Curriculum Overview
The curriculum for the B.Tech Electrical Engineering program at Aisect University Hazaribagh is structured to provide a comprehensive understanding of fundamental principles, advanced topics, and practical applications in electrical engineering. It includes core courses, departmental electives, science electives, and laboratory sessions designed to foster innovation, critical thinking, and problem-solving skills.
| SEMESTER | COURSE CODE | COURSE TITLE | CREDIT STRUCTURE (L-T-P-C) | PREREQUISITES |
|---|---|---|---|---|
| Semester I | PH101 | Engineering Physics | 3-1-0-4 | - |
| MA101 | Mathematics I | 3-1-0-4 | - | |
| CH101 | Chemistry for Engineers | 3-1-0-4 | - | |
| EC101 | Introduction to Electrical Engineering | 3-0-0-3 | - | |
| EE101 | Basic Electrical Circuits | 3-1-0-4 | - | |
| CS101 | Introduction to Programming | 2-0-2-3 | - | |
| EN101 | Communication Skills | 2-0-0-2 | - | |
| GE101 | Engineering Graphics | 2-1-0-3 | - | |
| EP101 | Basic Electronics | 2-0-2-3 | - | |
| PH102 | Physics Laboratory | 0-0-2-2 | - | |
| CH102 | Chemistry Laboratory | 0-0-2-2 | - | |
| CS102 | Programming Laboratory | 0-0-2-2 | - | |
| Semester II | PH201 | Engineering Physics II | 3-1-0-4 | PH101 |
| MA201 | Mathematics II | 3-1-0-4 | MA101 | |
| EC201 | Circuit Analysis | 3-1-0-4 | EE101 | |
| EE201 | Electromagnetic Fields | 3-1-0-4 | - | |
| EE202 | Signals and Systems | 3-1-0-4 | - | |
| CS201 | Data Structures and Algorithms | 3-0-2-5 | CS101 | |
| EN201 | Professional Communication | 2-0-0-2 | - | |
| GE201 | Engineering Drawing | 2-1-0-3 | - | |
| EP201 | Analog Electronics | 3-0-2-5 | - | |
| PH202 | Physics Laboratory II | 0-0-2-2 | - | |
| EC202 | Circuit Analysis Laboratory | 0-0-2-2 | - | |
| CS202 | Data Structures Laboratory | 0-0-2-2 | - | |
| EP202 | Analog Electronics Laboratory | 0-0-2-2 | - | |
| Semester III | PH301 | Engineering Physics III | 3-1-0-4 | PH201 |
| MA301 | Mathematics III | 3-1-0-4 | MA201 | |
| EE301 | Power Systems Analysis | 3-1-0-4 | EC201 | |
| EE302 | Digital Electronics | 3-1-0-4 | - | |
| EE303 | Control Systems | 3-1-0-4 | - | |
| EE304 | Microprocessors and Microcontrollers | 3-1-2-6 | - | |
| CS301 | Database Management Systems | 3-0-2-5 | CS201 | |
| EN301 | Technical Writing | 2-0-0-2 | - | |
| EP301 | Digital Electronics Laboratory | 0-0-2-2 | - | |
| EE305 | Control Systems Laboratory | 0-0-2-2 | - | |
| EE306 | Microcontroller Laboratory | 0-0-2-2 | - | |
| EE307 | Power Systems Analysis Laboratory | 0-0-2-2 | - | |
| EP302 | Digital Electronics Laboratory | 0-0-2-2 | - | |
| Semester IV | PH401 | Engineering Physics IV | 3-1-0-4 | PH301 |
| MA401 | Mathematics IV | 3-1-0-4 | MA301 | |
| EE401 | Power Electronics | 3-1-0-4 | - | |
| EE402 | Communication Systems | 3-1-0-4 | - | |
| EE403 | Electromagnetic Compatibility | 3-1-0-4 | - | |
| EE404 | Embedded Systems | 3-1-2-6 | - | |
| CS401 | Computer Networks | 3-0-2-5 | CS201 | |
| EN401 | Business Communication | 2-0-0-2 | - | |
| EP401 | Power Electronics Laboratory | 0-0-2-2 | - | |
| EE405 | Communication Systems Laboratory | 0-0-2-2 | - | |
| EE406 | Embedded Systems Laboratory | 0-0-2-2 | - | |
| EE407 | EMC Laboratory | 0-0-2-2 | - | |
| EP402 | Power Electronics Laboratory | 0-0-2-2 | - | |
| Semester V | EE501 | Renewable Energy Systems | 3-1-0-4 | - |
| EE502 | Digital Signal Processing | 3-1-0-4 | - | |
| EE503 | Advanced Control Systems | 3-1-0-4 | - | |
| EE504 | Machine Learning for Electrical Engineers | 3-1-0-4 | - | |
| EE505 | Power System Protection | 3-1-0-4 | - | |
| CS501 | Machine Learning | 3-0-2-5 | CS201 | |
| EN501 | Leadership and Team Management | 2-0-0-2 | - | |
| EP501 | Digital Signal Processing Laboratory | 0-0-2-2 | - | |
| EE506 | Renewable Energy Systems Laboratory | 0-0-2-2 | - | |
| EE507 | Control Systems Laboratory | 0-0-2-2 | - | |
| EE508 | ML for Electrical Engineering Laboratory | 0-0-2-2 | - | |
| EP502 | DSP Laboratory | 0-0-2-2 | - | |
| EE509 | Power System Protection Laboratory | 0-0-2-2 | - | |
| Semester VI | EE601 | Smart Grid Technologies | 3-1-0-4 | - |
| EE602 | Advanced Power Electronics | 3-1-0-4 | - | |
| EE603 | Robotics and Automation | 3-1-0-4 | - | |
| EE604 | Energy Storage Systems | 3-1-0-4 | - | |
| EE605 | Signal Processing in Communications | 3-1-0-4 | - | |
| CS601 | Cloud Computing | 3-0-2-5 | CS201 | |
| EN601 | Ethics and Professional Responsibility | 2-0-0-2 | - | |
| EP601 | Smart Grid Laboratory | 0-0-2-2 | - | |
| EE606 | Advanced Power Electronics Laboratory | 0-0-2-2 | - | |
| EE607 | Robotics and Automation Laboratory | 0-0-2-2 | - | |
| EE608 | Energy Storage Systems Laboratory | 0-0-2-2 | - | |
| EP602 | Signal Processing Laboratory | 0-0-2-2 | - | |
| EE609 | Smart Grid Laboratory | 0-0-2-2 | - | |
| Semester VII | EE701 | Capstone Project I | 3-0-6-9 | - |
| EE702 | Research Methodology | 2-0-0-2 | - | |
| EE703 | Advanced Topics in Electrical Engineering | 3-1-0-4 | - | |
| EE704 | Industrial Training | 0-0-0-6 | - | |
| CS701 | Artificial Intelligence | 3-0-2-5 | CS201 | |
| EN701 | Project Management | 2-0-0-2 | - | |
| EP701 | Capstone Project Laboratory I | 0-0-6-6 | - | |
| EE705 | Advanced Topics in Electrical Engineering Laboratory | 0-0-2-2 | - | |
| EE706 | Capstone Project Laboratory II | 0-0-6-6 | - | |
| EE707 | Research Methodology Laboratory | 0-0-2-2 | - | |
| EE708 | Industrial Training Laboratory | 0-0-0-6 | - | |
| EP702 | Capstone Project Laboratory III | 0-0-6-6 | - | |
| EE709 | Advanced Topics in Electrical Engineering Laboratory | 0-0-2-2 | - | |
| Semester VIII | EE801 | Capstone Project II | 3-0-6-9 | - |
| EE802 | Final Year Thesis | 3-0-6-9 | - | |
| EE803 | Elective Course I | 3-1-0-4 | - | |
| EE804 | Elective Course II | 3-1-0-4 | - | |
| CS801 | Capstone Project in AI | 3-0-2-5 | CS201 | |
| EN801 | Entrepreneurship Development | 2-0-0-2 | - | |
| EP801 | Capstone Project Laboratory IV | 0-0-6-6 | - | |
| EE805 | Final Year Thesis Laboratory | 0-0-6-6 | - | |
| EE806 | Elective Course Laboratory I | 0-0-2-2 | - | |
| EE807 | Elective Course Laboratory II | 0-0-2-2 | - | |
| EE808 | Capstone Project Laboratory V | 0-0-6-6 | - | |
| EP802 | Final Year Thesis Laboratory | 0-0-6-6 | - | |
| EE809 | Elective Course Laboratory III | 0-0-2-2 | - |
Advanced Departmental Electives
Digital Signal Processing: This course covers the fundamentals of digital signal processing, including sampling theorem, discrete-time signals and systems, z-transforms, and Fast Fourier Transform (FFT). Students will learn to implement filters using MATLAB and apply DSP techniques in audio and image processing.
Power System Protection: The course explores the principles of power system protection, including relay characteristics, fault analysis, and protective relaying schemes. Practical applications include designing protection systems for transformers, transmission lines, and generators.
Machine Learning for Electrical Engineers: This interdisciplinary course combines machine learning algorithms with electrical engineering concepts. Students will learn to apply neural networks, support vector machines, and deep learning models to solve problems in power systems, communications, and control systems.
Advanced Power Electronics: Focused on high-efficiency power conversion techniques, this course covers topics such as resonant converters, soft-switching techniques, and wide-bandgap semiconductor devices. Students will design and simulate advanced power electronic circuits using software tools like PSpice and MATLAB.
Robotics and Automation: This course introduces students to robotics principles, including kinematics, dynamics, control systems, and sensor integration. Practical components involve building and programming autonomous robots using microcontrollers and industrial automation platforms.
Renewable Energy Systems: The curriculum covers solar photovoltaic systems, wind turbines, hydroelectric power generation, and energy storage technologies. Students will learn to model and simulate renewable energy systems and design hybrid systems for various applications.
Smart Grid Technologies: This course focuses on smart grid architecture, including communication protocols, demand response programs, and grid modernization strategies. Students will study real-time monitoring systems, energy management platforms, and cybersecurity in power systems.
Embedded Systems Design: Designed to teach students how to design embedded systems for specific applications, this course covers microcontroller architectures, real-time operating systems, and hardware-software co-design. Practical sessions involve building IoT-based projects using ARM processors and FPGA boards.
Signal Processing in Communications: This course explores how signal processing techniques are applied in communication systems, including modulation schemes, channel coding, and error correction. Students will implement communication protocols and analyze system performance using simulation tools.
Energy Storage Systems: The course covers battery technologies, supercapacitors, fuel cells, and other energy storage solutions. Students will learn to design and optimize energy storage systems for grid applications, electric vehicles, and portable electronics.
Control Systems Design: This advanced course delves into modern control system design techniques, including state-space representation, optimal control, and robust control. Students will use MATLAB/Simulink to model and simulate complex control systems and implement them in real-time applications.
Power System Analysis: The course provides a comprehensive overview of power system analysis methods, including load flow studies, short circuit calculations, and stability analysis. Students will analyze large-scale power networks and design solutions for system reliability.
Communication Systems: This foundational course covers analog and digital communication systems, including modulation techniques, noise analysis, and channel capacity. Practical components involve designing communication links using software tools and testing physical transmission media.
Electromagnetic Compatibility: Students will study electromagnetic interference (EMI) sources, propagation mechanisms, and mitigation strategies. The course includes practical sessions on EMI/EMC testing and compliance with international standards.
Advanced Control Systems: This course covers advanced control system design, including adaptive control, nonlinear control, and model predictive control. Students will learn to apply these techniques in industrial automation and robotics applications.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is rooted in the belief that engineering education should emphasize hands-on experience and real-world problem-solving. This approach ensures that students develop both theoretical knowledge and practical skills necessary for success in industry.
The structure of project-based learning includes mini-projects in early semesters, followed by capstone projects in later years. Mini-projects typically last 4-6 weeks and involve small groups working on specific aspects of a larger engineering challenge.
Mini-projects are evaluated based on design documentation, implementation quality, presentation skills, and team collaboration. Each project is supervised by a faculty mentor who guides students through the development process and provides feedback at critical milestones.
The final-year thesis or capstone project spans 8-12 weeks and involves independent research or large-scale system design. Students select projects based on their interests and career goals, often aligned with ongoing research initiatives in the department.
Faculty mentors are assigned based on expertise and availability, ensuring that each student receives personalized guidance throughout the project lifecycle. Evaluation criteria include innovation, technical depth, presentation quality, documentation standards, and impact analysis.