Curriculum Overview
The curriculum for the Engineering program at University of Science and Technology Meghalaya is designed to provide students with a comprehensive and rigorous education in engineering principles and practices. The program is structured over eight semesters, with each semester building upon the previous one to ensure a deep and holistic understanding of engineering disciplines.
Course Structure
The curriculum is divided into core subjects, departmental electives, science electives, and laboratory courses. Core subjects provide foundational knowledge in engineering principles, while departmental electives allow students to specialize in their chosen field. Science electives enhance analytical and problem-solving skills, and laboratory courses provide hands-on experience with real-world applications.
Semester-wise Course Breakdown
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | None |
| 1 | ENG104 | Engineering Graphics | 2-1-0-3 | None |
| 1 | ENG105 | Programming for Engineers | 2-1-0-3 | None |
| 1 | ENG106 | Engineering Mechanics | 3-1-0-4 | None |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits | 3-1-0-4 | ENG102 |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | ENG102 |
| 2 | ENG204 | Fluid Mechanics | 3-1-0-4 | ENG102 |
| 2 | ENG205 | Materials Science | 3-1-0-4 | ENG103 |
| 2 | ENG206 | Computer Programming Lab | 0-0-3-2 | ENG105 |
| 3 | ENG301 | Advanced Mathematics | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG303 | Control Systems | 3-1-0-4 | ENG202 |
| 3 | ENG304 | Manufacturing Processes | 3-1-0-4 | ENG106 |
| 3 | ENG305 | Structural Analysis | 3-1-0-4 | ENG106 |
| 3 | ENG306 | Database Systems | 3-1-0-4 | ENG105 |
| 4 | ENG401 | Advanced Control Systems | 3-1-0-4 | ENG303 |
| 4 | ENG402 | Heat Transfer | 3-1-0-4 | ENG203 |
| 4 | ENG403 | Machine Design | 3-1-0-4 | ENG106 |
| 4 | ENG404 | Environmental Engineering | 3-1-0-4 | ENG204 |
| 4 | ENG405 | Software Engineering | 3-1-0-4 | ENG306 |
| 4 | ENG406 | Embedded Systems | 3-1-0-4 | ENG105 |
| 5 | ENG501 | Artificial Intelligence | 3-1-0-4 | ENG302 |
| 5 | ENG502 | Machine Learning | 3-1-0-4 | ENG501 |
| 5 | ENG503 | Cybersecurity | 3-1-0-4 | ENG306 |
| 5 | ENG504 | Data Analytics | 3-1-0-4 | ENG302 |
| 5 | ENG505 | Renewable Energy | 3-1-0-4 | ENG203 |
| 5 | ENG506 | Robotics | 3-1-0-4 | ENG403 |
| 6 | ENG601 | Advanced Algorithms | 3-1-0-4 | ENG302 |
| 6 | ENG602 | Computer Vision | 3-1-0-4 | ENG501 |
| 6 | ENG603 | Neural Networks | 3-1-0-4 | ENG502 |
| 6 | ENG604 | Power Electronics | 3-1-0-4 | ENG202 |
| 6 | ENG605 | Advanced Materials | 3-1-0-4 | ENG205 |
| 6 | ENG606 | Project Management | 3-1-0-4 | ENG306 |
| 7 | ENG701 | Research Methodology | 3-1-0-4 | ENG301 |
| 7 | ENG702 | Capstone Project I | 3-1-0-4 | ENG501 |
| 7 | ENG703 | Industrial Internship | 0-0-6-2 | ENG306 |
| 8 | ENG801 | Capstone Project II | 3-1-0-4 | ENG702 |
| 8 | ENG802 | Entrepreneurship | 3-1-0-4 | ENG606 |
| 8 | ENG803 | Professional Ethics | 3-1-0-4 | None |
Advanced Departmental Elective Courses
Departmental electives play a crucial role in allowing students to specialize in their chosen field and gain in-depth knowledge in specific areas. These courses are designed to provide advanced technical skills and real-world applications, preparing students for careers in specialized engineering roles.
Artificial Intelligence
This course introduces students to the fundamentals of artificial intelligence, including machine learning, deep learning, and neural networks. Students will explore algorithms for problem-solving, knowledge representation, and reasoning. The course includes practical projects on image recognition, natural language processing, and robotics.
Machine Learning
The Machine Learning course focuses on supervised and unsupervised learning algorithms, including decision trees, clustering, regression, and classification. Students will learn to implement machine learning models using Python and scikit-learn, and will work on real-world datasets to build predictive models.
Cybersecurity
This course covers the principles of cybersecurity, including network security, cryptography, and ethical hacking. Students will learn to identify vulnerabilities, implement security protocols, and develop secure software systems. The course includes hands-on labs on penetration testing and security auditing.
Data Analytics
Data Analytics teaches students how to collect, process, and analyze large datasets using statistical methods and machine learning techniques. The course includes practical applications in business intelligence, data visualization, and predictive analytics using tools like R, Python, and SQL.
Renewable Energy
This course explores the principles of renewable energy systems, including solar, wind, and hydroelectric power. Students will study energy conversion processes, system design, and environmental impact assessment. The course includes field visits to renewable energy installations and project work on sustainable energy solutions.
Robotics
The Robotics course covers the design and programming of robotic systems, including sensors, actuators, and control systems. Students will build and program robots to perform specific tasks, and will explore applications in manufacturing, healthcare, and exploration.
Advanced Algorithms
This course delves into advanced algorithmic techniques, including graph algorithms, dynamic programming, and optimization methods. Students will learn to analyze the complexity of algorithms and apply them to solve complex computational problems in engineering and computer science.
Computer Vision
Computer Vision introduces students to the principles of image processing and pattern recognition. The course covers topics such as edge detection, feature extraction, and object recognition. Students will implement computer vision algorithms using OpenCV and TensorFlow.
Neural Networks
This course explores the architecture and training of neural networks, including feedforward, convolutional, and recurrent networks. Students will learn to design and train neural networks for various applications, including image classification, speech recognition, and natural language processing.
Power Electronics
The Power Electronics course covers the principles of power conversion and control, including rectifiers, inverters, and DC-DC converters. Students will study the design and application of power electronic circuits in renewable energy systems, electric drives, and power supplies.
Advanced Materials
This course explores the properties and applications of advanced materials, including composites, nanomaterials, and smart materials. Students will study the synthesis, characterization, and performance evaluation of materials for engineering applications.
Project Management
Project Management teaches students how to plan, execute, and monitor engineering projects. The course covers project lifecycle, risk management, resource allocation, and quality control. Students will work on real-world projects and develop project management skills using industry-standard tools.
Project-Based Learning Philosophy
The department strongly emphasizes project-based learning as a core component of the curriculum. This approach ensures that students gain practical experience and apply theoretical concepts to real-world problems. The program includes both mini-projects and a final-year thesis/capstone project, providing students with opportunities to develop technical and professional skills.
Mini-Projects
Mini-projects are undertaken during the third and fourth semesters. These projects are typically team-based and focus on solving specific engineering problems. Students work under the guidance of faculty mentors and are evaluated based on their technical contribution, teamwork, and presentation skills.
Final-Year Thesis/Capstone Project
The final-year project is a comprehensive endeavor that allows students to integrate their learning and demonstrate their expertise in a chosen area of engineering. Students select a project topic in consultation with faculty mentors and work on it for the entire academic year. The project includes literature review, design, implementation, testing, and documentation. Students present their work in a final defense and are evaluated based on the quality of the project, innovation, and presentation.
Project Selection and Mentorship
Students are encouraged to choose projects that align with their interests and career goals. The department provides a list of project ideas and research areas, and students can also propose their own projects. Faculty mentors are assigned based on the project topic and the expertise of the faculty member. The mentorship process includes regular meetings, progress reviews, and guidance on technical and professional development.