Digital Commons Network^™

Robust And Trustworthy Deep Learning: Attacks, Defenses And Designs, Bingyin Zhao

All Dissertations

Deep neural networks (DNNs) have achieved unprecedented success in many fields. However, robustness and trustworthiness have become emerging concerns since DNNs are vulnerable to various attacks and susceptible to data distributional shifts. Attacks such as data poisoning and out-of-distribution scenarios such as natural corruption significantly undermine the performance and robustness of DNNs in model training and inference and impose uncertainty and insecurity on the deployment in real-world applications. Thus, it is crucial to investigate threats and challenges against deep neural networks, develop corresponding countermeasures, and dig into design tactics to secure their safety and reliability. The works investigated in this …

Generalizable Deep-Learning-Based Wireless Indoor Localization, Ali Owfi

All Theses

The growing interest in indoor localization has been driven by its wide range of applications in areas such as smart homes, industrial automation, and healthcare. With the increasing reliance on wireless devices for location-based services, accurate estimation of device positions within indoor environments has become crucial. Deep learning approaches have shown promise in leveraging wireless parameters like Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) to achieve precise localization. However, despite their success in achieving high accuracy, these deep learning models suffer from limited generalizability, making them unsuitable for deployment in new or dynamic environments without retraining. To …

Improving Inference Speed Of Perception Systems In Autonomous Unmanned Ground Vehicles, Bradley Selee

All Theses

Autonomous vehicle (AV) development has become one of the largest research challenges in businesses and research institutions. While much research has been done, autonomous driving still requires extensive amounts of research due to its immense, multi-factorial difficulty. Autonomous vehicles rely on many complex systems to function, make accurate decisions, and, above all, provide maximum safety. One of the most crucial components of autonomous driving is the perception system.

The perception system allows the vehicle to identify its surroundings and make accurate, but safe, decisions through the use of computer vision techniques like object detection, image segmentation, and path planning. Due …

Adversarial Deep Learning And Security With A Hardware Perspective, Joseph Clements

All Dissertations

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep …

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

Non-Destructive Terrain Evaluation And Modeling For Off-Road Autonomy, Howard Brand

All Dissertations

In recent years, there has been an increased interest in implementing intelligent robotic systems in outdoor environments. Paramount to accomplishing this objective is being able to conduct successful robotic navigation in unprepared outdoor environments. This presents unique challenges in that there is a risk of catastrophic immobilization in terrain regions which, though unoccupied, cannot provide traction support for vehicle mobility. Methods for providing prior knowledge and perception of traction support is therefore an interest and focus of research.

In the advent of ever advancing machine learning models, “learn-as-you-go” approaches have emerged as topics of interest for mobility prediction. These approaches, …

Improving The Human-Machine Interaction Of Ai Systems For System Health Monitoring, Ryan Nguyen

All Dissertations

System health monitoring aids in the longevity of fielded systems or products. Providing a fault diagnosis or a prognosis can evaluate a system's current health. A diagnosis is the type of issue that could lead to a system's end-of-life (EOL); a prognosis is the remaining useful life (RUL) between the current state and the EOL. Fault diagnosis and RUL prediction can be acquired through (1) physics-based methods (PbM), (2) data-driven methods (DDM), or (3) hybrid modeling methods. DDM accurately provide a fault diagnosis, but the amount of data required is significant. This study reduces the amount of required data by …

Deep Learning Based Localization Of Zigbee Interference Sources Using Channel State Information, Dylan Kensler

All Theses

As the field of Internet of Things (IoT) continues to grow, a variety of wireless signals fill the ambient wireless environment. These signals are used for communication, however, recently wireless sensing has been studied, in which these signals can be used to gather information about the surrounding space. With the development of 802.11n, a newer standard of WiFi, more complex information is available about the environment a signal propagates through. This information called Channel State Information (CSI) can be used in wireless sensing. With the help of Deep Learning, this work attempts to generate a fingerprinting technique for localizing a …

Analysis Of Deep Learning Methods For Wired Ethernet Physical Layer Security Of Operational Technology, Lucas Torlay

All Theses

The cybersecurity of power systems is jeopardized by the threat of spoofing and man-in-the-middle style attacks due to a lack of physical layer device authentication techniques for operational technology (OT) communication networks. OT networks cannot support the active probing cybersecurity methods that are popular in information technology (IT) networks. Furthermore, both active and passive scanning techniques are susceptible to medium access control (MAC) address spoofing when operating at Layer 2 of the Open Systems Interconnection (OSI) model. This thesis aims to analyze the role of deep learning in passively authenticating Ethernet devices by their communication signals. This method operates at …