This study explores the utilization of the NavIC-L5 SPS signal in a bi-static radar configuration, wherein the satellite serves as the signal transmitter, and a receiver, po sitioned at an elevated location on the ground, ...

This project extends the study of fair allocation and division in graph structures by building upon the concept of (1,β)-compact allocations for paths, as introduced in the seminal work on compact graph allocations. ...

This research report investigates the robustness of Federated Learning (FL) by understanding, examining, and comparing the performance of Federated Averaging (FedAvg) and our proposed approach, i.e., Latency Rigid Federated ...

The project titled "Development of Large Language Models and Tools to Study Patents and Develop Insights" aims to use advanced AI to help analyze and understand patent information better. With more patents being filed each ...

This project focuses on addressing challenges faced by college students through a unified platform called Homiese. Initially conceived as "CollegeMate," the project evolved to better cater to students’ needs for mentorship, ...

This project addresses the imperative role of technology libraries in digital VLSI design, focus- ing on enhancing the accuracy and adaptability of standard cell attribute modeling. Traditional libraries encounter challenges ...

Very-Large-Scale Integration (VLSI) design is constantly evolving with advancements in semiconductor technology. Static Timing Analysis (STA) is a crucial aspect of VLSI design that ensures circuit reliability and functionality ...

We are designing a 1-bit reconfigurable unit cell capable of enabling beam steering of circularly polarized (CP) reflected waves when a linearly polarized (LP) plane wave is incident on it. The unit cell is designed to ...

As artificial intelligence (AI) and machine learning (ML) systems increasingly influence high- stakes decisions in areas such as healthcare, finance, and hiring, concerns regarding bias and fairness have emerged as critical ...

This research explores the application of FL to the MNIST dataset, focusing on impact of system heterogeneity. The study involves two clients collaboratively training a machine learning model using the FedAvg algorithm, ...