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. …

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 …

Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van

Turkish Journal of Electrical Engineering and Computer Sciences

Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …

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 …

Surface Directed Microwave Assisted Fabrication Of Znmn2o4 And Its Composite As Anode Materials For Lithium Ion Battery, Anirudh Singh

Electronic Theses & Dissertations

The Lithium-ion batteries (LIBs) are widely utilized in portable electronics and electric/hybrid electric vehicles. However, the limited theoretical capacity of graphite (372 mA h g⁻¹) restricts further advancements. In our recent work, a series of ZnMn₂O₄ materials with various additives were synthesized using the microwave reaction method and evaluated as anodes for LIBs. Additives such as 2-methylimidazole (2MIM), polyvinyl alcohol (PVA), and cetyltrimethylammonium bromide (CTAB) were employed to prepare ZnMn₂O₄ with distinct morphologies. Our findings revealed that ZnMn₂O₄  with pure phases was achieved when 2MIM and CTAB were used as additives. …

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. …

Developing And Evaluating U.S. Army Bridging For Ship Simulation, Jacob C. Hodges

Theses and Dissertations

This research investigates the intricacies of ship simulation and developmental processes for integrating wet gap crossing capabilities into a simulated environment. The US Army Mark III Bridge Erection Boat (BEB) and Improved Ribbon Bridge (IRB) were modeled in commercial off the shelf (COTS) and Government Co-Owned (GCO) navigation software suites to determine their performance capabilities and limitations. Each model was constructed using naval architecture concepts to accurately capture their hydrostatic and hydrodynamic effects before being validated against available performance data and expert elicitation from expert US Army watercraft operators. Models were configured and tested in simulated river flows with current …

A Cradle To Grave Life Cycle Assessment Of Captured Landfill Methane Abatement Strategies, Joshua Caleb Richard

Theses and Dissertations

Landfill methane mitigation strategies are commonly evaluated using percentage-based leakage assumptions and linear scaling models. These approaches may misrepresent system performance at varying throughput levels and real-world operating scenarios. This thesis develops a system-level life cycle assessment framework that incorporates both fixed infrastructure losses and employs sensitivity analysis to more accurately characterize emissions from landfill gas management pathways. Three scenarios are evaluated: methane capture and flare, methane capture and combustion for electricity generation, and methane upgrading with pipeline injection as renewable natural gas. Results demonstrate that emissions models over- dependance on static factors may inadvertently lead to under-reporting and provide …

Experimental Investigation Of The Effectiveness Of Various Stitching Methods On Arresting Mode-Ii Interlaminar Damage Propagation In Geometrically-Scaled Stitched Resin-Infused Composites, Allison Jean White

Theses and Dissertations

This thesis presents an experimental and analytical investigation of mode-II interlaminar fracture in geometrically-scaled quasi-isotropic resin-infused composites, with emphasis on through-thickness stitching as reinforcement. A global-local framework integrating load-displacement response, size effect fracture energy analysis, and digital image correlation-based separation measurements enabled direct comparison between unstitched and stitched configurations. Unstitched laminates exhibited pseudo-ductile behavior but significant post-peak load drops (16.7–26.8%) and a small fracture process zone (FPZ) of 0.73 mm, limiting stable crack growth. Stitching preserved pre-peak stiffness while reducing post-peak load drops by 43–95%, increasing effective fracture energy by 39.7% (to 2.413 N/mm), and expanding the FPZ to 2.85 …

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 Assisted Development Of Al0.2cufemnni High Entropy Alloy Through Selective Laser Melting, Hareharen K Mr

Theses and Dissertations

High Entropy Alloys (HEAs) are an emerging class of advanced materials that have gained significant attention due to their exceptional mechanical strength, thermal stability, and structural performance. Unlike conventional alloys based on a single principal element, HEAs are composed of multiple elements in a near-equiatomic ratio. Despite these advantages, designing HEAs with tailored properties is difficult because of the enormous number of possible combinations and the limitations of traditional trial-and-error methods. To overcome these challenges, this study presents a machine learning (ML) based approach to accelerate the design and development of an HEA.

In this work, a newly designed composition, …

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 …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

Vegetated Canopy Heterogeneity Footprints In The Roughness Sublayer, Giulia Salmaso, Raul Bayoan Cal, Marc Calaf

Mechanical and Materials Engineering Faculty Publications and Presentations

Turbulent flows over horizontally homogeneous rough surfaces are categorized as rough‐wall boundary layer flows, while flows over homogeneous vegetated canopies are better described through a mixing‐layer analogy. At present, numerous studies have investigated canopy density as a transition mechanism between rough‐wall and mixing‐layer‐type flows. Yet, most considered canopies have been spatially homogeneous, with few exceptions investigating agricultural arrangements. However, most vegetated canopies are not homogeneously distributed, but instead contain gaps and spatial heterogeneities of different scales. In these cases, it remains unclear which are the dominant flow traits, and how spatial heterogeneity affects them. To help overcome these knowledge gaps, …

Development Of Carbon-Fiber-Reinforced Composite Rotor Sleeves For High-Speed, Multi-Layer Ferrite-Based Interior Permanent-Magnet Motors, Andrew Walters

Theses and Dissertations

This research investigated whether a non-wound carbon fiber reinforced composite (CFRC) sleeve could serve as an effective retention mechanism for a high-speed, multi-layer ferrite-based interior permanent magnet motor. Material characterization testing was conducted on IM7/CYCOM-5320 and IM7/8552, with IM7/8552 selected due to its superior commercial availability. Experimentally obtained properties for both materials fell within acceptable tolerances of published values. Sleeves were manufactured to rotor design specifications (50-mm width, 1.2-mm thickness, 88.6-mm diameter) and tested per ASTM D2290 Procedure A to determine apparent hoop-failure stress. These experimental results validated a finite element model of the D2290 test, confirming realistic strain patterns …

Harmonic Response Analysis Of Asymmetric Double-Wishbone Suspension Systems For Amphibious Vehicle Stability, Harper K. Mccraw

Theses and Dissertations

Amphibious vehicles in the surf zone face severe hydrodynamic forcing, yet their internal suspension dynamics remain under-characterized. This research quantifies the non-linear dynamic response of an asymmetric double-wishbone suspension under regular wave impact. Using a physical model in a wave flume and a Qualisys motion-capture system, global rigid-body roll angles were processed through a decoupled kinematic numerical framework to isolate time-domain strut displacements. To evaluate stability, the data was transformed into the frequency domain to calculate Total Harmonic Distortion (THD). Results indicate that all test configurations exhibited significant non-linearity, primarily driven by mechanical clipping as struts reached their physical travel …

The Effects Of Experiential Learning On Engineering Self-Efficacy: A Study Of The Impact Of Inperson And Virtual Internships, Co-Ops, And Research Experiences, Sunny Bharat Patel

Theses and Dissertations

This dissertation examined the relationship between participation in Engineering Learning Experiences (ELEs) and engineering self-efficacy, with particular attention to differences among ELE types (internships, cooperative education, and undergraduate research) and delivery modalities (in-person versus hybrid/virtual). Grounded in social cognitive theory, this study sought to better understand how work-related experiential formats influence students’ confidence in their ability to succeed in engineering tasks and professional contexts. As engineering programs and industry increasingly adopt flexible and technology-mediated environments, examining how these experiences shape beliefs about engineering capability is both timely and necessary. Using a mixed-methods approach, quantitative analyses compared self-efficacy outcomes across students …

Liquid-Phase Chemical Melting Deposition For Anchored Nanoparticle–Nanofiber Architectures, Hiep Pham, Kiernan O'Boyle, Gracie Boyer, Jonghyun Park

Mechanical and Aerospace Engineering Faculty Research & Creative Works

We report chemical melting deposition (CMD), a manufacturing strategy designed to overcome the low mass loading and weak interfacial bonding inherent to vapor-based synthesis. Unlike conventional vapor routes, CMD leverages a transient liquid-phase transfer (TLPT) mechanism driven by the differential thermal degradation of carrier fibers to transfer and anchor nanoparticles directly onto target fibers. This process thermodynamically drives the wetting and interfacial fusion of nanoparticles, establishing a liquid-phase contact pathway that enables markedly higher active material loading. To validate the structural resilience of this fused architecture against extreme volumetric stress, we utilized lead oxide (PbO) as a model system, which …

Design And Uncertainty Methodology In High Efficiency Filtration Media Testing, Jeremy Andrei Adriano

Theses and Dissertations

Electrospun nanofiber media are being developed as potential alternatives to conventional HEPA filtration materials due to their small fiber diameters and tunable microstructures. To support future testing of these materials, the Small-Scale Test Stand (SSTS) at the Institute for Clean Energy Technology (ICET) requires modifications to ensure accurate and repeatable aerosol filtration measurements. Electrospun filters introduce challenges including fragile media structures and uncertain filtration efficiencies that may allow particle penetration to downstream instrumentation. This work evaluates adaptations to the SSTS design to support testing of electrospun filtration media. In addition, an uncertainty framework for aerosol measurements obtained using a Scanning …

Feasibility-Aware Deep Reinforcement Learning For Sustainable Timber Procurement Under Hurricane Demand Uncertainty, Jarod Wright

Theses and Dissertations

The timber supply chain connects landowners and mills to provide wood products but faces challenges from stochastic demand, seasonal variations, and disruptions such as hurricanes. Fur- thermore, sustainability concerns like transportation emissions create trade-offs in procurement. This study proposes a feasibility-aware Deep Reinforcement Learning framework for sustainable timber procurement and inventory control under joint demand–hurricane uncertainty. We develop a stochastic mathematical model capturing mill-landowner interactions, seasonal demand, hurricane- driven pricing, and carbon emissions. The problem is formulated as a constrained Markov decision process and solved using Proximal Policy Optimization with a feasibility-enforcing layer. A Mississippi-based case study with 2,100 landowners …

Cortical Bone Density And Thickness Assessment Of Intraradicular Sites In Adolescent Patients Using Cbct Imaging, Sara Endo

UNLV Theses, Dissertations, Professional Papers, and Capstones

Background: The indications for the use of cone beam computed tomography (CBCT) in orthodontics has grown since it was first introduced and dentists are discovering new ways that 3D images enhance diagnosis and treatment planning. Evaluating bone quality and quantity can be measured as bone density and thickness in CBCT imaging and is helpful for temporary anchorage device (TAD) placement in orthodontic treatment. TADs rely on primary stability and are commonly used in orthodontic treatment to increase anchorage and expand the limit that nonsurgical orthodontics can provide.

Objectives: This study aims to assess the bone thickness and density at different …

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, …

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 …