Physical Sciences and Mathematics Commons^™

Remote Side-Channel Disassembly On Field-Programmable Gate Arrays, Brandon R. Baggett

<strong> Theses and Dissertations </strong>

Over the last two decades, side-channel vulnerabilities have shown to be a major threat to embedded devices. Most side-channel research has developed our understanding of the vulnerabilities to cryptographic devices due to their implementation and how we can protect them. However, side-channel leakage can yield useful information about many other processes that run on the device. One promising area that has received little attention is the side-channel leakage due to the execution of assembly instructions. There has been some work in this area that has demonstrated the idea’s potential, but so far, this research has assumed the adversary has physical …

Gravel Conveyance System: An Evaluation On E. Coli Removal Capabilities, Madison Grace

<strong> Theses and Dissertations </strong>

In many rural, low-income communities such as the Black Belt of Alabama, there is a lack of wastewater infrastructure and solutions leaving homeowners non-compliant to state and federal law and with potential health risks on their property. New regulations have allowed for onsite, surface discharge for single family homes that are not connected to a centralized sewer or large flow development. These new regulations also contain strict disinfection criteria for the treated wastewater that will be discharged. A low-cost and low-maintenance treatment system that provides adequate disinfection is necessary to allow homeowners to take advantage of the new opportunity for …

Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen

Theses and Dissertations

This work investigates digital twin (DT) applications for electric power system (EPS) resilience. A novel DT architecture is proposed consisting of a physical twin, a virtual twin, an intelligent agent, and data communications. Requirements for the virtual twin are identified. Guidelines are provided for generating, capturing, and storing data to train the intelligent agent. The relationship between the DT development process and an existing controller hardware-in-the-loop (CHIL) process is discussed. To demonstrate the proposed DT architecture and development process, a DT for a battery energy storage system (BESS) is created based on the simulation of an industrial nanogrid. The creation …

Toward A Greater Comprehension Of The Paraná Epicontinental Sea During The Late Paleozoic Ice Age: The Glacial And Postglacial Record In The Western Paraná Basin (Brazil) And The Kaokoveld Fjord Network (Namibia), Eduardo Luiz Menozzo Da Rosa

Theses and Dissertations

The late Paleozoic Ice Age (LPIA; 362 to 255 Ma) was an icehouse interval that drove substantial changes in Earth’s surficial systems. Numerous paradigms regarding aspects of the LPIA were established throughout ~150 years of research based primarily on the evolving state of knowledge in sedimentology. The near-field lithological evidence for widespread glaciation across sedimentary basins of Gondwana are the building blocks for comprehensive paleoclimatic, biologic, paleogeographic, and ice volume models. Nevertheless, the embodied glacial record from several late Paleozoic Gondwanan depocenters still rely on insufficient and/or outdated sedimentologic and stratigraphic studies, which can lead to misinterpretations in models built …

Short Range Correlation Measurements In The Quasielastic Region With An 11 Gev Beam, Casey Morean

Doctoral Dissertations

Electron scattering is a significant means of studying internal high momentum
nucleon and quark distributions in nuclei. Thomas Jefferson National Accelerator
Facility (JLab) with its 11GeV beam is capable of studying high momentum nucleons
with unmatched precision. The role of short range nucleon configurations and
quark distributions is significant for understanding the dynamics of nuclei and their
underlying components. Scattering cross section measurements in the kinematic
regime x > 1, where the free nucleon is forbidden, are sensitive to high momentum
nucleons, which are believed to come from short range correlations (SRCs). SRCs are
strongly interacting, high momentum nucleons with a …

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …

Studies On Electrochemical Hydrogen Isotope Separation, Liyanage Mayura Sankalpa Silva

All Dissertations

Graphene-integrated Proton Exchange Membrane (PEM) electrochemical cells have emerged as a novel area of scientific investigation in the realm of hydrogen isotope separation. Chemical Vapor Deposited (CVD) graphene has been especially useful due to its large-scale production capability for scaling-up purposes. The research described in this dissertation explores the role that inadvertent introduction of cations, notably ammonium and copper, during the CVD graphene transfer onto PEM substrates, such as Nafion, might play in affecting hydrogen ion transport and isotope separation in PEM electrochemical cells. An extensive review of existing literature exposed a gap concerning unintentional cation introductions during graphene transfer, …

An Investigation Of The Accretion Processes In T Tauri And Herbig Ae/Be Systems Using High Resolution Optical And Near-Infrared Spectroscopy, Joshua Kern

All Dissertations

Star and planet formation is intimately tied to the accretion of material from the environments in which they form. During the formation process, disks of gas and dust develop in young stellar objects through which material is facilitated to the star and forming planets. Theoretical models of these accretion processes invoke viscous spreading via hydrodynamics, as well as more complex interactions with magnetic fields be it from the stellar component or the formation environment in order to catalyze these mass flows. These accretion models predict various scenarios including magnetospheric accretion as well as supersonic accretion flows in the disk atmosphere …

Thermal Energy Storage Using High Temperature Borehole Heat Exchangers In Unconsolidated Materials, Kayla Bicknell

All Theses

Thermal energy storage is a potential method for storing excess energy produced when supply is greater than demand. The use of the subsurface for storing thermal energy has become more recognized as a viable alternative to conventional methods of energy storage. However, high temperature borehole thermal energy storage has yet to be researched in-depth. Therefore, the goal of this project is to determine the feasibility of using the subsurface to store thermal energy at relatively high temperatures.

The focus of this work is to determine what design elements would make a borehole thermal energy storage system most effective and produce …

The Spatial And Temporal Distribution Of Large Rock Blocks And Control On Landscape Evolution In The Ozarks, Chelsea Moran

Graduate Theses and Dissertations

Geologists often use landscape form to infer landscape processes through time. While climate and tectonics shape geomorphic form, the potential range of spatial or temporal scales that can shape any specific landscape can render landscape process-form based hypotheses too general for consideration. Contributions by mathematical modeling have helped bridge the gap between inferring processes from form, notably in how sediment transport dynamics shape hillslopes. However, few models encapsulate the movement of large rock blocks ( >2 meters across) and their potential impact as hillslope sediment transport disruptors. The Upper Buffalo River watershed (BRW) in the Ozarks of northern Arkansas has …

Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu

Graduate Theses and Dissertations

With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …

Bayesian Learning Of Spatiotemporal Source Distribution For Beached Microplastic In The Gulf Of Mexico, David Pojunas

Graduate Theses and Dissertations

Over the last several decades, plastic waste has gradually accumulated while slowly degrading in terrestrial and oceanic environments. Recently, there has been an increased effort to identify the possible sources of plastic to understand how they affect vulnerable beaches. This issue is of particular concern in the Gulf of Mexico due to the presence of oil, natural gas, and plastic production. In this thesis, we expand upon existing Bayesian plastic attribution models and develop a rigorous statistical framework to map observed beached microplastics to their sources. Within this framework, we combine Lagrangian backtracking simulations of floating particles using nurdle beaching …

Big Data Applications And Challenges In Giscience (Case Studies: Natural Disaster And Public Health Crisis Management), Amir Masoud Forati

Theses and Dissertations

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

Disease Of Lung Infection Detection Using Cnn Model -Bayesian Optimization, Poojitha Gutha

Electronic Theses, Projects, and Dissertations

Auscultation plays a role, in diagnosing and identifying diseases during examinations. However, it requires training and expertise, for application. This study aims to tackle this challenge by introducing a model that categorizes respiratory sounds into eight groups: URTI, Healthy, Asthma, COPD, LRTI, Bronchiectasis, Pneumonia, and Bronchiolitis. To achieve this categorization the study utilizes a Convolutional Neural Network (CNN) model that has been optimized using techniques. The dataset used in the study consists of 920 audio samples obtained from 126 patients with durations ranging from 10 to 90 seconds. Impressively, the model demonstrates a noteworthy 83% validation accuracy and an impressive …

Feasibility Study Of Off-The-Shelf Components On A Split-Cycle Motor And Esc Testbed, Hayden C. Lotspeich

Computer Science and Engineering Theses

This project aims to create a testbed for split-cycle flapping wing systems that allows for testing of different motors and ESC protocols to find a suitable set for a flapping wing system. In order for a flapping-wing drone to be able to maneuver, it has to be able to flap its wings at different speeds when flapping forward and flapping backwards. The arching back-and-forth motion is what the output wing would be connected to, so this system is used to calculate the maximum split-cycle time ratio that can be achieved when set up with different motors and ESC protocols.

Enhancing Biomedical Imaging With Ai: Compression, Prediction, And Multi-Modal Integration For Clinical Advancement, Mohammad Sadegh Nasr

Computer Science and Engineering Dissertations

This dissertation delves into the enhancement of biomedical image analysis through the deployment of artificial intelligence methodologies, focusing on the transition from theoretical innovation to practical clinical utility. Spanning four cornerstone projects, the work encapsulates the development of predictive models for spatial transcriptomics, efficient image compression for cancer pathology slides, and critical evaluations of histopathology slide search engines. The first project employs Random Forest Regression and spatial point processes to forecast cell distribution patterns, thereby offering a novel perspective on gene expression in embryogenesis at a single-molecule resolution. The second venture introduces a Variational Autoencoder (VAE) that sets a new …

An Intelligent Multi-Modal Framework Towards Assessing Human Cognition, Ashish Jaiswal

Computer Science and Engineering Dissertations

Cognition is the mental process of acquiring knowledge and understanding through thought, experience, and senses. Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions that can result in physiological complications. However, tools to understand and assess fatigue are minimal. This thesis primarily focuses on building an experimental setup that induces cognitive fatigue (CF) …

Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh

Computer Science and Engineering Theses

Substructure discovery is a process in data analysis and data mining that involves identifying and extracting meaningful patterns, structures, or components within a larger dataset. These substructures can be of various types, such as frequent patterns, motifs, or any other relevant features within the data. The growth of the internet and the proliferation of mobile devices have led to the generation of enormous amounts of data. Companies like Facebook and Twitter can generate large datasets from user interactions on their websites, such as connections between users and user generated content. Moreover, advances in processing power and storage capacity have made …

Enhancing Indoors Robotic Traversability Estimation With Sensor Fusion, Christos Sevastopoulos

Computer Science and Engineering Dissertations

Generally speaking, traversability estimation illustrates the ability to navigate or move through a particular environment (indoors or outdoors). Indoor environments are governed by uncertainty and stochasticity arising from their complex structures encapsulating both static elements like furniture and walls, as well as entities such as moving humans. In our research, we underline the importance of blending semantic and spatial information for ensuring secure navigation for a mobile robot. We show that RGB sensors suffer from constrained situational awareness of the surroundings, thus highlighting the need to incorporate spatial and geometric data, which can collaborate synergistically to enhance overall perception and …

Deep Generative Sculpting Models For Single Image 3d Reconstruction, Jason Jennings

Computer Science and Engineering Dissertations

In the field of computer vision, learning representations of images is an important task. This dissertation introduces deep generative sculpting models (DGSM), deep learning models that learn 3D representations of objects from 2D images. DGSMs use convolutional networks combined with a differentiable renderer to attempt to "sculpt" a base 3D mesh, such as a sphere, to faithfully represent an object in the scene, and render it to reconstruct the input image. The core methodology revolves around the encoding of the input image into latent variables. These variables are decoded into interpretable scene parameters, describing the object's translation, rotation, scale, texture, …

Constructing Large Open-Source Corpora And Leveraging Language Models For Simulink Toolchain Testing And Analysis, Sohil Lal Shrestha

Computer Science and Engineering Dissertations

In several safety-critical industries such as automotive, aerospace, healthcare, and industrial automation, MATLAB/Simulink has emerged as the de-facto standard tool for system modeling and analysis, model compilation into executable code, and code deployment onto embedded hardware. Within the context of cyber-physical system (CPS) development, it is imperative to both rigorously test the development tools, such as MathWorks’ Simulink, and understand modeling practices and model evolution. The existing body of work faces limitations primarily stemming from two factors: (1) contemporary testing methodologies often prove inefficient in identifying critical toolchain bugs due to a paucity of explicit toolchain specifications and (2) there …

Design Of Single Precision Floating Point Unit (32-Bit Numbers) According To Ieee 754 Standard Using Verilog, And Creation Of An Education Model For Advanced Digital Logic And Design Courses, Kartikey Sharan

Computer Science and Engineering Theses

In today’s day and age of arithmetic, Floating Point Arithmetic is by far the most industry sanctioned way of approximating real number arithmetic for making numerical calculations on all computers used by industries on an everyday basis. In the year 1985, IEEE 754 standard was established that defined a single universal standard for all different arithmetic formats [1]. Before this, for a long period each computer had a different arithmetic format and size for bases, significand, and exponents. This format allowed industries all around the world to compute floating point arithmetic in a universal way and facilitated open communication between …

Hemln-Sd: Substructure Discovery In Heterogeneous Multilayer Networks, Kiran Bolaj

Computer Science and Engineering Theses

Graph mining analyzes the real-world graphs for finding core substructures in chemical compounds (e.g., Benzene), identify the structure that occurs frequently in a given graph or forest. These identified structures are important as they reveal an inherent feature or property in the given graph or forest. Substructures represent interesting and repeating patterns found within an application, offering insights into hidden regularities. Therefore, the process of finding these interesting and frequent patterns in an unsupervised manner is known as substructure discovery. SUBDUE was the first main-memory algorithm developed for substructure discovery. Since then, for scalability, the algorithm has been extended to …

Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole

Computer Science and Engineering Theses

Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is …

Stream Restoration Effectiveness In Mullins Creek In Fayetteville, Arkansas, Amadeo Scott

Crop, Soil and Environmental Sciences Undergraduate Honors Theses

Lotic waterways are vital for habitat, food, water, and flood protection, but urbanization poses a major threat to their integrity. Runoff from urban surfaces leads to pollution, flashiness, loss of biodiversity, and other symptoms, also known as Urban Stream Syndrome (USS). To combat USS, streams can be restored, but most restorations are not monitored so their long-term effectiveness is unknown. This study quantitatively evaluated a decade-old stream restoration in Fayetteville, Arkansas, to assess its effectiveness in combating USS and achieving set restoration goals, and to gain insights for future restoration projects. Restoration goals included decreasing erosion and sedimentation, increasing pool …

Clueless: Revolutionizing Sustainable Fashion And Combating Overconsumption, Tanya Ravichandran

Graphic Communication

“Clueless” revolutionizes sustainable fashion by combating wardrobe overconsumption and the industry’s carbon footprint, using AI to suggest personalized outfits from existing wardrobes tailored to weather and wear history. It enhances user engagement through features like outfit ‘shuffle’ and provides insights into wardrobe utilization and carbon impact.

It’s more than an app; it’s a step towards a greener wardrobe and a healthier planet.

Analysis And Assessment Of Land Use / Land Cover Impact On Human And Natural Ecosystems In The Salton Sea Watershed, 2013 - 2021, Diego Ramirez

Electronic Theses, Projects, and Dissertations

This study represents an interdisciplinary analysis of the changing landscape of the Salton Sea Watershed from 2013 to 2021, focusing on land use land cover (LULC) category changes, climatic variations, and socioeconomic factors. The findings of this research show a shift in land cover categories, portrayed by the changes of natural landscapes and vegetative areas into rapidly increasing urbanized expansion and increased impervious surfaces. These changes pose concerns about increased temperature in the region, a decrease in overall water availability and groundwater infiltration, and an increase in pollution. The study explores 10 sub-watersheds within the Salton Sea Watershed basin, focusing …

Twitter Policing, Hemanth Kumar Medisetty

Electronic Theses, Projects, and Dissertations

Police departments are frequently utilizing social media platforms to actively interact with the public. Social media offers an opportunity to share information, facilitate communication, and foster stronger connections between police departments and the communities they serve. In this context sentiment analysis of social media data has become a tool, for identifying sentiments and tracking emerging trends.

This project utilizes sentiment analysis to examine the social media interactions with particular data obtained from the Twitter (X). Initially, the project gathers social media data, from twitter mentioned accounts on Twitter utilizing web scraping techniques. Afterwards, we perform a thorough sentiment analysis using …

A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang

Theses and Dissertations

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …