Digital Commons Network^™

3d Face Modeling From 2d Images Using Deep Neural Networks, Mario Alberto De La Cruz Armendariz

Open Access Theses & Dissertations

Applications of 3D face reconstruction include biometric authentication, personalized avatars and digital identity, medical visualization, forensic analysis, and broader human-computer interaction. We propose an approach to 3D face reconstruction that can generate a fully textured 3D facial model using only two grayscale images: a front view and a profile view of the subject. Once trained, the system can perform the reconstruction autonomously without manual intervention. Unlike traditional methods requiring multi-camera setups, depth sensors, or cloud-based processing, the proposed approach runs fully offline on a standard CPU, supporting dynamic execution across CPU cores and eliminating the need for a dedicated GPU. …

Design And Validation Of Model Predictive Controllers For Lateral Vehicle Control, Keren Flores

Open Access Theses & Dissertations

This thesis develops, implements, and evaluates a Model Predictive Control (MPC) framework for lateral vehicle guidance using a linearized bicycle model. The complete control architecture is formulated from first principles, beginning with the derivation of the continuous-time planar dynamics and their conversion into a discrete-time state-space representation suitable for real-time control. The MPC problem is constructed explicitly through the development of prediction models, output selection matrices, and quadratic cost formulations. The controller is evaluated through a Python simulation environment that includes reference trajectory generation, nonlinear vehicle propagation, and constraint handling. Robustness is examined using systematic perturbations to vehicle mass, tire …

Microgrid Assessment And Ml-Based Power System Faults Detection Leveraging Real-Time Co-Simulation, Diego Normando Gandara Mendez

Open Access Theses & Dissertations

The rapid growth of distributed energy resources (DERs) and the increasing reliance on data-driven decision making have reshaped the operational challenges of modern electric power systems. As microgrids become more prominent in distribution networks, utilities require methods that unify planning, control, and real-time situational awareness to ensure resilient operation under faulted or uncertain conditions. The goal of this MSEE thesis is to design and validate a latency-aware ML framework for rapid, reliable fault detection in distribution grids. To achieve the goal of the thesis, there are three specific objectives. Objective 1 evaluates optimized microgrid configurations under varying DER levels and …

Comparison Of Ti-6al-4v Powder And Bulk Specimen Properties Across Manufacturers In Laser Powder-Blown Ded, Alejandro Javier Hernandez

Open Access Theses & Dissertations

The investigation and comparison of 4 different Ti64 powder feedstocks from 3 different manufacturers was done to determine if the quality of the powder had a profound effect on the properties of the bulk specimens despite using the same processing parameters. The characteristics of the powder observed are the atomization process used, particle size distribution, flowability, and its density. The Ti64 printing parameters for directed energy deposition that were kept consistent were the feed rate, scanning speed, and laser power. Coupons with varying build angles (0-30°) were printed to measure the impact of overhangs on bulk specimen properties (ultimate tensile …

Further Insights Into The Network Link Outlier Factor's (Nlof) Light-Load Penalty, Sunday Oluwaleke Ogundele

Open Access Theses & Dissertations

This research investigates the performance of the Network Link Outlier Factor with Most Likely Links (NLOF:MLL), under varying network load conditions. Earlier studies reported that the NLOF:MLL algorithm experienced a noticeable drop in fault-localization accuracy when operating in lightly loaded networks. To further examine this limitation, 240 experiments were carried out to observe how the algorithm responds as overall network load increases. The evaluation focused on the classification performance metrics: precision, recall, and F1-score. The results show that NLOF:MLL’s effectiveness improves as network load increases but that the rate of improvement slows progressively, eventually stabilizing in a pattern consistent with …

Powder Degradation Analysis In Reused Pure Tungsten For Electron Beam Melting (Ebm), Hernan Valenzuela

Open Access Theses & Dissertations

Additive manufacturing (AM) enables unparalleled design freedom and geometric complexity, driving its adoption in aerospace and medical fields. A major advantage of metal powder AM technologies, such as Electron Beam Melting (EBM), is its ability to reuse unused metal powder, reducing material waste and costs. However, reusing powder repeatedly alters its properties, affecting the final product. Despite its significance, research on powder degradation remains limited, primarily focusing on alloys like Ti-6Al-4V. This thesis investigates the degradation of pure tungsten powder reused over multiple Electron Beam Melting (EBM) cycles. A literature review revealed general powder-reuse mechanisms across additive manufacturing (AM) technologies …

Optimizing Pv Solar Array Design By Analysis Of The Industrial And Training Assessment Center (Itac) Database, Angel Samuel Fernandez

Theses and Dissertations

Since 2017, the Industrial Training and Assessment Center (ITAC) at UTRGV has included photovoltaic (PV) system evaluations in its industrial energy assessments. A review of these reports showed inconsistencies in PV system design, including variations in sizing methods, performance assumptions, and application of NEC requirements. To address these issues, this thesis develops a standardized and NEC-compliant methodology for designing PV systems, incorporating demand-based sizing, solar-geometry principles, and electrical calculations guided by NEC Articles 690 and 705.

This thesis focuses on demand-based PV system sizing and incorporates NEC-guided electrical design to ensure technical compliance and safety. System performance is evaluated using …

A Human Factors Analysis And Classification System (Hfacs) Approach To Compare Cardiovascular, Orthopedics, Trauma Care, And Neurosurgery, Asfandyar Khan

Doctoral Dissertations and Master's Theses

Background: Despite significant progress in patient safety, human error continues to occur at high rates in procedural settings. Traditional safety models tend to focus on retrospective incident analysis, often overlooking the systemic conditions that allow errors to occur. The Human Factors Analysis and Classification System (HFACS) offers a proactive lens to understand how and where errors emerge and is useful for identifying active and latent failures.

Objective: To evaluate the utility and reliability of the Human Factors Analysis and Classification System (HFACS) in categorizing and comparing the pattern of frequencies (events) in cardiovascular, orthopedic, trauma care, and neurosurgical procedure.

Design, …

Thermal Management With Supercritical Carbon Dioxide Under Extreme Applications, Devon Hardy

Doctoral Dissertations and Master's Theses

A unique challenge in thermal system design is minimizing the power required to operate cooling systems while maintaining effective heat removal. Traditional cooling systems utilize single-phase fluids where the heat transfer mechanisms are well understood. To meet a variety of cooling demands, a range of technologies are available, including microchannel forced convection, jet impingement, porous media, nanofluids, pin fin arrays, and film cooling, each offering distinct advantages and limitations. While the performance of single- or two-phase cooling systems are generally predictable, in extreme thermal environments, new cooling solutions are needed to improve overall system efficiency and reliability.

Supercritical fluids, particularly …

Propeller Noise Prediction Of An Urban Air Mobility Vehicle In Urban Environment, Jie Hua

Doctoral Dissertations and Master's Theses

Urban Air Mobility (UAM) vehicles operate in complex, unsteady aerodynamic environments where wake interactions, gust and turbulence significantly affect both performance and noise generation. Understanding these mechanisms is critical for developing quieter and more efficient eVTOL systems that meet future community noise and certification requirements. This dissertation focuses on the high-fidelity numerical investigation of propeller noise in three representative unsteady loading scenarios: (i) transition (tilt) flight, (ii) edgewise inflow, and (iii) wake–propeller interaction. Each case emphasizes different physical mechanisms that dominate noise generation in UAM operations. Hybrid turbulence-resolving approaches—Detached Eddy Simulation (DES) and Delayed Detached Eddy Simulation (DDES)—are coupled with …

Further Evaluation Of Speed Tables On Roadway Curves: Simulation And Expeditionary Deployments, Nathan Reineke

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The road network leading up to a United States military base is known as an Entry Control Facility, or ECF. Entry control facilities are used to shield military installations by monitoring vehicles and refusing access to unauthorized vehicles. Passive control devices, including speed tables, are used to disrupt or delay threat vehicles attempting to navigate ECFs at high speeds. The purpose of this research was to recommend optimized configurations of speed tables in combination with roadway curves which allow safe, efficient traversal for authorized vehicles and disruption or delay for threat vehicles.

First, an analytical model of a Tesla Model …

Machine Learning-Enabled Safety-Critical Model Predictive Control For Uncertain Dynamical Systems, Hossein Nejatbakhsh Esfahani

All Dissertations

This dissertation explores the interactions between Model Predictive Control (MPC), Safety-critical Control, and Artificial Intelligence (AI)/Machine Learning (ML) methods, with a particular focus on Reinforcement Learning (RL) and Bayesian Optimization (BO). We then leverage AI/ML to address several challenges in the control design and safe operation of uncertain dynamical systems. In many applications, ranging from autonomous vehicles and robotics to energy systems and industrial processes, ensuring safety is as essential as satisfying control objectives. The use of Control Barrier Functions (CBFs) within the MPC framework recently has emerged as a powerful tool to guarantee safety by enforcing constraints in optimal …

Reverse (Bio)Engineering: A Machine Learning Approach To Optimize Baseball Pitcher Health And Performance, Robert C. Moore

All Dissertations

Ball tracking systems are becoming ubiquitous in sport, creating an unprecedented opportunity for big data applications to optimize human health and performance. These applications are especially common in baseball, a sport known for analyzing ball flight data to quantify performance. Analysts routinely use ball flight data to identify the attributes of top performing pitchers, finding that the best pitchers throw with optimal combinations of release speed and spin to precise locations. However, for certain pitchers, the throwing motion required to produce optimal ball flight places exceedingly high biomechanical load on the elbow, and consequently injury rates continue to rise. This …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

All Dissertations

Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

All Dissertations

This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Integrating Dft And Machine Learning To Predict Structural Properties In High Entropy Alloys, Nathan Linton

All Dissertations

In the past decade, a paradigm shift in the design of metal alloys has been observed. These new alloys are commonly referred to as high entropy alloys (HEAs), multi-principal element alloys (MPEAs), or complex, concentrated alloys (CCAs). In contrast to conventional alloys, which consist of one main element (for example 80%) with other elements in small amounts, HEAs are made of four or more main elements ranging from 5 to 35% each element. Due to the large presence of multiple elements, HEAs have shown substantial material property improvements over conventional alloys such as steel. For example, they have high ductility …

Accident Data Analytics And Ai-Based Auditory Enhancement For Highway Construction Safety, Thinh Nguyen

All Dissertations

This research develops data-driven and AI-enhanced approaches to improve worker safety in highway construction environments. The study integrates advanced analytics and auditory signal enhancement to identify high-risk worker behaviors and improve real-time safety warnings. The work consists of three core studies. The first study applies Sequential Pattern Mining and Social Network Analysis to more than 1,000 construction accident reports to identify high-risk worker actions and their hidden sequential relationships with accident types and consequences. These insights reveal sector-specific risk patterns, supporting the creation of targeted, data-informed safety interventions. The second study introduces an AI-based auditory enhancement model built on a …

Universal Systems Simulation Via Constraint Hypergraphs With Applications To Digital Twins, John Morris

All Dissertations

The characterization of systems encompasses a variety of modeling frameworks designed to capture specific behaviors and components of various system domains. Whatever the framework, the core elements of a system representation are the information of the system and a description of how that information is related. The relations in deterministic systems are functions, which, when composed to form executable processes, can be used to simulate system data. A declarative modeling framework is one that encodes mechanisms for preparing these simulations within the model structure, allowing an external agent to form the execution processes required for a given context. To date, …

End-To-End Direct Current For Standalone Power Network, Eyad Ahmad Aldarsi

All Dissertations

The primary issue that faces the humanity is climate change and because of that greenhouse gases (GHGs) emissions are rising. The consequences of this issue have emphasized the necessity for replacing the dominant method of electricity generation, which mostly utilizes fossil fuels, to be centered on access to all, green, low-cost, and renewable sources of energy. Currently, photovoltaics (PV) and wind turbines are the two technologies so far that can convert the source of renewable energy, which is produced by sun irradiance and wind respectively, into large-scale electric power that can be distributed into the electricity grid. The work in …

Molecular Dynamics Simulations Of Biologically Relevant Interfaces: From Tear Film Lipid Layers To Rna Adsorption On Silica Surfaces., Mahbuba Khanom

Electronic Theses and Dissertations

Molecular dynamics (MD) simulation is an important tool in biochemical sciences for studying complex molecular processes and interfacial interactions at the molecular level. Biological interfaces play key roles in processes such as ocular lubrication and biomolecular adsorption. This dissertation studies interfacial phenomena involving two distinct yet conceptually related systems: the tear film lipid layer (TFLL) at the air–water interface at the surface of the human eye, and RNA adsorption on silica surfaces. These studies provide insight into how interfacial composition, structure, and environmental conditions affect larger-scale behavior. Atomistic MD simulations were used to model the TFLL. The TFLL is essential …

Scalable Carbon-Based Electrodes For Hole-Transport Layer-Free Perovskite Solar Cells., Sharmin Akter

Electronic Theses and Dissertations

Perovskite solar cells (PSCs) have rapidly reached certified single-junction efficiencies near 26%, but deployment is still limited by operational instability, scale-up challenges, and the cost and complexity of typical device stacks. This dissertation explores a simpler, scalable approach: HTL-free PSCs using low-cost, chemically robust carbon (e.g., carbon black) counter electrodes compatible with low-temperature, solution processing. This work demonstrates how carbon-ink formulation, scalable deposition (blade vs. slot-die), and work-function-tuning additives control film quality, interfacial energetics, and device performance. Carbon inks were developed for both coating methods by systematically varying carbon-to-binder ratio, polymer chemistry, and solvent system. Among the binders screened, ethyl …

Finite Element Analysis For The Development Of A High-Efficiency Novel Dual-Stator Electric Generator For Renewable Energy Conversion Applications, Sunday Omokhoya Joseph

Engineering

Renewable energy has become a persuasive solution for reducing the negative impacts of conventional energy sources and providing sustainable options to rural communities without access to the power grid. As the development of renewable energy advances, electric machines are crucial for efficiently converting and integrating these energy resources into the electrical grid. However, conventional single-stator alternating current electric machines face several challenges that hinder efficient energy conversion, such as harmonic distortion, high core and copper losses due to multi­ple winding groups, and structural complexity. These issues limit their performance, particularly in renewable energy applications such as wind and hydrokinetic power, …

State And Parameter Estimation Of Synchronous Generator Using Particle Filter In Classical Model Of Generator, Sauraj Poudel

Engineering

The accurate parameterization of machines in power systems is crucial during modeling to study the behavior of the modern power system. However, the parameters of synchronous machines are susceptible to change from the original manufacturer’s specifications due to factors like degradation of the windings and core or for rewound machines. Traditional methods to update these parameters require taking generators offline for various testing methods, which results in substantial revenue losses to the utility and sometimes blackouts in small remote microgrids. To overcome this challenge, this study proposes an approach for online parameter estimation by using available data from the generator …

An Audio Data-Driven Roadway Digital Twin And Its Underlying Framework For A Digitized Transportation Construction Environment, Anisha Deria, Pedro J. Chacon Dominguez, Yong-Cheol Lee, Jin W. Choi

Michigan Tech Publications

The increasing need to build and maintain transportation systems has led project managers to manage multiple projects simultaneously. Roadway projects often entail several miles of job site, making it difficult to keep track of progress and maintenance activities. To improve the situation, this study proposes an audio data-driven roadway digital twin framework for real-time and remote monitoring of construction projects. The latent characteristics of a digital twin required for establishing a digitized work environment were investigated. As a primary method of seamlessly linking virtual and physical environments, audio data classified and analyzed by deep neural network (DNN) has been employed …

Processing-Driven Crystallization, Morphology, And Mechanics In Pekk Composites, Nicholas Ryan Enos

Dissertations

This dissertation aims to understand how thermoplastic composites can be processed at faster manufacturing rates while identifying how processing variables influence crystallization, morphology, and mechanical properties. Throughout this dissertation, the influence of nano-scale additives, carbon fibers, rapid cooling, shear, and molecular weight on polymer physics, crystallization, morphology, and mechanics is investigated.

The inclusion of carbon nanotubes and graphene nanoplatelets accelerated crystallization, increased nucleation density, reduced lamellar thickness, and shifted governing Hoffman-Lauritzen growth regimes in PEKK. Velisaris-Seferis modelling identified a surface-induced nucleation mechanism in PEKK nanocomposites, and fast scanning calorimetry allowed for the study of rapid cooling rates, helping to identify …

Human Identification And Action Recognition Using Small Data And Deep Domain Adaptation, Alexander M. Glandon

Electrical & Computer Engineering Theses & Dissertations

Human identification and human action recognition problems are two important research areas for real-world security and surveillance applications. In both human identification and action recognition, it is necessary to operate by collecting small datasets in the field, possibly in a short time window of observation. This dissertation studies and develops computational modeling and high-performance machine learning (ML) and deep learning (DL) models for human identification and human action recognition using small amounts of data. These methods and computational models may be useful for different security and surveillance applications.

This dissertation on human recognition develops a ML computational model to estimate …

On The Scalability Of Anisotropic Mesh Adaptation On Distributed And Shared Memory Architectures For Numerical Approximations, Kevin Mark Garner Jr.

Computer Science Theses & Dissertations

Mesh generation is a critical component in numerical approximations of Partial Differential Equations (PDEs). One such example includes Computational Fluid Dynamics (CFD), as CFD simulations in turn are crucial for applications in many industries, such as personalized healthcare and the design of aerospace vehicles. Generating high quality meshes for large-scale CFD problems presents a significant bottleneck in the CFD workflow. This dissertation proposes “fast,” parallel 3D mesh generation methodologies that are designed to leverage the concurrency offered by emerging High-Performance Computing (HPC) architectures. First, a distributed memory method is presented that integrates a sequential state-of-the-art isotropic, advancing front local reconnection-based …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Diabetes-Induced Mitral Valve Degeneration: Tissue-Engineered Solutions And Antioxidant Therapy, Samantha Hahn

All Theses

Mitral valve (MV) fibrosis is a progressive pathological condition characterized by leaflet thickening and stiffening, ultimately compromising valve function. This process is accelerated in diabetes, where chronic hyperglycemia elevates reactive oxygen species (ROS) and alters the mechanics of the extracellular matrix (ECM), collectively promoting the activation of valvular interstitial cells (VICs). Under healthy conditions, VICs maintain a quiescent, fibroblast-like phenotype responsible for sustaining ECM homeostasis. However, pathological biochemical cues or mechanical stress drive VICs toward a myofibroblast phenotype. While this transformation is essential for short-term repair, its persistence contributes to maladaptive MV remodeling and fibrosis. Diabetic stressors may be mitigated …

Mitral Annular Calcification In Diabetes: Valvular Interstitial Cells, Antioxidant Therapy, And Tissue Engineering Approaches, Raleigh J. Keenan

All Theses

Mitral annular calcification (MAC) is a chronic, progressive, and degenerative condition that impairs mitral valve function and increases the risk of cardiovascular disease. The mitral annulus is a saddle-shaped, fibrous structure that is primarily composed of collagen and elastin. The annulus also contains interspersed lipids that are involved in cellular metabolism, as well as inflammatory and osteogenic signaling. These lipids increase the annulus’ susceptibility to calcification, under conditions that increase mechanical stress (hypertension) and promote atherosclerosis (diabetes mellitus). These conditions intensify calcification by promoting the activation of valvular interstitial cells (VICs), increasing fibrosis, and inducing osteogenic differentiation. Although it is …