Digital Commons Network^™

The Rise In Height And Curb Weight Of The Top-Selling Vehicles In The United States From 1991 - 2025: Implications For Engineering Design And Vulnerable Road Users, Samuel L. Steinman-Friedman

Civil Engineering Theses

Road fatalities are higher in the United States than in other high-income regions. In 2024, 39,254 people died on roads in the United States compared to 19,940 in the European Union. These two regions have comparable exposure metrics, such as population size, roadway network length, and fatalities per mile traveled. Light trucks dominate the U.S. market, whereas hatchbacks are prevalent in Europe. The trendline of the average height and curb weight of the top-selling vehicles in the United States has increased by 0.24 inches and 23.52 pounds per year since 1991. The average 2025 top-selling U.S. passenger vehicle is 7.5 …

A Machine Learning–Based Framework For Traffic Classification And Threshold-Based Load Management In 5g Network Slicing, Safi Ibrahim, Younis S. Younis, Kamal S. Hamza, Mohamed M. Ashour

Mansoura Engineering Journal

This study proposes a machine learning–based framework that applies machine learning techniques to improve the efficiency of 5G network slicing through automated traffic classification and threshold-based load management . The proposed model optimizes resource allocation among the three standardized 5G slice types: enhanced Mobile Broadband (eMBB), ultra-Reliable Low-Latency Communication (URLLC), and massive Machine-Type Communication (mMTC). Two supervised learning algorithms—K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)—are trained using Quality of Service (QoS) parameters such as packet delay, loss rate, and Quality Class Identifier (QCI). Experimental evaluations were conducted on two large-scale datasets containing over 400,000 traffic instances, demonstrating that the …

Autonomous Coupler Alignment Using Position-Based Visual Servoing In A Ros~2 Framework, Nickolas Giffen

Graduate Research Theses & Dissertations

As robotic arms are becoming increasingly common alongside humans as collaborative robots, their high precision in motion enables tasks to be performed at significantly higher speeds with reduced disruption in the environment. The Fermi National Accelerator Laboratory is exploring this application by incorporating a UR16e from Universal Robots in a cleanroom setting during assembly of couplers to superconducting radio frequency cavities as part of the PIP-II project. The goal of the robotic assembly process is to precisely position the UR16e robot so that the coupler flange, mounted on the robot’s end-effector, is accurately aligned with and pressed against the cavity …

Cross-Layer Supervisory Control For Low-Altitude Uav Swarm Networks, Nitin Singh Rathore

Computer Science and Engineering Theses

Low-altitude unmanned aerial vehicle (UAV) swarms are increasingly used in applications such as aerial sensing, disaster response, and communication support, where reliable operation under dynamic and uncertain conditions is essential. In these environments, performance degradation arises from multiple sources, including external disturbances, sensing uncertainty, and communication impairments. Although these effects originate from different layers of the system, such as dynamics, observation, and networking, they often manifest as similar tracking or coordination errors. Conventional control approaches, which rely primarily on error-driven feedback, do not explicitly account for the underlying cause of these deviations, limiting their effectiveness in multi-agent settings. This thesis …

Automated Object Detection And Change Quantification In Underground Mines Using Lidar Point Clouds And 360◦ Image Processing, Ana Fabiola Patricia Tejada Peralta, Roya Bakzadeh, Sina Siahidouzazar, Pedram Roghanchi

Mining Engineering Faculty Publications

Underground mining environments pose significant challenges for automated hazard detection due to low illumination, restricted visibility, and the absence of Global Navigation Satellite System (GNSS) coverage. These factors limit situational awareness and delay inspection efforts, particularly after disruptive events when rapid assessment is essential for safety. This study addresses this problem by developing a dual-pipeline framework for 2D–3D detection that uses 360° imaging and LiDAR-based machine learning to identify people, vehicles, and positional changes in underground settings without requiring personnel to re-enter hazardous areas. The objective was to create a system capable of recognizing objects and monitoring spatial changes under …

Guitar Amplifier Directivity, Rachel C. Edelman, Brian E. Anderson, Samuel D. Bellows, Timothy W. Leishman

Directivity

No abstract provided.

Ev Charging And V2g Operation For Distribution System Vpp Including Model Predictive Control, Rosemary E. Alden, Simone Silvestri, Malcolm D. Mcculloch, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Future smart grid virtual power plants (VPPs) are considered for development based on industry communication standards for electric vehicle (EV) chargers such as Open Charge Point Protocol (OCPP), IEC 15118, and IEC 61851. To support research and development of computationally intelligent controls for distributed EV batteries, a python-based API OpenDSS VPP framework is utilized with thousands of experimental smart meter profiles, the IEEE 123 node test feeder, and hundreds of national survey-based EV modules for conventional and optimal charging and vehicle-to-grid (V2G) control development to mitigate any voltage violations and reduce peak load. A methodology is proposed for model-predictive control …

Advancing Ethical Innovation In Human–Ai Collaboration: Trust And Legitimacy In Technology-Mediated Teams, Sanket Ramchandra Patole

Human Resource Development Theses and Dissertations

Human Resource Development (HRD) confronts a central paradox in the digital age. The technologies designed to broaden access to learning and collaboration often reproduce the same social hierarchies that HRD seeks to challenge. Artificial intelligence (AI), digital collaboration platforms, and algorithmic management are widespread features of organizational life. Yet these systems do not generate inclusive outcomes for all workers. Women of Color (WoC), situated at the intersection of racialized, gendered, and technological structures, experience digital transformation as both possibility and constraint. They encounter opaque decision systems, diminished authority in virtual teams, and digitally-mediated microaggressions, while also developing new capacities for …

Beyond Permissions: A Privacy And Security Analysis Of Sensor And Network Activity On Xr Devices Using Xrmonitor, Gayatri Sravya Siripurapu

Computer Science and Engineering Theses - Archive

Standalone virtual reality headsets integrate multiple sensors and cameras whose runtime behavior is not transparently documented, creating potential privacy and security risks that are difficult to audit using existing tools. The relationship between software feature activation and the underlying hardware engaged at runtime remains opaque, limiting the ability of users and researchers to assess what data is being collected and when.

This thesis presents XRMonitor, a unified framework for monitoring and analyzing sensor activation behavior and network traffic on Meta Quest devices. The framework is motivated by the lack of transparency in how standalone XR devices engage their hardware sensors …

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …

Sapphire Optical Fiber Bragg Grating Sensors Based On Dispersive Microwave-Photonic Frequency-Time Domain Analysis, Ruimin Jie, Chen Zhu, Farhan Mumtaz, Koustav Dey, Bohong Zhang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Sapphire fiber Bragg gratings (SFBGs) have attracted growing interest for high temperature sensing in harsh environments, yet their interrogation typically relies on optical spectrum measurements, demanding a high-resolution optical spectrum analyzer (OSA) that is bulky, expensive, and constrained in acquisition speed. Moreover, the inherently multimode nature of sapphire fiber further complicates spectrum-based demodulation, thereby limiting the achievable sensing resolution. In this paper, we propose and experimentally demonstrate a microwave-photonic interrogation approach for SFBG sensors. Instead of measuring the optical reflection spectrum, the complex frequency response in the microwave domain of an SFBG is acquired using a vector network analyzer (VNA) …

Interpretable Machine Learning For Bridge-Pier Scour Prediction And Flood Resilience, Adil Khan, Dalya Ismael

Engineering Technology Faculty Publications

Bridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 …

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

Mxene-Enabled Wearable Biosensors: A Design Framework For Autonomous Biosensing, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

MXenes, a rapidly expanding family of two-dimensional transition metal carbides and nitrides, have emerged as leading materials for wearable bioelectronics due to their metallic conductivity, termination-rich surfaces, mechanical compliance, and tunable interlayer structures. However, wearable biosensor performance does not arise from conductivity alone, but from coupled interactions among surface termination chemistry, heterointerface engineering, hierarchical architecture, and device integration under dynamic physiological conditions. This review establishes a predictive structure-interface-device framework linking MXene chemistry to system-level performance across electrochemical, mechanical, gas, optical, and energy-storage modalities. We analyze how termination-controlled adsorption governs charge transfer and selectivity, how heterojunction formation modulates carrier density and …

Pillpetz: A Smarter Way To Encourage Medication Adherence In Children, John H. Begley

CMC Senior Theses

Medication adherence is usually framed as a problem of patient behavior, but this thesis argues that it is equally a problem of design. Children who take daily medication face barriers that adults often do not: developing executive function, dependence on caregivers, shifting school and home routines, privacy concerns, and stigma around being perceived as different. The standard prescription bottle, by contrast, was designed primarily for dispensing efficiency, safety, and accidental ingestion prevention—not for sustained daily use by a developing child.

This thesis proposes PillPetz, a smart pill case and digital companion that uses routine, play, dose confirmation, and caregiver-connected support …

Enhancing Measurement Accuracy: The Impact Of Missing Data On Parameter Estimation In Mass-Spring-Damper Systems, Michkath Omanda Bouraima, Steven Thompson, Maciej J. Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In sensor-driven dynamic systems, missing data can severely degrade parameter estimation accuracy. This article investigates the impact of missing data on phase estimation in a mass-spring-damper system using an information-theoretic framework based on the Cramér-Rao Lower Bound (CRLB). Closed-form CRLB expressions are derived for four scenarios: complete data, missing completely at random (MCAR) deletion, MCAR-based imputation, and missing at random (MAR) missingness via a selection-weighted formulation. These bounds are used as theoretical benchmarks to evaluate classical imputation methods (last observation carried forward (LOCF), linear interpolation) and advanced approaches (Kalman filtering, Rauch-Tung-Striebel (RTS) smoothing, Bayesian inference, and transformer-based imputation) through Monte …

Evaluating The Impact Of Cognitive Distraction On Spaceflight-Relevant Task Performance Using Surface Electromyography And Motion Capture, Allyson K. Mitchell

UNF Graduate Theses and Dissertations

Cognitive distraction poses a risk to astronaut performance during complex, multitasking operations in spaceflight environments. This study examined the effects of cognitive load on neuromuscular coordination and task execution using surface electromyography (sEMG) and motion capture. Thirteen participants performed spaceflight-relevant tasks under undistracted and distracted conditions, with distraction induced through verbal questioning. EMG signals from eight upper-extremity muscles were processed using envelope filtering, peak normalization, and time normalization to enable inter-subject comparison, and group-level mean activation with standard deviation was analyzed. While overall muscle activation was similar between conditions, phase-dependent differences were observed, with undistracted trials showing higher activation during …

Experimental Validation Of Optical Wireless Communication And Power Transfer For Uav Applications, Fnu Dhruv

UNF Graduate Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) have revolutionized emergency response, disaster assessment, and search-and-rescue operations. However, their operational efficacy is fundamentally constrained by limited battery endurance and the susceptibility of traditional radio-frequency communication to disruption in adverse weather. To address these limitations, this thesis proposes and experimentally validates a novel architecture integrating Free-Space Optical (FSO) communication with Simultaneous Lightweight Information and Power Transfer (SLIPT). This system utilizes a split-beam configuration to concurrently enable high-bandwidth data transmission and optical energy harvesting to replenish the UAV's battery pack. The research was conducted in three progressive phases. Initially, system feasibility was established through rigorous optical …

Development Of Thermal Joints For Conduction-Cooling Applications, J. Lewis, G. Ciovati

Mechanical & Aerospace Engineering Faculty Publications

In support of the development of conduction-cooled superconducting radiofrequency (SRF) niobium cavities for use in continuous-wave linear accelerators, an experimental study of thermal contact resistance was performed on bolted joints using high-purity niobium, aluminium, and copper along with Apiezon N grease and indium foil as interfacial materials. The geometry of each joint investigated aims at replicating the design adopted in conduction-cooled SRF cavities currently under development at Jefferson Lab. The materials' thermal conductivity and the joints' thermal resistance were measured in the temperature range of 3.5 - 10 K. The results showed that the low thermal contact resistance of 2-4 …

Enhancing The Performance Of Disk-Based Key-Value Stores: From Learned Index Acceleration To I/O-Efficient Hybrid Caching, Sujit Maharjan

Computer Science and Engineering Dissertations - Archive

The exponential growth of data in modern computing environments has rendered the efficient extraction of information from massive datasets a critical systemic requirement. Key-value (KV) storage systems serve as the backbone for these operations; however, their performance is consistently bottlenecked by two primary functional requirements: identifying the data's location and managing the physical cost of accessing the storage device. Data locations are typically identified via an index, while disk I/O is minimized through caching. This dissertation presents LearnedStore, TurboIndex, and ReadBooster, which break these performance bottlenecks by introducing architectural modifications to the index and cache. LearnedStore accelerates operations by adapting …

Adaptive Synchronization In Digital Twin–Enabled Iot Systems: A Unified Framework For Energy, Fidelity, And Latency Trade-Offs, Uzma Zehra

Computer Science and Engineering Theses

Digital twin technology has emerged as a foundational paradigm for enabling real-time monitoring, analysis, and control in Internet of Things (IoT) systems by maintaining virtual representations of physical processes. Its effectiveness, however, critically depends on timely and accurate synchronization between distributed sensing devices and their corresponding digital counterparts. Frequent synchronization improves reconstruction fidelity and system responsiveness but incurs significant communication energy consumption and network latency. In contrast, infrequent synchronization conserves communication resources but can lead to stale or inaccurate digital twin states, particularly in environments with rapidly changing dynamics. These opposing effects give rise to a fundamental trade-off among energy …

Exploring Relational Farming Futures For Responsible Innovation In Precision Agriculture Through Q-Method And Speculative Design, Bhavna Joshi, Maaz Gardezi, Edward Prutzer, Awais Hameed Khan, Asim Zia, Benjamin E. K. Ryan, Ali Dadkhah, Donna M. Rizzo, John T. Mcmaine, David Clay

Biosystems and Agricultural Engineering Faculty Publications

This article draws on concepts of speculative design and responsible innovation to examine the possible impacts of data-driven precision agriculture on farmers and farm work. We use an innovative mixed-method approach to research design. Using artistic depiction of futures of precision agriculture in design workshop and in-depth semistructured interviews conducted with the farmers in Vermont and South Dakota, we explain how farmers perceive different futures of precision agriculture. Specifically, in our design workshop, we combined visual arts and social science methods, namely Q-method, to explore existing and new relationships between humans, technologies, and environment in the design, deployment, and use …

Charge‐Governed Mechanistic Evaluation Of Aav2 Clarification Via Functionalized Flat Sheet And Hollow Fiber Deconstructed Depth Filters, Mara Baughman‐Leach, Mattie Brock, Malgorzata Chwatko, Dibakar Bhattacharyya

Chemical and Materials Engineering Faculty Publications

The expansion of adeno-associated virus (AAV)-based therapeutics has increased the demand for efficient clarification. In this study, a functionalized deconstructed depth filtration platform was developed to dissect how membrane–virus charge, modulated by pH, influences separation. Three charge regimes, positive (pH 4), neutral (pH 6), and negative (pH 8) between the membranes and AAVs, were examined. The flat-sheet system, composed of sequential 1.2, 0.8, 0.45, and 0.1 µm membranes, enabled the first layer-by-layer analysis of depth filtration behavior. This revealed that Stage 1 and Stage 4 dominate clarification. Remarkably, substantial DNA capture (> 75%) was observed even in the largest-pore stage, …

Noninvasive Diffuse Optical Monitoring Of Cerebral Blood Flow And Oxygenation Responses To Intermittent Hypoxia In Neonatal Rats, Pegah Safavi, Mehrana Mohtasebi, Chowdhury Azimul Haque, Faezeh Akbari, Xuhui Liu, Yiqi Yuan, Li Chen, Lei Chen, Guoqiang Yu

Biomedical Engineering Faculty Publications

Significance: Intermittent hypoxia (IH) is common in preterm neonates and can cause hypoxic–ischemic brain injury. Simultaneous monitoring of cerebral blood flow (CBF) and oxygenation is essential to detect oxygen delivery-extraction mismatches and guide intervention.

Aim: We aimed to adapt and test an innovative diffuse speckle contrast flow oximetry (DSCFO) system for continuous monitoring of cerebral hemodynamics during IH in neonatal rats, a model approximating human neonates.

Approach: Two compact laser diodes and a miniature CMOS camera were integrated into a fiber-free probe for continuous monitoring of changes in relative CBF (rCBF) and oxy- and deoxy-hemoglobin concentrations (Δ[HbO₂] and …

Simulation And Characterization Of Fuel Cell System Incorporated Internet Of Things, Ahmad N. Alhjouj

Mechanical Engineering Theses

Small air-cooled proton exchange membrane fuel cell (PEMFC) systems are increasingly relevant for portable and distributed applications, yet practical modeling and monitoring methods remain limited: high-fidelity physics-based models are too costly for real-time use, while purely data-driven models discard physical interpretability. This thesis develops an experimentally grounded gray-box framework for the characterization, reduced-order modeling, and IoT-enabled monitoring of a small hydrogen PEMFC system. A low-order nonlinear state-space model couples an electrochemical voltage layer to a lumped thermal layer, with parameters identified in stages using self-adaptive differential evolution. Identifiability analysis shows that static loss parameters are well constrained, while dynamic parameters …

Computational Study Of Rotating Detonation Combustors, Aditya Balasubramaniam

Mechanical and Aerospace Engineering Theses

Rotating detonation combustors (RDCs) are pressure-gain combustion devices that sustain one or more continuously rotating detonation waves, offering potential thermodynamic and performance advantages over conventional deflagration-based systems. Their behavior depends strongly on combustor geometry and operating conditions. Understanding these effects is therefore essential for the design and optimization of practical RDCs. Accordingly, this thesis numerically investigates annular RDCs with two primary objectives: (1) to evaluate the effects of propellant mass flux and (2) to assess the influence of annular width on detonation-wave dynamics and combustor performance.

A finite-volume framework is used to solve the compressible reactive Euler equations with hydrogen–air …

Scalable Quantum Network Routing Through Reinforcement Learning And Resource Optimization, Tasdiqul Islam

Computer Science and Engineering Dissertations

Long-distance quantum communication depends on distributing high-delity entanglement across quantum repeaters. Entangled states are fragile: they decohere in memory, are consumed when used, and lose delity after each swap. Quantum routing therefore diers from classical routing: an algorithm must decide not only the path, but when to generate, store, swap, and consume entanglement before they lose their usefulness. This dissertation studies scalable resource allocation and routing for quantum networks under delity, memory, and concurrency constraints. It rst addresses re- peater deployment with heuristics that nd near-optimal locations while cutting com- putation from days to seconds versus integer linear programming (ILP). …

Early Detection And Objective Assessment Of Neonatal Hypoxic-Ischemic Encephalopathy Severity, Soheila Norasteh

Bioengineering Dissertations

Hypoxic–ischemic encephalopathy (HIE) is a neonatal brain injury caused by reduced oxygen and blood flow to the brain around the time of birth. It remains a major cause of neonatal mortality and long-term neurodevelopmental impairment worldwide. Therapeutic hypothermia is the standard treatment for moderate-to severe HIE and improves outcomes when initiated within the first six hours of life. Therefore, accurate assessment of injury severity during this period is essential. Cur rently, HIE severity is determined primarily through neurological examination and classified as mild, moderate, or severe. However, these examinations are subjective, cannot provide continuous monitoring of brain function, and may …

Array Signal Processing And Machine Learning In 5g/6g Networks, Roopesh Kumar Polaganga

Electrical Engineering Dissertations - Archive

This dissertation investigates advanced methodologies in Array Signal Processing (ASP) and Machine Learning (ML) to enhance the performance, efficiency, and intelligence of next-generation wireless networks, with a primary focus on 5G and emerging 6G systems. As wireless networks face rapid traffic growth, increasingly heterogeneous service requirements, and more complex propagation environments, conventional design and optimization approaches become insufficient to meet evolving demands in reliability, capacity, spectral efficiency, and energy efficiency. On the network intelligence side, this work develops data-driven frameworks for causal discovery, scheduler enhancement, session-duration prediction, and Radio Resource Control (RRC) state optimization using real-world telecommunication network data. On …

Passive Communication Across Diverse Swarm Formations And Scales Utilizing Wake Signatures For Messaging And Object Inference, Bryan Varela

Open Access Master's Theses

Passive wake signatures in fluid flows can support perception and low-rate communication in swarms. In cluttered or contested underwater environments, conventional acoustic, radio, and optical links can be power-hungry, intermittent, or undesirable when low observability is required. Wake-mediated cues offer a local, directional channel that does not require line of sight because each agent naturally sheds coherent vortices that persist downstream and can be sampled by followers with only a small number of probes.

This study evaluates whether sparse downstream probes are sufficient to infer agent attributes and decode simple messages from wakes generated by established source shapes. Computational fluid …