Comprehensive Course Listing
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisite |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 4-0-0-4 | - |
| 1 | PHY101 | Physics of Materials | 3-0-0-3 | - |
| 1 | CSE101 | Introduction to Programming using Python | 2-0-2-2 | - |
| 1 | ENG102 | Engineering Drawing and Computer Graphics | 2-0-2-2 | - |
| 1 | MAT101 | Mathematics for Engineers | 4-0-0-4 | - |
| 1 | CHM101 | Chemistry for Engineers | 3-0-0-3 | - |
| 2 | ENG201 | Circuit Theory | 4-0-0-4 | MAT101 |
| 2 | ENG202 | Signals and Systems | 3-0-0-3 | MAT101 |
| 2 | CSE201 | Logic Design | 3-0-0-3 | - |
| 2 | ENG203 | Electromagnetic Fields | 3-0-0-3 | MAT101 |
| 2 | ENG204 | Digital Electronics Lab | 0-0-2-2 | - |
| 3 | ENG301 | Microprocessors and Microcontrollers | 3-0-0-3 | ENG201 |
| 3 | ENG302 | Control Systems | 3-0-0-3 | ENG202 |
| 3 | ENG303 | Communication Systems | 3-0-0-3 | ENG202 |
| 3 | ENG304 | Embedded Systems | 3-0-0-3 | CSE201 |
| 3 | ENG305 | Communication Lab | 0-0-2-2 | - |
| 4 | ENG401 | VLSI Design | 3-0-0-3 | ENG301 |
| 4 | ENG402 | Power Electronics | 3-0-0-3 | ENG201 |
| 4 | ENG403 | Biomedical Instrumentation | 3-0-0-3 | ENG202 |
| 4 | ENG404 | Robotics and Automation | 3-0-0-3 | ENG302 |
| 4 | ENG405 | Capstone Project | 0-0-4-6 | All Core Subjects |
| 5 | ENG501 | Machine Learning for Electronics | 3-0-0-3 | ENG202 |
| 5 | ENG502 | Wireless Sensor Networks | 3-0-0-3 | ENG303 |
| 5 | ENG503 | Advanced Control Systems | 3-0-0-3 | ENG302 |
| 5 | ENG504 | Signal Processing Applications | 3-0-0-3 | ENG202 |
| 5 | ENG505 | Renewable Energy Systems | 3-0-0-3 | ENG201 |
| 6 | ENG601 | Advanced VLSI Design | 3-0-0-3 | ENG401 |
| 6 | ENG602 | Internet of Things | 3-0-0-3 | ENG304 |
| 6 | ENG603 | Cybersecurity in Electronics | 3-0-0-3 | ENG303 |
| 6 | ENG604 | Advanced Embedded Systems | 3-0-0-3 | ENG304 |
| 6 | ENG605 | Research Methodology | 2-0-0-2 | - |
| 7 | ENG701 | Specialized Research Project | 0-0-4-6 | All Core Subjects |
| 7 | ENG702 | Electronics Design Workshop | 0-0-2-2 | - |
| 8 | ENG801 | Final Year Thesis | 0-0-6-8 | All Core Subjects |
| 8 | ENG802 | Industry Internship | 0-0-0-4 | - |
Advanced Departmental Elective Courses
Machine Learning for Electronics: This course explores the intersection of machine learning and electronics engineering, focusing on how ML algorithms can be implemented in hardware systems. Students learn to build neural networks using FPGAs and ARM processors, with applications in image recognition, speech processing, and autonomous systems.
Wireless Sensor Networks: Designed for students interested in IoT and embedded networking, this course covers wireless communication protocols, sensor data fusion, network topology design, and real-time monitoring systems. Practical labs involve deploying networks in campus environments and analyzing performance metrics.
Advanced Control Systems: Building upon basic control theory, this advanced elective delves into nonlinear control, adaptive control, and optimal control methods. Students model complex systems such as drones and industrial robots, simulating their behavior using MATLAB/Simulink.
Signal Processing Applications: This course bridges signal processing theory with practical applications in audio, video, and biomedical engineering. Students implement filters, perform spectral analysis, and apply digital signal processing techniques in real-world scenarios.
Renewable Energy Systems: Focused on sustainable technologies, this elective covers solar, wind, and hydroelectric power generation systems. Students design inverters, evaluate energy storage solutions, and analyze grid integration challenges for renewable sources.
Advanced VLSI Design: This course teaches advanced techniques in chip architecture, logic synthesis, and physical implementation of integrated circuits. Students learn to optimize designs for performance, area, and power consumption using industry-standard tools like Cadence and Synopsys.
Internet of Things: Exploring the future of connectivity, this course covers IoT architectures, sensor integration, cloud computing platforms, and smart city applications. Students develop end-to-end IoT systems including hardware, middleware, and application layers.
Cybersecurity in Electronics: With increasing reliance on connected devices, cybersecurity has become a critical concern. This elective focuses on securing embedded systems, detecting threats in networked environments, and implementing secure communication protocols.
Advanced Embedded Systems: Students explore advanced architectures, real-time operating systems, and microcontroller programming for high-performance applications. Projects include building autonomous vehicles, smart home devices, and industrial automation systems.
Project-Based Learning Philosophy
At TRINITY, project-based learning is central to our educational philosophy. We believe that students learn best when they engage actively with real-world problems and develop solutions from concept to implementation.
The structure of our project framework spans multiple levels:
- Mini Projects (Year 2): Students work in teams on small-scale projects related to circuit design, microcontroller programming, or signal processing. These projects are assessed based on innovation, technical execution, and teamwork.
- Capstone Project (Year 4): The final project involves developing a complete system from scratch. Students select their topics in consultation with faculty mentors, conduct feasibility studies, design components, prototype the system, and present findings to an industry panel.
Evaluation criteria include:
- Technical Competency
- Innovation and Creativity
- Team Collaboration
- Documentation Quality
- Presentation Skills
- Industry Relevance
Faculty mentors are assigned based on student interests and project scope. Students are encouraged to propose innovative ideas that align with current industry trends, such as AI-enabled sensors or sustainable power solutions.