Curriculum
The curriculum at Aditya University Kakinada is meticulously designed to provide a comprehensive understanding of electrical engineering principles while fostering creativity and innovation. The program spans eight semesters, with a blend of core courses, departmental electives, science electives, and practical lab components.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | EE101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | EE102 | Physics for Engineers | 3-1-0-4 | - |
| 1 | EE103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | EE104 | Basic Electrical Circuits and Networks | 3-1-0-4 | - |
| 1 | EE105 | Introduction to Engineering Design | 2-0-2-3 | - |
| 2 | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Electromagnetic Fields and Waves | 3-1-0-4 | EE102 |
| 2 | EE203 | Analog Electronics I | 3-1-0-4 | EE104 |
| 2 | EE204 | Digital Electronics I | 3-1-0-4 | EE104 |
| 2 | EE205 | Signals and Systems | 3-1-0-4 | EE101 |
| 3 | EE301 | Network Theory and Analysis | 3-1-0-4 | EE201, EE204 |
| 3 | EE302 | Power Systems Analysis | 3-1-0-4 | EE201, EE205 |
| 3 | EE303 | Analog Electronics II | 3-1-0-4 | EE203 |
| 3 | EE304 | Digital Electronics II | 3-1-0-4 | EE204 |
| 3 | EE305 | Control Systems | 3-1-0-4 | EE205 |
| 4 | EE401 | Power Electronics and Drives | 3-1-0-4 | EE302, EE303 |
| 4 | EE402 | Microprocessors and Microcontrollers | 3-1-0-4 | EE304, EE305 |
| 4 | EE403 | Communication Engineering | 3-1-0-4 | EE205 |
| 4 | EE404 | Electromagnetic Compatibility | 3-1-0-4 | EE202 |
| 4 | EE405 | Renewable Energy Systems | 3-1-0-4 | EE302 |
| 5 | EE501 | Digital Signal Processing | 3-1-0-4 | EE205 |
| 5 | EE502 | Smart Grid Technologies | 3-1-0-4 | EE302 |
| 5 | EE503 | VLSI Design | 3-1-0-4 | EE304 |
| 5 | EE504 | Artificial Intelligence for Electrical Engineering | 3-1-0-4 | EE205, EE501 |
| 5 | EE505 | Advanced Control Systems | 3-1-0-4 | EE305 |
| 6 | EE601 | Industrial Automation | 3-1-0-4 | EE305, EE402 |
| 6 | EE602 | Energy Storage Systems | 3-1-0-4 | EE505 |
| 6 | EE603 | Internet of Things (IoT) | 3-1-0-4 | EE402, EE501 |
| 6 | EE604 | Wireless Communication Systems | 3-1-0-4 | EE403 |
| 6 | EE605 | Electrical Machine Design | 3-1-0-4 | EE302 |
| 7 | EE701 | Research Methodology and Project Planning | 2-0-2-3 | - |
| 7 | EE702 | Advanced Power Systems | 3-1-0-4 | EE302, EE502 |
| 7 | EE703 | Embedded Systems Design | 3-1-0-4 | EE402, EE503 |
| 7 | EE704 | Machine Learning for Signal Processing | 3-1-0-4 | EE501, EE504 |
| 7 | EE705 | Capstone Project I | 2-0-6-6 | EE301, EE402, EE505 |
| 8 | EE801 | Capstone Project II | 2-0-6-6 | EE705 |
| 8 | EE802 | Thesis Research | 2-0-6-6 | EE701 |
Advanced departmental elective courses play a crucial role in enhancing the depth and breadth of knowledge for Electrical Engineering students. These courses are designed to provide specialized skills and prepare students for specific career paths or further academic research.
Advanced Departmental Elective Courses
- Digital Signal Processing (DSP): This course explores the mathematical foundations of digital signal processing, including discrete-time signals and systems, Fourier transforms, z-transforms, and filter design. Students learn to implement DSP algorithms using MATLAB and Python. The course emphasizes practical applications in audio processing, image enhancement, and biomedical signal analysis.
- Smart Grid Technologies: This course covers the integration of renewable energy sources into existing power grids, smart metering systems, demand response programs, and grid stability management. Students study both theoretical concepts and real-world case studies from national utilities to understand how modern grids operate efficiently under varying conditions.
- VLSI Design: This course introduces students to the design of very large-scale integrated circuits using hardware description languages such as VHDL and Verilog. Students learn about logic synthesis, timing analysis, and physical layout design. The course includes lab sessions where students design and simulate digital circuits using industry-standard tools.
- Artificial Intelligence for Electrical Engineering: This interdisciplinary course combines AI methodologies with electrical engineering principles to solve complex problems in power systems, communication networks, and control systems. Students explore machine learning algorithms such as neural networks, decision trees, and support vector machines applied to real-world engineering challenges.
- Industrial Automation: This course focuses on the application of automation technologies in industrial environments, including programmable logic controllers (PLCs), SCADA systems, and sensor integration. Students gain hands-on experience with industrial equipment and learn how to design automated processes that improve efficiency and reduce costs.
- Energy Storage Systems: This course examines various energy storage technologies such as batteries, supercapacitors, and compressed air systems. Students study the physics behind each technology, evaluate their performance characteristics, and understand how they integrate into power systems to enhance reliability and sustainability.
- Internet of Things (IoT): This course explores the architecture, protocols, and applications of IoT systems in various domains including smart cities, healthcare, agriculture, and manufacturing. Students design and implement IoT projects using microcontrollers, wireless sensors, and cloud platforms.
- Wireless Communication Systems: This course covers the fundamentals of wireless communication including modulation techniques, multiple access methods, channel coding, and antenna design. Students study modern standards such as 5G, LTE, and Wi-Fi, and explore how these technologies enable seamless connectivity in mobile devices and networks.
- Electrical Machine Design: This course delves into the design and analysis of electrical machines such as transformers, motors, and generators. Students learn about electromagnetic principles, material selection, thermal management, and optimization techniques used in machine design.
- Power Electronics and Drives: This course focuses on converting electrical power from one form to another using semiconductor switches and converters. Topics include rectifiers, inverters, DC-DC converters, and motor drives. Students gain practical experience with power electronics lab equipment and learn to design efficient power conversion systems.
The department's philosophy on project-based learning emphasizes hands-on experiences that integrate theoretical knowledge with real-world applications. Projects are assigned at different stages of the program to ensure continuous development of technical skills and collaborative abilities.
Project-Based Learning Structure
Mini-projects are introduced in the second year, where students work in small teams on specific engineering problems. These projects are supervised by faculty members and typically last 2-4 weeks. Students are expected to present their findings at the end of each project and submit detailed reports documenting their approach and results.
The final-year capstone project is a comprehensive endeavor that spans the entire academic year. Students select topics related to their area of interest or aligned with industry needs. Faculty mentors guide students through the research process, helping them refine their ideas, conduct experiments, and develop prototypes. The project culminates in a public presentation where students showcase their work to faculty, peers, and industry representatives.
Evaluation criteria for projects are based on several factors including technical depth, innovation, teamwork, presentation quality, and adherence to deadlines. Students receive feedback throughout the process to ensure continuous improvement and learning.