Physical Sciences and Mathematics Commons^™

Towards A Practical Method For Monitoring Kinetic Processes In Polymers With Low-Frequency Raman Spectroscopy, Robert Vito Chimenti

Theses and Dissertations

Unlike liquids and crystalline solids, glassy materials exist in a constant state of structural nonequilibrium. Therefore, a comprehensive understanding of material kinetics is critical for understanding the structure-property-processing relationships of polymeric materials. Amorphous materials universally display low-frequency Raman features related to the phonon density of states resulting in a broad disorder band for Raman shifts below 100 cm-1, which is related to the conformational entropy and the modulus. This disorder band is dominated by the Boson peak, a feature due to phonon scattering because of disorder and can be related to the transverse sound velocity of the material, and a …

Mri Image Regression Cnn For Bone Marrow Lesion Volume Prediction, Kevin Yanagisawa

Theses and Dissertations

Bone marrow lesions (BMLs), occurs from fluid build up in the soft tissues inside your bone. This can be seen on magnetic resonance imaging (MRI) scans and is characterized by excess water signals in the bone marrow space. This disease is commonly caused by osteoarthritis (OA), a degenerative join disease where tissues within the joint breakdown over time [1]. These BMLs are an emerging target for OA, as they are commonly related to pain and worsening of the diseased area until surgical intervention is required [2]–[4]. In order to assess the BMLs, MRIs were utilized as input into a regression …

An Investigation Into Applications Of Canonical Polyadic Decomposition & Ensemble Learning In Forecasting Thermal Data Streams In Direct Laser Deposition Processes, Jonathan Storey

Theses and Dissertations

Additive manufacturing (AM) is a process of creating objects from 3D model data by adding layers of material. AM technologies present several advantages compared to traditional manufacturing technologies, such as producing less material waste and being capable of producing parts with greater geometric complexity. However, deficiencies in the printing process due to high process uncertainty can affect the microstructural properties of a fabricated part leading to defects. In metal AM, previous studies have linked defects in parts with melt pool temperature fluctuations, with the size of the melt pool and the scan pattern being key factors associated with part defects. …

Robust And Uncertainty-Aware Image Classification Using Bayesian Vision Transformer Model, Fazlur Rahman Bin Karim

Theses and Dissertations

Transformer Neural Networks have emerged as the predominant architecture for addressing a wide range of Natural Language Processing (NLP) applications such as machine translation, speech recognition, sentiment analysis, text anomaly detection, etc. This noteworthy achievement of Transformer Neural Networks in the NLP field has sparked a growing interest in integrating and utilizing Transformer models in computer vision tasks. The Vision Transformer (ViT) model efficiently captures long-range dependencies by employing a self-attention mechanism to transform different image data into meaningful, significant representations. Recently, the Vision Transformer (ViT) has exhibited incredible performance in solving image classification problems by utilizing ViT models, thereby …

Analyzing The Effects Of Ultrafast Laser Processing On Mechanical Properties Of 3d-Printed Pla Parts, Darshan Pramodbhai Yadav

Theses and Dissertations

Recent advances in additive manufacturing technologies have already led to wide-scale adoption of 3D-printed parts in various industries. The expansion in choice of materials that can be processed, particularly using Fused Deposition Modeling (FDM), and the steady advancements in dimensional accuracy control have extended the range of applications far beyond rapid prototyping. However, additive manufacturing still has considerable limitations compared to traditional and subtractive manufacturing processes. This work addresses limitations associated with the as-deposited surface roughness of 3D-printed parts. The effects of roughness-induced stress concentrations were studied on ultimate tensile strength and fatigue life. The samples were manufactured using a …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

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 …

The Private Pilot Check Ride: Applying The Spacing Effect Theory To Predict Time To Proficiency For The Practical Test, Michael Scott Harwin

Theses and Dissertations

This study examined the relationship between a set of targeted factors and the total flight time students needed to become ready to take the private pilot check ride. The study was grounded in Ebbinghaus’s (1885/1913/2013) forgetting curve theory and spacing effect, and Ausubel’s (1963) theory of meaningful learning. The research factors included (a) training time to proficiency, which represented the number of training days needed to become check-ride ready; (b) flight training program (Part 61 vs. Part 141); (c) organization offering the training program (2- or 4-year college/university vs. FBO); (d) scheduling policy (mandated vs. student-driven); and demographical variables, which …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

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 …

Direct Measurement Of The 114cd(��, ��)115cd Cross Section In The 1 Ev To 300 Kev Energy Range, Kofi Tutu Addo Assumin-Gyimah

Theses and Dissertations

The large thermal cross section of cadmium makes it ideal for many practical applications where screening of thermal neutrons is desired. For example, in non-destructive assay techniques, or for astrophysical studies of the s-process. All such applications require precise knowledge of the neutron-capture cross section on cadmium. Although there are some data on neutron-capture cross sections particularly at thermal energies and at energies relevant for astrophysics, there is very little data at most other energies. Further, the evaluated cross sections from the ENDF and JENDL databases disagree at high energies. Therefore, there is a critical need for precise knowledge of …

From Waste To Energy: The Electrochemical Reduction Of Co2 Using Recycled Nanostructured Catalysts, Ibrahim Badawy

Theses and Dissertations

The reduction of carbon dioxide (CO₂RR) using electrochemistry is a promising solution for the burgeoning global energy crisis. The overall vision of its implementation relies on renewable energy sources to power the reaction creating carbon neutral products and effectively closing the carbon cycle. Research in this field has come a long way since its inception in the mid-1900s. However, there remain significant hurdles and important considerations to overcome in order to reach full commercialization. Most electrocatalysts tested for CO₂RR have been designed solely for maximum performance while ignoring the environmental consequences if such a material were …

Extending The Convolution In Graph Neural Networks To Solve Materials Science And Node Classification Problems, Steph-Yves Mike Louis

Theses and Dissertations

The usage of graph to represent one's data in machine learning has grown in popularity in both academia and the industry due to its inherent benefits. With its flexible nature and immediate translation to real life observed objects, graph representation had a considerable contribution in advancing the state-of-the-art performance of machine learning in materials.

In this dissertation proposal, we discuss how machines can learn from graph encoded data and provide excellent results through graph neural networks (GNN). Notably, we focus our adaptation of graph neural networks on three tasks: predicting crystal materials properties, nullifying the negative impact of inferior graph …

Predicting Material Structures And Properties Using Deep Learning And Machine Learning Algorithms, Yuqi Song

Theses and Dissertations

Discovering new materials and understanding their crystal structures and chemical properties are critical tasks in the material sciences. Although computational methodologies such as Density Functional Theory (DFT), provide a convenient means for calculating certain properties of materials or predicting crystal structures when combined with search algorithms, DFT is computationally too demanding for structure prediction and property calculation for most material families, especially for those materials with a large number of atoms. This dissertation aims to address this limitation by developing novel deep learning and machine learning algorithms for effective prediction of material crystal structures and properties. Our data-driven machine learning …

Role Of Defect Type In Optimizing Photoelectrochemical Hydrogen Production Catalysts, Mohamed Mahrous

Theses and Dissertations

The search for new energy sources has become a global challenge due to the increasing demand for energy and the negative impact of traditional energy sources on the environment. The photoelectrochemical water splitting has emerged as a promising alternative source for producing hydrogen, which can be used as a clean fuel. However, it is necessary to tailor the properties of the light-active material that will be used to absorb sunlight and split water. This research project aimed at providing detailed insights into the effect of varying the type and concentration of defects on the optical and electronic properties of diamond …

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …

Methodology For Designing Assistive Robots For Activities Of Daily Living Assistance, Javier Dario Sanjuan De Caro

Theses and Dissertations

The growing prevalence of Upper or Lower Extremities Dysfunctions (ULED), often linked to central nervous disorders such as stroke, Spinal Cord Injury (SCI), and Multiple Sclerosis (MS), underscores the urgent need for innovative support solutions. Over 5.35 million Americans currently live with ULED, a situation that places a significant socioeconomic burden on families and society. Despite invaluable support from caregivers and family members, the need for more scalable, practical solutions persists.

Wheelchair-mounted assistive robots emerge as a promising alternative in this context. These devices, offering continuous and reliable assistance, significantly alleviate caregiver fatigue and enhance the independence and quality of …

Eddy Current Defect Response Analysis Using Sum Of Gaussian Methods, James William Earnest

Theses and Dissertations

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics.

Secure And Efficient Federated Learning, Xingyu Li

Theses and Dissertations

In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to …

Utilizing Fluorescent Nanoscale Particles To Create A Map Of The Electric Double Layer, Quintus Owen

Theses and Dissertations

The interactions between charged particles in solution and an applied electric field follow several models, most notably the Gouy-Chapman-Stern model, for the establishment of an electric double layer along the electrode, but these models make several assumptions of ionic concentrations and an infinite bulk solution. As more scientific progress is made for the finite and single molecule reactions inside microfluidic cells, the limitations of the models become more extreme. Thus, creating an accurate map of the precise response of charged nanoparticles in an electric field becomes increasingly vital. Another compounding factor is Brownian motion’s inverse relationship with size: large easily …

Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang

Theses and Dissertations

Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …

Semantics-Based Data Security Models, Theppatorn Rhujittawiwat

Theses and Dissertations

In this dissertation, we studied how an adversary could attack databases and how the system could prevent or recover from such an attack. Our motivation to improve the current security capabilities of database management systems. We provided better recovery capabilities of database management systems by incorporating data provenance. We also expand our study to express security and privacy needs of data in the Internet of Things (IoT) environments such as a smart home environment. For this, we proposed a stream data security model to theoretically represent the data in the IoT network. We built a dynamic authorization model on our …

Polyethersulfone Thin-Film Nanocomposite Membrane Embedded With Amine-Functionalized Graphene Oxide For Desalination Applications, Ahmed Bahaeldin

Theses and Dissertations

Thin-film nanocomposite (TFN) desalination membranes were prepared based on a polyethersulfone (PES) support, where the polyamide (PA) layer was embedded with amine-functionalized graphene oxide (GO). The effect of adding various concentrations of functionalized and un-functionalized GO on the desalination performance, hydrophilicity, and morphology of the membranes was additionally assessed throughout this work. Scanning electron microscopy (SEM) measurements were used to assess the morphology of the membranes in combination with Brunauer-Emmett-Teller (BET) analysis. Contact angle measurements were used to gauge the hydrophilicity of the synthesized membranes. The membrane with the best desalination performance contained 1x10^-3 wt/vol% of functionalized GO in …

Using Dielectric Scatters To Selectively Excite Embedded Eigenstates In Cavity Resonators, Olugbenga Joshua Gbidi

Theses and Dissertations

Bound states in the continuum (BICs) are waves that remain in the continuous spectrum of radiating waves that carry energy, however, still localized within the spectrum. BICs, also embedded eigenmodes, exhibit high quality factors that have been observed in optical and acoustic waveguides, photonic structures, and other material systems. Presently, there are limited means to select these BICs in terms of the quality factor and their excitation. In this work, we show that a different type of BIC, Quasi-BICs (Q-BICs), in open resonators can have their quality attuned by introducing embedded scatters. Using microwave cavities and dielectric scatters as an …

Mechanisms Of Emulsion Destabilization: An Investigation Of Surfactant, Stabilizer, And Detergent Based Formulations Using Diffusing Wave Spectroscopy, Jordan N. Nowaczyk

Theses and Dissertations

Conventional approaches for studying emulsions, such as microscopy and macroscopic phase tracking, present challenges when it comes to establishing detailed mechanistic descriptions of the impact of emulsifier and stabilizer additives. Additionally, while a combination of sizing methods and macroscopic phase tracking can provide insights into droplet size changes and concentration, the use of multiple measurements can be cumbersome and error-prone. It is the focus of this work, to present a new method for studying water in oil (W/O) emulsions that involves using diffusing wave spectroscopy (DWS) to examine the impact of three different surface stabilizing additives at varying concentrations. By …

Material Extrusion-Based Additive Manufacturing: G-Code And Firmware Attacks And Defense Frameworks, Haris Rais

Theses and Dissertations

Additive Manufacturing (AM) refers to a group of manufacturing processes that create physical objects by sequentially depositing thin layers. AM enables highly customized production with minimal material wastage, rapid and inexpensive prototyping, and the production of complex assemblies as single parts in smaller production facilities. These features make AM an essential component of Industry 4.0 or Smart Manufacturing. It is now used to print functional components for aircraft, rocket engines, automobiles, medical implants, and more. However, the increased popularity of AM also raises concerns about cybersecurity. Researchers have demonstrated strength degradation attacks on printed objects by injecting cavities in the …

Towards Structured Planning And Learning At The State Fisheries Agency Scale, Caleb A. Aldridge

Theses and Dissertations

Inland recreational fisheries has grown philosophically and scientifically to consider economic and sociopolitical aspects (non-biological) in addition to the biological. However, integrating biological and non-biological aspects of inland fisheries has been challenging. Thus, an opportunity exists to develop approaches and tools which operationalize planning and decision-making processes which include biological and non-biological aspects of a fishery. This dissertation expands the idea that a core set of goals and objectives is shared among and within inland fisheries agencies; that many routine operations of inland fisheries managers can be regimented or standardized; and the novel concept that current information and operations can …

Use Of Machine Learning And Natural Language Processing To Enhance Traffic Safety Analysis, Md Abu Sayed

Theses and Dissertations

Despite significant advances in vehicle technologies, safety data collection and analysis, and engineering advancements, tens of thousands of Americans die every year in motor vehicle crashes. Alarmingly, the trend of fatal and serious injury crashes appears to be heading in the wrong direction. In 2021, the actual rate of fatalities exceeded the predicted rate. This worrisome trend prompts and necessitates the development of advanced and holistic approaches to determining the causes of a crash (particularly fatal and major injuries). These approaches range from analyzing problems from multiple perspectives, utilizing available data sources, and employing the most suitable tools and technologies …

Water Hardness Removal By Electrochemical Precipitation In A Continuous Flow Condition Using Conductive Concrete As Cathode, Tahsin Tareque

Theses and Dissertations

This study focuses on the electrochemical precipitation (EP) process to reduce excess water hardness from the Lower Rio Grande Valley (LRGV) tap water using electrically conductive concrete as cathode in a continuous flow condition. LRGV tap water is extremely hard with hardness more than 350 mg/L as CaCO3. Humans can pleasantly consume water with hardness less than 150 mg/l as CaCO3 according to World Health Organization (WHO). Hard water is also known to cause mechanical problems to boilers and heat exchangers. In this process, electricity is passed through electrodes submerged in electrolyte, which causes an alkaline environment around the cathode …

Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar

Theses and Dissertations

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …