Curriculum Overview

The curriculum for the engineering program at The Assam Royal Global University is meticulously designed to provide students with a comprehensive foundation in core engineering principles while offering flexibility to explore specialized areas of interest. It spans four years and includes core courses, departmental electives, science electives, and laboratory sessions that integrate theory with practical application.

Course Structure

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	ENG101	Engineering Mathematics I	3-1-0-4	-
1	ENG102	Engineering Physics	3-1-0-4	-
1	ENG103	Introduction to Programming	2-0-2-3	-
1	ENG104	Engineering Chemistry	3-1-0-4	-
1	ENG105	English for Engineers	2-0-0-2	-
1	ENG106	Engineering Graphics & Design	2-0-2-3	-
2	ENG201	Engineering Mathematics II	3-1-0-4	ENG101
2	ENG202	Electrical Circuits and Networks	3-1-0-4	ENG102
2	ENG203	Thermodynamics	3-1-0-4	ENG102
2	ENG204	Materials Science	3-1-0-4	ENG104
2	ENG205	Fluid Mechanics	3-1-0-4	ENG101
2	ENG206	Engineering Mechanics	3-1-0-4	ENG101
3	ENG301	Control Systems	3-1-0-4	ENG201, ENG202
3	ENG302	Data Structures & Algorithms	3-1-0-4	ENG103
3	ENG303	Digital Electronics	3-1-0-4	ENG202
3	ENG304	Computer Architecture	3-1-0-4	ENG203
3	ENG305	Signals & Systems	3-1-0-4	ENG201
3	ENG306	Structural Analysis	3-1-0-4	ENG206
4	ENG401	Machine Design	3-1-0-4	ENG306
4	ENG402	Power Generation Systems	3-1-0-4	ENG202
4	ENG403	Advanced Algorithms	3-1-0-4	ENG302
4	ENG404	Renewable Energy Systems	3-1-0-4	ENG203
4	ENG405	Embedded Systems	3-1-0-4	ENG303
4	ENG406	Project Management	2-0-0-2	-

Advanced Departmental Electives

Students in their later semesters are introduced to advanced departmental electives that allow them to specialize in areas of interest and align with current industry trends. These courses provide in-depth knowledge and hands-on experience in specific domains:

• Advanced Machine Learning: This course explores deep learning architectures, reinforcement learning, natural language processing, and computer vision applications. Students work on real datasets and implement models using TensorFlow and PyTorch frameworks.
• Cybersecurity Fundamentals: Designed for students interested in protecting digital assets, this course covers network security, cryptography, ethical hacking, and risk management strategies used by leading organizations.
• Smart Grid Technologies: This elective focuses on modern power grid systems, including renewable energy integration, demand response mechanisms, and intelligent control systems. Students analyze smart grid operations through simulation software.
• Biomedical Instrumentation: Combines engineering principles with biological sciences to design medical devices and diagnostic tools. The course includes lab sessions where students build prototypes of heart rate monitors and glucose sensors.
• Robotics and Automation: Teaches robot kinematics, control systems, sensor integration, and programming languages like ROS (Robot Operating System). Students build autonomous robots capable of performing complex tasks in simulated environments.
• Environmental Impact Assessment: Prepares students to evaluate the environmental implications of engineering projects. The course covers regulatory frameworks, life cycle analysis, and sustainable development practices applicable to civil and industrial infrastructure.
• Finite Element Analysis: Introduces numerical methods for solving engineering problems using software tools like ANSYS and ABAQUS. Students apply FEA techniques to structural, thermal, and fluid dynamics problems in various industries.
• Sustainable Construction Materials: Focuses on developing eco-friendly building materials and techniques that reduce carbon footprint while maintaining structural integrity. The course includes field visits to green construction sites and material testing labs.
• Industrial IoT and Edge Computing: Explores how internet of things (IoT) devices connect to edge computing platforms for real-time data processing in manufacturing environments. Students implement IoT-based solutions using Raspberry Pi, Arduino, and cloud services.
• Quantitative Finance and Risk Modeling: Designed for students interested in financial engineering, this course covers stochastic processes, derivative pricing models, portfolio optimization, and algorithmic trading strategies used by hedge funds and investment banks.

Project-Based Learning Philosophy

Our department strongly believes in project-based learning as a core component of engineering education. Projects serve as a bridge between classroom knowledge and real-world applications, enabling students to develop critical thinking, teamwork, and problem-solving skills essential for professional success.

Mini-Projects (First Two Years)

In the first two years, students engage in small-scale projects that reinforce fundamental concepts taught in core courses. These projects are typically completed in groups of 3-5 members and involve designing, building, and testing simple systems or solving engineering problems using available resources.

Final-Year Thesis/Capstone Project (Third/Fourth Years)

The final-year capstone project is a significant undertaking that allows students to apply their entire academic learning to address a complex engineering challenge. Students select projects in consultation with faculty mentors and work on them over the course of the semester. The project involves research, design, implementation, testing, and documentation phases.

Project Selection Process

Students begin the project selection process during their third year by attending faculty presentations, reviewing available projects, and identifying areas of interest. Each student submits a proposal outlining the problem statement, methodology, expected outcomes, and timeline. Faculty mentors guide students throughout the development phase, providing feedback on progress and helping resolve technical issues.

Evaluation Criteria

Projects are evaluated based on several criteria including innovation, technical depth, presentation quality, adherence to deadlines, teamwork effectiveness, and final deliverables. Students must present their work to a panel of faculty members and industry experts who assess the overall impact and feasibility of the proposed solution.