Comprehensive Course Listing
The Digital Systems program at Electronics Service And Training Centre is structured over 8 semesters, combining foundational science subjects, core engineering courses, departmental electives, and hands-on laboratory experiences. Each semester builds upon previous knowledge while introducing advanced concepts in digital system design.
| Year | Semester | Course Code | Full Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|---|
| First Year | I | PH101 | Physics for Engineers | 3-1-0-4 | - |
| First Year | I | MA101 | Mathematics I | 4-0-0-4 | - |
| First Year | I | EC101 | Introduction to Electronics | 3-1-0-4 | - |
| First Year | I | CS101 | Programming Fundamentals | 3-1-0-4 | - |
| First Year | I | EN101 | English for Communication | 2-0-0-2 | - |
| First Year | II | PH102 | Physics II | 3-1-0-4 | PH101 |
| First Year | II | MA102 | Mathematics II | 4-0-0-4 | MA101 |
| First Year | II | EC102 | Electrical Circuits | 3-1-0-4 | EC101 |
| First Year | II | CS102 | Data Structures and Algorithms | 3-1-0-4 | CS101 |
| First Year | II | EN102 | Communication Skills | 2-0-0-2 | - |
| Second Year | III | EC201 | Digital Electronics | 3-1-0-4 | EC102 |
| Second Year | III | CS201 | Computer Organization | 3-1-0-4 | CS102 |
| Second Year | III | MA201 | Probability and Statistics | 3-0-0-3 | MA102 |
| Second Year | III | EC202 | Electromagnetic Fields | 3-1-0-4 | PH102 |
| Second Year | III | CS202 | Operating Systems | 3-1-0-4 | CS102 |
| Second Year | IV | EC203 | Analog Electronics | 3-1-0-4 | EC102 |
| Second Year | IV | CS203 | Database Management Systems | 3-1-0-4 | CS102 |
| Second Year | IV | EC204 | Signals and Systems | 3-1-0-4 | MA102 |
| Second Year | IV | CS204 | Software Engineering | 3-1-0-4 | CS102 |
| Third Year | V | EC301 | Microprocessor Architecture | 3-1-0-4 | EC201 |
| Third Year | V | CS301 | Computer Networks | 3-1-0-4 | CS201 |
| Third Year | V | EC302 | VLSI Design | 3-1-0-4 | EC201 |
| Third Year | V | CS302 | Artificial Intelligence | 3-1-0-4 | CS203 |
| Third Year | V | EC303 | Digital Signal Processing | 3-1-0-4 | EC204 |
| Third Year | VI | EC304 | Embedded Systems | 3-1-0-4 | EC201 |
| Third Year | VI | CS303 | Distributed Systems | 3-1-0-4 | CS201 |
| Third Year | VI | EC305 | Control Systems | 3-1-0-4 | EC204 |
| Third Year | VI | CS304 | Cybersecurity Fundamentals | 3-1-0-4 | CS204 |
| Fourth Year | VII | EC401 | Advanced Embedded Design | 3-1-0-4 | EC304 |
| Fourth Year | VII | CS401 | Machine Learning | 3-1-0-4 | CS203 |
| Fourth Year | VII | EC402 | Hardware Security | 3-1-0-4 | EC302 |
| Fourth Year | VII | CS402 | Cloud Computing | 3-1-0-4 | CS201 |
| Fourth Year | VIII | EC403 | Capstone Project | 0-0-6-6 | All previous courses |
| Fourth Year | VIII | CS403 | Research Thesis | 0-0-6-6 | All previous courses |
Advanced Departmental Elective Courses
These advanced courses are designed to deepen student expertise in specialized areas of digital systems, offering in-depth knowledge and practical applications aligned with current industry trends.
Advanced Neural Networks: This course delves into the mathematical foundations of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Students will implement these models using frameworks like TensorFlow and PyTorch while exploring their applications in embedded systems. The course emphasizes hardware acceleration techniques for AI inference engines and provides hands-on experience with FPGA-based neural network implementations.
AI Accelerators: Focused on designing and optimizing specialized computing platforms for artificial intelligence tasks, this course covers the architecture of ASICs, TPUs, and GPUs used in machine learning. Students learn how to optimize algorithms for these accelerators, evaluate performance metrics, and design custom hardware modules that support scalable AI inference. The lab component includes working with real accelerator chips and simulating performance improvements using industry tools.
Cryptography in Hardware: This course explores the implementation of cryptographic protocols at the hardware level, focusing on secure embedded systems. Topics include symmetric and asymmetric encryption algorithms, hash functions, digital signatures, and secure key exchange mechanisms. Students will design and simulate cryptographic modules on FPGAs, ensuring compliance with international standards such as AES, RSA, and ECC.
Secure Embedded Systems Design: This elective addresses the challenges of building secure systems from the ground up. It covers threat modeling, secure boot processes, memory protection, secure communication protocols, and fault injection resistance. Students will work on designing and implementing secure microcontroller-based systems that can withstand various types of attacks, including side-channel and timing attacks.
Hardware Implementation of ML Models: This course bridges the gap between software-based machine learning models and their hardware implementations. It teaches students how to map neural networks onto specialized hardware platforms such as FPGAs, ASICs, or neuromorphic chips. The focus is on optimizing model size, latency, and power consumption while maintaining accuracy.
Real-Time Operating Systems (RTOS): Students learn the principles of real-time systems and how to design and implement RTOS for embedded applications. Topics include scheduling algorithms, interrupt handling, memory management, and system reliability. The lab component involves programming microcontrollers with popular RTOS such as FreeRTOS or Zephyr.
Low-Power Design Techniques: With the increasing demand for battery-powered devices, this course teaches students how to design systems that minimize power consumption without sacrificing performance. It covers power estimation tools, dynamic voltage scaling, clock gating, and sleep modes. Students will evaluate and optimize the energy efficiency of various digital circuits using simulation tools.
Microcontroller Architecture: This course examines the internal architecture of microcontrollers, focusing on memory mapping, peripheral integration, interrupt controllers, and power management units. Students will study how different vendors implement these components and analyze their performance trade-offs in embedded applications.
VLSI Design Automation: This course introduces students to the tools and methodologies used in VLSI design automation. It covers logic synthesis, placement, routing, timing closure, and verification processes. Students will use industry-standard EDA tools like Synopsys, Cadence, and Mentor Graphics to design and simulate digital systems.
FPGA-Based System Design: Focused on designing digital systems using Field-Programmable Gate Arrays (FPGAs), this course covers HDL design with Verilog and VHDL, IP core development, and system integration. Students will implement complex digital systems such as processors, filters, and communication protocols on Xilinx or Intel FPGA platforms.
Quantum Computing Fundamentals: As quantum computing becomes more accessible, this course explores the theoretical and practical aspects of quantum algorithms and their implementation in digital systems. It covers qubit modeling, quantum gates, error correction, and hybrid classical-quantum architectures. Students will simulate quantum circuits using platforms like IBM Qiskit or Google Cirq.
Hardware Security: This course explores advanced topics in hardware security, including tamper resistance, fault injection attacks, side-channel analysis, and secure chip design. Students will implement secure cryptographic functions and evaluate the vulnerability of digital systems to various types of hardware-based threats.
Digital Signal Processing (DSP) for Communication: This course combines DSP techniques with communication theory, focusing on how digital signals are processed and transmitted in modern communication systems. It covers modulation schemes, channel coding, filtering, and synchronization methods used in wireless and wired networks.
Edge-AI Implementation: With the rise of edge computing, this course focuses on deploying machine learning models directly on resource-constrained devices such as smartphones, sensors, and microcontrollers. Students will learn how to optimize models for edge deployment, reduce latency, and maintain accuracy in low-power environments.
Project-Based Learning Philosophy
The Digital Systems program emphasizes project-based learning as a cornerstone of its educational philosophy. Projects are designed to simulate real-world challenges, encouraging students to apply theoretical knowledge in practical contexts while developing problem-solving skills.
Mini-projects begin in the third year and are integrated into core courses, allowing students to experiment with design concepts and explore emerging technologies. These projects are typically completed in teams of 3–5 students and involve a structured process that includes concept development, prototyping, testing, documentation, and presentation.
The final-year thesis or capstone project is a comprehensive endeavor that spans the entire fourth year. Students select their topics under faculty mentorship, ensuring alignment with current industry trends and research directions. The project involves extensive literature review, system design, implementation, experimentation, and evaluation. A formal proposal defense is followed by an oral presentation and written report.
Faculty mentors guide students throughout their project journey, providing technical insights, feedback, and career advice. The program also offers opportunities for students to present their work at national conferences, publish papers in journals, or enter competitions such as the IEEE Design Contest or the National Innovation Challenge.