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Wideband Microwave Radar for Autonomous Navigation

This research aims to develop a deeper knowledge of how radar systems are used for autonomous navigation. Small on chip radar systems can be implemented in various technologies, such automobiles, in order to improve safety for the general public. Imaging at millimeter wave frequencies can provide high image resolution of the objects in question. Radar


Augmented reality computing environments further the richness of the mobile experience by providing applications a continuous vision experience, where visual information continuously provides context for applications. In a modern mobile system, the typical user is exposed to potential mass aggregation of sensitive information, posing both privacy and security deficiencies.To address such deficiencies, the team proposes

Vertical Control for Underwater Robotics

Underwater lateral robotic manipulation is used for multidimensional maneuverability for underwater vehicles. Bio-inspired mechanisms were built and tested for buoyancy and lateral position control for an underwater robot. To create a natural buoyancy within the robot, we built fish-like bladders, using air pockets to lower the density. Ballasts displace water between the surrounding water and

Robotic Mapping using WiFi

In the near future, robots will be required to autonomously navigate in increasingly unstructured and complex environments, such as indoors or underground, where GPS may not be available. There have been many advances in Simultaneous Localization and Mapping (SLAM) in the past two decades, but there are still many drawbacks to current methods. This research

The Reinforcement Learning Trojan Horse: Data Poisoning in Autonomous Driving Simulations

The objective of this research is to identify the presence of a specific, but potentially catastrophic, mathematical characteristic within a key machine learning aspect of the control system of autonomous vehicles. The conclusions of the study point to the presence of a mechanism in which a malicious adversary could include a seemingly undetectable backdoor into

Redesign of the Mixed-Mode Fracture Fixture for More Accurate Damage Tolerant Structures

Polymer Matrix Composites (PMCs) and nanocomposites have become widely preferred in high application designs, like aircraft, satellites, and other critical components, due to their high strength ratio to their weight. However, one of their drawbacks is delamination. Delamination is a mode failure that causes the material to separate into layers and lose its mechanical toughness.

Characterization of Critical Networks using Heterogeneous Data to Create a Comprehensive Characterization of a Robust Critical Network

The objective of this project is to create a data set and tools for analyzing potential risks in critical networks. By focusing on power networks and collecting a series of heterogeneous data (e.g., topology, demand, outages), a comprehensive characterization of complex networks considering transmission and distribution has been created. Outage and network topology data for

Optical Characterization of Silver-Doped Germanium-Chalcogenide Thin-films

The purpose of this research is to optically characterize germanium-based chalcogenide thin films and evaluate how their properties change when the composition is altered and when they are doped with silver. Using techniques such as UV-Vis spectroscopy, profilometry, and ellipsometry, parameters that describe the optical characteristics are found, including the absorption coefficient, refractive index, optical


Alpha-loss is a tunable loss function with a parameter alpha that bridges log-loss for (alpha = 1) and 0-1 loss for (alpha = infinity). In machine learning literature, the theoretically optimal loss function is the 0-1 loss function. Unfortunately, 0-1 loss is computationally intractable due to being non-differentiable and discontinuous. Thus, it will be extremely