3-1-0-4 | EE205 |
| 3 | EE305 | Microprocessors & Microcontrollers | 3-1-0-4 | EE206 |
| 3 | EE306 | Communication Systems | 3-1-0-4 | EE205 |
| 4 | EE401 | Mathematics IV | 3-1-0-4 | EE301 |
| 4 | EE402 | Power Electronics | 3-1-0-4 | EE302 |
| 4 | EE403 | Electrical Machines | 3-1-0-4 | EE302 |
| 4 | EE404 | Digital Signal Processing | 3-1-0-4 | EE205 |
| 4 | EE405 | Embedded Systems | 3-1-0-4 | EE305 |
| 4 | EE406 | Renewable Energy Systems | 3-1-0-4 | EE303 |
| 5 | EE501 | Advanced Control Systems | 3-1-0-4 | EE304 |
| 5 | EE502 | Power System Protection | 3-1-0-4 | EE303 |
| 5 | EE503 | Industrial Electronics | 3-1-0-4 | EE402 |
| 5 | EE504 | Wireless Communication | 3-1-0-4 | EE306 |
| 5 | EE505 | VLSI Design | 3-1-0-4 | EE305 |
| 5 | EE506 | Biomedical Instrumentation | 3-1-0-4 | EE205 |
| 6 | EE601 | Smart Grid Technologies | 3-1-0-4 | EE502 |
| 6 | EE602 | Data Analytics for Electrical Systems | 3-1-0-4 | EE404 |
| 6 | EE603 | Research Methodology | 2-0-0-2 | - |
| 6 | EE604 | Project Management | 2-0-0-2 | - |
| 6 | EE605 | Elective I | 3-1-0-4 | - |
| 6 | EE606 | Elective II | 3-1-0-4 | - |
| 7 | EE701 | Mini Project I | 0-0-6-3 | - |
| 7 | EE702 | Mini Project II | 0-0-6-3 | - |
| 7 | EE703 | Elective III | 3-1-0-4 | - |
| 7 | EE704 | Elective IV | 3-1-0-4 | - |
| 8 | EE801 | Final Year Thesis/Capstone Project | 0-0-12-6 | - |
Detailed Course Descriptions for Advanced Departmental Electives
Advanced departmental electives in the Electrical program at Bishamber Sahai Institute Of Technology are designed to provide students with specialized knowledge and skills relevant to current industry trends. These courses go beyond standard curriculum offerings, offering deep dives into emerging fields that drive innovation.
Power Electronics
This course explores the design and application of power electronic converters used in various industries such as renewable energy systems, electric vehicles, and industrial automation. Students learn about different topologies including rectifiers, inverters, DC-DC converters, and AC-AC converters. The course emphasizes practical applications through laboratory sessions where students build and test actual power conversion circuits.
Smart Grid Technologies
As the traditional power grid evolves into a more intelligent and responsive system, this course delves into smart grid concepts including advanced metering infrastructure, demand response systems, and integration of renewable energy sources. Students are introduced to topics like grid stability analysis, cyber security in power systems, and real-time monitoring using IoT technologies.
Renewable Energy Systems
This course provides a comprehensive overview of renewable energy technologies including solar photovoltaic systems, wind turbines, hydroelectric plants, and geothermal systems. Students learn about system design, performance evaluation, and economic analysis of renewable energy projects. Practical components involve simulations and case studies of successful installations in India and abroad.
Digital Signal Processing
Digital signal processing is essential for modern communication systems, audio/video processing, and biomedical applications. This course covers discrete-time signals and systems, z-transforms, Fast Fourier Transform (FFT), and filter design techniques. Students gain hands-on experience with software tools like MATLAB and Python for implementing DSP algorithms.
Control Systems
This course builds upon earlier concepts in control theory and introduces advanced topics such as state-space representation, robust control, and nonlinear systems. Students learn to model complex systems, design controllers using classical and modern methods, and analyze stability and performance characteristics of feedback control systems.
Embedded Systems
Embedded systems are at the heart of modern devices ranging from smartphones to industrial machinery. This course covers microcontroller architectures, real-time operating systems, embedded software development, and hardware-software co-design. Students work on projects involving sensor integration, data acquisition, and control applications using platforms like Arduino and Raspberry Pi.
Biomedical Instrumentation
This interdisciplinary course bridges electrical engineering with medical sciences. Students learn about bioelectricity, medical imaging techniques, and instrumentation for physiological monitoring. The curriculum includes hands-on labs where students design and test biomedical devices such as ECG monitors, pulse oximeters, and pacemakers.
VLSI Design
Very Large Scale Integration (VLSI) is critical in the development of integrated circuits used in modern electronics. This course covers semiconductor physics, circuit design, layout techniques, and testing methodologies. Students gain experience with CAD tools like Cadence and Synopsys, learning how to design custom chips for specific applications.
Wireless Communication
With the proliferation of mobile devices and IoT, wireless communication has become a cornerstone of modern technology. This course covers modulation techniques, multiple access schemes, error correction codes, and network protocols. Students explore current standards like 5G and future technologies such as satellite communications.
Data Analytics for Electrical Systems
This emerging field combines data science with electrical engineering to extract insights from large datasets in power systems, communication networks, and control systems. Topics include statistical modeling, machine learning algorithms, predictive analytics, and visualization techniques. Students work on real-world problems using tools like Python, R, and TensorFlow.
Project-Based Learning Framework
The Electrical program at Bishamber Sahai Institute Of Technology emphasizes project-based learning as a core component of student development. This approach fosters creativity, critical thinking, and practical application of theoretical knowledge.
The project-based learning framework is structured across three phases: Mini Projects, Major Projects, and Final-Year Thesis/Capstone Project.
Mini Projects
Mini projects are introduced in the seventh semester, allowing students to apply concepts learned in earlier semesters. These projects typically last 3-4 months and involve small teams of 2-4 students working under faculty supervision. Students select topics aligned with their interests or emerging trends in electrical engineering.
Major Projects
In the eighth semester, students engage in more substantial projects that often involve collaboration with industry partners or research institutions. These projects require advanced skills in system design, implementation, testing, and documentation. Students are expected to present their findings at departmental symposiums and potentially publish papers in journals.
Final-Year Thesis/Capstone Project
The final-year project represents the culmination of a student's academic journey. It involves extensive research, design, and development work culminating in a comprehensive report and presentation. Students are encouraged to pursue innovative ideas that address real-world challenges or contribute to ongoing research efforts.
Project Selection and Mentorship
Students select their projects based on faculty availability, research interests, and alignment with industry needs. Each project is assigned a faculty mentor who provides guidance throughout the process. Regular meetings, progress reports, and milestone reviews ensure timely completion and quality outcomes.