Electrical Engineering

Conventional Von Neumann Computing and its limitations, Basic neuroscience concepts: neurons, synapses, plasticity, Overview of neuromorphic computing, Spiking Neural Networks, Hardware primitives, System architectures, Applications: Edge AI, Robotics.

Discrete time systems and the z-transform, sampling and stability analysis techniques, digital controller design, microcomputer implementation of digital systems, quantization and round off noise analysis.

This course offers a comprehensive, hands-on study of Distributed Energy Resources (DERs) and their role in the transition to a decentralized power grid. Through lab-based learning using EDIBON smart system and real-world research projects at Alfred University Advanced Power Grid Lab, students will explore how to integrate, control, and optimize DERs within the gride using industry standards.

This course explores the design, implementation, and operation of microgrids and Virtual Power Plants (VPPs). Students will investigate how to aggregate distributed energy resources (DERs) into intelligent, controllable systems that support grid reliability and sustainability. Through lab work, case studies, and simulation with EDIBON's AEL-MGP hardware and SCADA software, students will build practical knowledge of control strategies, interoperability, and grid-tied vs. islanded operation modes.

This course includes challenges in Power Systems, such as symmetrical components, system design, and modifications using two software packages (GEVERNOVA ADMS and AEMS), as well as two hardware simulation systems (DERs and Microgrid).

Introduction to Complementary Metal Oxide Semiconductor (CMOS) Technology, Limitations of CMOS, Going beyond Semiconductors, Ferroelectronics, Spintronics, Phase-change/transition materials, memristors, RRAM, Current state and future applications.