Engineering Commons^™

Experimental Material Analysis And Load Monitoring Of Polyurethane–Steel Belts Subjected To Static And Cyclic Dynamic Loading: Implications For Continuous Mooring Line Systems In Wave Energy Converters, Youssef Mohamed

Electronic Theses and Dissertations

This study experimentally investigates the mechanical and viscoelastic behavior of a steel-cord-reinforced polyurethane synchronous belt capable of withstanding continuous cyclic loading as a potential compliant mooring element for WEC applications. Quasi-static and cyclic tensile tests were conducted to characterize the belt's mechanical response. The quasi-static determined that the belt behaved as a stiff, low-strain tensile member. Under approximately 100 kgf, the belt extended by about 0.107mm over a 250mm gauge length, apparent tensile modulus of 5.41GPa, an axial rigidity of 2.44MN, and an equivalent line stiffness of 9.75MN/m. Cyclic tensile testing showed high dynamic stiffness with stiffness-transfer ratio of 0.924. …

Evaluating Diagnostic Information Preservation In Weakly Supervised Medical Imaging, Vinceline Bertrand

Electronic Theses and Dissertations

Weakly supervised medical imaging models trained with coarse image-level labels often report strong performance on metrics such as accuracy and AUC. This thesis argues that these metrics can be misleading: a model may succeed on a coarse diagnostic task while failing to preserve the fine-grained information needed for consequential clinical decisions. It makes this failure measurable through the diagnostic gap, defined as the divergence between coarse and fine-grained diagnostic preservation, across three connected studies. The first shows that near-perfect ovarian ultrasound accuracy reflects visual separability rather than pathological understanding. The second measures the diagnostic gap in a mammographic pipeline, where …

Tackling Oversmoothing, Heterogeneity, And Label Distributions For Robust Graph Learning, Yufei Jin

Electronic Theses and Dissertations

With the tremendous development of graph neural networks, graph learning has become a dominant solution applied to various applications naturally integrated with graph structures, including traffic networks [82], molecule networks [40, 22, 1, 30, 32], social networks [4], etc. While most existing graph learning solutions can handle homogeneous graphs (graphs with a single node type and a single edge type) and homophily graphs (graphs where node labels tend to be the same as their neighbors) for multi-class node classification downstream tasks well, in real world applications, graph structures can be more complex with heterogeneous graphs (graphs with multiple node types …

Analysis Of Flood Modeling For The Replication Of An Extreme Rainfall Event In South Florida, Lusnel Fritznel Ferdinand

Electronic Theses and Dissertations

Flooding is the most common natural disaster in the United States, and trends show an increase in flood risk for the foreseeable future. Florida averages 45 flood disasters per year, at an annual cost of $86 million. Communities in Florida are susceptible to a variety of flood-inducing sources, including tropical cyclones, extreme rainfall, and rising sea levels, which can impact the tourism industry and the economic well-being of residents. The first objective of the thesis was to use flood modeling to replicate the extreme rainfall event that caused heavy flooding in Broward County, Florida, from April 12-13, 2023. A commercial …

Comparative Performance Evaluation Of A Gpu-Accelerated Watershed Model For Flood Inundation Mapping, Maliha Ali

Electronic Theses and Dissertations

This study evaluates PyWMP (Python-based Watershed Modeling and Planning), which is a flood modeling framework developed by the Center for Water Resiliency and Risk Reduction (CWR3) at Florida Atlantic University (FAU). The framework includes a 2D Rain-on-Mesh (ROM) solver, which is an explicit finite-volume shallow-water equation model. PyWMP is compared against HEC-RAS 2D and Flood Modeller for their rain-on-grid capabilities for watershed-scale flood inundation mapping under three design storm scenarios. The performance of PyWMP was evaluated using both quantitative and spatial metrics. Mean absolute error values showed that the average depth difference between each model is under 1 foot. Percent …

Load Balancing Algorithms For Cloud Computing Systems, Mark Rakesh Christian

Electronic Theses and Dissertations

Cloud computing depends on load balancing to allocate user requests among virtual machines (VMs), affecting response time, utilization, and scalability. Round Robin, Equally Spread Current Execution, and standard Throttled scheduling are widely used; however, standard Throttled selects among eligible VMs using an implementation-dependent order with no explicit preference. This thesis proposes the Weighted Throttled Load Balancing (WTLB) algorithm, which introduces an explicit, deterministic selection priority among eligible VMs, implemented by modifying the Throttled scheduler within CloudAnalyst.

Evaluated under an identical six-region, multi-data-center configuration against Round Robin, ESCE, and standard Throttled, WTLB preserves the aggregate response-time, processing-time, and cost profile of …

Assessment Of Anaerobic Co-Digestion Of High Strength Organic Feedstocks Diverted From Landfills, Sumaiya Sharmin

Electronic Theses and Dissertations

The increasing generation of high-strength organic wastes such as food waste and fats, oils, and grease (FOG) presents challenges for landfill capacity and waste management. Anaerobic co-digestion offers a sustainable alternative by converting these wastes into renewable energy and nutrient-rich digestate. This study evaluated the co-digestion performance of single fruit waste residuals and mixed food waste with wastewater sludge to identify substrate combinations that maximize methane production while maintaining process stability. Laboratory-scale batch experiments were conducted under mesophilic conditions using thickened waste activated sludge (TWAS) with varying proportions of food waste and co-substrates, including seaweed, aquatic weed, grease trap waste, …

Object Avoidance Onboard An Autonomous Underwater Vehicle In Support Of Geomagnetic Based Navigation System, Eric Benavidez

Electronic Theses and Dissertations

The objective of this thesis was to collect and analyze magnetic field data in the vicinity of the Mercy, Tracey, and Jay Scutti with an autonomous underwater vehicle (AUV) and a forward mounted magnetometer. The collected data was used to develop geomagnetic contour maps and magnetic thresholds of known anomalies to compare the results with validated sources such as NOAA.

In addition, this collected data was used to develop a Gazebo simulation which accurately modeled the magnetic field data and ocean effects of the Fort Lauderdale Shipwreck Trail. This Gazebo simulation allowed for proper testing and refinement of object avoidance …

Evaluation Of Microwave Treatment Of Pfos-Laden Granular Activated Carbon, Rachel Melo Fonseca

Electronic Theses and Dissertations

This study investigated the microwave treatment as a potential regeneration approach for PFOS-laden granular activated carbon (GAC). PFOS was used as a model legacy PFAS, and calcium was selected to examine how inorganic constituents may influence PFAS loading and fluorine behavior during treatment. Microwave temperature profiling showed that GAC temperatures increased with treatment time and power level, reaching approximately 400-950°C. Selected microwave conditions reduced PFOS-derived extractable organic fluorine (EOF), with apparent EOF reduction increasing from 65.5% after 1 min to 96.8% after 4 min. Calcium increased PFOS-derived EOF loading before microwave treatment but did not show a consistent effect on …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

Characterization Of Various Corrosion-Resistant Rebars Embedded In Concrete After 20 Years Of Exposure, Samantha Mavrak

Electronic Theses and Dissertations

In aggressive marine environments, the deterioration of reinforced concrete structures is primarily caused by chloride-induced corrosion of embedded carbon steel reinforcement, resulting in concrete cracking, spalling, and costly maintenance or premature replacement. Corrosion-resistant reinforcement has been developed to mitigate these effects; however, long-term field performance data remain limited. This study evaluates the long-term corrosion performance of several corrosion-resistant rebars embedded in simulated deck slab concrete specimens following approximately 20 years of chloride exposure.

Concrete specimens containing 304SS, 316SS, and 2304 duplex SS, two types of clad reinforcement (316SS with carbon steel core), and an intermediate alloy with 12% Cr were …

Enhancement Of A Copolyester’S Impact Resistance Via Inorganic Reinforcement, David Felipe Gonzalez

Electronic Theses and Dissertations

Thermoplastic copolyester elastomers (TPEEs) are widely used in engineering applications because of their flexibility, toughness, and chemical resistance. However, prolonged exposure to marine environments can reduce their mechanical performance through moisture-induced degradation. This research investigated the use of titanium dioxide (TiO2) nanoparticles and APTES-functionalized TiO2 nanoparticles to improve the mechanical, thermal, and environmental durability of Hytrel 5556. Nanocomposites containing 1 wt.% and 2 wt.% TiO2 were fabricated through melt blending and compression molding. Mechanical properties were evaluated through tensile, compression, flexural, and impact testing, while thermal behavior was characterized using differential scanning calorimetry (DSC). Nanoparticle dispersion was examined using scanning …

Resource Constraint Evacuation Route Planning A Capacity-Aware Charge-Encoded State-Space Approach, Praveen Borra

Electronic Theses and Dissertations

Emergency Management Information Systems (EMIS) are defined as a set of tools that assist decision-makers in risk assessment and disaster response for significant multi-hazard threats and disasters. Over the past several decades, EMIS have become increasingly important for understanding, managing, and governing transportation systems during large-scale emergency events. One of the primary objectives of EMIS is to efficiently utilize spatial and network datasets to support evacuation planning, identify critical transportation patterns during emergencies, and allocate resources effectively. However, the increasing complexity and scale of modern transportation systems present significant challenges in developing reliable evacuation planning solutions.

One of the most …

Design And Fpga Deployment Of Quantized Convolutional Spiking Neural Networks For Ecg Arrhythmia Classification, Olamilekan Banjo

Electronic Theses and Dissertations

Wearable ECG monitors enable continuous cardiac surveillance, yet most remain limited to basic heart rate metrics or coarse atrial fibrillation detection, relying on cloud-based analysis that introduces latency, connectivity dependence, and battery drain. Deploying advanced multi-class arrhythmia classification directly on-device is constrained by the tight memory, power, and computational budgets of wearable hardware. This dissertation presents a Quantized Convolutional Spiking Neural Network (QCSNN) for real-time ECG arrhythmia detection on edge hardware, developed across three progressive phases.

Phase 1 introduces a separately trained two-stage QCSNN architecture — a binary classifier cascaded with a four-class classifier — trained directly via surrogate gradient …

Hybrid Patrol–Staging Optimization For Freeway Service Patrols Under Cost Control: A Deterministic Milp With Dynamic Segment Adjustment & Environmental Co-Benefit, Mauricio Micolta

Electronic Theses and Dissertations

Freeway Service Patrol (FSP) programs are central to Traffic Incident Management (TIM), delivering rapid response, clearance, and motorist assistance on high-volume corridors. The prevailing continuous roaming patrol model provides broad coverage but generates substantial non-productive vehicle-miles traveled (VMT), increases responders’ exposure to risk, and treats Service Level Agreement (SLA) compliance as a statistical outcome rather than a fixed operational constraint. Existing literature lacks a framework that simultaneously optimizes corridor segmentation, jointly deploys patrol and staged units across space, time, and direction, and enforces response-time SLA as a binding constraint.

This dissertation introduces the Segmental-Spatio-Temporal Hybrid Service Model (SSTHSM), a two-tier …

Operational Feasibility Of Reinforcement Learning For Vehicle Routing Under Heterogeneous Fleet Capacity Constraints, Freddy Giovanny Aviles Moreno

Electronic Theses and Dissertations

Reinforcement learning methods have demonstrated strong performance on vehicle routing benchmarks, yet their behavior under severe capacity constraints remains unexplored. This dissertation investigates whether PPO-based neural routing policies maintain operational viability when vehicle capacity is severely constrained, as occurs in resource-limited rural logistics settings.

Through controlled experiments on synthetic instances and validation on real-world rural healthcare networks in Florida, this research reveals a critical capacity threshold effect. Moderate capacity reductions from 40 to 20 produce negligible performance loss (4.1%), while severe reductions to capacity 10 trigger catastrophic failure with 243% degradation, manifested through degenerate single-customer routing patterns. Convergence analysis identifies …

A Computational Micropatterning Approach To Study Caveolae And Protein Localization In Vascular Biology, Andrew B. Grespin

Electronic Theses and Dissertations

Caveolae are specialized, flask-shaped membrane invaginations highly expressed in endothelium and dysregulated in atherosclerosis. Caveolae play a central role in buffering membrane tension, yet the principles governing their spatial organization remain elusive. Thus, we sought to generate the most comprehensive and systematic analysis of blood vessel caveolar spatial organization. However, cell culturing, the backbone of human-focused biological research, does not standardly do well to model physiologically relevant cell behaviors such as migration or polarity-based tissue formation, particularly for punctate proteins and structures like caveolae. Micropatterns are cell-adhesive shapes that biophysically confine cell(s) to a user defined geometry which stereotype organelle …

An Ai-Integrated Methodology For Secure Software And System Development, Ian Matthew Campbell Coston

Electronic Theses and Dissertations

Securing interconnected software systems requires more than layering existing frameworks on top of each other. Most current Secure Software and System Development Lifecycle (S-SDLC) models treat security as a phase rather than a design condition, leaving real gaps in governance, access control, and automated enforcement that become critical failure points in Internet of Things (IoT) environments where devices are resource-constrained, long-lived, and frequently insecure by default.

This dissertation introduces the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, a novel approach that unifies DevSecOps automation, the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF), and …

Performance Analysis Of Video Coding For Machines With Vision Transformers, Vaishnavi Dhulipudi

Electronic Theses and Dissertations

This thesis investigates the performance of Video Coding for Machines (VCM) with Vision Transformer based object detection models. While existing VCM studies and tool designs have largely been developed under CNN-based assumptions, recent advances in computer vision have shown the growing importance of transformer based models. Motivated by this shift, this work studies whether VCM compressed data remains suitable for Vision Transformer based inference in addition to conventional CNN-based task networks.

To address this problem, three representative transformer based object detection models were selected: DETR, SWIN, and YOLOS. These models were chosen to represent different architectural styles, namely a CNN …

Spad Camera Image Analysis, Pratheen Reddy Pininti

Electronic Theses and Dissertations

I present a thorough noise characterization of the Canon MS-500, a Single-Photon Avalanche Diode (SPAD) camera system, tested under both lit and dark conditions. The camera outputs 10-bit digital number (DN) values produced by an internal processing pipeline whose design is not publicly documented. All analyses in this thesis therefore describe the camera’s DN output — the signal that any downstream detection, tracking, or classification system will actually receive — rather than the photon-counting statistics of the underlying SPAD array. All computations were performed on the native 10-bit data. Where a measured quantity has a known photon-counting analog, the relationship …

Aquaculture Ecosystem Modeling Aeration Efficiency Optimization Using Cfd, Gabriela Reyes Gomez

Electronic Theses and Dissertations

The Harbor Branch Oceanographic Institute (HBOI) is developing the Intelligent and Resource Efficient Pond Aquaculture (IREPA) project, which aims to improve aquaculture management through the integration of predictive models and automated technologies. This approach seeks to transition traditional practices toward data-driven systems capable of supporting informed decision-making for aeration management.

This study investigates the effectiveness of different aerator configurations on hydrodynamic circulation and dissolved oxygen (DO) distribution in aquaculture ponds using computational fluid dynamics (CFD). A model was developed in ANSYS Fluent, where the aerator was represented using a power-based formulation and implemented as a moving wall boundary condition. Dissolved …

Adsorption Of Pfas On Bridged Functionalized Organosilica Materials, Elisha Lawerh Kabutey

Electronic Theses and Dissertations

PFAS are hazardous contaminants that have a devastating impact on human health and the environment. Their hazardous nature has led to the development of various adsorption methods for removing these contaminants from water sources. In this study, functionalized organosilica materials were synthesized from bis[3-(trimethoxysilyl)propyl] amine using the sol-gel method. The surface amino groups of the organosilica were converted into amine hydrochloride groups. Their adsorption properties were evaluated using salts of perfluorooctanoic acid, perfluorooctanesulfonic acid, and perfluorobutanesulfonic acid. Results showed excellent adsorption capacity of the materials. Adsorption of PFAS leads to particle agglomeration and flotation of the spent material. A column …

A Machine Learning Framework For Early-Stage Cost Estimation Of Transportation Projects, Pritom Paul

Electronic Theses and Dissertations

To address the limitations of traditional planning-level cost estimating methods, such as reliance on statewide averages, heavy reliance on engineering judgment, and high levels of inaccuracy, this study developed two data-driven frameworks and corresponding tools. The first framework focused on resurfacing projects and used treatment-specific simple linear regression based on lane miles and geographic location. The second addressed a broader group of construction projects and used multiple linear regression with project length, route type, and right-of-way cost as key predictors. In both frameworks, the final estimates are computed by aggregating multiple estimates produced by county-, region-, and state-level models. The …

Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu

Electronic Theses and Dissertations

Post-vote tampering during the collation and transmission of election results remains a persistent challenge in Nigerian elections, enabling manipulation of already-cast votes and weakening public trust in electoral outcomes. Existing technological interventions, including biometric voter accreditation and digital result transmission systems, improve voter authentication but do not adequately secure the post-vote result collation process. This thesis proposes a blockchain-enabled framework designed to protect the integrity of election results during the collation and transmission stages. Using a Design Science Research methodology, the study develops a permissioned blockchain framework based on Hyperledger Fabric that records polling-unit results as immutable ledger entries and …

Optimization Of A Point Absorber Wave Energy Converter (Wec), Rutwa Dilipkumar Nagar

Electronic Theses and Dissertations

This study examines the performance and optimization of a point absorber wave energy converter with a partially submerged spherical float. A comprehensive open-source computational framework integrates frequency-domain hydrodynamic analysis with time-domain simulation using Capytaine, BEMIO, and WEC-Sim. Hydrodynamic coefficients such as added mass, radiation damping, and excitation forces are calculated and integrated into the governing equation of motion using the Cummins formulation. A parametric analysis varies the float diameter and geometric configurations across several cases. Hydrodynamic coefficients and response amplitude operators are assessed to analyze system behavior and resonance characteristics. Results indicate consistent trends in normalized added mass, with radiation …

On Time-Series Analysis By Structured Matrix Decompositions With Applications To Signal Direction-Of-Arrival Estimation, Georgios Ierotheos Orfanidis

Electronic Theses and Dissertations

Modern autonomous systems operating in highly dynamic, non-stationary environments require reliable inference from short, potentially corrupted time-series measurements, where conventional statistical methods relying on large-sample support and stationarity assumptions become fundamentally inapplicable. This dissertation develops a unified, model-free theoretical framework for time-series analysis, with a particular application to signal Direction-of-Arrival (DoA) estimation, grounded in structured matrix decompositions under both the L2-norm and L1-norm formulations.

We first approach the problem from a conventional viewpoint by carrying out standard matrix analysis directly on Hankel-structured representations of time-series data. In this context, we demonstrate that L1-norm decompositions of Hankel matrices offer strong resistance …

Computer Vision And Deep Learning-Based Decision Support System Using Eye Motion Tracking For Nystagmus Detection, Kowshik Balasubramanian

Electronic Theses and Dissertations

This thesis presents the design, implementation, and experimental validation of an artificial intelligence (AI)-driven system for detecting and quantifying nystagmus an involuntary, rhythmic oscillation of the eyes intended as a portable, low-cost complement to conventional Videonystagmography (VNG). The complete pipeline integrates six algorithmic stages: face landmark detection, contrast enhancement, background-aware pixel thresholding, grid-based vertical column filtering, connected-component cluster analysis, and centroid computation, operating in real time on standard smartphone video to extract a sub-pixel normalized iris position time-series without any specialized eye-tracking hardware or infrared illumination. The system supports diagnostic decision-making, highlighting its promise for incorporation into telemedicine settings. The …

Influence Of Web Thickness And Concrete Cover On The Fire Resistance Of Aashto Type Iv Prestressed Concrete Bridge Girders, Fnu Nidhi

Electronic Theses and Dissertations

Bridge fires caused by vehicle accidents, fuel tanker explosions and storage of combustible material beneath highway structures have led to severe structural damage and catastrophic collapse of prestressed concrete bridges. Critical structural collapse due to fire incidents such as I-85 prestressed concrete bridge girder collapse in Atlanta, 2017 and I-95 steel plate girder bridge partial collapse in Philadelphia, 2023 demonstrated the vulnerability of girders when exposed to extreme hydrocarbon fire temperatures exceeding 1000°C within few minutes. These events highlight the urgent need to better understand the thermal and structural behavior of bridge girders and explore design modifications that can enhance …

Extraction And Interpretation Of Eeg Features For Diagnosis And Severity Prediction Of Ad And Ftd Using Deep Learning, Tuan Vo

Electronic Theses and Dissertations

Alzheimer’s disease (AD) is the most common form of dementia and is characterized by progressive cognitive decline and memory impairment. Frontotemporal dementia (FTD), the second most prevalent form, primarily affects the frontal and temporal lobes and often leads to changes in personality, behavior, and language. Due to overlapping clinical symptoms, FTD is frequently misdiagnosed as AD. Electroencephalography (EEG) offers a portable, non-invasive, and cost-effective method for studying brain activity; however, its diagnostic utility for differentiating dementia subtypes is limited by signal complexity and noise. In this dissertation, I propose an EEG-based feature extraction framework that leverages deep learning to identify …

Llm For Clinical Named Entity Recognition: A Study On Rag With Pubmed And Umls, Apoorv Tripathi

Electronic Theses and Dissertations

The first step of biomedical NLP is recognizing clinical named entities, which consist of identifying and categorizing a variety of clinical entities such as diseases, symptoms, genetics, diagnostic tests, procedures, etc. from a body of unstructured clinical text. This study presents a PubMed and UMLS based Retrieval Augmented Generation framework which improves the performance of the Large Language Models to identify clinical entities by providing context. In particular, the framework consists of a two-stage pipeline, where candidate tokens are identified from initial LLM-based classification and refined with retrieved context from either PubMed or UMLS. The proposed framework is assessed across …