Engineering Commons^™

Multi-Modal Data-Efficient Learning For 3d Machine Vision, Zhimin Chen

All Dissertations

The rapid progress of 3D computer vision has enabled a wide range of applications in autonomous driving, robotics, and augmented reality. Despite this growth, training robust 3D perception models remains challenging due to limited labeled data, the complexity of integrating multiple modalities, and the inherently imbalanced and long-tailed nature of 3D datasets. This dissertation addresses these challenges by proposing data-efficient, multi-modal learning frameworks that improve the accuracy, generalization, and scalability of 3D scene understanding.

In the semi-supervised setting, this work presents novel approaches that combine limited annotations with large amounts of unlabeled data to enhance 3D object classification and retrieval. …

Synthesis And Characterization Of Chemically Recyclable Polypropylene-Based Covalent Adaptable Networks, Andrii Tiiara

All Dissertations

Polyolefins (POs) are inexpensive engineering materials with excellent physical and mechanical properties, accounting for nearly 60% of all thermoplastics. However, large-scale recycling remains limited, with only ~15% undergoing mechanical recovery. This dissertation addresses this challenge by fragmenting and functionalizing polypropylene (PP) chains to generate macromonomers and employing them, along with industrial reactive PP macromonomers, to synthesize, depolymerize, and repolymerize chemically recyclable polypropylene-based polyolefins (CR-POs). These CR-POs incorporate ester linkages that form covalent adaptable networks (CANs), maintaining a gel fraction of ~70% while remaining melt-reprocessable via extrusion and compression molding.

First, the fragmentation of PP is investigated by microwave irradiation. Microwave …

Robust Data-Driven Predictive Control Of Nonlinear Systems Under Modeling Uncertainty, Pegah Ghafghanbari

All Dissertations

Data-driven predictive control enables designing controllers directly from data, making it attractive for complex systems with hard-to-model dynamics. However, practical deployment is challenged by modeling inaccuracies and changing operating conditions. This dissertation develops predictive control frameworks that incorporate robustness and adaptability to address these issues in uncertain nonlinear systems.

The first part employs the Linear Parameter-Varying (LPV) framework, which represents nonlinear dynamics through simple linear form representation. To characterize the plant-model-mismatch often caused by limited data and numerical calculations, Bayesian Neural Networks (BNNs) are used, and their uncertainty estimates are integrated into two robust control approaches. The first is a …

Surface Reaction And Transportation Of Radium-226 In Marine Systems, Jiusi Wang

All Dissertations

Historical uses of radium (Ra)-containing self-luminous paint, such as on gauges and dials in naval ships, have led to the potential for such paint, and thus Ra, to be released into the marine environment. Radium-226 (²²⁶Ra) behavior in marine systems is not fully described in the literature, presenting a knowledge gap in the process of evaluating potential ecological impact of Ra in marine systems. This study explores various mechanisms contributing to Ra environmental transport in marine systems on a laboratory scale, including leaching, sorption, and diffusion. ²²⁶Ra leaching from watch hand dials containing radioactive luminous paint is …

Advancing Life Cycle Assessment Of Mass Timber: Environmental Performance, Circular Design And End-Of-Life Strategies, Muzan Williams Ijeoma

All Dissertations

Mass timber has emerged as a leading candidate for low-carbon construction, offering the dual benefits of reduced embodied emissions and biogenic carbon storage. Yet, fully realizing its sustainability potential requires a comprehensive and context-specific understanding of its environmental performance across the entire life cycle, from raw material production to building operation and beyond end-of-life (EoL). This dissertation advances life cycle assessment (LCA) practices for mass timber by addressing critical challenges across multiple phases of its life cycle. This dissertation is comprised of three published papers, and two submitted manuscripts. First, I conducted a critical review and meta-analysis of mass …

Hydrodynamics Of Perforated Heave Plates, Muhammad Usman

All Dissertations

Ocean waves are a clean and renewable energy source that can help reduce dependence on fossil fuels and combat climate change. To capture this energy, researchers have developed devices known as wave energy converters (WECs). One common type, called a two-body point absorber WEC, uses a buoy floating on the ocean surface connected to a submerged reaction plate, known as the heave plate, which lies underwater. The relative up-and-down motion between the buoy and the heave plate, driven by ocean waves, generates usable power via a power take-off (PTO) system. Traditionally, these submerged heave plates are solid structures. However, this …

Opposed-Piston Two Stroke Engine As A High Power Density Solution In Heavy Duty Applications: An Experimental Evaluation With Diesel And Ethanol, Ankur Bhatt

All Dissertations

With an increasing demand to reduce greenhouse gas (GHG) emissions, the conventional powertrain technology is at a saturation point. There is reinvigorated research interest in alternative powertrain designs that offer potentially higher efficiencies and lower engine-out emissions compared to conventional powertrains. The opposed piston two-stroke (OP2S) engine is one such alternative engine architecture that has demonstrated a reduction in engine-out emissions and increased efficiency compared to conventional four-stroke diesel engines. Like any two-stroke engine, the scavenging process and the composition of the internal residuals are predominantly governed by the pressure differential between the intake and the exhaust ports. Without dedicated …

Towards Trustworthy Federated Learning, Alina Basharat

Theses and Dissertations

Federated learning is a collaborative training model in which multiple clients optimize aglobal model by transmitting updates to a coordinating server while keeping raw data on-device, thereby reducing direct data exposure and enabling iterative global improvement. However, the iterative communication process is vulnerable to malicious attackers that either deliberately destroy the model or curious to infer raw data. Moreover, learning from multiple agents may result in unfair results. To enhance trustworthiness within this setting, we employ two-sided norm-based screening (TNBS) that removes both abnormally large and abnormally small updates, pair it with a q-fair objective to emphasize high-loss (disadvantaged) clients, …

When Textures Trim Drag: Micro-Structures For Adaptive Drag Reduction And Flow Authority, Miguel Angel Olvera

Theses and Dissertations

Turbulent boundary layers are a major source of drag and performance loss. This thesis investigates Triangular Porous Texturing (TPT), apex-forward microstructures with optional bleed channels, as a passive concept for simultaneously reducing skin friction and strengthening boundary layer momentum near separation. Low-speed wind tunnel tests are conducted over tripped turbulent boundary layers on flat plates with three surfaces: a smooth baseline, a solid-crest TPT array isolating the knife-edge “slip-cut” effect, and a porous-bleed TPT array whose paired micro-channels vent a small fraction of the free stream. Time-resolved PIV provides wall-normal velocity fields; skin friction is inferred from near-wall shear, and …

Modeling Flood Risks For Small-Scale Coastal Watershed Across Shared Socioeconomic Pathway Scenarios, Sebastian Loschner

Graduate Theses and Dissertations (2019 - present)

Coastal flooding, driven by the mixture of natural processes and human activities such as precipitation, waves, sea level rise, and urbanization, remains a growing concern. While recent flood-risk studies use IPCC's Shared Socioeconomic Pathways with climate projections to assess future flood exposure, smaller coastal watersheds, < 120,000 acres, remain insufficiently studied. Projected sea level rise is expected to intensify these impacts in smaller systems, potentially causing more severe flooding than reported for larger watersheds. To address this gap, this study used the SRH-2D model integrated with Aquaveo's Surface-water Modeling System to simulate flooding in the lower Fish River basin, Alabama. A coastal watershed sensitive to both riverine and tidal influences. Simulations tested different sea levels based on SSP scenarios corresponding to low, moderate, and high emissions for the years 2050 and 2100. Results indicate that the midcentury scenarios remained even in inundation extent and volume, between 9.9-10.4% compared to the 2020 baseline, the latter scenarios had a wider range in results of between 25.9-50.3% compared to the same baseline. Across scenarios, the most vulnerable and inundated areas of the smaller coastal watershed were the narrow points where upstream flow convene with downstream backwater. These findings highlight the need for futuristic location-specific flood mitigation strategies.

Learning To Accelerate Tightening Of Convex Relaxations Of The Ac Optimal Power Flow Problem, Faith Cengil, Harsha Nagarajan, Russell Bent, Sandra Eksioglu, Burak Eksioglu

Industrial Engineering Faculty Publications and Presentations

We propose a novel machine learning (ML)-based approach to significantly reduce the run times of the optimality-based bound tightening (OBBT) algorithm for strengthening the convex relaxations of the non-convex Alternating Current Optimal Power Flow (AC-OPF) problem. While OBBT can yield near-global solutions via tight convex relaxations, its runtime remains a critical bottleneck on large-scale power grids. Our key contribution is a dynamic policy that selects smaller subsets of voltage magnitude and phase-angle difference variables for sequential bound tightening at every iteration of the OBBT algorithm. This ensures that the bound-tightening process remains adaptive, thereby circumventing the stalling in the optimality …

Experimental Measurement Demonstration Of Initial And Steady-State Heights Of A Co2 Vertical Jet Fountain In Still Air, Brock Mcrae

Chemical Engineering Undergraduate Honors Theses

Atmospheric releases of heavier-than-air gases pose acute near-field hazards in low wind, yet the near-source behavior of vertical dense-gas jets in still air is sparsely documented. This thesis demonstrates a repeatable laboratory method for measuring the initial and steady-state rise heights of a carbon dioxide (CO₂) jet issued vertically from a floor nozzle in the CHRC ultra-low-speed wind tunnel under still-air conditions. Controlled mixtures (pure CO₂, 50/50 CO₂-air, 25/75 CO₂-air) were released at four flow rates (75, 100, 125, 150 LPM) with five repeats per condition. A fixed camera and tape-measure scale enabled video extraction of jet height using Kinovea …

Calibration And Validation Of Cyclic Stretching Platform For Stiffness Testing Of Engineered Heart Tissue, Jillian Welsh

Chemical Engineering Undergraduate Honors Theses

Cardiac fibrosis (CF) is a major contributor to the high mortality rate of heart failure which affects over 23 million people worldwide. CF involves cardiac fibroblasts depositing extracellular matrix, made primarily of collagen type I, which stiffens the heart walls over time. As testing new drugs for CF is deterred by the high cost and time required for animal and clinical trials, this research helps to solve the problem by providing an in vitro platform to study fibrosis.

The work involved creating tissues containing human cardiac fibroblast (HCFB) cells. Spring testing was conducted to determine the force applied by the …

Expanderizing Higher-Order Random Walks, Vedat Levi Alev, Shravas Rao

Computer Science Faculty Publications and Presentations

We study a variant of the down-up (also known as the Glauber dynamics) and up-down walks over an 𝑛-partite simplicial complex, which we call expanderized higher-order random walks—where the sequence of updated coordinates corresponds to the sequence of vertices visited by a random walk over an auxiliary expander graph 𝐻. When 𝐻 is the clique with self-loops on [𝑛], this random walk reduces to the usual down-up walk, and when 𝐻 is the directed cycle on [𝑛], this random walk reduces to the well-known systematic scan Glauber dynamics. We show that whenever the usual higher-order random walks satisfy a log-Sobolev …

Coupling Material Formulation Strategies To Tailor Piezoelectric Properties Of Ceramic Lattices, Amanda Lauren Borgaro

Open Access Theses & Dissertations

This thesis examines the material formulation and infiltration processes required to create interpenetrating ceramic composites with a particular focus on developing piezoelectric ceramic-epoxy lattice structures (PCELS). Following this, a high–solid loading barium titanite photopolymer resin (BT40) was developed to produce BTO scaffolds with controlled porosity suitable for metal infusion. The resin’s shear-thinning behavior and scattering-dominated curing response enabled the formation of stable, uniform ceramic structures whose pore networks directly influence infiltration behavior. Building on this foundation, a low-temperature Field’s Metal (FM) infiltration method was established using controlled thermal equilibration and the melting of pre-formed alloy rods. By maintaining the ceramic …

Computational Methods For Complex Electromagnetic Geometries And Media, Edgar Bustamante

Open Access Theses & Dissertations

Additive manufacturing has enabled electromagnetic devices with increasingly complex geometries, but existing numerical tools remain limited in their ability to model and design such structures. This dissertation presents two major advancements that address these restrictions in the finite-difference frequency-domain method (FDFD) and the spatially-variant lattice algorithm (SVLA). These two numerical methods provide a foundation for future exploration in the simulation, optimization, and realization of next-generation electromagnetic devices. First, a general bianisotropic FDFD formulation based on the vector wave equation is presented that enables practical modeling of metamaterials using effective medium homogenized parameters rather than explicitly resolving subwavelength metamaterial features. The …

Evaluating Large Language Models For Requirements Engineering And Verification: A Comparative Analysis Of Gpt-5 And Gemini 3 Pro, Guadalupe Nevarez

Open Access Theses & Dissertations

Large Language Models (LLMs) are increasingly viewed as viable tools for Systems Engineering, yet empirical data regarding their effectiveness in generating engineering-quality requirements remains limited. This thesis compares the performance of GPT-5 and Gemini 3 Pro in generating system functional requirements for the Miner Guardian Drone, a prototype UAV designed for minefield remediation. Both models were provided with identical prompts and tasked with producing 15 functional requirements. Two independent raters evaluated the outputs using a nine-dimensional quality rubric aligned with IEEE 29148 and INCOSE guidelines to ensure rigorous assessment. The difference between the models was analyzed using descriptive statistics and …

Calibration Of Low-Cost Pm2.5 Sensors Using Data Continuously Generated At A Reference Station In El Paso, Texas, Brenda Lizbeth Hernandez

Open Access Theses & Dissertations

Low-cost sensors (LCS) offer a cost-effective way to expand air-quality monitoring networks, but they require strong calibration methods to ensure reliable performance under changing environmental conditions. In this study, Clarity LCSs were co-located with a state reference monitor for 6 months to evaluate fixed-time (2-, 3-, 4-, and 6-week) and rolling (2- and 3-week) calibration approaches. A multivariate regression model using PM1, PM2.5, PM10, and relative humidity was used to generate calibration coefficients for a “base sensor.” Data from two additional sensors were normalized to the base unit using linear regression, and their calibration coefficients were applied across the network. …

Development And Optimization Of A Deep Level Transient Spectroscopy System, Samuel Ruiz

Open Access Theses & Dissertations

Deep-level transient spectroscopy (DLTS) remains one of the most widely used techniques for identifying electrically active defects that affect leakage current, carrier lifetime, and overall reliability in semiconductor devices. Legacy boxcar or capacitance meter implementations, however, often struggle with limited signal-to-noise ratio, labor-intensive data collection, and poor adaptability across diverse material systems. This thesis presents the design and optimization of a semi-automated, lock-in-amplifier-based DLTS platform. By pairing a Zurich HF2LI with PID-controlled cryogenic sweeps, precision signal generation, and MATLAB driven acquisition and processing scripts, the system will (i) enhance SNR through phase-sensitive detection, programmable low pass filtering, and parasitics calibration; …

Image And Signal Processing Methods For Clinical Decision Support: Reference-Based Lung Segmentation On Chest X-Rays And Ecg-Based Study Of Acute Hyperkalemia Using Machine Learning, Basavarajaiah Shanmukhayya Totada

Open Access Theses & Dissertations

ABSTRACT

Study-I: An Image Processing Pipeline for Reference Guided Lung Region Detection in Chest Radiographs with Shape Similarity Matching Accurate and reliable segmentation of the lung region in chest X-ray (CXR) images is essential for computer-aided diagnosis (CAD) systems, particularly in the early detection and monitoring of lung disorders. Traditional segmentation techniques often rely on manual annotations, limiting scalability and adaptability. The proposed reference-guided approach selects the most similar healthy CXRs dynamically, ensuring flexibility while benefiting from dataset-specific reference images. Building upon prior approaches that utilize shape similarity-based selection and SIFT-flow, this study introduces a fully automated segmentation pipeline that …

Effect Of Build Parameters On Mechanical Properties And Residual Stresses Of Lp-Ded Inconel 718 Overhanging Structures, Angel Fernando Bravo

Open Access Theses & Dissertations

Additive manufacturing of Inconel 718 via laser powder direct energy deposition (LP-DED) offers design flexibility for repair and fabrication of large components. However, mechanical properties remain sensitive to process parameters and geometric constraints, which impact the consistency of material properties. This study investigates the effect of different build angles (0°, 20°, 30°, and 35°) at different laser powers (1070W and 2000W) on the mechanical properties and residual stresses. Eight overhanging structures were fabricated using Inconel 718 and subjected to various heat treatments (SR + HIP + SOL + Aging). Numerical simulations were developed to predict residual stresses in the samples, …

Study Of Fatigue Endurance Of Alsi10mg Post Heat Treatments And Subsequently Aged At Service Temperature, Luis Alfredo Barrera

Open Access Theses & Dissertations

Laser Powder Bed Fusion (LBPF) AlSi10Mg is used in the aerospace and automotive industries for its refined microstructure and ductility compared to cast alloys. However, LPBF processes can inherently lead to defects that may act as fatigue crack initiators, such as lack of fusion, balling, and keyhole porosity. This work focuses on the analysis of high-cycle fatigue (HCF) endurance of AlSi10Mg under expected service conditions. For this study, AlSi10Mg specimens were printed on an EOS M290 LPBF system and heat-treated by stress relief (SR1), Hot Isostatic Pressing (HIP), and Hot Isostatic Pressing + T6 (HIP+T6), as per ASTM 3318. Specimens …

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 …

Efficient Adaptive Spline-Based Path Planning For In-Space Servicing, Assembly, And Manufacturing Applications, Christian Lozoya

Open Access Theses & Dissertations

Autonomous robotic systems operating in cluttered and partially observed environments require trajectory generation methods that produce smooth and dynamically feasible motion while reacting to locally sensed obstacles. This requirement is especially pronounced for free-flyer and in-space servicing, assembly, and manufacturing (ISAM) platforms, where onboard sensing is sparse, global environmental information is unavailable, and communication or computational resources are constrained. In such settings, motion plans must be updated online using incomplete and rapidly changing local observations, while avoiding excessive replanning that can lead to oscillatory or unstable behavior. Many existing approaches either rely on dense optimization over extended horizons, which is …

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 …

Gpu-Based Electromagnetic Microwave Tomography For Brain Imaging And Stroke Detection, Pablo Sotelo Torres

Open Access Theses & Dissertations

Each year, an estimated 795,000 people in the U.S. suffer a stroke, with approximately 610,000 being first-time cases. Of these, 87% are ischemic strokes, while the remaining 13% are hemorrhagic. Current imaging methods, such as Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI), provide useful information into brain tissue properties; while each technique has its advantages, they remain expensive, non-portable, and often too slow for emergency bedside or in-ambulance use. Electromagnetic Microwave Tomography (EMT) offers a promising alternative: an affordable, portable, rapid, and safe method for stroke detection. By contrasting dielectric properties between healthy and affected …

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 …