Search and navigate to colleges and courses
Apply
Scholarships & exams
Fees
₹12,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹8,00,000
Fees
₹12,00,000
Placement
92.0%
Avg Package
₹5,00,000
Highest Package
₹8,00,000
Seats
250
Students
250
Seats
250
Students
250
The Electrical Engineering curriculum at JAWAHARLAL INSTITUTE OF TECHNOLOGY BORAWAN is structured over eight semesters, ensuring a balanced progression from foundational sciences to advanced engineering principles and specialized applications.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | PHYS101 | Physics I | 3-1-0-4 | - |
| 1 | MATH101 | Mathematics I | 4-0-0-4 | - |
| 1 | CS101 | Introduction to Programming | 2-0-2-3 | - |
| 1 | CHEM101 | Chemistry I | 3-0-0-3 | - |
| 1 | ENG101 | English for Engineers | 2-0-0-2 | - |
| 1 | HSS101 | History and Social Sciences | 2-0-0-2 | - |
| 2 | PHYS102 | Physics II | 3-1-0-4 | PHYS101 |
| 2 | MATH102 | Mathematics II | 4-0-0-4 | MATH101 |
| 2 | CHEM102 | Chemistry II | 3-0-0-3 | CHEM101 |
| 2 | EE101 | Basic Electrical Engineering | 3-1-0-4 | - |
| 2 | ME101 | Introduction to Mechanical Engineering | 2-0-0-2 | - |
| 3 | MATH201 | Mathematics III | 4-0-0-4 | MATH102 |
| 3 | EE201 | Circuit Analysis | 3-1-0-4 | EE101 |
| 3 | EE202 | Digital Electronics | 3-1-0-4 | - |
| 3 | EE203 | Analog Electronics | 3-1-0-4 | - |
| 3 | EE204 | Signals and Systems | 3-1-0-4 | MATH201 |
| 3 | EE205 | Electromagnetic Fields | 3-1-0-4 | - |
| 4 | EE301 | Power Systems | 3-1-0-4 | EE201 |
| 4 | EE302 | Control Systems | 3-1-0-4 | EE204 |
| 4 | EE303 | Microprocessors and Microcontrollers | 3-1-0-4 | - |
| 4 | EE304 | Communication Systems | 3-1-0-4 | EE204 |
| 4 | EE305 | Electrical Machines | 3-1-0-4 | EE201 |
| 5 | EE401 | Power Electronics | 3-1-0-4 | EE203 |
| 5 | EE402 | Renewable Energy Systems | 3-1-0-4 | - |
| 5 | EE403 | Embedded Systems | 3-1-0-4 | EE303 |
| 5 | EE404 | VLSI Design | 3-1-0-4 | - |
| 5 | EE405 | Electromagnetic Compatibility | 3-1-0-4 | EE205 |
| 6 | EE501 | Advanced Control Systems | 3-1-0-4 | EE302 |
| 6 | EE502 | Artificial Intelligence and Machine Learning | 3-1-0-4 | - |
| 6 | EE503 | Advanced Signal Processing | 3-1-0-4 | EE204 |
| 6 | EE504 | Industrial Automation | 3-1-0-4 | - |
| 6 | EE505 | Energy Storage Technologies | 3-1-0-4 | - |
| 7 | EE601 | Capstone Project I | 2-0-0-2 | - |
| 7 | EE602 | Research Methodology | 2-0-0-2 | - |
| 7 | EE603 | Special Topics in Electrical Engineering | 2-0-0-2 | - |
| 8 | EE701 | Capstone Project II | 4-0-0-4 | EE601 |
| 8 | EE702 | Internship | 2-0-0-2 | - |
Departmental electives in the Electrical Engineering program are designed to offer students specialized knowledge and practical skills aligned with current industry trends. These courses provide depth and breadth in specific areas, enabling students to tailor their education according to personal interests and career aspirations.
This course delves into power conversion techniques, motor drives, inverters, and energy-efficient systems. Students learn about switch-mode power supplies, variable frequency drives, and applications in electric vehicles and renewable energy systems. The course emphasizes both theoretical analysis and practical implementation through laboratory sessions.
This elective explores solar photovoltaic systems, wind turbines, hydroelectric power generation, and grid integration challenges. Students study energy storage solutions, smart grid technologies, and policy frameworks related to sustainable energy. The course includes hands-on projects involving real-world renewable energy installations.
Focused on hardware-software co-design, this course covers microcontroller architectures, real-time operating systems, embedded C programming, and IoT applications. Students develop practical skills in designing intelligent devices using ARM Cortex-M series processors and development boards such as Arduino and Raspberry Pi.
This advanced course introduces students to integrated circuit design methodologies, semiconductor physics, CMOS technology, and layout design rules. Topics include logic synthesis, verification techniques, and advanced process nodes. Students gain experience with EDA tools like Cadence and Mentor Graphics for designing custom circuits.
Students learn about electromagnetic interference sources, propagation mechanisms, shielding methods, and compliance testing procedures. The course includes practical labs on EMI/EMC measurement and mitigation techniques using spectrum analyzers, network analyzers, and specialized software tools.
This interdisciplinary course combines machine learning algorithms with electrical engineering concepts. Students study neural networks, deep learning architectures, pattern recognition, and intelligent control systems. The course includes case studies on AI applications in power systems, signal processing, and automation.
Focusing on digital signal processing techniques, this course covers filtering, transforms (FFT, DCT), spectral analysis, and data modeling. Students learn to implement algorithms using MATLAB, Python, and other computational platforms. The course includes projects on speech recognition, image processing, and biomedical signal analysis.
This elective explores modern control theory, including state-space representation, optimal control, robust control, and nonlinear systems. Students apply these concepts to robotics, aerospace systems, industrial processes, and smart grid applications. The course includes simulation exercises using Simulink and Python-based control libraries.
This course covers programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, and industrial communication protocols. Students gain hands-on experience with industrial automation tools and participate in projects simulating real-world manufacturing environments.
Building upon foundational knowledge of power systems, this course covers advanced topics such as stability analysis, load flow studies, fault analysis, and protection schemes. Students learn to model complex power networks and perform simulations using industry-standard software tools like ETAP and PSCAD.
The department strongly believes in project-based learning as a means of bridging the gap between theory and practice. From the second year onwards, students engage in mandatory mini-projects that require them to apply core concepts in solving real-world problems. These projects are designed to enhance problem-solving abilities, teamwork, and communication skills.
The final-year capstone project is a comprehensive endeavor where students work closely with faculty mentors to design, implement, and present an original solution to a significant engineering challenge. The project can be theoretical, experimental, or applied, depending on the student's interest and mentorship guidance.
Students have the freedom to choose their projects based on personal interests, emerging technologies, or industry needs. Faculty members from various specializations serve as mentors, providing technical support, research direction, and career guidance throughout the project lifecycle.
