Course Structure Overview
The Electrical Engineering program at M DIT Polytechnic College is structured over 8 semesters, with a blend of core courses, departmental electives, science electives, and laboratory sessions designed to provide students with a well-rounded education.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | ENG101 | English for Engineers | 3-0-0-3 | - |
| I | MAT101 | Mathematics I | 4-0-0-4 | - |
| I | PHY101 | Physics for Engineers | 3-0-0-3 | - |
| I | CSE101 | Introduction to Programming | 2-0-2-4 | - |
| I | EE101 | Basic Electrical Engineering | 3-0-0-3 | - |
| I | LAB101 | Basic Lab Session | 0-0-2-2 | - |
| II | MAT102 | Mathematics II | 4-0-0-4 | MAT101 |
| II | PHY102 | Modern Physics | 3-0-0-3 | PHY101 |
| II | EE102 | Circuit Analysis | 3-0-0-3 | EE101 |
| II | EE103 | Digital Logic Design | 3-0-0-3 | - |
| II | CSE102 | Data Structures and Algorithms | 3-0-0-3 | CSE101 |
| II | LAB102 | Circuit Lab Session | 0-0-2-2 | - |
| III | MAT201 | Mathematics III | 4-0-0-4 | MAT102 |
| III | EE201 | Electromagnetic Fields | 3-0-0-3 | PHY102 |
| III | EE202 | Analog Electronics | 3-0-0-3 | EE102 |
| III | EE203 | Signals and Systems | 3-0-0-3 | MAT201 |
| III | EE204 | Control Systems | 3-0-0-3 | EE102 |
| III | LAB201 | Analog Electronics Lab | 0-0-2-2 | - |
| IV | MAT202 | Mathematics IV | 4-0-0-4 | MAT201 |
| IV | EE301 | Digital Electronics | 3-0-0-3 | EE203 |
| IV | EE302 | Electrical Machines | 3-0-0-3 | EE201 |
| IV | EE303 | Power Electronics | 3-0-0-3 | EE202 |
| IV | EE304 | Communication Systems | 3-0-0-3 | EE203 |
| IV | LAB202 | Digital Electronics Lab | 0-0-2-2 | - |
| V | EE401 | Power Systems Analysis | 3-0-0-3 | EE302 |
| V | EE402 | Microprocessors and Microcontrollers | 3-0-0-3 | EE301 |
| V | EE403 | Embedded Systems | 3-0-0-3 | EE301 |
| V | EE404 | Renewable Energy Sources | 3-0-0-3 | - |
| V | LAB301 | Power Systems Lab | 0-0-2-2 | - |
| VI | EE501 | Industrial Automation | 3-0-0-3 | EE402 |
| VI | EE502 | VLSI Design | 3-0-0-3 | EE301 |
| VI | EE503 | Smart Grid Technologies | 3-0-0-3 | EE401 |
| VI | EE504 | Artificial Intelligence in Electrical Engineering | 3-0-0-3 | - |
| VI | LAB302 | Embedded Systems Lab | 0-0-2-2 | - |
| VII | EE601 | Advanced Control Systems | 3-0-0-3 | EE404 |
| VII | EE602 | RF and Microwave Engineering | 3-0-0-3 | EE201 |
| VII | EE603 | Signal Processing Applications | 3-0-0-3 | EE203 |
| VII | EE604 | Electromagnetic Compatibility | 3-0-0-3 | EE201 |
| VII | LAB401 | Advanced Lab Session | 0-0-2-2 | - |
| VIII | EE701 | Capstone Project | 0-0-6-6 | - |
| VIII | EE702 | Research Methodology | 3-0-0-3 | - |
| VIII | EE703 | Elective I | 3-0-0-3 | - |
| VIII | EE704 | Elective II | 3-0-0-3 | - |
| VIII | EE705 | Mini Project | 0-0-6-6 | - |
Advanced Departmental Electives
The following advanced departmental elective courses are offered in the final years of the program:
- Power System Protection and Stability: This course covers protection schemes for power systems, stability analysis, and fault calculation techniques. Students learn to design protective relays and analyze system behavior under various fault conditions.
- Advanced Power Electronics: Focuses on high-frequency converters, resonant converters, and soft-switching techniques. The course includes practical sessions on designing power supplies for renewable energy systems.
- Wireless Communication Systems: Covers modern wireless technologies including 4G/5G networks, Wi-Fi standards, satellite communications, and IoT protocols. Students implement communication systems using software-defined radios.
- Neural Networks and Deep Learning for Signal Processing: Integrates machine learning concepts with signal processing applications. Students work on projects involving image recognition, speech processing, and pattern classification.
- Advanced Microcontroller Applications: Builds upon basic microcontroller programming to cover advanced architectures, real-time operating systems, and embedded software design principles.
- Energy Storage Technologies: Explores various battery technologies, supercapacitors, and hybrid storage systems. Students evaluate performance metrics and economic viability of different energy storage solutions.
- Automation in Manufacturing Systems: Focuses on industrial robotics, PLC programming, and automation control strategies for manufacturing environments.
- Electromagnetic Compatibility Design: Teaches students to design electronic systems that comply with EMC regulations. Topics include EMI/EMC testing procedures, shielding techniques, and regulatory standards.
- Smart Grids and Demand Response: Addresses smart grid architecture, demand response programs, and integration of distributed energy resources into the power system.
- Advanced Embedded Systems Architecture: Explores advanced embedded platforms including ARM Cortex-A series processors, real-time operating systems, and hardware-software co-design methodologies.
Project-Based Learning Philosophy
Our approach to project-based learning emphasizes practical application of theoretical concepts, teamwork, and innovation. The program incorporates two types of projects: mini-projects and final-year capstone projects.
Mini Projects (Semester VII): Students work in teams of 3-5 members on a defined problem statement related to their chosen specialization track. These projects typically last 6-8 weeks and involve designing, prototyping, testing, and documenting a solution. Evaluation criteria include project design quality, implementation skills, teamwork, presentation, and documentation.
Final-Year Capstone Project (Semester VIII): This is the most significant component of the program, lasting 12-14 weeks. Students select a research topic or industrial challenge in consultation with faculty mentors. The project involves extensive literature review, experimental design, data collection and analysis, and presentation to an evaluation committee.
Students are encouraged to choose projects that align with their career interests or address real-world problems. Faculty mentors guide students throughout the process, providing expertise and feedback on technical aspects and research methodologies.
The project selection process involves a proposal submission phase where students present their ideas and receive guidance from advisors. Projects may be selected from industry partners, government agencies, or academic research areas. The goal is to produce work that contributes meaningfully to knowledge or solves practical problems.