Engineering Commons^™

Investing Hysteresis In Floodplain Dynamics Of Lakes In The Middle St. Johns River Using Sentinel-1 Sar Imagery, Keenan Hubbard

Doctoral Dissertations and Master's Theses

Floodplain dynamics are often complex, with hysteresis potentially affecting the temporal relationship between flood stage and flood extent during subsequent inundation phases. This study leverages Sentinel-1 synthetic aperture radar (SAR) imagery to map flood extent in the Middle St. Johns River Lake floodplains and examine the presence of hysteresis during flood events. SAR scenes corresponding to river gauge readings were analyzed from the rising and falling limbs of a flood hydrograph. By comparing these flood maps, we assess differences in inundated areas at equivalent water levels during each stage of the flood event. The findings aim to enhance flood monitoring …

Experimental Analysis Of Satellite Operator Training Using Game-Based Virtual Reality Simulation, Lana Laskey

Doctoral Dissertations and Master's Theses

Satellite data plays a vital role in modern global infrastructure by enabling communications, navigation, and weather forecasting. As demand for satellite technology grows, so does the need for highly trained satellite ground operators. Traditional training regimens for satellite operators employ simulation using two-dimensional computer console displays paired with the varied ability of trainees to generate abstract mental imagery of the scenario. However, this development of mental imagery imposes a considerable learning curve and cognitive workload on the trainee, which may negatively impact the user experience and knowledge gained during the training scenario.

This experimental study investigated the effects of game-based …

Effect Of Vehicular Electrification On Transportation Emissions In Florida, Shreya Sapkota Dhakal

Doctoral Dissertations and Master's Theses

Vehicular emissions from fuel-based passenger cars emit an array of gases and particles that are detrimental for human health and the environment. In that regard, electric vehicles (EVs) present a viable, sustainable solution. This study investigated the impact of electrification of passenger cars on air quality in Florida, in five major urban counties namely Miami-Dade, Duval, Hillsborough, Orange, and Leon. Between 2018 and 2022, these counties experienced a significant increase of 219.50 ± 52.32% in EV adoption, coupled with a 11.55 ± 6.13% decrease in fuel-based vehicle usage. Herein, we characterize five pollutants primarily generated from fuel-based passenger vehicles- carbon …

Mno2 Nanoscale Interface Modification, Alexander C. Skoppe

Doctoral Dissertations and Master's Theses

Interface modification of carbon fibers has been shown to improve the mechanical performance of composites. In addition, interface modification of carbon fiber composites can impart multifunctionality into the resulting composite. This work will explore ZnO and MnO2 as interface modifications for use on carbon fibers. When exposed to high temperatures, carbon fibers undergo fiber degradation, leading to the need for low-temperature hydrothermal processes. This work will develop and characterize a nanoscale ZnO and MnO2 interface modification for use on carbon fibers. These nanomodifications will be developed with low-temperature processes, minimizing the fiber degradation that the fibers undergo. Fourier transform infrared …

Aircraft Bird Strike Risk Prediction Using Machine Learning And Analytic Hierarchy Process, Jason Anthony Powell

Doctoral Dissertations and Master's Theses

To address the limitations of Next Generation Radar-based bird strike forecasting, this study modeled 12 spatiotemporal weather features from the National Oceanic and Atmospheric Administration alongside bird strike risk using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), XGBoost regression tree, and Bayesian network algorithms. Five years of bird strike data from four geographically diverse airfields served as the target risk variable, categorized as low, moderate, or severe based on Department of the Air Force risk models. The ensemble model, which combines the LSTM-RNN and XGBoost regression algorithms, yielded the most accurate forecasts, achieving 80% to 93% accuracy across all airfields, …

Statistical Monitoring Of Hard Faults In Digital Systems, Dany Akshay Deep Isukapalli

Electrical Engineering Theses and Dissertations

Achieving a high test coverage is crucial for helping to ensure that integrated circuits are working correctly and are non-defective. Although scan-based structural tests are used throughout the industry, high-level functional tests may be needed to detect some defects— especially those that are environmentally sensitive. Unfortunately, the character of functional test makes it difficult to obtain high coverage, and it is even hard to estimate coverage because fault simulation times of large circuits are long. As a result, some method is required for predicting the ability of a functional test that has not been fault simulated to detect defects. In …

Electron Beam Irradiation Effects On Bulk Metals And High Entropy Alloys (Crmnv And Crmntiv) And The Synthesis And Characterization Of Irradiation-Induced Damage In Polycrystalline Metal And High Entropy Alloy Thin Films, Najmin Ara Sultana

Mechanical & Aerospace Engineering Theses & Dissertations

Accelerator beam exit windows are designed to withstand intense radiation and mechanical stress while preserving beam transmission and structural integrity. This dissertation explores the structural and mechanical responses of pure metals and high-entropy alloys (HEAs), in both bulk and thin-film forms, under high-dose electron beam (e-beam) irradiation to identify optimal materials for next-generation accelerator exit windows. Monte Carlo simulations using FLUKA, conducted at Jefferson Lab, revealed that amongst Ni, Ti, Cr, and V, Ni exhibits the highest power dissipation, while Ti depicts the lowest power dissipation, with Cr and V demonstrating intermediate characteristics. Bulk polycrystalline (PC) and single-crystalline (SC) samples …

Towards Efficient Privacy-Preserving Deep Learning: He-Friendly Structures, Flexible Pruning, He-Efficient Architectures, And Secure Transformer Token Drop, Yifei Cai

Electrical & Computer Engineering Theses & Dissertations

Deep learning (DL) has become a powerful tool for solving complex problems, but developing DL models typically requires vast datasets, high computational resources, and expert knowledge—barriers that limit accessibility. Machine Learning as a Service (MLaaS) addresses this challenge by allowing resource-rich providers to deliver pre-trained DL models as services. However, privacy concerns arise: clients hesitate to share sensitive data, while providers protect their proprietary models. To address this, privacy-preserving MLaaS integrates cryptographic techniques into DL computations, as seen in frameworks like Cryptonets, SecureML, GAZELLE, CrypTFlow2, Cheetah, and BOLT. Among them, Homomorphic Encryption (HE) enables computation on encrypted data but remains …

Advanced Study Of Nickel-Titanium Alloy: Effects Of Point Defects On Mechanical And Thermodynamic Properties, Diego Armando Juarez Rosales

Open Access Theses & Dissertations

High-throughput first-principles calculations of point defects are emerging as a powerful tool to accelerate materials discovery in applications [1]. Substitutional, antisite, and vacancy defects can play an important role in the mechanical and thermal properties of intermetallic alloys [2]. In this present work I compute the thermal and mechanical properties of shape-memory alloy nickel-titaniun (NiTi) in the B19â?? martensitic and B2 austenitic phases from molecular dynamics (MD), using a second nearest neighbor (2NN) modified embedded atom method (MEAM) [3] classical potential in the temperature range from 200K to 600K and composition range from 45 atomic percent to 55 atomic percent …

Optimization Of Thermoelectric Energy Harvester To Power Wireless Onboard Bearing Health Sensors During Rail Service, Danna Cecilia Capitanachi Avila

Theses and Dissertations

This work presents the optimization of a thermoelectric energy harvester designed to supply power to a wireless onboard bearing health monitoring device for critical freight rail components. The study demonstrates an enhancement in the circuitry through the integration of an energy management subsystem (E-Peas AEM20940), which features a boost converter with lower self-start voltage. The harvesting system was tested on dynamic bearing test rigs to closely replicate field service conditions. Test plans were conducted using common freight speeds and loads to simulate a representative urban route. The thermoelectric energy harvester was able to operate at temperature differentials as low as …

Investigation Of The Effect Of Material Density And Particle Size On Mixed Powder Spreading Behavior In Powder Bed Fusion Process, Alfred Kofi Apianing Achenie

Theses and Dissertations

Powder spreading marks a crucial step in the Laser powder bed fusion process, directly impacting the powder bed uniformity and paving the way for subsequent stages in the process. The laser powder bed fusion process depends heavily on the composition of the metallic powder feedstock. While most existing studies focus on pre-alloyed powders, limited work has explored the use of elemental powder blends as feedstock as used in in-situ alloying. Pre-alloyed powders are often costly, exhibit irregular morphologies and offer limited flexibility in material selection. This study uses Discrete Element Method (DEM) simulations to investigate how variations in material density …

Secure Query On Encrypted Data By Using Fully Homomorphic Encryption, Mrinmoy Bera

Master’s Dissertations

Cloud service providers typically store user data in an encrypted form (data at rest). However, when a user performs a query, the server first decrypts the data, processes the query on plaintext, and then sends the result back to the user (data in transit). This process exposes a critical vulnerability—if the cloud server is ever compromised, the decrypted data becomes accessible to the attacker. To address this security gap, we design a secure query protocol that eliminates the need to decrypt data on the server side. Fully Homomorphic Encryption (FHE) offers a groundbreaking solution by enabling arbitrary computations directly on …

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …

Integrating Ensemble Hydrologic Forecasts With Policy Optimization Models For Forecast-Informed Reservoir Operations (Firo), Alessandra Estefania Perez Salas

Theses and Dissertations

Effective reservoir management in hydrologically variable regions such as California’s Central Valley faces increasing pressure to balance flood control, water supply reliability, hydropower generation, and ecosystem health. Traditional rule-based operations, while operationally simple, often lack the adaptability required to respond to real-time hydrologic conditions and forecast uncertainty. This study presents an integration of a policy tree optimization model with ensemble streamflow forecasts to enhance Forecast-Informed Reservoir Operations (FIRO) at Folsom Reservoir. Building on historical data for inflow, storage, and release, an interpretable, threshold-based decision framework was developed and coupled with 14-day ensemble inflow forecasts from the California-Nevada River Forecast Center’s …

Microstructure And Defects Of Aa6061 In The Laser Powder Bed Fusion Additive Manufacturing, Sivaji Karna

Theses and Dissertations

AA6061 is a widely used aluminum alloy known for its excellent thermal conductivity, strength, and corrosion resistance. However, its application in Laser Powder Bed Fusion (LPBF) additive manufacturing is restricted by solidification cracking and porosity. This study examines defect formation, microstructural evolution, and precipitate behavior in AA6061 processed under various LPBF conditions, including room temperature and heated substrates (500°C), as well as pulsed wave laser configurations. Microstructural characterization was conducted using optical microscopy, scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM).

A maximum relative density of 99.17% was …

Hydrogen Permeation And Mechanical Behavior Of Hydrogen-Charged Cr-Coated And Oxidized Zr-Alloy Fuel Cladding, Daniel Cole Viands

Theses and Dissertations

Following the Fukushima nuclear accident in 2011, accident tolerant fuel (ATF) became a major research interest to enhance the safety of light water reactors. Because the current fleet of light water reactors and their associated fuel cycle infrastructure are well-established, developing ATF that does not significantly alter the current fuel form or change its supporting infrastructure is ideal. Cr-coated Zircaloy fuel cladding is therefore an excellent short-term solution to enhance accident tolerance. Thin layers of Cr-coating offer notable resistance to corrosion, high-temperature oxidation, physical wear, and hydrogen permeation. Cold spray (CS) and physical vapor deposition (PVD) are two well-established Cr-coating …

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

Navigating Perceptions: How Organizational Type And Message Source Affect Willingness To Fly In Automated Air Taxis, Cody James Sweatt

Doctoral Dissertations and Master's Theses

A new era of transportation and business is beginning to emerge. Advanced air mobility (AAM), which uses highly automated aircraft that transport passengers and cargo in the underutilized, low-altitude airspace above and around urban areas, is rapidly becoming a reality (FAA, 2023). These new companies will utilize air taxis, which are automated, electric, vertical takeoff and landing (eVTOL) aircraft and will potentially foster a revolutionary new commerce infrastructure. Unfortunately, it is not currently understood how the credibility of a message source or specific type of organization that owns and operates an automated air taxi company influences a customer’s willingness to …

A Wearable-Based Approach To Spinal Posture Tracking And Assessment, Sydney Marie Sherman, Noah Jeffery

Biomedical Engineering: Graduate Reports and Projects

This paper describes the design, manufacturing, and testing of a novel spinal posture tracking wearable device with embedded sensors. Currently on the market, most of the spinal posture tracking devices only focus on one segment of the spine and therefore do not track the entirety of human posture. The objective for this project was to create a wearable device in the form of a “smart shirt” that offers insights into posture trends throughout the day by tracking the entire spine. To begin the project, multiple designs were developed for the sensor array and wearable materials. These concepts were compared against …

Seconds From Impact: Anticipatory Vehicular Crash Prediction Using Video Vision Transormers, Ryan P. Geisen

Master's Theses

Vehicular collisions represent a significant public health concern, necessitating re search into advanced emergency notification systems. While deep learning has shown promise in accident detection, a research gap persists in applying state-of-the-art transformer architectures to the task of anticipatory, real-time crash prediction from video. This thesis addresses this gap by developing and evaluating a Video Vision Transformer (ViViT) for the binary classification of imminent vehicular collisions. Utilizing a curated dataset of 1,493 unique collision sequences, this study systemati cally investigates the impact of temporal context by comparing the ViViT against a single-frame Vision Transformer (ViT) baseline and conducting comprehensive exper …

Hardware Accelerated Simulation Of Buck Converters Using Physics-Informed Neural Networks, James Clayton Crews

Theses and Dissertations

Physics-informed neural networks (PINNs) are an emerging machine learning method for learning the behavior of physical systems described by governing differential equations. Dc-dc power-electronic converters are used in a variety of industry applications such as motor drives or power supplies where real-time simulation is critical for control and safety. This thesis investigates physics-informed machine learning as an approach to develop a real-time digital twin for dc-dc power converters. Traditional numerical integration methods are used to approximate discretized behavior, and the results are compared with a trained PINN model. Modern ML frameworks (such as PyTorch and TensorFlow/Keras) are used to quickly …

Electrochemically Active Liquid Organic Hydrogen Carriers For Energy Generation And Storage, Jinyao Tang

Theses and Dissertations

The growing demand for sustainable energy has spurred interest in hydrogen as a clean energy carrier, yet its widespread adoption is limited by storage and transport challenges. Liquid organic hydrogen carriers (LOHCs) offer a promising solution by enabling reversible hydrogen storage through chemical redox reactions under ambient conditions. Compared to conventional thermal methods, LOHC electrochemistry provides improved energy efficiency, operational safety, and integration into electrochemical systems. However, key challenges remain, such as catalyst deactivation, limited redox reversibility, and low system-level performance that hinder broader application. Alcohols like isopropanol (IPA) and cyclohexanol (CHOL), and amines like ethylamine, have emerged as promising …

Saw Biosensor For The Detection Of Cyanobacteria And Cyanotoxin, Tally Bovender

Theses and Dissertations

The growing prevalence of cyanobacterial blooms and associated cyanotoxins, such as microcystin-LR (MC-LR), presents a significant threat to water quality and public health. Conventional detection methods, including ELISA and PCR, are often time-consuming, expensive, and require trained personnel, limiting their application in field settings. Hence, a miniaturized, quick testing sensor is proposed to circumvent the current sensing challenges for real time, in-situ diagnostics of MC-LR. The proposed high-frequency ultrasonic sensor employs surface acoustic waves (SAW). The sensor was designed using a multi-physics simulation tool to understand wave propagation in the piezoelectric substrate (lithium tantalate) and optimize the design of the …

Design Of Supercritical Reactors To Understand Supercritical Water Oxidation Process And Hydrothermal Flames, Victor Dubceac

Theses and Dissertations

Supercritical water oxidation (SCWO) has been a topic of immense interest as an effective technique/tool for hazardous aqueous waste disposal. SCWO can be achieved by introducing an oxidizer (e.g., H2O2), or a hydrothermal flame as an internal source of both heat and oxidizer species in an aqueous environment at conditions above the critical point of water (P > 218 atm and T > 647 K). SCWO poses important environmental advantages for the treatment of harmful organic materials contained in waste streams. An SCWO reactor has the potential to be compact in design and therefore can be an ideal alternative to existing technology …

Optical And Thermal Characterization Of Vertically Conducting Β-Ga₂O₃ Diodes Grown On 4h-Sic Substrates For Short-Wavelength (<254 Nm) Detection, Muhammad Hassan Tahir

Theses and Dissertations

The advancement of compact, thermally stable, and efficient devices for deepultraviolet (DUV) detection below 254 nm is critical for solar-blind sensing, radiation monitoring, and high-power electronics. This thesis presents the growth, fabrication, and detailed optical and thermal characterization of vertically conducting β-gallium oxide (βGa₂O₃) Schottky barrier diodes (SBDs) grown on n-type 4H-Silicon Carbide (4H-SiC) substrates using metal-organic chemical vapor deposition (MOCVD).

β-Ga₂O₃ is an ultra-wide bandgap (UWBG) semiconductor (~4.8 eV) with intrinsic DUV transparency and high breakdown electric field, making it an ideal candidate for short-wavelength photodetection. However, its low thermal conductivity limits its vertical device performance. To overcome this, …

Effect Of Growth Conditions On The Performance Of Vertically Conducting Beta-Ga2o3 Diodes On 4h-Sic Substrate By Mocvd, Nifat Jahan Nipa

Theses and Dissertations

This thesis highlights the critical influence of doping strategies, specifically co-delta doping with silicon (Si) and indium (In), on improving the structural quality and device performance of β-Ga₂O₃-based Schottky barrier diodes (SBDs). To achieve better thermal management, β-Ga₂O₃ thin film was grown on bulk 4H-SiC substrates using the Metal-Organic Chemical Vapor Deposition (MOCVD) technique, renowned for its precise control over composition, thickness and doping profile. The ultra-wide bandgap (UWBG), high critical breakdown field and low turn-on resistance of β-Ga₂O₃ make it a strong candidate for next-generation power electronics and short-wavelength detection in extreme environments. However, the presence of structural defects …

Addressing Key Challenges In Mhz Power Converters, Aqarib Hussain

Theses and Dissertations

Power electronics are essential in applications such as aerospace, electric vehicles, and electric ships where compact, efficient, and high power-density converters are increasingly demanded. This push toward miniaturization has led to converter designs operating at switching frequencies in the megahertz (MHz) range. Higher-frequency operation enables significant reductions in passive component sizes—particularly transformers and inductors—thereby facilitating dense packaging. However, these benefits come with new challenges, including increased thermal stress, complex semiconductor behavior, higher-frequency magnetic design constraints, and elevated electromagnetic interference (EMI).

This research addresses several key challenges in MHz converter design. The first part involves the development of a 1-MHz, 1-kW …

Self-Regulated Thermal Management With Shape-Memory Alloy Torsional Tubes, Paula Sanjuan Espejo

Doctoral Dissertations and Master's Theses

The need for dynamic thermal management that adapts to varying system needs and requirements is a growing topic of interest in different engineering disciplines, most prominently aerospace and electronics. This demand for improved thermal management systems comes from the general increase of system efficiencies leading to an increase in component energy density. Shape-memory alloy actuators, which respond with a mechanical shape recovery to variations in temperature, can be used as self-regulated thermal management actuators that are able to respond to environmental thermal changes autonomously. In this dissertation, modeling and experimental analysis of a two-way shape-memory effect trained SMA torsional tube …

Embeddable Multi-Material Wireless Micro-Sensors Utilizing Additive Manufacturing And Enhanced Microstructure, Nicholas Reed

Doctoral Dissertations and Master's Theses

The development of embeddable, multi-material wireless microsensors offers transformative potential for structural health monitoring (SHM) in aerospace applications. This work integrates additive manufacturing (AM) techniques with advanced microstructural design to produce flexible, high-resolution sensors that can be directly embedded into polymer substrates. By utilizing the vat photopolymerization process, customized embedded sensors are seamlessly integrated into polymer AM structures. These sensors are then continuously refined, targeting increases in performance, both internal and external. Microstructural enhancements are explored to modify the rheological properties of the fabricated embedded sensing channels and enhance the adhesive bonding between the embedded sensor and the AM structure. …