Comprehensive Course Structure
The engineering program at Sanjeev Agrawal Global Educational University Bhopal is structured to provide students with a comprehensive and progressive learning experience over four years. The curriculum is designed to build upon foundational knowledge while offering specialized tracks in various engineering disciplines. Each semester includes a combination of core courses, departmental electives, science electives, and laboratory sessions that collectively contribute to the development of well-rounded engineers.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHM101 | Chemistry for Engineering Applications | 3-1-0-4 | - |
| 1 | ENG102 | Engineering Graphics and Design | 2-1-0-3 | - |
| 1 | ENG103 | Introduction to Engineering | 2-0-0-2 | - |
| 1 | ENG104 | Computer Programming | 3-0-2-5 | - |
| 1 | L101 | Engineering Mathematics I Lab | 0-0-3-1 | - |
| 1 | L102 | Physics Lab | 0-0-3-1 | - |
| 1 | L103 | Chemistry Lab | 0-0-3-1 | - |
| 1 | L104 | Programming Lab | 0-0-3-1 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Mechanics of Materials | 3-1-0-4 | PHY101 |
| 2 | ENG203 | Electrical Circuits | 3-1-0-4 | PHY101 |
| 2 | ENG204 | Thermodynamics | 3-1-0-4 | CHM101 |
| 2 | ENG205 | Engineering Mechanics | 3-1-0-4 | PHY101 |
| 2 | ENG206 | Introduction to Programming | 2-0-2-4 | ENG104 |
| 2 | L201 | Mathematics II Lab | 0-0-3-1 | ENG101 |
| 2 | L202 | Mechanics of Materials Lab | 0-0-3-1 | ENG202 |
| 2 | L203 | Electrical Circuits Lab | 0-0-3-1 | ENG203 |
| 2 | L204 | Thermodynamics Lab | 0-0-3-1 | ENG204 |
| 3 | ENG301 | Control Systems | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Signals and Systems | 3-1-0-4 | ENG201 |
| 3 | ENG303 | Materials Science | 3-1-0-4 | CHM101 |
| 3 | ENG304 | Fluid Mechanics | 3-1-0-4 | ENG201 |
| 3 | ENG305 | Electromagnetic Fields | 3-1-0-4 | ENG203 |
| 3 | ENG306 | Software Engineering | 3-1-0-4 | ENG206 |
| 3 | L301 | Control Systems Lab | 0-0-3-1 | ENG301 |
| 3 | L302 | Signals and Systems Lab | 0-0-3-1 | ENG302 |
| 3 | L303 | Materials Science Lab | 0-0-3-1 | ENG303 |
| 3 | L304 | Fluid Mechanics Lab | 0-0-3-1 | ENG304 |
| 4 | ENG401 | Advanced Mathematics for Engineering | 3-1-0-4 | ENG201 |
| 4 | ENG402 | Advanced Thermodynamics | 3-1-0-4 | ENG204 |
| 4 | ENG403 | Advanced Control Systems | 3-1-0-4 | ENG301 |
| 4 | ENG404 | Advanced Signals and Systems | 3-1-0-4 | ENG302 |
| 4 | ENG405 | Research Methodology | 2-1-0-3 | - |
| 4 | ENG406 | Capstone Project I | 2-0-0-2 | - |
| 4 | L401 | Advanced Mathematics Lab | 0-0-3-1 | ENG401 |
| 4 | L402 | Advanced Thermodynamics Lab | 0-0-3-1 | ENG402 |
| 4 | L403 | Advanced Control Systems Lab | 0-0-3-1 | ENG403 |
| 4 | L404 | Advanced Signals and Systems Lab | 0-0-3-1 | ENG404 |
Advanced Departmental Elective Courses
The advanced departmental elective courses offered in the engineering program at Sanjeev Agrawal Global Educational University Bhopal are designed to provide students with specialized knowledge and skills in their chosen fields. These courses build upon the foundational knowledge acquired in earlier semesters and prepare students for advanced research, industry applications, or graduate studies.
Advanced Machine Learning
This course provides comprehensive coverage of advanced machine learning techniques including deep learning architectures, reinforcement learning algorithms, and neural network optimization methods. Students learn to implement complex models using frameworks such as TensorFlow and PyTorch, with a focus on real-world applications in computer vision, natural language processing, and robotics.
Deep Reinforcement Learning
The Deep Reinforcement Learning course explores the intersection of machine learning and control theory, focusing on how agents can learn optimal policies through interaction with environments. Students study advanced algorithms such as Q-learning, policy gradients, and actor-critic methods, with applications in autonomous systems and game playing.
Natural Language Processing
This course covers the fundamentals of computational linguistics and statistical methods for processing human language. Topics include sentiment analysis, machine translation, text summarization, and dialogue systems. Students develop practical skills in building language understanding systems using modern NLP frameworks and techniques.
Computer Vision Applications
The Computer Vision Applications course provides students with advanced knowledge of image and video processing techniques, including object detection, segmentation, and recognition. The curriculum covers convolutional neural networks, feature extraction methods, and real-time computer vision applications in surveillance, autonomous vehicles, and medical imaging.
Power System Analysis
This course delves into the analysis and design of electrical power systems, covering topics such as load flow analysis, stability studies, protection schemes, and renewable energy integration. Students learn to model and simulate complex power systems using industry-standard software tools.
Renewable Energy Integration
The Renewable Energy Integration course focuses on the challenges and solutions associated with incorporating renewable energy sources into existing power grids. Students study solar and wind energy technologies, energy storage systems, and smart grid technologies that enable efficient integration of distributed renewable resources.
Advanced Manufacturing Processes
This advanced course covers cutting-edge manufacturing technologies including additive manufacturing, precision machining, and automation systems. Students explore 3D printing techniques, CNC programming, and industrial robotics applications in modern manufacturing environments.
Finite Element Analysis
The Finite Element Analysis course provides students with comprehensive knowledge of numerical methods for solving engineering problems. Students learn to model complex structures, analyze stress distributions, and simulate real-world conditions using advanced FEA software tools.
Embedded Systems Design
This course focuses on the design and implementation of embedded systems for various applications including IoT devices, automotive systems, and industrial automation. Students gain hands-on experience with microcontrollers, real-time operating systems, and hardware-software integration techniques.
Smart Grid Technologies
The Smart Grid Technologies course explores the evolution of power systems towards intelligent, interconnected networks. Students study advanced communication protocols, demand response systems, and energy management technologies that enable efficient and sustainable electricity distribution.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that practical experience enhances theoretical knowledge and develops critical problem-solving skills. The program emphasizes hands-on learning through both mini-projects and capstone projects that mirror real-world engineering challenges.
Mini-Projects Structure
Mini-projects are integrated throughout the curriculum and typically span 2-3 months. These projects are designed to reinforce concepts learned in core courses while introducing students to practical engineering challenges. Each mini-project is assigned by faculty members and focuses on a specific technical problem or application.
Final-Year Thesis/Capstone Project
The final-year thesis/capstone project represents the culmination of the student's engineering education. Students work on comprehensive, open-ended projects that require integration of knowledge from multiple disciplines. These projects often involve collaboration with industry partners and address real-world challenges.
Evaluation Criteria
Project evaluation is based on multiple criteria including technical competency, innovation, presentation skills, teamwork, and project documentation. Students must demonstrate their ability to apply theoretical concepts to practical problems while working within realistic constraints such as time, budget, and available resources.
Project Selection Process
Students select their projects in consultation with faculty mentors based on their interests, career aspirations, and academic strengths. The selection process involves proposal development, mentor matching, and initial planning sessions to ensure that projects are appropriately scoped for the student's capabilities and available resources.