Course Structure Overview
The engineering curriculum at Integral University Lucknow is meticulously designed to ensure a progressive learning experience that aligns with industry standards and academic excellence. The program spans eight semesters, with each semester consisting of core courses, departmental electives, science electives, and laboratory components.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | ENG101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | ENG102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | ENG103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | ENG104 | Computer Programming | 2-0-2-3 | - |
| 1 | ENG105 | Engineering Graphics | 2-0-2-3 | - |
| 1 | ENG106 | Workshop Practice | 0-0-4-2 | - |
| 2 | ENG201 | Engineering Mathematics II | 3-1-0-4 | ENG101 |
| 2 | ENG202 | Electrical Circuits and Networks | 3-1-0-4 | - |
| 2 | ENG203 | Thermodynamics | 3-1-0-4 | - |
| 2 | ENG204 | Material Science | 3-1-0-4 | - |
| 2 | ENG205 | Engineering Mechanics | 3-1-0-4 | - |
| 2 | ENG206 | Basic Electronics | 2-0-2-3 | - |
| 3 | ENG301 | Engineering Mathematics III | 3-1-0-4 | ENG201 |
| 3 | ENG302 | Fluid Mechanics | 3-1-0-4 | ENG203 |
| 3 | ENG303 | Mechanics of Materials | 3-1-0-4 | - |
| 3 | ENG304 | Digital Electronics | 3-1-0-4 | ENG206 |
| 3 | ENG305 | Computer Architecture | 3-1-0-4 | ENG204 |
| 3 | ENG306 | Strength of Materials | 3-1-0-4 | - |
| 4 | ENG401 | Control Systems | 3-1-0-4 | ENG301 |
| 4 | ENG402 | Signals and Systems | 3-1-0-4 | - |
| 4 | ENG403 | Machine Design | 3-1-0-4 | ENG306 |
| 4 | ENG404 | Power Plant Engineering | 3-1-0-4 | - |
| 4 | ENG405 | Industrial Engineering | 3-1-0-4 | - |
| 4 | ENG406 | Electromagnetic Fields | 3-1-0-4 | ENG202 |
| 5 | ENG501 | Advanced Mathematics | 3-1-0-4 | ENG301 |
| 5 | ENG502 | Design of Experiments | 3-1-0-4 | - |
| 5 | ENG503 | Advanced Materials | 3-1-0-4 | - |
| 5 | ENG504 | Process Control | 3-1-0-4 | - |
| 5 | ENG505 | Renewable Energy Systems | 3-1-0-4 | - |
| 5 | ENG506 | Operations Research | 3-1-0-4 | - |
| 6 | ENG601 | Research Methodology | 2-0-2-3 | - |
| 6 | ENG602 | Advanced Control Systems | 3-1-0-4 | ENG401 |
| 6 | ENG603 | Artificial Intelligence | 3-1-0-4 | - |
| 6 | ENG604 | Cybersecurity | 3-1-0-4 | - |
| 6 | ENG605 | Neural Networks | 3-1-0-4 | - |
| 6 | ENG606 | Machine Learning | 3-1-0-4 | - |
| 7 | ENG701 | Capstone Project I | 2-0-4-4 | - |
| 7 | ENG702 | Special Topics in Engineering | 3-1-0-4 | - |
| 7 | ENG703 | Advanced Computer Architecture | 3-1-0-4 | ENG305 |
| 7 | ENG704 | Embedded Systems | 3-1-0-4 | - |
| 7 | ENG705 | Smart Grids | 3-1-0-4 | - |
| 7 | ENG706 | Internet of Things (IoT) | 3-1-0-4 | - |
| 8 | ENG801 | Capstone Project II | 2-0-6-6 | - |
| 8 | ENG802 | Industry Internship | 0-0-8-8 | - |
| 8 | ENG803 | Project Management | 2-0-2-3 | - |
| 8 | ENG804 | Entrepreneurship | 2-0-2-3 | - |
| 8 | ENG805 | Ethics in Engineering | 2-0-2-3 | - |
| 8 | ENG806 | Final Thesis | 0-0-10-10 | - |
Advanced Departmental Elective Courses
Departmental electives form a crucial part of the curriculum, allowing students to explore specialized areas and develop expertise in their chosen fields. These courses are designed to provide depth and breadth in engineering disciplines while maintaining alignment with current industry trends.
Artificial Intelligence and Machine Learning
This course introduces students to fundamental concepts in AI and ML, including neural networks, deep learning algorithms, natural language processing, and computer vision. Students engage in hands-on projects involving data preprocessing, model training, and performance evaluation using frameworks such as TensorFlow and PyTorch.
Cybersecurity
The cybersecurity course covers network security protocols, cryptography, ethical hacking, digital forensics, and risk management strategies. Through simulations and real-world case studies, students gain practical skills in identifying vulnerabilities and implementing robust defense mechanisms.
Renewable Energy Systems
This elective explores solar, wind, hydroelectric, and geothermal energy technologies. Students study energy conversion processes, grid integration challenges, and policy frameworks supporting renewable energy adoption. Practical components include designing small-scale renewable systems and analyzing their economic viability.
Advanced Materials Science
The course delves into the structure-property relationships of advanced materials such as composites, nanomaterials, smart materials, and biomaterials. Students learn about synthesis techniques, characterization methods, and applications in various engineering fields.
Smart Grid Technologies
This course examines the integration of renewable energy sources into electrical grids, including grid stability, power quality, demand response systems, and energy storage solutions. Students work on modeling and simulating smart grid scenarios using tools like MATLAB and PowerWorld.
Internet of Things (IoT)
The IoT elective focuses on sensor networks, embedded systems, wireless communication protocols, and cloud computing integration. Students build IoT applications for smart cities, agriculture, healthcare, and industrial automation, gaining experience in hardware-software co-design.
Embedded Systems Design
This course covers microcontroller architectures, real-time operating systems, hardware-software interfacing, and embedded software development. Practical labs involve programming ARM-based microcontrollers, designing embedded applications, and integrating sensors with control systems.
Operations Research
The course introduces mathematical optimization techniques used in engineering decision-making, including linear programming, integer programming, queuing theory, and simulation modeling. Students apply these methods to solve real-world problems in logistics, manufacturing, and resource allocation.
Advanced Control Systems
This elective builds upon foundational control systems knowledge by covering advanced topics such as state-space representation, robust control, adaptive control, and nonlinear systems. Students design controllers for complex dynamic systems using MATLAB and Simulink tools.
Neural Networks and Deep Learning
The course explores deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs). Students implement models for image recognition, natural language understanding, and time series forecasting.
Project-Based Learning Philosophy
The department emphasizes project-based learning as a core pedagogical approach to ensure that students develop practical skills alongside theoretical knowledge. This methodology encourages collaborative teamwork, critical thinking, and innovation throughout the academic journey.
Mini-Projects
Mini-projects are introduced in the third year, allowing students to apply classroom concepts to real-world scenarios. These projects typically last 2-3 months and require students to work in teams of 4-6 members under faculty supervision. Projects are selected based on student interests, faculty expertise, and industry relevance.
Final-Year Thesis/Capstone Project
The final-year capstone project is a comprehensive endeavor that integrates all knowledge acquired during the undergraduate program. Students propose individual or group projects aligned with their specialization tracks. The process involves literature review, problem definition, design, implementation, testing, and documentation.
Students must select a faculty mentor who guides them through each phase of the project. The mentorship includes regular meetings, feedback sessions, and technical reviews. Projects are evaluated based on innovation, technical merit, presentation quality, and overall impact.
Faculty mentors come from diverse backgrounds, including academia, industry, and research institutions. Their expertise spans multiple domains, ensuring that students receive well-rounded guidance tailored to their project needs.