Electrical and Computer Engineering Commons^™

Development Of Interactive Games On An Affordable Braille Display, Daniel Tsivkovski, Dylan Ravel, Maryam Etezad

Student Scholar Symposium Abstracts and Posters

Developing an affordable and STEM learning-focused Braille display addresses a significant disparity in the market for Braille displays, where most fail to provide a cost-effective, accessible, and education-oriented solution. This research aims to bridge this gap through innovative hardware and software development, offering a comprehensive learning experience to elementary school children (K-6) who are blind/visually impaired. The hardware features a piezo-electric tactile display that displays up to six Braille characters at once or a shape in an 8x8 pin array configuration. The educational software includes a user-friendly website packed with engaging STEM activities specifically designed for blind/visually impaired children. The …

Praxly: An Online Ide For The Praxis Cs Test Pseudocode, Benjamin Saupp

James Madison Undergraduate Research Journal (JMURJ)

No abstract provided.

Characterization Of A Magnetically Contained Hot Filament Plasma Source With A Wide-Sweeping Langmuir Probe, Jonas Rowan

Doctoral Dissertations and Master's Theses

Ionospheric plasma research in the Space and Atmospheric Instrumentation Laboratory’s Space Plasma Chamber has been hindered by the lack of a suitable plasma diagnostic instrument and understanding of its hot-filament plasma source. This thesis describes efforts made to remedy both problems. A wide-range Sweeping Langmuir Probe was developed with a ±35 V sweeping range to fully analyze ion and electron saturation regions in the entire IV curve. A method was derived to estimate the chamber source’s filament temperatures. The new Langmuir probe was integrated into a refurbished automated system designed in Python to measure plasma parameters for various chamber conditions, …

Breaking New Ground: Division Directly In Memory, M. Hassan Najafi, Mehran Shoushtari Moghadam

Faculty Scholarship

In-memory computing (IMC) has emerged as a promising paradigm for overcoming the limitations of traditional von Neumann architectures by reducing data movement and enhancing computational efficiency. Despite significant advancements in this area, implementing complex arithmetic operations, such as division, directly within memory has remained an elusive challenge. This paper introduces a pioneering technique for performing division operations directly in memory, representing the first successful integration of such functionality into the IMC framework. Our approach leverages an innovative circuit based on an unconventional model of computing–stochastic computing (SC). Our technique extends the computational capabilities of IMC systems and paves the way …

A Wireless Flex Sensors Based Man-Machine Interface, Maram F. Badkook, Razan A. Alshehri, Saeed Qaiser

Effat Undergraduate Research Journal

Abstract. The recent technological advancements are focusing on developing the smart systems to facilitate the subscribers and to improve their lifestyle. The machine learning algorithms and artificial intelligence are becoming the elementary tools, which are used in the establishment of modern smart systems across the globe. In this study, a flex sensors-based Man to Machine Interface (MMI) is proposed. It is beneficial for a category of people with reduced mobility or special needs like deaf, dumb, parallelized, etc. The system prototype is realized by using an array of flex sensors and a front-end processor, wirelessly connected to the central processing …

Pressure Switch Tester, Michael Starasinich, Andrew Dobson, Jordan Huschen, Mitchell Smith, Melvin Brown Iii

Honors Capstones

Abstract—- The Pressure Switch Test Stand is a comprehensive and automated testing solution designed to streamline the actuation and validation of simultaneously. At the core of the system is a PLC-controlled architecture that enables precise control and simulation of pressure conditions, replicating real-world scenarios to ensure accurate and reliable switch performance.

This test stand integrates robust mechanical fixtures, a reliable pneumatic control system, custom-designed electrical circuits, and a modular software interface, working in unison to deliver consistent and high-fidelity testing outcomes. Each pressure switch undergoes a sequence of pressure ramps and holds, with the system continuously monitoring switch states, activation …

Validating An Electronics Cooler Experiment And Optimizing Its Performance Using Ansys Icepak, Daniel V. Curl

Mechanical Engineering Undergraduate Honors Theses

Researchers at the University of Arkansas' Mechanical and Electrical Engineering Research Departments have designed and built a cold plate and substrate design for a 10 kV SiC MOSFET power module. Many variables define the design of the cold plate and substrate system and have quantifiable effects on the device’s performance. These variables include but are not limited to geometry and material selection of the fins and substrate. Manufacturing many test designs, running experiments, and comparing their performance is a time-consuming and expensive design optimization method that may be effective for some low-cost applications. Still, simulation is often a more cost-effective …

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

Automation Programming For Uhv-Cvd Growth Of Group Iv Materials, William Hay

Electrical Engineering and Computer Science Undergraduate Honors Theses

The automation programming of a UHV-CVD reactor for group IV materials was performed. The reactor was programmed to perform all the necessary functions for a Germanium (Ge) growth with little response from the user. The overall goal was to fix an Argon purge program to clean out the process chamber of gases and create an Excel output program to ease any work on the user. There were also general fixes and address name changes that occurred throughout the program. Then, a one step and two step growth of a Ge material was formed in a UHV-CVD reaction for analysis. Germanium …

Defect Characterization Of Sic Schottky Barrier Diode Using Deep Level Transient Spectroscopy, Zachary I. Mccoy

Electrical Engineering and Computer Science Undergraduate Honors Theses

The aim of this project is to use deep level transient spectroscopy (DLTS) to energetically evaluate defects by using the carrier concentration dependence on temperature and where these carriers may experience deviation from standard predictions. These defects become apparent through multiple C-V measurements that are swept through a temperature range. By analyzing trends with respect to temperature, the defects can be energetically “located,” and thus identified. This identification can help streamline semiconductor material manufacturing/growth because DLTS identifies potential defect causes by providing data to characterize semiconductor mid-band defects. DLTS can assess nearly all parameters associated with traps/defects including density, thermal …

Design, Simulation, And Testing Of Planar Transformer For Smart Green Power Node (Sgpn) Isolation Using Ansys, Brooke Scott

Electrical Engineering and Computer Science Undergraduate Honors Theses

The goal of this project was to design and simulate a small, high-frequency, planar, ferrite-core transformer which can be used in a variety of applications, the most relevant of which is photovoltaic (PV) converter systems, which often require bulky transformers to function properly. The current research involved several processes: (1) Understanding and designing a tutorial for the use of Ansys software to aid in the efficient development and modeling of transformers, (2) designing and simulating a planar transformer in Ansys to be used in a solar converter, specifically the Smart Green Power Node (SGPN), and (3) testing the planar transformer …

Investigation And Characterization Of Silicon Carbide Power Mosfets, Vincent Hassman

Electrical Engineering and Computer Science Undergraduate Honors Theses

As engineers constantly seek to make solar power systems smaller, cheaper, and more efficient, technological enhancements in the electronics that run them are required. One of the most fundamental electronic components of any solar system is the inverter, which takes DC current from the solar cell and turns it into AC current that is supported by the grid. This is done with metal oxide semiconductor field effect transistors (MOSFETs). Through more advanced MOSFET technology, inverters can switch at a higher frequency, increasing efficiency and reducing size and cost.

MOSFET performance can be enhanced by swapping the traditional silicon substrate with …

Emerging Applications On Reconfigurable Computing Platforms, Zhenyu Xu

All Dissertations

Security and high-performance computing have become two of the most critical demands in modern technology. The increasing complexity of digital systems, the need for real-time processing, and the emergence of sophisticated cyber threats require computing solutions that balance computational power with adaptability and security. Reconfigurable computing platforms, particularly Field-Programmable Gate Arrays (FPGAs), offer a promising solution to these challenges by combining flexibility with hardware acceleration. Many new applications have emerged with the developing of reconfigurable platforms.

FPGAs can be utilized in two primary directions: as control and high-precision measurement units for security-sensitive applications, and as accelerators capable of outperforming GPUs …

Optimizing And Training An Svm-Based Breast Cancer Tumor Classifier, Kevin Lopatka

Master's Theses

With advancements in technology, turning to machine learning has become a popular choice for aiding clinicians in the diagnoses of breast cancer malignancies. While the neural networking approach has been vetted thoroughly, this work aims to take advantage of traditional machine learning techniques; mainly support vector machine learning and the optimizing of feature extraction. The discrete-wavelet transform is used in the feature extraction stage of machine learning. Previous works that use this feature extraction technique are analyzed and expanded upon by utilizing a variety of different wavelets as well as other color-spaces with the goal of achieving higher result metrics …

Modeling And Analysis Of A Hybrid Ac/Dc Green Seaport Power System With Photovoltaic Generation And Battery Energy Storage Systems, Corey Zaas

Master's Theses

This thesis investigates the modeling, simulation, and analysis of a Green Seaport power system integrating photovoltaic (PV) generation, battery energy storage systems (BESS), and coordinated protection within a hybrid AC/DC framework. The work improves a previously established model, enhancing it with detailed protection studies and simultaneous AC/DC power flow analysis using EasyPower software. The system was evaluated under a wide range of operating conditions, including varying solar irradiance, battery charge and discharge states, and both full and zero load conditions. Key performance indicators included voltage regulation, power factor behavior, equipment loading, protection device coordination, and total system power losses. Simulation …

Adaptive Delay Compensation Frameworks For Distributed Real-Time Co-Simulation In Power Systems, Elutunji Buraimoh

All Dissertations

This dissertation presents a model-free, adaptive delay prediction and compensation framework for geographically distributed real-time power system co-simulation environments. Communication delays—both constant and real-time-varying—significantly degrade the accuracy, fidelity, and stability of co-simulated systems, particularly in dynamic and transient analyses of partitioned power systems. To address this, a predictor-based framework is developed that compensates for delays without requiring system models, computationally intensive signal transformations, or manual intervention.

The proposed solution leverages a Damping Impedance Method as the interface algorithm, combined with a sliding-mode control-inspired predictor system. Both single-parameter and multi-parameter predictor configurations are implemented, with the multi-parameter design providing an additional …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Enhancing Photovoltaic Inverter Reliability Through Advanced Hardware Protection And Simulation Techniques, Buck Brown

All Dissertations

Power electronics converter reliability is an issue in today’s grid, and it is an issue that will only become more prevalent as the nation and world move toward greater renewable energy penetration. While a significant amount of research has been conducted pertaining to power electronics converter reliability, (1) there lacks a universal way to protect the most critical component in a power electronics converter—the power device—from the two most common failure modes—short- and open-circuit faults; and (2) there does not exist a tool that considers both the component- and system-levels of a photovoltaic (PV) inverter for the purposes of lifetime …

On The Optimal Design Of Coreless Afpm Machines With Halbach Array Rotors For Electric Aircraft Propulsion, Matin Vatani, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper provides a comprehensive analysis of the winding factor and winding harmonic distribution in coreless stator axial flux permanent magnet (AFPM) machines. The topology of this type of machine and its similarities to conventional cored AFPM machines are discussed. Popular pole/coil number combinations in coreless stator AFPM machines are identified and analyzed in detail. It is demonstrated that the winding factor in these machines varies with radius and can be expressed as the product of the conductor distribution factor, pitch factor, and coil group factor. The formulas for each factor are derived and presented in multiple forms based on …

Hybrid Fea And Meta-Modeling For De Optimization Of A Highly Saturated Spoke Ipm, Oluwaseun A. Badewa, Marcelo Silva, Rosemary E. Alden, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces a novel approach for high performance electric motor design that combines machine learning (ML)-based meta-modeling with a differential evolution (DE) optimization algorithm. The method leverages finite element analysis (FEA) results to train the ML meta-model, enabling efficient design optimization for high-power density cored machines, such as spoke interior permanent magnet motors (IPM), which exhibit complex nonlinearities and saturation effects. This hybrid ML-DE framework seeks to provide an alternative for physics-based electric motor design and optimization, offering significant reductions in computational effort while maintaining accuracy. The meta-model’s accuracy in capturing the nonlinear relationships between design parameters, core losses, …

Optimal Design Of Coreless Axial Flux Pm Machines Using A Hybrid Machine Learning And Differential Evolution Method, Matin Vatani, David R. Stewart, Pedram Asef, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Coreless stator axial flux permanent magnet (AFPM) machines require computationally intensive three dimensional finite element analysis (FEA) for accurate performance evaluation, making optimization time-consuming and impractical for large-scale design studies. This paper presents a hybrid optimization approach that integrates differential evolution (DE) with artificial neural networks (ANNs) to accelerate the optimization of coreless AFPM machines. In this method, DE driven FEA simulations generate a dataset used to train an ANN surrogate model, significantly reducing reliance on direct FEA computations. The effectiveness of this approach is demonstrated through a multi-objective DE optimization, where the ANN’s predictions are validated against FEA results. …

Winding Factors And Harmonics Of Coreless Axial Flux Pm Machines, Matin Vatani, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper provides a comprehensive analysis of the winding factor and winding harmonic distribution in coreless stator axial flux permanent magnet (AFPM) machines. The topology of this type of machine and its similarities to conventional cored AFPM machines are discussed. Popular pole/coil number combinations in coreless stator AFPM machines are identified and analyzed in detail. It is demonstrated that the winding factor in these machines varies with radius and can be expressed as the product of the conductor distribution factor, pitch factor, and coil group factor. The formulas for each factor are derived and presented in multiple forms based on …

Implementation Of Residual Tandem Neural Networks For Photonic Inverse Design, Ponthea A. Zahraii

Electrical Engineering and Computer Science (MS) Theses

Deep-learning approaches can greatly benefit the modeling and design of nanophotonic and optical structures. Traditional full-wave simulations are time and resource-intensive, which can act as a bottleneck in photonic design. On the other hand, deep-learning approaches for designing the response of nanophotonic geometries can be computationally inexpensive and produce accurate and efficient results. In this project, we specifically investigate the case of optical forces near meta-structures. We propose using an inverse design approach with residual blocks to account for the deep nature of this architecture and inherently address the non-uniqueness problem. A tandem approach, which consists of two interconnected models, …

Sonochemically Synthesized Zno Nanowires (Nws)-Based Sensor For Non-Invasive Glucose Detection In Sweat, G. M. Mehedi Hossain, Ahmed Hasnain Jalal, Hasina Huq, Nazmul Islam, Karen Lozano, Nezih Pala, Fahmida Alam

Electrical and Computer Engineering Faculty Publications

Glucose is a crucial metabolic marker, providing critical insights into energy regulation and insulin sensitivity, making its monitoring essential for managing diabetes and overall metabolic health. In this context, flexible biosensors have gained considerable attention for their potential to enable real-time and non-invasive glucose monitoring. One promising advancement in this domain is using Zinc-Oxide (ZnO) nanostructures through sonochemical synthesis. ZnO has a wide bandgap of ∼3.37 eV and a large binding energy of 60 meV. This research focuses on ZnO-Nanowires' growth for the first time on a flexible polymer substrate. The average length and diameter of the nanowires are 2.5µm …

Optimization-Based Distributed Controller For Multi-Agents System In Microgrid Secondary Control, Fahad S. Alshammari, Ayman El-Refaie, Saleh Alyahya, Sheroz Khan

Electrical and Computer Engineering Faculty Research and Publications

Micro-grids function to connect to power system power produced by the renewable energy resources. In islanded micro-grids, grid-forming units collaborate to maintain the micro-grids voltage and frequency by utilizing droop control technique that includes primary, secondary and tertiary levels. Secondary control intervenes to improve power sharing and restore voltage and frequency to their nominal levels. However, the conventional droop control applied to a grid with mismatched line parameters experiences a trade-off between reactive power sharing and voltage regulations. This paper applies real-time trajectory tracking convex optimization to ensure by communicating power sharing between units in a consensus topology. The optimization …

Understanding The Breadth And Impact Of The Ias [President’S Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Modality Distillation Using A Sam-Guided Multimodal Teacher For Unimodal Wildfire Segmentation And Temperature Prediction, Michael N. Marinaccio

All Theses

Wildfires are one of the world’s most devastating natural disasters that affect the environment, communities, and more critically, humans that live in and around those communities. Due to the threat of large-scale destruction in landscapes and human inhabited areas, it has become increasingly more important to develop wildfire detection, management, and suppression strategies to mitigate and prevent these negative outcomes. Wildfire research encompasses many different areas. Most notably, the development of communication, navigation, remote sensing, and monitoring systems. In wildfire monitoring, limitations discovered in-ground and satellite observation have shifted the focus toward Unmanned Aerial Vehicle (UAV) based wildfire research, which …

Lightlink: Visible Light Communication For Batteryless Devices With Variable Computational Load, Charles Phillips

All Theses

Visible Light Communication (VLC) devices have been experimentally proven to work as a suitable communication medium for batteryless devices. However, the effects of practical load have yet to be fully explored. To that end, we have developed LightLink, a new MAC and PHY layer protocol for VLC within batteryless devices, and have studied various ways that computational load can affect transmission accuracy in realistic scenarios. Our key findings point us towards an adaptive VLC reception system based on inferred environmental variables.

Real Time Adaptive Control Of A Pid Via Genetic Algorithm Machine Learning Systems, Cemre Tas

Graduate Theses and Dissertations

The most common control method that is utilized by all industries across the world is the proportional-integrative-derivative controller (PID) due the relatively low cost and complexity of the system. However, there are draw-backs with PIDs, it is not adaptative to a changing system, so it works on nominal systems, and it starts breaking down when a system begins to have a non-linear response. The method chosen to overcome both is the utilization of machine learning with the use of genetic algorithms.

This method allows any PID system to be capable of adapting in real-time, while not adding significant additional cost …

A Study Of The Impact Of Balancing, Geometric Transformation, Generative Networks, And Roi Techniques In Eye Diseases Classification, Sghaira Hareb Alnuaimi

Theses

Automatic detection of ocular diseases helps medical professionals efficiently identify eye disorders, reduce diagnostic errors, and accelerate diagnoses to prevent blindness. Deep learning has been successfully utilized in various fields, including medical image classification. However, in spite of these advancements, challenges remain in ocular disease classification.

The objective of this work is to address these challenges using data processing, data augmentation in combination with Region of Interest (ROI) techniques. Medical datasets often suffer from scarcity, imbalance, and low-quality images, leading to inaccurate classification. To mitigate these issues, we utilize the ODIR dataset, which contains 7,000 labelled training images for both …