Electrical and Computer Engineering Commons^™

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Rail Anomalies Dataset For Semantic Segmentation Analysis, Saarah Majid, Arian Izadi, Paul Stanik

Undergraduate Research Symposium Posters

Rail maintenance is a core necessity of upkeep, maintenance, deterioration, and safety implications of large rail systems. Doing so in an orderly and uniform way, however, has proven difficult with the thousands of miles that railways span. Use of computer vision on locomotives during normal operation can allow for a more streamlined process that would eliminate the need for costly specialized equipment and disruptions of normal operations. This project aims to efficiently detect vegetation overgrowth, mud-pumping, and standing water on railways. With the use of machine learning algorithms and dataset curation, models can be trained, allowing them to gain a …

Spatiotemporal Feature Extraction From Vertical Grf Signals For Parkinson’S Disease Severity Assessment Using Physionet And Gaitpd, Nitya Jadeja

Undergraduate Research Symposium Posters

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that impairs motor coordination, often resulting in measurable gait disturbances. Vertical ground reaction force (VGRF) signals provide a quantitative means of capturing these abnormalities and monitoring disease progression. This study focuses on the extraction and analysis of spatiotemporal gait features such as stride variability, stance duration, and force imbalance, from the PhysioNet Gait dataset to better understand their correlation with PD severity. Using gait analysis and machine learning techniques, recent computational models are reviewed and evaluated for their ability to assess PD progression through VGRF data. The analysis compares data-driven approaches with …

Real-Time Ai-Enabled Therapeutic Device For Improving Peripheral Perfusion And Glucose Regulation In Type 2 Diabetes, Torryana Tanis

Undergraduate Research Symposium Posters

Type 2 Diabetes Mellitus is frequently accompanied by peripheral microvascular disease, leading to impaired lower limb perfusion, impaired healing, and vulnerability to ulcers and amputation. The existing medical devices are predominantly aimed at systemic glucose control and do not have local therapeutic intervention to improve circulation or allow muscular glucose delivery. To meet this crucial demand, we have developed a portable, two-in-one biofeedback device that combines non-invasive blood glucose monitoring and Electronic Muscle Stimulation (EMS) therapy for the calf muscle. The glucose monitoring subsystem implements near-infrared (NIR) spectroscopy to measure skin light absorptance on the index finger. Real-time blood glucose …

Digital Twin Of The Unlv Campus For Safe Autonomous Vehicle Simulation, Carlos Funes

Undergraduate Research Symposium Posters

With the rise of artificial intelligence and machine learning algorithms, self-driving cars are becoming increasingly prevalent on our roads. By utilizing these technologies, we can reduce the number of accidents caused by distracted driving. Before implementing these systems in vehicles, however, it is essential to conduct numerous tests. Traditional evaluation of driverless cars on real-world roads can be both expensive and hazardous. To address this, creating a digital twin of an actual road minimizes unexpected hazards, allowing researchers to safely and efficiently test self-driving car programs in high-risk scenarios using computer simulations. This research outlines the process of generating a …

Diving Video Analysis Using Multitask Learning Action Quality Assessment (Mtl-Aqa), Taylor Gauthier

Undergraduate Research Symposium Posters

In many sports, videos are being used to assist in judging. For example, they are being used to review quick actions or confirm the actions being performed. For diving specifically, the videos of a dive can be at most 3 seconds long, and divers can perform a range of somersaults and twists within that time frame, all of which affect the score, classification, and dive number. Using these videos, a Multitask Learning Action Quality Assessment (ML-AQA) program, based on machine learning, from a vast dataset, is able to produce an Action Quality Score out of 100, Factorized Action Recognition (classifications), …

Capacity, Allocation And Update Dynamics Of Human Memory Systems, Shaoying Wang

Electronic Theses and Dissertations

Information is encoded and stored in three types of memory: sensory memory (SM), short-term memory (STM), and long-term memory (LTM). SM has a large capacity but retains information for only a brief period. When information transfers to STM, only a limited amount can be stored. Information in STM can then be transferred to LTM, which has a much larger capacity and longer retention time. STM is often conceptualized as working memory (WM) to highlight its role in active information processing. Due to the limited capacity of STM, it is commonly believed that STM serves as the bottleneck for information processing. …

Parentcoach: Designing An Mhealth Parenting App To Enhance Parental Involvement In Adhd Support, Franceli L. Cibrian, Nancy Herrera, Jesus A. Beltran, Lucas M. Silva, Mikaela Pulse, Kayla Anderson, Cassie Zeiler, Luc Rieffel, Daniel I. Lee, Sabrina E. B. Schuck, Kimberley D. Lakes

Engineering Faculty Articles and Research

Introduction: Parents play a vital role in supporting self-regulation and managing behaviors in children with Attention-Deficit/Hyperactivity Disorder (ADHD). However, many face barriers to accessing consistent, evidence-based support. Mobile health (mHealth) technologies offer a promising way to deliver flexible, low-burden guidance for parents on best practices and strategies to support their children's self-regulation. However, designing them is non-trivial.

Objective: This paper introduces ParentCoach, a mobile application designed to support parents of children with ADHD through brief daily lessons, reflection prompts, and skill-building activities.

Methods: ParentCoach was developed in two phases: (1) secondary analysis of qualitative data from over 30 families …

Microgrid Black Start Challenges: The Role Of Grid-Forming Inverters, Gab-Su Seo, Wenzong Wang, Behrooz Mirafzal

Faculty Publications

Grid-forming (GFM) inverters are becoming increasingly important for future power systems, particularly in establishing and restarting microgrids after blackouts. The use of GFM inverters enables microgrids to operate independently of utility power and provide key advantages over synchronous generators in black start scenarios, including rapid startup and stable voltage and frequency support for critical loads. However, inverter-driven black start introduces unique challenges and operational considerations. This article examines key challenges and solutions, emphasizing inverter design, control strategies, and microgrid system requirements. Drawing on analysis, simulation, and experimental results, this article highlights the central role of GFM inverters in ensuring reliable …

Robust Control Of Lcl-Filtered Three-Phase Grid-Tied Inverters Using H∞ Synthesis: Design, Analysis, And Experimental Validation, Mohammad Rousan

Thesis/ Dissertation Defenses

This thesis presents the design, development, and practical implementation of various robust current control strategies for LCL-filtered gridtied inverters, with the aim of maintaining robust stability over a range of plant perturbations, while ensuring high-quality current delivery to the utility grid. Chapter One presents the literature review, while Chapter Two focuses on the modeling of the system under study. In the third Chapter, the system dynamics are augmented with an appropriate servo-compensator to ensure that, at steady-state, the grid current accurately tracks its sinusoidal reference with zero steady-state error, even in the presence of model uncertainties. This augmented system serves …

Cybersecurity Risks Of Freight Rail As Critical Infrastructure, Kira Sun

Discovery Undergraduate Interdisciplinary Research Internship

Our project implements simulated train engineers to operate model train engines on a hybrid twin of a freight rail system. We can then use the model and simulate cyber-security attacks to demonstrate the risks and effects of the attacks. Using existing model train hardware and an Arduino running open-source software, DCC-EX and JMRI, we can control the train engines and various track components and sensors. We program each engine to make safe decisions about what speed and direction to take, using information provided by the various sensors and light signals around the track. When attacks occur, the engines can have …

Towards Robust Autonomous Systems: Handling Multi-Modal Uncertainties In Gps-Denied Environments, Vivya Kalidindi

Doctoral Dissertations

This dissertation focuses on designing a robust and uncertainty-aware framework for autonomous systems operating in GPS-denied environments, such as indoor infrastructures, underground tunnels, and lunar surfaces. The proposed framework addresses the challenges posed by multi-modal uncertainties, including sensor noise, distributional shifts under adverse conditions, and conflicting decision-making preferences. These challenges compromise the reliability and adaptability of autonomous platforms. To overcome these challenges, the proposed framework adopts a layered architecture that integrates advanced methodologies across the sensing, perception, and decision-making layers. At the sensing layer, an Edge-Kalman Filter combined with a density ratio-based update mechanism is employed to reduce aleatoric uncertainty …

Inverse Design For Generating Initial Conditions In Scientific Simulations, Leslie Horace, Christin Whitton, Vanessa Job, William Jones, Nathan A. Debardeleben

Computing Sciences

We propose a conditional normalizing flow (CNF) surrogate model to solve generative, many-to-one inverse problems in scientific simulations governed by partial differential equations (PDEs) with time-evolving interactions between heterogeneous materials. We present two case studies: electrostatic potential and heat diffusion, which serve as proxy simulations for generating diverse sets of initial conditions that can reproduce an observed output state (transient or steady). Finally, we provide a comprehensive overview of the synthetic datasets, the model specification, each stage of the experimental workflow, evaluation of training performance, and uncertainty quantification for the generated samples.

Test Data: Raised Or Recessed? Finding The Optimal Gate Architecture For Improving The Static Performance Of Graphene Transistors, Ivan Puchades, Tzu-Jung Huang, Andrew Spencer, Luke Ingraham, Anibal Pacheco

Data

As silicon CMOS technology approaches its scaling limits, graphene offers a compelling alternative as the active material channel in transistors due to its high carrier mobility and atomically thin profile, which provide strong electrostatic control and promise high-performance analog applications. However, roadblocks such as device-to-device variation, high contact resistance, poor dielectric interfaces, and non-uniform graphene quality have limited the adoption of graphene field effect transistors (GFETs). Hence, further investigations are required for mitigating these issues at a material, e.g., by improving graphene transfer, and device level, e.g., by finding an appropriate gate architecture. In this work, we directly compare two …

Exploratory Study Of Semiconductor Nanomembranes In Em Applications, Grant D. Heileman

Electrical and Computer Engineering ETDs

Antenna systems are a cornerstone of modern technologies, playing an increasingly vital role in their advancement. As demand for compact, high-performance, and adaptable communication platforms grows reconfigurable antenna technologies are becoming essential. This research explores a novel front-end reconfigurable antenna system (FERAS) architecture that leverages the mechanical flexibility and photoconductive behavior of semiconductor nanomembrane (SNM) devices. By exploiting the emergent properties of ultra-thin silicon (Si) or gallium arsenide (GaAs) nanomaterials and optically exciting these samples using vertical-cavity surface-emitting laser (VCSEL) arrays, this study develops lightweight, low-cost, deployable antenna structures for satellite communications, remote sensing, GPS, and radar. Despite their significant …

User Interface And Watchstation Improvements Required For Multi-Vehicle Usv Operations, Val Schmidt, Joshua Bergeron

Faculty Publications

In October 2024, the University of New Hampshire and NOAA’s Uncrewed Systems Office embarked on a mapping mission in the Gulf of Maine, simultaneously operating two DriX Un-crewed Surface Vehicles. Goals of the project were focused on testing hypotheses related to concepts of operation, including the safety of operations, cognitive loading of operators, management of vehicle endurance, vehicle logistics, maintenance and field support, refueling and a host of others.

Experience With The Stem For Success Internship, Ujwal S. Thirunagari

STEM Month

In this article, I discuss my experience working with the STEM for Success team. I detail some of the challenges I faced in completing the CRT and LED sections, how I addressed these challenges, and the widely applicable skills that I learned.

Television Guidebook, Ujwal S. Thirunagari

STEM Month

In this television guidebook, we work to clearly explain television technology by including detailed illustrations and introducing fundamental concepts. This guidebook first explains the Cathode Ray Tube by laying out the basics of circuits, thermionic emission, the electron beam gun, and the deflection yoke. Then, the guidebook tackles the modernly adopted LED panel technology used in most phones and computers by explaining the fundamentals of light and liquid crystal. In future iterations of the guidebook, we hope to delve into more detail on color CRTs, thin film transistors, OLED panels, and Micro-LED technology.

Microstrip Antenna Design Based On Ai And Machine Learning, Waleed Mohamed Sha Moulavi

Thesis/ Dissertation Defenses

Microstrip patch antennas (MPAs) rely on precise impedance matching for efficient power transfer between the antenna and feed line. This is often achieved using a number of different techniques, one of which is the quarter-wavelength transformer (QWT). However, optimizing the width of the QWT presents significant computational and analytical challenges due to the unknown antenna impedance and the absence of explicit design relationships. This thesis aims to overcome these limitations by developing and comparatively evaluating artificial intelligence (AI) models for QWT width optimization. Methods involved Random Forest (RF) and a novel Probabilistic Deep Neural Network (PDNN) on a custom dataset …

Modeling Social Isolation: A Data-Driven Index Function Design And Implementation, Jeremis N. Morales Morales, Carmen Caiseda, Phyllis Muniu, Joshua Atsu, Folashade B. Agusto

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

On The Derivation Of The Ungerboeck Observation Model For Offset Qpsk, Michael Rice

Faculty Publications

This report derives maximum likelihood sequence estimator for Offset QPSK (OQPSK) operating over frequency selective channel. The maximum likelihood sequence estimator takes the form of the Viterbi Algorithm operating on a trellis defined intersymbol interference caused by the frequency selective channel. Because the distorted pulse shape does not satisfy the Nyquist no-ISI criterion, the matched filter output samples contain correleted noise. The derivation uses Ungerboeck’s method to create a recursive causal metric suitable for use with the Viterbi Algorithm.

Deep Reinforcement Learning Based Control For Enhanced Frequency Response With Multi-Energy Storage Systems, Abu Shouaib Hasan, Rui Fan, Wei Gao, Di Wu

Electrical and Computer Engineering: Faculty Scholarship

This paper proposes an advanced strategy for managing multiple battery energy storage systems (BESS) to enhance frequency support during contingencies. A novel deep reinforcement learning (DRL) framework based on a guided surrogate-gradient-based evolutionary strategy (GSES) was developed to dynamically regulate BESS outputs for rapid power injection or absorption. This approach effectively mitigates the rate of change of frequency (ROCOF) and stabilizes the system frequency under varying operating conditions. Parallel computing techniques are employed to accelerate training and ensure robust performance. In addition, a genetic algorithm is implemented to determine the placement of BESS within the grid network, strategically minimizing ROCOF …

A Fully Automated Drilling Machine For Printed Circuit Boards With Superior Path Optimization, Mohamed Mammdouh, Ahmed Khaled, Reem Mahmoud, Osama Desouki, Sameh O. Abdellatif

Electrical Engineering

This paper addresses a critical challenge in Printed Circuit Board (PCB) manufacturing by proposing an AI-driven, fully automated drilling machine that employs sophisticated path-planning techniques. Current methodologies often fail to adequately assess designs with varying hole sizes, diverse component placements, and complex geometries, leading to compromised precision and increased manufacturing times. Our innovative approach leverages advanced algorithms to intelligently analyze PCB designs and optimize drilling paths, significantly reducing production time and minimizing errors. By automating the drilling process, we enhance overall productivity while ensuring precise hole placement, essential for maintaining high-quality circuit boards. Utilizing KiCAD EDA software, we automate the …

“How Can Ai Data Centers In The U.S. Meet Projected Electricity Demands By 2030?, Aiden M. Matano

Sustainable Supply Chain Management

The rapid expansion of artificial intelligence is driving a sharp increase in U.S. electricity demand, with AI-driven data centers emerging as a central contributor through 2030. This paper asks how U.S. AI data centers can meet projected electricity needs while simultaneously reducing carbon emissions. Using a supply-chain and systems perspective, it analyzes the full energy chain of AI data centers: upstream electricity supply and grid deliverability, midstream facility design and grid interaction, and downstream operational practices, waste management, and disclosure. Drawing on recent projections from the International Energy Agency, Lawrence Berkeley National Laboratory, and U.S. federal guidance, the paper shows …

Design Considerations For Conversational Agents To Assess The Social-Emotional Well-Being Of Young Children In Low-Income South African Communities, Lucretia A. Williams, Elizabeth A. Ankrah, Catherine E. Draper, Caylee J. Cook, Franceli L. Cibrian, Jesus A. Beltran, Kimberley D. Lakes, Gillian R. Hayes

Engineering Faculty Articles and Research

A variety of digital technologies have been used to support early childhood development (ECD) programs in low-income South African communities. Even though technology has provided opportunities to increase access to health interventions, the lack of trust and socio-economic constraints under which these tools would need to work pose complex challenges. We examine home visitors’ work processes, experiences, and preferences of a conversational agent to support their work of administering social-emotional well-being assessments to young children ages 0-5. Analysis of the results of focus groups with 51 home visitors indicates the need for designing conversational agents that support ECD in the …

Localized Nuclear Utility For Erau Environmental Improvement, Timothy Schroeder, Jacob Blanton, Christopher Anderson

Sustainability Conference

The reliance of Embry Riddle Aeronautical University (ERAU) on energy supplied by Florida Power & Light (FPL), which currently draws large amounts of power from fossil fuel sources, is continuously suboptimal both environmentally and financially. This study proposes assessing the feasibility of deploying a campus scale nuclear reactor tailored to ERAU’s power consumption profile. This serves as a path toward reducing greenhouse gas emissions and limiting dependence on external fossil fuel generation. The methods used are primarily literature review(s), and numerical analysis on comparable use cases. The literature reviews focused on design theory and yield of nuclear reactors on a …

Advancing Power System Reliability And Security With Efficient And Resilient Graph Neural Network Frameworks, Seyed Hamed Haghshenas

USF Tampa Graduate Theses and Dissertations

Enhancing the reliability and security of smart grids is critical for ensuring their seamless operation and resilience against disruptions. The increasing integration of distributed energy resources, advanced measurement devices, and cyber-physical elements introduces both opportunities and challenges for grid management. While these advancements provide enhanced visibility and operational control, they also expose the grid to vulnerabilities from cyber-physical stresses, such as cyber-attacks, equipment failures, and fluctuating power demands. Traditional methods for reliability assessment and threat detection often rely on model-based approaches that struggle to adapt to the complexity and dynamic nature of modern smart grids. These limitations necessitate novel data-driven …

Abdominal Ultrasound Image Dataset For Organ Classification And Disease Detection, Sifat Zina Karim

Research Data

This is a dataset of Ultrasound (US) images of abdominal organs. US imaging is widely accessible and a very common diagnostic tool, as it is non-invasive and does not involve radiation risk. This dataset was curated solely for research in deep learning, with potential applications in supervised, semi-supervised, and unsupervised learning to support disease detection in resource-constrained settings.

The dataset comprises 5,468 unique images of different abdominal organs, namely: Abdominal Aorta (0), Gallbladder (1), Hepatic Vein (2), Kidneys (3), Liver (4), Ovaries (5), Pancreas (6), Portal Vein (7), Spleen (8), and the Urinary System (9), which includes the Urinary Bladder, …

Harnessing Electrochemical Hydrogen Evolution In Α-Fe2o3/2h–Mos2 Heterojunction: A Theoretical And Experimental Study On Electronic Modulation And Basal Plane Activation, Mohamed Sayed, Ossama Metwalli, Sameh O. Abdellatif, Kai Yin, Mohamed Saber, Ahmed Khalil, Hossam Abdelwahan

Electrical Engineering

Hydrogen production through water electrolysis is a promising method for storing renewable energy and reducing greenhouse gas emissions. However, the efficiency and stability of hydrogen production electrodes remain challenging issues. In this study, we present a rationally designed α-Fe2O3/2H–MoS2 heterojunction that unlocks the electrocatalytic potential of the inert basal plane of the semiconducting 2H–MoS2 phase. The heterostructure, synthesized via a facile hydrothermal method, features α-Fe2O3 nanocubes anchored onto flowerlike 2H–MoS2 nanosheets. The electrochemical performance of the α-Fe2O3/2H–MoS2 heterojunction electrode was evaluated by linear sweep voltammetry, Tafel polarization, and electrochemical impedance spectroscopy. The results showed that the α-Fe2O3/2H–MoS2 electrode exhibited lower …

Photochemical And Electrochemical Assessment Of Uio-66-Nh₂/G-C₃N₄ Thin-Film Heterostructures As Potential Candidates For Hydrogen Evolution: An Experimental Study Augmented By Dft Insights, Nour Abouseada, Maryam Galal, Sameh O. Abdellatif, Khaled Kirah

Electrical Engineering

The global shift towards carbon-neutral energy systems has catalyzed an intensified focus on sustainable hydrogen production, with photo and electrochemical water splitting emerging as a particularly promising pathway. This study elucidates the design, simulation, and synthesis of advanced photo and electrocatalytic materials tailored for the hydrogen evolution reaction (HER), concentrating on heterostructures formed by zirconium-based metal-organic frameworks (MOFs)— specifically, UiO-66 and its amine-functionalized derivative, UiO-66-NH₂—in conjunction with graphitic carbon nitride (g-C₃N₄). Employing density functional theory (DFT) simulations, we pre-screened the electronic properties of the MOFs, revealing that amine functionalization significantly narrows the bandgap and optimizes band alignment, thereby enhancing photocatalytic …