Comprehensive Course Schedule Across All 8 Semesters

Detailed Course Descriptions for Advanced Departmental Electives

Each departmental elective course is designed to provide students with in-depth knowledge and practical skills relevant to their chosen specialization. Here are descriptions of ten advanced departmental electives:

• EE503 - Artificial Intelligence in Electrical Engineering: This course explores the intersection of AI and electrical engineering, focusing on machine learning algorithms applied to power systems, signal processing, control systems, and embedded applications. Students learn to implement neural networks for predictive maintenance, fault detection, and optimization problems in electrical infrastructure.
• EE508 - Cybersecurity in Electrical Systems: This course covers the vulnerabilities inherent in modern electrical systems and introduces cybersecurity frameworks tailored for smart grids, industrial control systems, and embedded devices. Students study attack vectors, defensive mechanisms, and compliance standards such as NIST SP 800-82 and IEC 62443.
• EE605 - Machine Learning for Power Systems: Focused on applying machine learning techniques to enhance power system operations, this course covers topics like load forecasting, anomaly detection, optimal power flow, and grid stability analysis. Students gain hands-on experience with Python-based tools like TensorFlow and scikit-learn.
• EE608 - Grid Modernization: This course examines the transformation of traditional power grids into smart grids through digital technologies. It includes discussions on IoT integration, real-time monitoring, demand response programs, and regulatory frameworks governing grid evolution.
• EE703 - Electrical Engineering Project I: A foundational project-based course where students begin developing their capstone idea under faculty supervision. Emphasis is placed on problem identification, literature review, feasibility study, and initial design concepts.
• EE802 - Final Year Project Work II: The culmination of the student's academic journey, this course involves full-scale implementation, testing, documentation, and presentation of a complex engineering solution. Students demonstrate mastery in applying theoretical knowledge to practical challenges.
• EE509 - Advanced Signal Processing Techniques: This advanced course delves into modern signal processing methodologies including wavelet transforms, adaptive filtering, beamforming, and spectral estimation. Applications include radar systems, biomedical signal analysis, audio processing, and sensor fusion.
• EE604 - Power Quality and Harmonics: Students explore the causes and effects of power quality issues in electrical systems, including harmonics, voltage fluctuations, flicker, and transients. The course covers measurement techniques, mitigation strategies, and international standards like IEEE 519.
• EE607 - Robotic Control and Navigation: This course focuses on the control architecture of robotic systems used in industrial automation and energy sectors. Topics include kinematics, path planning, sensor integration, autonomous navigation, and mobile robot design principles.
• EE708 - Advanced Project Management: Designed to equip students with tools and methodologies for managing large-scale engineering projects, this course covers risk management, resource allocation, timeline optimization, and stakeholder communication. It includes case studies from real-world electrical engineering ventures.

Project-Based Learning Philosophy

At SHA SHIB COLLEGE OF TECHNOLOGY, we believe that effective learning occurs when students actively engage with real-world problems through structured project-based experiences. Our approach emphasizes hands-on experimentation, collaborative teamwork, and iterative design processes.

The mandatory mini-projects introduced in the second year serve as stepping stones to larger capstone initiatives. These projects are assigned based on student interest and faculty availability, ensuring personalized mentorship and guidance. Each project is evaluated using a rubric that assesses technical proficiency, creativity, teamwork, presentation skills, and adherence to deadlines.

By the final year, students are expected to complete an advanced capstone project that addresses a significant challenge within their chosen specialization area. Projects are selected in consultation with faculty advisors who provide ongoing support throughout the development cycle. Students are encouraged to collaborate with industry partners, leveraging real-world datasets and requirements for enhanced relevance.

Our evaluation criteria emphasize not only the final deliverables but also the process of innovation, problem-solving, and ethical decision-making. Students must document their project journey through detailed reports, oral presentations, and live demonstrations that showcase both technical depth and communication excellence.