Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| I | IE101 | Mathematics I | 3-1-0-4 | - |
| I | IE102 | Physics | 3-1-0-4 | - |
| I | IE103 | Chemistry | 3-1-0-4 | - |
| I | IE104 | Engineering Drawing | 2-1-0-3 | - |
| I | IE105 | Basic Electrical Engineering | 3-1-0-4 | - |
| I | IE106 | Computer Programming | 2-1-0-3 | - |
| I | IE107 | Environmental Science | 2-0-0-2 | - |
| I | IE108 | Engineering Mechanics | 3-1-0-4 | - |
| II | IE201 | Mathematics II | 3-1-0-4 | IE101 |
| II | IE202 | Electrical Circuits and Networks | 3-1-0-4 | IE105 |
| II | IE203 | Electronic Devices and Circuits | 3-1-0-4 | - |
| II | IE204 | Digital Logic Design | 3-1-0-4 | - |
| II | IE205 | Applied Mechanics | 3-1-0-4 | IE108 |
| II | IE206 | Computer Programming Lab | 0-0-2-1 | IE106 |
| II | IE207 | Electrical Circuits Lab | 0-0-2-1 | IE202 |
| III | IE301 | Mathematics III | 3-1-0-4 | IE201 |
| III | IE302 | Signals and Systems | 3-1-0-4 | - |
| III | IE303 | Process Control Systems | 3-1-0-4 | - |
| III | IE304 | Sensors and Transducers | 3-1-0-4 | - |
| III | IE305 | Industrial Communication Networks | 3-1-0-4 | - |
| III | IE306 | Measurement and Instrumentation | 3-1-0-4 | - |
| III | IE307 | Process Control Lab | 0-0-2-1 | IE303 |
| III | IE308 | Sensors and Transducers Lab | 0-0-2-1 | IE304 |
| IV | IE401 | Mathematics IV | 3-1-0-4 | IE301 |
| IV | IE402 | Microcontroller and Embedded Systems | 3-1-0-4 | - |
| IV | IE403 | PLC Programming and Applications | 3-1-0-4 | - |
| IV | IE404 | SCADA Systems | 3-1-0-4 | - |
| IV | IE405 | Industrial Automation | 3-1-0-4 | - |
| IV | IE406 | Advanced Process Control | 3-1-0-4 | - |
| IV | IE407 | Embedded Systems Lab | 0-0-2-1 | IE402 |
| IV | IE408 | PLC and SCADA Lab | 0-0-2-1 | IE403, IE404 |
| V | IE501 | Industrial Project I | 0-0-6-3 | - |
| V | IE502 | AI and Machine Learning in Instrumentation | 3-1-0-4 | - |
| V | IE503 | Renewable Energy Systems | 3-1-0-4 | - |
| V | IE504 | Cybersecurity in Industrial Systems | 3-1-0-4 | - |
| V | IE505 | Data Analytics for Process Optimization | 3-1-0-4 | - |
| V | IE506 | Smart Manufacturing | 3-1-0-4 | - |
| V | IE507 | Project Lab I | 0-0-4-2 | IE501 |
| VI | IE601 | Industrial Project II | 0-0-8-4 | - |
| VI | IE602 | Capstone Project | 0-0-12-6 | - |
| VI | IE603 | Internship | 0-0-12-6 | - |
| VI | IE604 | Elective Course I | 3-1-0-4 | - |
| VI | IE605 | Elective Course II | 3-1-0-4 | - |
| VI | IE606 | Project Lab II | 0-0-4-2 | IE601 |
Detailed Description of Advanced Departmental Electives
The department offers several advanced elective courses that allow students to specialize in emerging areas of instrumentation engineering. These courses are designed to provide in-depth knowledge and practical skills required for career advancement in specific fields.
AI and Machine Learning in Instrumentation
This course explores the integration of artificial intelligence and machine learning techniques into instrumentation systems. Students learn about neural networks, deep learning algorithms, and statistical modeling applied to process control and predictive maintenance. The curriculum includes hands-on projects involving real datasets from industrial environments, enabling students to develop intelligent systems that can adapt to changing conditions.
Renewable Energy Systems
This elective focuses on the instrumentation aspects of renewable energy technologies such as solar panels, wind turbines, and hydroelectric generators. Students study the control systems required for efficient power generation, grid integration, and energy storage solutions. Practical sessions involve working with real-time monitoring equipment and simulation software to optimize performance and reliability.
Cybersecurity in Industrial Systems
With increasing digitization of industrial processes, cybersecurity has become a critical concern. This course covers threats specific to industrial control systems, security protocols for SCADA networks, and best practices for protecting sensitive data. Students gain experience in identifying vulnerabilities, implementing firewalls, and conducting penetration testing on industrial environments.
Data Analytics for Process Optimization
Modern industries rely heavily on data-driven decision-making. This course teaches students how to collect, analyze, and interpret large volumes of operational data using tools like Python, R, and MATLAB. The focus is on applying analytics techniques to improve process efficiency, reduce waste, and enhance product quality.
Smart Manufacturing
Smart manufacturing involves the use of advanced technologies such as IoT, robotics, and automation to create flexible and efficient production systems. This course introduces students to concepts like Industry 4.0, digital twins, and smart factory architectures. Projects include designing automated assembly lines and implementing real-time monitoring systems.
Advanced Process Control
This advanced elective delves into complex control strategies beyond basic PID controllers. Topics include multivariable control, robust control, and optimal control theory. Students learn to model and simulate industrial processes using advanced software tools, preparing them for roles in R&D and system design.
Internet of Things (IoT) Applications
The Internet of Things has revolutionized how devices communicate and share data. This course covers the architecture of IoT systems, wireless communication protocols, sensor networks, and cloud computing integration. Students develop IoT applications for various industries, from agriculture to healthcare, gaining practical experience in deploying connected solutions.
Embedded Systems Programming
Embedded systems are integral components of modern instrumentation devices. This course provides a comprehensive overview of embedded programming using C/C++ and microcontroller platforms like Arduino and Raspberry Pi. Students learn about real-time operating systems, memory management, and low-level hardware interfacing techniques.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a cornerstone of the educational experience. Mini-projects are introduced in early semesters to build foundational skills, while final-year capstone projects require students to apply comprehensive knowledge from all disciplines. Projects are selected based on student interest and faculty expertise.
Mini-projects typically span 4-6 weeks and involve small teams working under faculty supervision. These projects allow students to experiment with different technologies and methodologies, fostering creativity and problem-solving skills.
The final-year thesis/capstone project is a significant undertaking that requires students to propose, design, implement, and present a complete solution to a real-world problem. Faculty mentors guide students throughout the process, ensuring that projects meet academic standards and industry relevance.
Project selection involves discussions with faculty advisors who help match student interests with available research opportunities or industry needs. The evaluation criteria include technical merit, innovation, presentation quality, and team collaboration skills.