Course Structure Overview
The Electrical Engineering program at Annamacharya University Rajampet is structured over 8 semesters, with a carefully balanced mix of core courses, departmental electives, science electives, and practical lab sessions. The curriculum emphasizes both theoretical knowledge and hands-on application to ensure students are well-prepared for industry challenges.
Semester-wise Course Listing
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Pre-requisites |
|---|---|---|---|---|
| 1 | EE101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | EE102 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | EE103 | Programming for Engineers | 2-0-2-3 | None |
| 1 | EE104 | Physics for Engineers | 3-1-0-4 | None |
| 1 | EE105 | Chemistry for Engineers | 3-1-0-4 | None |
| 2 | EE201 | Engineering Mathematics II | 3-1-0-4 | EE101 |
| 2 | EE202 | Circuit Analysis | 3-1-0-4 | EE102 |
| 2 | EE203 | Electromagnetic Fields | 3-1-0-4 | EE104 |
| 2 | EE204 | Signals and Systems | 3-1-0-4 | EE101 |
| 2 | EE205 | Electronic Devices and Circuits | 3-1-0-4 | EE102 |
| 3 | EE301 | Power Electronics | 3-1-0-4 | EE205 |
| 3 | EE302 | Control Systems | 3-1-0-4 | EE204 |
| 3 | EE303 | Digital Signal Processing | 3-1-0-4 | EE204 |
| 3 | EE304 | Electromagnetic Field Theory | 3-1-0-4 | EE203 |
| 3 | EE305 | Computer Architecture | 3-1-0-4 | EE205 |
| 4 | EE401 | Power Generation, Transmission & Distribution | 3-1-0-4 | EE202 |
| 4 | EE402 | Renewable Energy Systems | 3-1-0-4 | EE202 |
| 4 | EE403 | Smart Grid Technologies | 3-1-0-4 | EE401 |
| 4 | EE404 | Industrial Automation | 3-1-0-4 | EE302 |
| 4 | EE405 | Embedded Systems Programming | 3-1-0-4 | EE205 |
| 5 | EE501 | Advanced Power Electronics | 3-1-0-4 | EE301 |
| 5 | EE502 | Robotics and Automation | 3-1-0-4 | EE302 |
| 5 | EE503 | Wireless Communications | 3-1-0-4 | EE204 |
| 5 | EE504 | VLSI Design | 3-1-0-4 | EE205 |
| 5 | EE505 | Signal Processing Applications | 3-1-0-4 | EE303 |
| 6 | EE601 | Machine Learning for Electrical Systems | 3-1-0-4 | EE505 |
| 6 | EE602 | Digital Image Processing | 3-1-0-4 | EE303 |
| 6 | EE603 | Antenna Design | 3-1-0-4 | EE203 |
| 6 | EE604 | Radar Systems | 3-1-0-4 | EE203 |
| 6 | EE605 | Battery Management Systems | 3-1-0-4 | EE402 |
| 7 | EE701 | Research Methodology | 2-0-2-3 | None |
| 7 | EE702 | Mini-Project I | 0-0-6-3 | EE401, EE302 |
| 7 | EE703 | Mini-Project II | 0-0-6-3 | EE401, EE302 |
| 8 | EE801 | Final Year Thesis/Capstone Project | 0-0-12-6 | EE702, EE703 |
Advanced Departmental Electives
Advanced departmental electives are offered in the later semesters to allow students to explore specialized areas of interest. These courses are designed by faculty members with industry experience and aim to bridge academic knowledge with practical applications.
Power Electronics
This course delves into advanced topics in power conversion, including DC-DC converters, inverters, and rectifiers. Students learn about switching devices, control strategies, and design considerations for high-efficiency power supplies. The course includes hands-on lab work involving real hardware prototypes.
Control Systems
This course covers modern control theory with emphasis on digital control systems, state-space methods, and optimal control. Students are introduced to tools like MATLAB/Simulink for system simulation and design, preparing them for complex control challenges in industries such as automotive and aerospace.
Digital Signal Processing
This course explores the mathematical foundations of digital signal processing, including discrete-time systems, Fourier transforms, and filter design. Practical applications include audio processing, image compression, and biomedical signal analysis, with labs using DSP chips and software tools like MATLAB.
Electromagnetic Field Theory
This course focuses on the mathematical formulation of electromagnetic fields and their propagation in various media. Topics include Maxwell's equations, waveguides, transmission lines, and antennas. Students engage in computational modeling and experimental validation of field theories.
Computer Architecture
This course introduces students to the design principles of computer systems, including instruction set architecture (ISA), pipeline design, memory hierarchy, and cache performance. Labs involve designing simple processors using Verilog or VHDL, giving students a deep understanding of hardware-software interaction.
Renewable Energy Systems
This course provides a comprehensive overview of solar, wind, hydro, and geothermal energy systems. Students study the physics behind each technology, efficiency metrics, and integration into power grids. The lab component includes building small-scale renewable energy systems and testing their performance under various conditions.
Embedded Systems Programming
This course teaches programming techniques for microcontrollers and real-time operating systems (RTOS). Students learn to develop embedded applications using languages like C/C++ and assembly, focusing on resource-constrained environments. Projects involve creating IoT devices with sensor integration and wireless communication capabilities.
Wireless Communications
This course covers the fundamentals of wireless communication systems, including modulation techniques, multiple access schemes, and error correction codes. Students work on simulations using tools like MATLAB and implement basic wireless communication protocols in lab settings.
VLSI Design
This course explores very large-scale integration (VLSI) design principles, including logic synthesis, physical design, and verification techniques. Students gain experience with EDA tools such as Cadence and Synopsys, and work on designing digital circuits from gate level to system level.
Machine Learning for Electrical Systems
This interdisciplinary course combines electrical engineering concepts with machine learning algorithms to solve real-world problems. Applications include predictive maintenance of power systems, fault detection in electrical networks, and optimization of energy consumption patterns.
Project-Based Learning Philosophy
Our department strongly believes in project-based learning as a means to enhance student engagement and practical understanding. This approach integrates theoretical knowledge with real-world problem-solving, fostering innovation and teamwork skills.
Mini-Projects Structure
Mini-projects are undertaken in the seventh semester and provide students with an opportunity to apply concepts learned in earlier courses. Each project is assigned a faculty mentor who guides the student through the design, implementation, and documentation phases. The evaluation criteria include technical depth, creativity, presentation quality, and teamwork.
Final-Year Thesis/Capstone Project
The final-year thesis is a significant component of the program, requiring students to undertake an independent research project or develop a substantial engineering solution. Students select their projects based on faculty expertise and personal interests, working closely with mentors throughout the process. The project must demonstrate originality, technical rigor, and practical relevance.
Faculty Mentor Selection
Students are encouraged to choose faculty mentors whose research interests align with their project ideas. The selection process involves a formal application, proposal presentation, and mentor-student matching based on mutual compatibility. Faculty mentors play a crucial role in guiding students through the research journey, ensuring academic excellence and professional development.