Comprehensive Course Structure
The curriculum at Mahapurusha Srimanta Sankaradeva Viswavidyalaya Nagaon is meticulously designed to provide students with a robust foundation in engineering principles while allowing flexibility for specialization. The program spans eight semesters, with each semester carrying a specific set of core courses, departmental electives, science electives, and laboratory sessions.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineering | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | ENG105 | Engineering Drawing & Computer Graphics | 2-0-2-3 | - |
| 1 | ENG106 | Communication Skills | 2-0-0-2 | - |
| 1 | ENG107 | Workshop Practice | 0-0-2-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Materials Science | 3-1-0-4 | ENG103 |
| 2 | ENG204 | Digital Electronics | 3-1-0-4 | ENG104 |
| 2 | ENG205 | Engineering Mechanics | 3-1-0-4 | - |
| 2 | ENG206 | Introduction to Programming | 2-0-2-3 | - |
| 2 | ENG207 | Lab Session: Basic Electronics | 0-0-2-2 | ENG104 |
| 3 | ENG301 | Probability & Statistics | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Mechanics of Solids | 3-1-0-4 | ENG205 |
| 3 | ENG303 | Fluid Mechanics | 3-1-0-4 | ENG202 |
| 3 | ENG304 | Signals & Systems | 3-1-0-4 | ENG201 |
| 3 | ENG305 | Electromagnetic Fields | 3-1-0-4 | ENG204 |
| 3 | ENG306 | Data Structures & Algorithms | 2-0-2-3 | ENG206 |
| 3 | ENG307 | Lab Session: Signals & Systems | 0-0-2-2 | ENG304 |
| 4 | ENG401 | Control Systems | 3-1-0-4 | ENG304 |
| 4 | ENG402 | Machine Design | 3-1-0-4 | ENG302 |
| 4 | ENG403 | Heat Transfer | 3-1-0-4 | ENG202 |
| 4 | ENG404 | Computer Networks | 3-1-0-4 | ENG304 |
| 4 | ENG405 | Embedded Systems | 3-1-0-4 | ENG204 |
| 4 | ENG406 | Software Engineering | 2-0-2-3 | ENG306 |
| 4 | ENG407 | Lab Session: Embedded Systems | 0-0-2-2 | ENG405 |
| 5 | ENG501 | Advanced Mathematics for Engineering | 3-1-0-4 | ENG201 |
| 5 | ENG502 | Operations Research | 3-1-0-4 | ENG301 |
| 5 | ENG503 | Design of Experiments | 3-1-0-4 | ENG301 |
| 5 | ENG504 | Artificial Intelligence | 3-1-0-4 | ENG306 |
| 5 | ENG505 | Cybersecurity Fundamentals | 3-1-0-4 | ENG404 |
| 5 | ENG506 | Industrial Management | 2-0-0-2 | - |
| 5 | ENG507 | Lab Session: AI & ML | 0-0-2-2 | ENG504 |
| 6 | ENG601 | Advanced Control Systems | 3-1-0-4 | ENG401 |
| 6 | ENG602 | Renewable Energy Systems | 3-1-0-4 | ENG202 |
| 6 | ENG603 | Project Management | 3-1-0-4 | ENG506 |
| 6 | ENG604 | Advanced Machine Learning | 3-1-0-4 | ENG504 |
| 6 | ENG605 | Quantitative Finance | 3-1-0-4 | ENG301 |
| 6 | ENG606 | Research Methodology | 2-0-0-2 | - |
| 6 | ENG607 | Lab Session: Advanced ML | 0-0-2-2 | ENG604 |
| 7 | ENG701 | Capstone Project I | 3-0-0-3 | ENG504, ENG604 |
| 7 | ENG702 | Advanced Biomedical Engineering | 3-1-0-4 | ENG202 |
| 7 | ENG703 | Smart Grid Technologies | 3-1-0-4 | ENG204 |
| 7 | ENG704 | Internet of Things (IoT) | 3-1-0-4 | ENG404 |
| 7 | ENG705 | Quantum Computing | 3-1-0-4 | ENG304 |
| 7 | ENG706 | Business Analytics | 2-0-0-2 | ENG301 |
| 7 | ENG707 | Lab Session: IoT & Quantum | 0-0-2-2 | ENG704, ENG705 |
| 8 | ENG801 | Capstone Project II | 3-0-0-3 | ENG701 |
| 8 | ENG802 | Entrepreneurship & Innovation | 2-0-0-2 | - |
| 8 | ENG803 | Sustainable Engineering Practices | 3-1-0-4 | - |
| 8 | ENG804 | Final Year Research Thesis | 2-0-0-2 | ENG606 |
| 8 | ENG805 | Industry Internship | 0-0-4-4 | - |
| 8 | ENG806 | Professional Ethics & Social Responsibility | 2-0-0-2 | - |
| 8 | ENG807 | Capstone Presentation & Viva Voce | 0-0-2-2 | ENG801 |
Advanced Departmental Elective Courses
Advanced departmental electives are designed to deepen students' understanding of specialized areas within their engineering discipline. These courses are offered by faculty members with deep expertise in emerging fields and industry trends.
One such course is Artificial Intelligence & Machine Learning, which delves into neural networks, deep learning frameworks, natural language processing, computer vision, reinforcement learning, and ethical implications of AI systems. Students learn to implement algorithms using Python, TensorFlow, and PyTorch. This course prepares students for roles in data science teams at leading tech companies.
Another key elective is Cybersecurity Fundamentals, which covers encryption techniques, network security protocols, threat modeling, penetration testing, secure coding practices, and incident response strategies. The course includes hands-on labs where students simulate real-world attacks and defend against them using tools like Wireshark, Nmap, and Metasploit.
Renewable Energy Systems explores solar photovoltaic systems, wind turbines, hydroelectric generation, energy storage technologies, smart grid integration, and carbon footprint reduction strategies. Students work on projects involving solar panel efficiency optimization and wind turbine design using simulation software like MATLAB/Simulink.
The course Advanced Control Systems focuses on modern control theory, state-space representation, optimal control, robust control, and adaptive systems. It includes practical sessions in MATLAB and Simulink for designing and simulating controllers for industrial processes.
Biomedical Engineering bridges engineering principles with medical applications. Students study biomechanics, bioinstrumentation, medical imaging techniques, tissue engineering, and drug delivery systems. Projects involve developing wearable health monitoring devices and analyzing physiological signals using signal processing tools.
Quantitative Finance introduces financial derivatives, risk management, portfolio optimization, stochastic calculus, and computational methods for pricing securities. Students use Python and R to analyze market data and build trading models that can be deployed in real-world scenarios.
Internet of Things (IoT) covers sensor networks, embedded systems programming, wireless communication protocols, cloud integration, and edge computing. Students design IoT applications for smart cities, agriculture, healthcare, and industrial automation using platforms like Arduino, Raspberry Pi, and AWS IoT Core.
Smart Grid Technologies examines power system dynamics, grid stability, renewable energy integration, demand response programs, and microgrids. Students learn to model electrical grids and optimize power distribution through simulation tools like PowerWorld and ETAP.
Quantum Computing explores qubit manipulation, quantum algorithms, error correction codes, and quantum cryptography. Students use IBM Qiskit and Microsoft Azure Quantum to run experiments on real quantum processors and simulate quantum circuits.
Sustainable Engineering Practices emphasizes eco-design principles, life cycle assessment, green manufacturing techniques, circular economy models, and environmental impact analysis. The course includes field visits to sustainable factories and case studies of successful sustainability initiatives in industry.
Project-Based Learning Philosophy
At Mahapurusha Srimanta Sankaradeva Viswavidyalaya Nagaon, project-based learning is central to the educational philosophy. Students engage in both mini-projects during their second year and a final-year capstone project that integrates knowledge from all semesters.
The Mini-Projects begin in the second semester with a focus on applying theoretical concepts to real-world problems. Students form teams of 3-4 members, select topics aligned with their interests, and work under faculty supervision for 6 weeks. Each project must include documentation, a prototype, and a presentation before a panel of experts.
The Final-Year Capstone Project is the culminating experience of the engineering program. Students propose projects related to current industry challenges or emerging technologies. They collaborate closely with faculty mentors, industry partners, and research labs. The project culminates in a detailed thesis, a demonstration, and a final viva voce examination.
Students have multiple avenues to select their projects. They can choose from pre-approved topics suggested by faculty members, propose independent ideas that align with departmental research goals, or collaborate with industry sponsors on applied problems. The selection process is transparent and involves peer reviews, mentor consultations, and project feasibility assessments.