Digital Commons Network^™

Functional Near-Infrared Spectroscopy As A Bedside Neuroimaging Tool For Neonatal Brain Monitoring And Injury Characterization, Lingkai Tang

Electronic Thesis and Dissertation Repository

Early brain injury in neonates is a common neurological complication associated with high mortality rates and long-term morbidities. Two common types of injury are intraventricular hemorrhage (IVH) and hypoxic ischemic encephalopathy (HIE). Currently used clinical neuroimaging tools for diagnosing or monitoring the injury have limitations, e.g., cranial ultrasound is not for continuous monitoring of the evolution of injury so it could miss the optimal timing for intervention, and magnetic resonance imaging (MRI) is not very accessible for infants in neonatal intensive care unit. Functional near-infrared spectroscopy (fNIRS) can be an alternative imaging technique as it can be used at the …

Approximate Computing And In-Memory Computing: The Best Of The Two Worlds!, Mohammed Essa Fawzy Essa

Theses and Dissertations

Machine learning (ML) has become ubiquitous, integrating into numerous real-life applications. However, meeting the computational demands of ML systems is challenging, as existing computing platforms are constrained by memory bandwidth, and technology scaling no longer yields substantial improvements in system performance. This work introduces novel hardware architectures to accelerate ML workloads, addressing both compute and memory challenges. In the compute domain, we explore various approximate computing techniques to assess their efficacy in accelerating ML computations. Subsequently, we propose the Approximate Tensor Processing Unit (APTPU), a hardware accelerator that utilizes approximate processing elements to replace direct quantization of inputs and weights …

Enhancing Strawberry Disease And Quality Detection: Integrating Vision Transformers With Blender-Enhanced Synthetic Data And Swinunet Segmentation Techniques, Kimia Aghamohammadesmaeilketabforoosh

Electronic Thesis and Dissertation Repository

Agricultural productivity in strawberry cultivation was enhanced through the application of machine learning in this study. Traditional methods for detecting diseases and assessing ripeness in strawberries were identified as labor-intensive and error-prone, which limited farming efficiency and reduced crop yields. To address these challenges, it was hypothesized that advanced machine learning models incorporating attention mechanisms could significantly improve these tasks. The objective was to evaluate the effectiveness of various models by optimizing them for specific agricultural applications. Two datasets of strawberry images were augmented, and three pretrained models—Vision Transformer (ViT), MobileNetV2, and ResNet18—were fine-tuned. Data quality was improved through background …

Application Of Machine Learning Techniques And The Unscented Kalman Filter To Real-Time Gas Turbine Clearance Prediction, Donald Earl Floyd

Theses and Dissertations

The growth in renewable energy sources and retirement of large baseload coal-fired power stations has led to an accompanying decrease in reliability and security of the electrical grid. Since renewable energy sources are typically non-dispatchable, this can lead to blackouts and/or brownouts for customers. Heavy duty gas turbine power plants (HDGT) offer a solution to this problem. HDGTs are dispatchable, clean, and offer flexibility in the fuel they consume, but operational limitations must be well understood to fully exploit their benefits.

One of the main operational limitations is the tip clearances in the gas turbine. In many cases, the gas …

Identifying Waterway Traffic Flow Patterns Using Modified Clustering, Shihao Pang

Graduate Theses and Dissertations

Efficient management of inland waterways is essential for the economic and operational efficiency of transportation networks. Characterization and prediction of waterway vessel traffic flow patterns by time of day are critical for optimizing planned disruptive events like maintenance activities. This study identifies and predicts inland waterway traffic flow patterns along the Lower Mississippi River (LMR) using a modified clustering approach. A five-year period of Automatic Identification System (AIS) data, which tracks vessel movements in real-time, is used for model development and evaluation. The model first segments the river into approximately one-mile-long traffic message channels (TMCs) to estimate vessel counts and …

Reconfigurable Over-The-Air Chamber: Measuring Radio Frequency Device Performance, Benjamin T. Arnold

Theses and Dissertations

Over-the-air (OTA) testing is particularly useful in determining the performance of multi-antenna communication devices in real-world environments. Traditional OTA testing technologies include the reverberation chamber (RC) and the multi-probe anechoic chamber. More recently, the reconfigurable OTA chamber (ROTAC), which is a RC that has probes lining its chamber walls, has been proposed. These probes are either driven with a source, possibly combined with a channel emulator, or are terminated with a tunable impedance. Controlling the excitations and terminations on the probes can alter the fields within the chamber and thereby synthesize an antenna response at the device under test (DUT). …

Emerging Technologies And Advanced Analyses For Non-Invasive Near-Surface Site Characterization, Aser Abbas

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation introduces novel techniques for estimating the soil small-strain shear modulus (G_max) and damping ratio (D), crucial for modeling soil behavior in various geotechnical engineering problems. For G_max estimation, a machine learning approach is proposed, capable of generating two-dimensional (2D) images of the subsurface shear wave velocity, which is directly related to G_max. The dissertation also presents a method for estimating frequency dependent attenuation coefficients from ambient vibrations collected using 2D arrays of seismic sensors deployed across the ground surface. These attenuation coefficients can then be used in an inversion process …

Ensemble Machine Learning At The Edge Using The Codec Classifier Structure And Weak Learners Guided By Mutual Information, Aj Beckwith

All Graduate Theses and Dissertations, Fall 2023 to Present

The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on resource-constrained edge devices. It achieves advantages over other tree ensemble methods due the separation of encoder and decoder tasks in the classifier. The encoder partitions feature space, and the decoder labels the regions in the partition. This functional separation of tasks enables the encoder design (partitioning) to be guided by maximizing the mutual information (MI) between class labels and the features (i.e. the encoded representation of the data) without regard to the error performance of the classifier. Experiments show maximizing MI leads …

Leveraging Machine Learning And Stochastic Programming To Address Vaccine Hesitancy In Public Health Resource Allocation, Hieu Trung Bui

Graduate Theses and Dissertations

Infectious disease outbreaks highlight the urgent need for effective strategies to distribute vaccines and allocate critical healthcare resources to contain the disease and reduce its negative impacts on the population. Managing these allocations is a significant challenge, especially in marginalized communities facing uncertainty in healthcare demand and logistical constraints. This dissertation addresses these challenges by investigating factors that influence dynamic changes in vaccine hesitancy (VH) and its implications for disease spread and healthcare resource demand. It develops optimization models for vaccine distribution and resource allocation under uncertainty, validated with data from the COVID-19 pandemic in the U.S. The first study …

Exploring Telehealth Utilization Through Data Analytics, Statistical Analyses, And Machine Learning Techniques, Aysenur Betul Cengil

Graduate Theses and Dissertations

This dissertation investigates the utilization of telehealth services, initially focusing on the Arkansas healthcare system and then extending the analysis nationwide. It aims to understand the factors influencing telehealth adoption and its impact on healthcare delivery. After examining telehealth utilization in Arkansas from 2018 to 2022, the research utilizes a comprehensive dataset from Epic Cosmos, which includes a wide range of patient and visit data from multiple healthcare facilities across the United States from 2018 to 2023. This timeframe allows for a detailed analysis of telehealth trends before, during, and after the COVID-19 pandemic. In Chapter 2, we analyze key …

The Design, Prototyping, And Validation Of A New Wearable Sensor System For Monitoring Lumbar Spinal Motion In Daily Activities, Brianna Bischoff

Theses and Dissertations

Lower back pain is a widespread problem affecting millions worldwide, because understanding its development and effective treatment remains challenging. Current treatment success is often evaluated using patient-reported outcomes, which tend to be qualitative and subjective in nature, making objective success measurement difficult. Wearable sensors can provide quantitative measurements, thereby helping physicians improve care for countless individuals around the world. These sensors also have the potential to provide longitudinal data on daily motion patterns, aiding in monitoring the progress of treatment plans for lower back pain. In this work it was hypothesized that a new wearable sensor garment that makes use …

Data-Driven Vibration-Based Condition Monitoring: Fundamentals, Applications, And Challenges, Sulaiman A. S. Aburakhia

Electronic Thesis and Dissertation Repository

Vibration-Based Condition Monitoring (VBCM) is commonly utilized in Prognostics and Health Management (PHM) due to its non-destructive nature and inherent advantages over alternative forms of condition monitoring. Furthermore, the rapid evolution of sensor fabrication and the rise of the Internet of Things (IoT) have facilitated large-scale VBCM systems across diverse domains, including industry, transportation, healthcare, agriculture, and wildlife monitoring. The recent advancements in computing technologies have significantly expanded the potential for VBCM by leveraging the synergy between signal processing and Machine Learning (ML). Accordingly, data-driven VBCM has emerged as a paradigm shift, improving the performance and reliability of VBCM systems. …

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

Computational Microscopy For Biomedical Imaging With Deep Learning Assisted Image Analysis, Yuwei Liu

Dissertations

Microscopy plays a crucial role across various scientific fields by enabling structural and functional imaging with microscopic resolution. In biomedicine, microscopy contributes to basic research and clinical diagnosis. Conventionally, optical microscopy derives its contrast from the amplitude of the optical wave and provides visualization of the physical structure of the sample qualitatively. To understand the function at the cellular or tissue level, there is a need to characterize the sample quantitatively and explore contrast mechanisms other than light intensity. Image enhancement or reconstruction from microscopic imaging systems is known as computational microscopy, and it involves the application of computational techniques …

Machine Learning-Based Design Of Doppler Tolerant Radar, Kyle Peter Wensell

Dissertations

In this work, machine learning theory is applied to the design of a radar detector in order to train a machine learning-based detector that is robust against Doppler shifts. The radar system is designed to work with data that would be otherwise intractable to conventional optimal detector design, such as transmitted noise waveforms and the effects of one-bit quantization at the receiver. The detection performance of the one-bit receiver is shown to match the performance of the derived square-law sign correlator detector. The resulting learning-based detector also introduces Doppler tolerance to the system, which allows for the successful detection of …

Securing The Skies: Safety-Constrained Decentralized Multi-Uav Coordination With Deep Reinforcement Learning, Jean-Elie Pierre

Electrical and Computer Engineering ETDs

In the dynamic landscape of autonomous aerial systems, the integration of uncrewed aerial vehicles (UAVs) has sparked a paradigm shift, offering unprecedented opportunities and challenges in collaborative decision-making and navigation. This thesis explores the application of multi-agent reinforcement learning (MARL) for the planning and coordination of UAVs in complex environments.

The first part of this thesis provides an introduction to single-agent reinforcement learning and MARL. We provide examples of the use of MARL for countering uncrewed aerial systems (C-UAS). We formulate the Counter-UAS problem as a multiagent partially observable Markov decision process (MAPOMDP), and we propose Multi-AGent partial observable deep …

Planetary Exploration Via Fully Automatic Topological Structure Extraction Using Adaptive Resonance, Jonathan Kissi

Electronic Thesis and Dissertation Repository

Renewed interest in Solar System exploration, along with ongoing improvements in computing, robotics and instrumentation technologies, have reinforced the case for remote science acquisition systems development in space exploration. Testing systems and procedures that allow for autonomously collected science has been the focus of analogue field deployments and mission planning for some time, with such systems becoming more relevant as missions increase in complexity and ambition. The introduction of lidar and laser scanning-type instruments into the geological and planetary sciences has proven popular, and, just as with the established image and photogrammetric methods, has found widespread use in several research …

Controlling Complex Dynamic Transportation Systems: Development And Adaptation Of A Novel Distributed Cooperative Multi-Agent Learning Technique, Russell Thomas Graves

Doctoral Dissertations

Intelligent transportation systems continue to increase complexity, scale, and scope as more devices contain embedded compute. Cooperation among vehicles, intersections, and other members of the greater traffic ecosystem at a system-of-systems level is critical to improving the efficiency of the multi-billion-dollar asset that is the U.S. roadway infrastructure. This work introduces a negotiations strategy among multi-agent reinforcement learning agents and applies this to both traffic signal control and supervisory control of vehicle platooning. The traffic signal control implementation builds off of many prior research thrusts, and was shown to improve vehicle throughput by an average of 671veh/hr over actuated traffic …

Multimodal Data Fusion And Machine Learning For Advancing Biomedical Applications, Md Inzamam Ul Haque

Doctoral Dissertations

This dissertation delves into the intricate landscape of biomedical imaging, examining the transformative potential of data fusion techniques to refine our understanding and diagnosis of health conditions. Daily influxes of diverse biomedical data prompt an exploration into the challenges arising from relying solely on individual imaging modalities. The central premise revolves around the imperative to combine information from varied sources to achieve a holistic comprehension of complex health issues.

The chapters included here contain articles and excerpts from published works. The study unfolds through an examination of four distinct applications of data fusion techniques. It commences with refining clinical task …

Techniques To Overcome Energy Storage Limitations In Electric Vehicles, Matthew J. Hansen

All Graduate Theses and Dissertations, Fall 2023 to Present

Electric vehicles are becoming increasingly popular, battery limitations (cost, size, and weight) complicate electric vehicle adoption. While important research on battery development is ongoing, this dissertation discusses two main approaches to overcome those limitations within the existing battery technology paradigm. Those thrusts are: improving battery health through an optimal charging strategy and minimizing necessary battery size through dynamic wireless power transfer. In this dissertation, relevant literature is discussed, with opportunities for further development considered. Within the two thrusts, three objectives sharpen the focus of the research presented here. First, a planning tool is defined for a battery electric bus fleet. …

Recommendation System Using Machine Learning For Fertilizer Prediction, Durga Rajesh Bommireddy

Electronic Theses, Projects, and Dissertations

This project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine …

Development And Evaluation Of A Modeling Platform For Evaluating Immunotherapeutic Efficacy In The Tumor Microenvironment., Dylan Andrew Goodin

Electronic Theses and Dissertations

The tumor microenvironment (TME) represents the complex outcome of numerous tumor, stromal, and immune interactions, and whose composition can significantly affect treatment response. Particularly, immunotherapeutic efficacy is subject to multiple tumor-specific TME interactions that may be difficult to evaluate/predict clinically. Mathematical modelling has been formulated to evaluate specific aspects of the TME, including vasculature, ECM deposition, and immune-tumor interactions. However, the computational challenge of simulating multiple TME interactions has led to sacrificing varying degrees of model generalizability and clinical relevance. This work describes increased computational performance of a 3D continuum model that simulates tumor tissue, ECM, and vasculature using a …

Mineral Matter Behavior During The Combustion Of Biomass And Coal Blends And Its Effect On Particulate Matter Emission, Ash Deposition, And Sulfur Dioxide Emission, Rajarshi Roy

Theses and Dissertations

Combustion of coal is one of the primary sources of electricity generation worldwide today. Coal contains different chemicals that cause particulate matter(PM) and sulfur dioxide (SO2) emissions. These are health hazards and are responsible for deteriorating the ambient air quality. Particulate matter also forms ash deposits inside the coal combustor, which in turn decreases the energy efficiency of the power plants. Using biomass as a fuel in these utility boilers can potentially reduce the problems of particulate matter emissions and ash deposition, and can significantly reduce the SO2 emissions. However, biomass needs to be pretreated to make its properties similar …

Developing A Sql Injection Exploitation Tool With Natural Language Generation, Kate Isabelle Boekweg

Theses and Dissertations

Websites are a popular tool in our modern world, used daily by many companies and individuals. However, they are also rife with vulnerabilities, including SQL injection (SQLI) vulnerabilities. SQLI attacks can lead to significant damage to the data stored within web applications and their databases. Due to the dangers posed by these attacks, many countermeasures have been researched and implemented to protect websites against this threat. Various tools have been developed to enhance the process of detecting SQLI vulnerabilities and active SQLI attacks. Many of these tools have integrated machine learning technologies, aiming to improve their efficiency and effectiveness. Penetration …

Application Of High-Deflection Strain Gauges To Characterize Spinal-Motion Phenotypes Among Patients With Clbp, Spencer Alan Baker

Theses and Dissertations

Chronic low back pain (CLBP) is a nonspecific and persistent ailment that entails many physiological, psychological, social, and economic consequences for individuals and societies. Although there is a plethora of treatments available to treat CLBP, each treatment has varying efficacy for different patients, and it is currently unknown how to best link patients to their ideal treatment. However, it is known that biopsychosocial influences associated with CLBP affect the way that we move. It has been hypothesized that identifying phenotypes of spinal motion could facilitate an objective and repeatable method of determining the optimal treatment for each patient. The objective …

Numerical Study Of Ozone Decomposition Reaction Behaviours In Gas-Solids Circulating Fluidized Bed Reactors, Zhengyuan Deng

Electronic Thesis and Dissertation Repository

This study numerically investigated the reaction behaviours of catalytic ozone decomposition reaction in a 10.2-meter-tall gas-solids circulating fluidized bed (CFB) reactor. A pseudo-homogeneous reactive transport model for ozone decomposition, integrated with the two-fluid model, was developed and validated using experimental data. Based on the model, the impacts of turbulence models, specularity coefficients, and simulation methods on reaction behaviours in the CFB riser reactor were explored. These three factors were found to significantly affect the hydrodynamic characteristics and the reaction behaviours in the riser. A comparative study of CFB riser and downer reactors was conducted. Operations in the direction of and …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Phase Field Modeling Of Fracture And Phase Separation Using Numerical Methods And Machine Learning, Revanth Mattey

Dissertations, Master's Theses and Master's Reports

Phase field modeling is a crucial tool in scientific and engineering disciplines due to its ability to simulate complex phenomena like phase transitions, interface dynamics, and pattern formation. It plays a vital role in understanding material behavior during processes such as solidification, phase separation, and fracture mechanics. Particularly in fracture mechanics, phase field modeling can be utilized to predict the crack path in complex materials. Understanding the failure behavior is vital for applications of any material. The specific contributions to the field of phase field fracture mechanics, are, Firstly, we propose a novel phase field fracture model to simulate the …

Explore Security And Machine Learning Applications In Next Generation Wireless Networks, Haolin Tang

Theses and Dissertations

Next-generation (NextG) or Beyond-Fifth-Generation (B5G) wireless networks have become a prominent focus in academic and industry circles. This is driven by the increasing demand for cutting-edge applications such as mobile health, self-driving cars, the metaverse, digital twins, virtual reality, and more. These diverse applications typically require high communication network performance, including spectrum utilization, data speed, and latency. New technologies are emerging to meet the communication requirements of various applications. Intelligent Reflecting Surface (IRS) and Artificial Intelligence (AI) are two representatives that have been demonstrated as promising and powerful technologies in NextG communications. While new technologies significantly enhance communication performance, they …

Improvements In Biomedical Image Analysis With Computational Intelligence And Data Fusion Techniques, Akanksha Maurya

Doctoral Dissertations

"An estimated 2 million new cases of basal cell carcinoma (BCC) are diagnosed each year in the United States, making it one of the most common skin cancers. Earlier detection of these cancers enables less invasive biopsies. Clinical detection consists of a preliminary visual observation of these skin lesions by an experienced dermatologist making it a specialized task highly dependent on their time, availability, and resources. Hence, there is a need for automating this process that can assist healthcare staff. In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. Telangiectasia …