Digital Commons Network^™

Fault Tolerant Quantum Computing With Lower Overhead, Benjamin E. Anker

Electrical and Computer Engineering ETDs

Quantum computation promises asymptotic speedups over classical algorithms, but realizing these advantages requires overcoming the noisiness of quantum hardware. Although fault-tolerant error correction can allow for reliable quantum computation even using unreliable components, the resource overheads required can substantially erode the asymptotic performance gains. This dissertation focuses on constructing and optimizing fault-tolerant procedures with lower overhead than previous methods were capable of. We present new frameworks for fault-tolerant syndrome extraction using flag gadgets with exponentially reduced ancilla requirements, explicit measurement schedules that achieve asymptotically fewer measurements than stabilizer generators, and a general method for making arbitrary Clifford circuits fault tolerant. …

Alternative Positioning, Navigation, And Timing In Global Navigation Satellite System Denied Environments, Joshua R. Atencio

Electrical and Computer Engineering ETDs

Global Navigation Satellite Systems (GNSS) provide the majority of critical positioning, navigation, and timing (PNT) services for civilian, commercial, and military applications. However, GNSS is vulnerable to service denial from spoofing and jamming from adversaries and environmental obstruction. These vulnerabilities highlight the need for resilient Alternative PNT (APNT) methods. This dissertation investigates APNT frameworks operating in GNSS denied environments. We develop coalition formation and matching theoretic models that allow users APNT services from anchor nodes under resource constraints and in adversarial or emergency conditions. The proposed frameworks optimize positioning accuracy, network utility, and system stability while accounting for geometric dilution …

Assessing The Impact Of Multi-Physics Effects On Photovoltaic Module Degradation Using Computational Modeling, James Yuan Hartley

Mechanical Engineering ETDs

This dissertation describes research to develop and apply multi-physics simulation capabilities using finite element methods to analyze photovoltaic module damage mechanisms. Analyses of full-scale solar modules under mechanical load are presented, including experimental validation against measurements of external deflection and internal strain. Module damage by solar cell breakage and interconnection fatigue are discussed, and the applicability of simplifying analyses using mathematical plate theory is assessed. Detailed sub-module component models undergoing thermal-mechanical stressors are also presented, to identify the design features and materials most influential to stress generation and to assess the representativeness of using sub-module assemblies in accelerated testing. Finally, …

Brain-Based Mechanisms Of Behavioral Impairment In Fetal Alcohol Spectrum Disorder (Fasd): The Neuroimaging Biomarkers Of Inhibitory Control, Zinia Pervin

Biomedical Engineering ETDs

The developing brain is highly susceptible to alcohol-induced toxicity, often resulting in long-term deficits in executive function and behavioral regulation. Inhibitory control impairments are among the most prominent deficits observed in Fetal Alcohol Spectrum Disorder (FASD). This study investigated the neural mechanisms of inhibitory dysfunction using a multimodal MEG–DTI approach in 67 children aged 6–8 years (34 with FASD, 33 controls) who performed a Go/No-Go task. Source-level MEG analyses revealed reduced stimulus-locked cortical activation in the anterior cingulate cortex and significant group-by-hemisphere interactions in the superior parietal cortex and cuneus. Time–frequency analyses showed diminished response-locked beta power in the sensory-motor …

Sensitivity-Informed Resonance Parameter Cross Section Adjustments, Matthew Juan Lazaric

Nuclear Engineering ETDs

This work details the process of developing several new features into the MCNP6.3 Monte Carlo code for use in uncertainty quantification and reduction. These new capabilities, which include the CLUTCH method of calculating k-eigenvalue cross section sensitivities and the Windowed Multipole method of calculating cross sections, are implemented and verified against previous implementations and existing cross section data, respectively. These capabilities are then combined to produce sensitivities of k-eigenvalue to resonance parameters, which are verified against direct perturbation sensitivity estimates. The resonance parameters are calibrated using linear Bayesian methods and the accuracy of the resulting cross section is evaluated via …

Representativeness And Sensitivity Analysis Of Accident Tolerant Fuel Cladding Behavior In Reactivity-Initiated Accidents, Melissa Andrea Moreno

Nuclear Engineering ETDs

Accident Tolerant Fuels (ATFs) such as FeCrAl are being developed to enhance safety margins in light water reactors during transients like Reactivity Initiated Accidents (RIAs). This dissertation evaluates the accuracy and representativeness of TRACE thermal hydraulic models for predicting FeCrAl C36M cladding behavior under steady state and transient flow boiling conditions. Benchmarking against University of New Mexico separate effects experiments shows that TRACE reproduces key thermal trends but underpredicts cladding temperatures near CHF by approximately 25 to 30%. A calibrated uncertainty and global sensitivity analysis, using Sobol indices, demonstrates that CHF and cladding temperature variability are dominated by mass flux, …

Statistical And Spectral Theory For Spatially Correlated Random Aperiodic Antenna Arrays, Thomas Edward Christian Jr

Electrical and Computer Engineering ETDs

Aperiodic phased arrays enable beam steering, interference suppression, and spectrum efficiency for 6G, radar, biomedical imaging, and distributed sensing. Minimum inter-element spacing and keep out zones induce spatial correlation, violating the i.i.d. element-position assumption behind classical probabilistic random array theory. This dissertation develops a unified probabilistic spectral framework for correlated (non-i.i.d.) arrays. Second moment power pattern analysis incorporates the pair-correlation function  and structure factor , recovering the i.i.d. limit when  and accommodating unequal excitations. Side lobe and main lobe fields deviate from Rayleigh/Exponential and are modeled by weighted Nakagami and Gamma-mixture distributions, parameterized via Monte Carlo. The blue noise spectral …

Investigating Workforce Development Gaps In Tribal Transportation Agencies, Benson Long Jr

Civil Engineering ETDs

Tribal transportation programs face challenges in recruiting and retaining qualified staff, prompting reliance on external consultants for key responsibilities. This study explores how workforce constraints and organizational structures influence tribal outsourcing decisions. Survey data from 57 tribes informed a logistic regression model examining how geographic isolation, population size, training access, and the presence of a transportation department affect preferences for internal work, actual task completion, and instances where tribes intended to perform tasks in-house but outsourced instead. Findings indicate that outsourcing is driven by limited internal capacity, including staffing shortages, lack of certified personnel, and training opportunities. Geography also plays …

Assessing Uranyl-Specific Dnazymes And Dna-Aptamers For Uranium Binding In Environmentally Relevant Waters, Ashley R. Apodaca-Sparks

Civil Engineering ETDs

The legacy of uranium mining affects many communities in the western United States, resulting in elevated levels of uranium in surface water. The ability to quickly and accurately detect uranium on site in affected communities can lead to real-time water quality analysis that informs risk assessment and remediation efforts. The objective of this work is to advance the application of biosensing technologies for the measurement of uranium in waters affected by mining legacy. The third chapter of this thesis compares ANDalyze by AlpHa Instruments, a commercially available uranium biosensor, and inductively coupled plasma mass spectrometry (an EPA-verified method for uranium …

Post-Fire Flood Modeling In Ruidoso, New Mexico: Improving Burn Scar Runoff Predictions, Pramesh Bhaila

Civil Engineering ETDs

Recent large wildfires in New Mexico have demonstrated severe hydrological driven by vegetation loss and the development of hydrophobic soil surfaces. This thesis evaluates the performance of three infiltration models—Curve Number (CN), Linear Constant (LC), and Green–Ampt (GA)— for the watershed impacted by the 2024 South Fork and Salt Fires near Ruidoso, New Mexico. All three models performed comparably during post‑fire simulations, with strong performance in 2024 and notable degradation in 2025, particularly for the CN. Post-fire analyses indicate substantial reductions in infiltration capacity, with CN increasing by 18–24%, LC initial deficit decreasing by 65–77%, effective hydraulic conductivity reduced by …

Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed

Turkish Journal of Electrical Engineering and Computer Sciences

High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

Exploring Ai-Driven Scaffolding For Critical Questioning In Argument Evaluation, Ebenezer A. Belete

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fast-paced changes caused by generative AI (GenAI) innovations call for exploring the potential benefits of GenAI in empowering 21st-century pedagogical strategies. Previous studies in the field of argumentation have shown how students can benefit from using critical questions. However, scaffolding argument evaluation through custom GenAI using critical questions has not been systematically investigated. This study involved two components: (1) designing and determining the usability of a GPT-powered conversational assistant (CQMAA Conversational Assistant) and (2) testing its impact on participants' efficacy for argument evaluation and their acceptance of GenAI as a learning tool through a pretest–posttest experiment. A convergent mixed-methods …

A Near Linear-Phase Analog Frequency Sampling Filter Design Framework Using A Second-Order Trust-Region Optimization Technique, Edreese Basharyar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) provide an efficient means of realizing finite impulse response (FIR)-like behavior in continuous-time systems, but their practical implementation is constrained by the requirement for perfect pole-zero cancellation along the imaginary axis. Because exact cancellation is physically unattainable due to component variations, ideal linear-phase Type 1 analog FSFs exhibit uncancelled poles that result in system instability. To address this limitation, this thesis introduces a near-linear-phase design framework for Type 1 analog FSFs that achieves both stability and design flexibility through the inclusion of a damping constant, ρ, which shifts the poles into the left half of …

An Optimization Method For Near-Linear Phase Analog Frequency Sampling Filter Design, Leonardo Ledesma

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) realize a desired frequency response by interpolating a frequency response through a set of harmonically related frequency samples from the filter’s frequency response and are magnitude and phase coefficients used in the filters transfer function. FSFs can be designed to have exact linear phase which makes the FSF attractive for many applications. A FSF’s system transfer function (STF) shows that the filter can be implemented by a series connection of a comb filter and a parallel array of resonators. However, the FSF requires that the zeros created by the comb filter cancel the imaginary axis …

Model Based Control And Hil Verification Of An Integrated Battery Management System, Catalin Sabou

UNLV Theses, Dissertations, Professional Papers, and Capstones

The rapid advancement of electric vehicle technologies necessitates highly reliable Battery Management Systems (BMS); however, validating embedded supervisory logic presents a notable challenge. While physical pack testing is accurate, it is costly and hazardous for early stage software evaluation. This thesis presents the design, implementation, and rigorous validation of an integrated BMS developed for the Battery Workforce Challenge, bridging the gap between model based design and safe hardware execution. The core of this work is a model based supervisory controller, developed in MATLAB/Simulink and executed on an STM32G4 embedded target. To facilitate embedded validation while preserving a representative battery environment, …

Improving And Supporting Flight Instructor’S Decisions For First Solo, Isabella Piasecki

Theses and Dissertations

Flight instructors have the burden of determining when a student is ready for their first solo flight, and many have expressed uncertainty over their own decision-making skills during this phase of a student’s training. Prior studies have examined flight instructors’ pre-solo decisions in other countries, but no such study has been conducted with American flight instructors. For this study, current flight instructors with multiple prior endorsements for a student pilot’s first solo were interviewed to identify the more abstract concepts they use to guide their decision. Qualitative themes were identified from their experiences. Using this information, a checklist was developed …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

Descriptive Analysis Of Career Planning And Industry Insight (Cognitive Component) Among Final-Year Mechanical Engineering Education Students, Fadliyanti Firdausia, Purnomo Purnomo, Eddy Sutadji, Tommy Tanu Wijaya, Muhammad Idris Effendi

Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan

This study aims to analyze the level of understanding of final-year Mechanical

Engineering Education students regarding career planning and industry insight as

cognitive components in preparing for the workforce. The study employed a quantitative

approach with a descriptive method involving 58 eighth-semester students who had

completed industrial internships. Data were collected using a Likert-scale questionnaire

consisting of 20 items on career planning and 18 items on industry insight, and were

analyzed using descriptive statistics, including mean scores and percentages. The results

indicate that students’ understanding of career planning falls into the high category, with

a mean score of 3.78, while …

Trussworthy - A Lunar Module Truss System, Anthony Wehrli, Sebnem Kalayci, Tyler Williams, Sofia Lopez-Linares Perez, Connor Mcdonagh, Presley Robb

Undergraduate Research and Scholarship Symposium

TrussWorthy is a modular truss-based construction platform designed to support the gradual development of sustainable infrastructure on the lunar surface. Aligned with NASA’s Moon to Mars objectives, the system emphasizes in-situ resource utilization (ISRU) by transforming lunar regolith into structural components through sintering and additive manufacturing. By reducing dependence on Earth-supplied materials, TrussWorthy aims to lower launch costs while enabling scalable, long-term construction capabilities. The design integrates autonomous excavation, 3D printing, and robotic assembly, building on technologies expected from upcoming Artemis and Commercial Lunar Payload Services missions. Engineered to withstand extreme thermal cycling, vacuum conditions, and reduced lunar gravity, the …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

A Study & Comparison Of Lift Boosters For An Unmanned Aerial Vehicle, Trevor Sean Dady

Theses and Dissertations

Unmanned aerial vehicles are becoming increasingly prevalent in modern society for tasks, such as package delivery, defense, and cinematography. Due to this surge in applications, many researchers have begun to search for improvements to unmanned aerial vehicles for their lift and thrust properties. Potential solutions to magnify an unmanned aerial vehicle’s thrust capability are the implementation of Coanda surfaces, nozzles, or bowl-shaped structures on each propeller. Coanda surfaces provide additional lift force by creating a vacuum in response to the adhesion of fluid to the smooth surface. Nozzles can constrict the flow of air, while also increasing its velocity. The …

Fault Location In Dc Microgrids Using Traveling Waves, Sajay Krishnan Paruthiyil

Electrical and Computer Engineering ETDs

In DC power systems, rapid fault location is crucial for maintaining reliable operation, particularly with the prevalence of DC-DC converters. This study investigates fault location techniques in DC systems utilizing Traveling Waves (TWs). Following data normalization, multi-resolution analysis employs discrete wavelet transform to capture high-frequency patterns of TW's wavelet coefficients. Parseval's theorem is utilized to quantify the energy of these coefficients. First, a curve-fitting technique is employed to estimate fault locations in DC microgrids. Then, two transfer learning approaches are proposed: first approach integrates Parseval energy curves into a Gaussian process estimator, while second employs feedforward neural network for fault …

Improving Human Visual Search: Enhancing Lung Cancer Nodule Detection In Medical Images, Christopher Khajira

Masters Theses

Human visual search involves the identification of relevant signals within information-rich environments, which is a fundamental problem in visual perception. While detection accuracy and response time are commonly used to evaluate performance in visual search, these measures do not reveal the underlying cognitive and computational structure that produces observable behavior. A key challenge lies in distinguishing between competing processing architectures, particularly in complex visual domains where different models can produce similar behavioral outcomes. This study addresses this challenge by developing a computational experimental framework for analyzing visual search behavior using System Factorial Technology (SFT). The experimental framework integrates naturalistic medical …