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Curriculum Overview

The curriculum for the Electrical Engineering program at G M University Davanagere is meticulously designed to provide a comprehensive understanding of electrical systems, electronics, control theory, power engineering, and emerging technologies. The program spans eight semesters over four years, integrating foundational science courses with advanced engineering principles.

Advanced Departmental Electives

The advanced departmental electives in the Electrical Engineering program are designed to deepen students' expertise in specialized areas. These courses offer a balance between theoretical understanding and practical application, preparing students for careers in emerging fields such as renewable energy, artificial intelligence, smart grids, and nanotechnology.

Power Electronics

This course focuses on the design and analysis of power electronic converters and inverters used in industrial and consumer applications. Students learn about switch-mode power supplies, motor drives, DC-AC conversion, and grid integration challenges. The course includes hands-on laboratory work with real-time simulation tools such as MATLAB/Simulink and hardware prototyping using FPGA platforms.

Wireless Communication

This elective introduces students to the principles of wireless communication systems including modulation techniques, multiplexing, channel coding, and antenna design. Students study modern standards like 5G, Wi-Fi, Bluetooth, and satellite communications. The course emphasizes practical implementation through lab experiments involving RF signal generation, measurement, and analysis.

Smart Grid Technologies

Smart grids represent the future of electricity distribution, integrating renewable energy sources, demand response systems, and intelligent monitoring technologies. This course explores grid stability, load forecasting, energy storage systems, and cybersecurity in power networks. Students engage in projects involving simulation of smart grid architectures using open-source tools like OpenDSS and GridLAB-D.

Artificial Intelligence in Electrical Engineering

This interdisciplinary course integrates machine learning algorithms with electrical engineering applications. Students explore topics such as neural networks, deep learning models, reinforcement learning, and their applications in power systems, signal processing, and control theory. The course includes programming assignments using Python libraries like TensorFlow and PyTorch.

Microgrid Operation and Control

As distributed energy resources become more prevalent, microgrids play a crucial role in ensuring reliable and efficient power delivery. This course covers the design, operation, and control strategies for microgrids including renewable energy integration, energy storage systems, and load management. Students work on case studies involving real-world microgrid implementations and develop control algorithms using simulation environments.

Nanotechnology in Electronics

This advanced elective delves into the fabrication and application of nanoscale electronic devices such as quantum dots, carbon nanotubes, and graphene-based components. Students study semiconductor physics at the nanometer scale, device modeling, and integration challenges in modern electronics. The course includes lab sessions involving scanning electron microscopy, atomic layer deposition, and nanofabrication techniques.

Advanced Control Theory

This course extends traditional control theory concepts to include robust control, optimal control, and nonlinear systems. Students learn about state-space representation, stability analysis, controller design methods, and model predictive control (MPC). The course includes practical implementation of control algorithms in MATLAB/Simulink and real-time testing on embedded platforms.

Digital Signal Processing

Students explore the mathematical foundations of digital signal processing including Fourier transforms, filter design, and spectral analysis. The course covers both theoretical concepts and practical applications in audio processing, image enhancement, biomedical signal analysis, and communication systems. Laboratory work involves implementing DSP algorithms using MATLAB, Python, and hardware accelerators.

Signal Processing for AI Applications

This course bridges the gap between signal processing and artificial intelligence by applying machine learning techniques to analyze and process signals. Students study feature extraction methods, classification algorithms, and neural networks tailored for signal processing tasks. The course includes projects involving voice recognition, image segmentation, and anomaly detection using deep learning models.

VLSI Design

Very Large Scale Integration (VLSI) design involves the creation of integrated circuits with thousands of transistors on a single chip. This course covers logic synthesis, layout design, timing analysis, and testing strategies for modern VLSI systems. Students gain experience using industry-standard tools such as Cadence, Synopsys, and Mentor Graphics for circuit design and verification.

Project-Based Learning Philosophy

The Electrical Engineering program at G M University Davanagere places a strong emphasis on project-based learning to ensure students develop practical skills alongside theoretical knowledge. The curriculum includes mandatory mini-projects in the third and fourth years, culminating in a final-year capstone project that serves as a culmination of all learned concepts.

Mini-projects are designed to be collaborative efforts where students work in small teams on real-world problems related to their specialization tracks. These projects often involve interaction with industry partners or faculty-led research initiatives, providing students with exposure to professional environments and current technological challenges.

The final-year thesis/capstone project is a significant component of the program, requiring students to conduct independent research or develop an innovative solution to a complex engineering problem. Students select their projects in consultation with faculty mentors who guide them through the process from initial concept development to final implementation and documentation.

Projects are evaluated based on multiple criteria including technical depth, innovation, presentation quality, and peer collaboration. Regular milestone reviews ensure that students stay on track and receive timely feedback for improvement. The program also encourages students to present their projects at national conferences or publish papers in academic journals, enhancing their visibility and professional development.

Faculty mentors are selected based on their expertise in specific areas relevant to student projects, ensuring high-quality guidance throughout the process. The university provides dedicated project spaces, access to advanced tools and equipment, and financial support for prototype development, making it easier for students to realize their ideas.