5G base stations operating in sub-6 GHz frequencies typically utilize full-dimensional or 3D beamforming with precise steering, using electronically scanned phased array antennas (PAA). Additionally, Radar systems for ...

Silicon retinas are artificial retinal devices designed to replicate the functionality of the biological retina using silicon-based technologies. These retinas aim to address visual impairment by capturing and processing ...

The increasing penetration of Inverter-Based Resources (IBRs) in power grids, driven by environmental concerns, has significantly altered the system's dynamic behavior. Unlike traditional Synchronous Generators (SGs), IBRs ...

Lightning continues to be the single largest natural cause of line outages. With high voltage lines spanning millions of kilometres around the globe, it has been a serious concern to transmission line engineers. Quite ...

Novel view synthesis refers to the problem of synthesizing novel viewpoints of a scene given the images from a few viewpoints. This is a fundamental problem in computer vision and graphics, and enables a vast variety of ...

Autonomous powering of electronic systems in applications spanning biomedical, automation, transportation and space industries relies on harvesting energy from the abundant ambient sources such as vibration, thermal, solar, ...

In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ...

Increased penetration of renewable energy sources like solar and wind is fundamentally altering the power flow dynamics in modern distribution networks. These distributed generations (DG) boost system reliability by enabling ...

Insulated gate bipolar transistors (IGBTs) are the dominant power semiconductor devices in high power applications, such as, locomotive traction and megawatt-level renewable energy systems. Power electronic converters in ...

The performance of deep neural network (DNN) training is a function of both the training compute latency and the latency to fetch and preprocess the input data needed to train the model. With advance- ments in GPUs and ...