Digital Commons Network^™

Aquaflow Pro, Ashton Henley, Jeannie Fritz, Khalin Rubbo

Williams Honors College, Honors Research Projects

Changing aquarium water is a hassle, and when done incorrectly, it will result in changes in water quality and potential harm to aquatic life. Aquarium water changes are crucial in removing toxins such as ammonia, nitrites, and nitrates. There is a need for an automated, stressless fish tank water-changing solution that ensures aquatic safety. To this end, an automatic temperature-controlled fish tank water changing system was designed, carrying out a water change for a designated main tank in which water heated within an inbound reservoir is pumped into the tank to replace the outbound water, responding to both the tank’s …

Epistemic Edge: Subjective Logic Guardrails For Llm-Driven Iot Actuation, Muntaser Syed, Marius Silaghi

Electrical Engineering and Computer Science Student Publications

Deploying large language models (LLMs) as decision-making agents in safety-critical Internet of Things (IoT) systems introduces risks that purely behavioral guardrails, such as action whitelists, cannot fully address. This paper presents Epistemic Edge, a four-tier neuro-symbolic pipeline that augments LLM-driven actuation with subjective logic (SL) uncertainty quantification, temporal decay, and dual guardrails combining epistemic threshold checks with behavioral whitelists. We evaluate seven locally-deployed models: three PrismML Bonsai 1-bit models (1.7B, 4B, 8B), three 4-bit quantized models (Qwen3-8B, Llama 3.2-3B, Phi-3.5-mini), and the DeepSeek-R18B reasoning model, across eight ablation conditions and five IoT actuation scenarios (2,800 controlled trials). We further validate …

Towards Optimal And Resilient Ac/Dc Microgrids: Control Design, Analysis, And Implementation, Jun Zhang

Electronic Theses and Dissertations

Microgrids serve as a small-scale power grid for utilizing renewable energy to enhance energy reliability, sustainability, and resilience. As an autonomous system, an islanded microgrid can disconnect from the utility grid and operate independently by maintaining system voltage and frequency. However, this new feature introduces coordination problems among distributed generators (DGs), such as 1) how to make sure the voltage profile and current sharing in DC microgrid with different types of converters; 2) how to reduce the impact of cyberattack when the system coordination is performed based on communication, and 3) how to calculate the steady state under a droop …

Brrbox, Shawn J. Myers, Lane Cline, Michael Davis, Christian Secrest

Williams Honors College, Honors Research Projects

This report details the project known as “The BRRBOX”, a reusable, insulated thermoelectric cooler developed to keep internal temperatures at refrigeration levels or cooler for at least 48 hours. The cooler will track its internal temperature during this period and be able to give the data at the end of its delivery cycle to keep up with food and pharmaceutical standards during delivery. The BRRBOX uses Peltier-based cooling alongside vacuum insulation panels and fans to achieve efficient thermal control. An onboard microcontroller will monitor temperature, record the data, and adjust the cooling output to minimize power consumption. The box will …

Automatic Pet Feeder Network, Daniel J. Herttna, David A. Bechtel, Alexander Deskovich

Williams Honors College, Honors Research Projects

This project will describe the design process and research behind the Automatic Pet Feeder Network, otherwise known as The Petwork. The system consists of a tank to hold the food, a latching system to drop the food at the desired time, a weight sensor to verify that the proper amount of food has been dispensed, a power supply unit that steps down 120V AC standard wall outlet power, & a microcontroller that will interpret the user's input from a mobile phone application. The networking portion is covered by having a primary and a secondary feeder. The mobile application will communicate …

Application Of Matlab Simulation For Quantum Wells And Absorption Modelling, Mohamed Nur

Electrical Engineering Theses

The Quantum-Well User Entered Simulation Tool (QUEST), originally developed at the University of Texas at Arlington in 2005, is a simulation program built in MATLAB for computing energy eigenvalues and wavefunctions in user-defined semiconductor quantum well structures. This thesis presents a new revision and extension of QUEST with three primary contributions: compatibility updates to the existing MATLAB codebase, intersubband absorption modelling, and a redesigned graphical user interface for ease-of-use in testing.

The modernization effort for this program addresses incompatibilities introduced by changes to the MATLAB runtime environment since QUEST’s original release in 2005, including corrections to the self-consistent Schrödinger-Poisson solver …

The Design And Analysis Of Robust Mems Devices For Extreme Space Environments, Joshua Taggart

Honors Undergraduate Theses

The purpose of this study is to analyze aluminum nitride (AlN) micro-electromechanical systems (MEMS) resonators designed for extreme-environment applications. The devices of study are Lamb wave, piezoelectric resonators designed and fabricated using conventional semiconductor manufacturing processes and operating around various frequencies in the megahertz range. The purpose of this study is to advance understanding of MEMS devices in extreme-temperature and radiated environments for outer-space applications.

Devices were tested under vacuum at temperatures ranging from room temperature (~21°C) to 800°C. Under these conditions, the device was measured both as a resonator and in an oscillator circuit. Results show that the resonant …

Distributed Vibration Sensing For Identification Of Loose Connectors In Coaxial Data Transmission Lines, Saidanvar Esanjonovich Valiev, Anthony C. Okafor, Jeremiah J. Rittenhouse, Jie Huang, Daniel S. Stutts

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This study investigates the effect of vibration-induced loose connections on signal reflection (S11) for loose connection identification in aerospace coaxial cables using distributed sensing approach, which is effective in filtering the noise and identifying minor discontinuities. In this approach, a sliding gated window is applied to S11 signal, a fast Fourier transform is performed over the gated windows, cross-correlation is computed between the baseline and vibration-affected signals, and the standard deviation is mapped along the cable length. Sinewave signals from 9 kHz to 5 GHz were swept through cables with vibrating connectors under three conditions: fully tightened, loosened by 180°, …

Multi-Timescale Monitoring And Modeling For Resilient Smart Grids: Pmu Anomaly Detection And Battery Digital Twins, Muhammad Imran Hossain

Graduate Theses, Dissertations, and Problem Reports (ETD)

Modern power systems face failures at very different timescales. Cyber-physical disturbances may emerge within seconds, while battery degradation develops over hundreds of operating cycles. Both monitoring problems share the same underlying difficulty: power systems produce measurement data in abundance, but reliably labeled examples of abnormal or degraded operation are scarce. Rare grid events are difficult to label, and battery degradation data are heterogeneous across cells, cycling protocols, and chemistries. This thesis addresses these challenges through two complementary domain-informed learning frameworks that constrain representation learning using information specific to each physical problem.

First, at the system level, T-BiGAN, a Transformer-augmented bidirectional …

Dataless Neural Networks For Boolean Satisfiability And Network Optimization, Andrew Evan Gautier

Graduate Theses, Dissertations, and Problem Reports (ETD)

Combinatorial optimization problems (COPs) require searching over a finite solution space subject to constraints, with the goal of satisfying an objective function. They arise in operations research, scheduling, resource allocation, circuit design, and many other fields. Many problems in combinatorial optimization (including satisfiability and network design) are NP-hard. Traditionally, researchers have built approximate solvers that return near- optimal solutions efficiently by developing increasingly sophisticated heuristics and meta- heuristics. Deep learning has provided new opportunities for improving combinatorial solvers by leveraging neural guidance to prune the search space. Traditional neural networks have distinct drawbacks in this context: separate training and …

Development Of Periodic Plasmonic Nano-Structures For Enhanced Labeled Bio-Sensing Systems, Kyle Zackary Smith

Graduate Theses, Dissertations, and Problem Reports (ETD)

The biomedical industry has seen sustained growth over the past half century, with a continually increasing demand for flexible, easy-to-use, and cost-effective tools. One large area of commercial interest has been point-of-use or point-of-care diagnostics, using optical based Lab-On-Chip (LOC) style systems. Label and label-free fluorescence detection systems are common benchtop modalities that have seen recent integration into these portable, cost-effective LOC applications. However, despite their maturity, there are still opportunities to improve device characteristics, specifically in reference to throughput, limit-of-detection (LOD), and hybrid integration (along with associated costs).

Optical research avenues at WVU have focused on improving these systems …

Deep-Learning-Based Generation Of Synthetic Contactless Fingerphotos, Christopher Harry Burton

Graduate Theses, Dissertations, and Problem Reports (ETD)

The collection of biometric data is a labor-intensive, high-resource process that presents significant logistical, privacy, and cost barriers for researchers and developers. To address these challenges, the biometrics community has increasingly turned to generative models capable of producing synthetic datasets that reflect the statistical properties of real data. While substantial progress has been made in synthetic fingerprint generation for contact-based modalities, the contactless fingerphoto domain has remained largely underserved. This work presents a deep learning-based approach to synthetic contactless fingerphoto generation using a Stable Diffusion model guided by multimodal conditions (text and image). The dataset used for training was collected …

A Digital Calibration Source For 21 Cm Cosmology Telescopes, Kalyani Balkrishna Bhopi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Precise calibration of radio telescope beams and gains is a central requirement for 21 cm intensity mapping experiments, which aim to measure large scale cosmological structure through the redshifted emission line of neutral hydrogen. Bright astrophysical foregrounds dominate the sky at these frequencies, and separating them from the cosmological signal demands precise control over instrumental systematics, particularly the telescope beam and its frequency-dependent response. Existing aerial calibration sources are incoherent broadband emitters, detectable only as total power. They provide no direct phase information and suffer from poor sensitivity in low signal-to-noise regimes.

We present the Precision Emitter for 21cm Array …

Hyperglycemia Detection From Single-Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola

Graduate Theses, Dissertations, and Problem Reports (ETD)

Diabetes Mellitus is known as high blood glucose. This high blood glucose level happens when the body has a problem with producing or using insulin. Insulin is a very important hormone that the pancreas makes to control how much glucose gets into the bloodstream and cells. Diabetes Mellitus has an effect on the body if it is not treated, such as damaging the blood vessels and nerves which can lead to stroke, kidney failure, heart attack and permanent loss of vision. Since people with diabetes need to measure their glucose levels in order to determine how much insulin or medicine …

Bi-Level Optimization Of Peer-To-Peer Trading In A Decentralized Energy Market, Marshal Miezah

Graduate Theses, Dissertations, and Problem Reports (ETD)

Distributed power generation based on rooftop photovoltaic (PV) systems integrated with battery storage emerges as a promising pathway for reducing greenhouse gas emissions and im- proving flexibility in modern power systems. This study develops a bi-level optimization model to examine how prosumers maximize profit through peer-to-peer (P2P) energy trading with consumers and the grid, and how consumers minimize cost by leveraging P2P trading. The bi-level problem is reformulated as a single-level mixed-integer programming (MIP) model using Karush- Kuhn-Tucker (KKT) conditions to improve tractability and preserve market-clearing behavior. For the model validation and case study development, prosumer and consumer data are …

Towards Increasing The Retention Of Freshman Engineering Students: Implementing A New Course, Ee 101 Introduction To Electrical And Computer Engineering, Omer Lateef, Mingyu Lu, Waseem Alaqqad, Charan Litchfield, Kenan Hatipoglu

2026 Scholarly Teaching Conference: Poster Session Papers

To assist with improving student retention in the undergraduate electrical and computer engineering program, a course was developed and implemented at the freshman level in the Spring of 2021.  This course was named EE 101: Introduction to Electrical and Computer Engineering and served to give freshman students an opportunity to meet the professors of the ECE department and experience live sessions dedicated to presenting “upcoming” topics and research endeavors of their expertise.  Students typically will not be attending the electrical or computer engineering subjects until their sophomore year and this course, set in second semester for a freshman, attempts to …

Numerical Analysis And Simulation Of Enhanced Performance In Nanowire Cds/Cdte Solar Cells: A Pathway To Greater Than 25% Efficient Cdte Solar Cell, Riasad Badhan

Theses and Dissertations--Electrical and Computer Engineering

This Thesis finds a pathway to a significantly high-efficient CdTe based solar cell by demonstrating and harvesting the advantages of a nano-structure configuration in CdTe based solar cells. Nanowire CdS window layer and the “control”, planar CdS window layer films were fabricated in the laboratory and compared for their optical transmission and other characteristics affecting the performance of the CdS-CdTe solar cell. Numerical simulations were performed for a comparative evaluation of the embedded nanowire CdS-CdTe solar cell device and the traditional planar CdS-CdTe solar cell device. Experimentally measured spectral transmission of nanowire CdS film was used in the simulation environment. …

Robotizing Complex Welding Processes Through Imitation Learning And Generative Models From Human Demonstration, Yue Cao

Theses and Dissertations--Electrical and Computer Engineering

Arc welding processes demand real-time adaptive control that current robotic systems cannot achieve autonomously. This dissertation develops a systematic framework to robotize complex welding by learning from human demonstration, integrating generative modeling, physics-informed reconstruction, and model-based imitation learning. First, human--robot collaboration systems are established for both Gas Tungsten Arc Welding (GTAW) and Double-Electrode Gas Metal Arc Welding, combining robotic teleoperation with virtual reality interfaces to capture high-quality operator demonstrations. Second, a physics-informed neural network framework reconstructs complete molten pool flow fields from high-speed imaging, enriching process understanding beyond direct sensor observation. Third, generative models, including a hybrid latent variational autoencoder …

Design Of Energy-Efficient, Scalable, And Flexible Tensor Processing Architectures With Electro-Photonic Integrated Circuits, Oluwaseun Alo

Theses and Dissertations--Electrical and Computer Engineering

In recent years, artificial intelligence has achieved remarkable success across domains such as computer vision, natural language processing, and scientific computing. This progress has been driven largely by advances in deep learning, particularly deep neural networks (DNNs), including convolutional neural networks (CNNs) and transformer-based models. While these models deliver unprecedented accuracy, often surpassing human performance, their computational complexity continues to grow rapidly due to multibillion- and trillion-parameter designs. As model sizes and deployment scales expand, the demand for energy-efficient and high-throughput hardware accelerators has intensified. Conventional electronic platforms based on CPUs, GPUs, ASICs, and FPGAs are increasingly constrained by the …

Efficient And Fair Resource Unit(Ru) Allocation Methods For The Ofdma Mechanism Under Ieee 802.11ax, Yang Yu

Doctoral

The IEEE 802.11ax standard was approved in February 2021. This standard allows users to access different channel bandwidths to transmit their packets under the Orthogonal Frequency Division Multiple Access (OFDMA) mechanism. As the number of Internet users and the number of Internet applications increase, the allocation of channel resources has been a challenge in WLANs. Different types of applications, such as real-time video conferencing, online gaming and large-scale data transfers, generate different requirements in terms of throughput, latency and reliability. Consequently, the diversity in users’ channel bandwidth demands is increasing. Under the OFDMA mechanism, the packets can be allocated into …

Improving Medical Diagnostics With Vision-Language Models: Convex Hull-Based Uncertainty Analysis, Ferhat Ozgur Catak, Murat Kuzlu, Taylor Patrick, Michel Audette

Engineering Technology Faculty Publications

In recent years, vision-language models (VLMs) have been applied to various fields, including healthcare, education, finance, and manufacturing, with remarkable performance. However, concerns remain regarding VLMs’ consistency and uncertainty, particularly in critical applications such as healthcare, which demand a high level of trust and reliability. This paper proposes a novel approach to evaluate uncertainty in VLMs’ responses using a convex hull approach on a healthcare application for visual question answering (VQA). For any VLM, temperature refers to a sampling parameter used in probabilistic generation, which controls the randomness of the model’s output. The LLM-CXR model is selected as the medical …

Nanomagnet Based Reservoir Computing And Quantum Control, Fahim F. Chowdhury

Theses and Dissertations

Conventional CMOS scaling has driven remarkable advances in computing but faces increasing physical and energy constraints, motivating alternative computing paradigms that integrate memory and computation while improving energy efficiency. Nanoscale magnetic systems offer a promising platform for such approaches because their intrinsic nonlinear dynamics and localized magnetic fields can support both classical and quantum information processing. This thesis investigates nanomagnetic systems for physical reservoir computing and, with primary emphasis, for localized quantum control of spin qubits.

The first part explores dipole-coupled nanomagnet arrays as physical reservoirs. Micromagnetic simulations demonstrate nonlinear dynamical behavior with high short-term memory and parity-check capacity, enabling …

Beyond The Square Pulse: Waveform Shape, Eeg Correlates, And The Pursuit Of Natural Sensation In Tens, Jason Whitson

Honors Undergraduate Theses

This study investigates the relationship between electrical stimulus characteristics of shape and charge on evoked sensations and electroencephalogram (EEG) data during multi-waveform transcutaneous electrical nerve stimulation (TENS) of the median nerve. Neuromodulation methods have traditionally had little control over the location and quality of their associated evoked sensation (e.g., electric, vibration, touch). This experiment utilized five unique stimulus waveforms during TENS stimulation. EEG data were collected concurrently to provide an introductory objective measure of the neural responses underlying these sensory changes. Eleven participants completed three tasks (thresholding, super-threshold stimulation, two-alternative forced choice) using a two-electrode TENS approach. Stimulus waveforms were …

Nanofibrous Materials And Nanoparticles For Combating Antimicrobial Resistance: Synthesis, Integration, And Translational Perspectives, Rewati Raman Ujjwal, Ashish Dilip Sutar, Rahul Shukla, Gymama Slaughter

Center for Bioelectronics Publications

Antimicrobial resistance (AMR) is a major global health challenge driven by mechanisms such as biofilm formation, efflux pumps, and genetic mutations. Nanoparticulate and fibrous materials have emerged as promising strategies to overcome these limitations through multimodal antimicrobial action and controlled drug delivery. This review highlights recent advances in electrospun nanofibrous systems, including natural and synthetic polymer-based scaffolds, stimuli-responsive nanofibers, and functionalized patches. Nanoparticle-loaded nanofiber systems demonstrate enhanced performance, including bacterial eradication, sustained drug release, and significant biofilm disruption. Multifunctional systems combining antimicrobial, antioxidant, and immunomodulatory properties further show synergism. Emerging innovations, such as piezoelectric and smart sensing systems, enable self-powered …

Linker-Free Gold-Sputtered Tapered Optical Fiber Plasmonic Sensors For High-Sensitivity Refractive Index Detection In Microfluidic Platforms, Erem Ujah, Gymama Slaughter

Center for Bioelectronics Publications

Gold-sputtered tapered optical fiber (Au-TOF) sensors were developed for high-sensitivity refractive-index (RI) detection using aqueous glucose standards spanning 1.33–1.41 RIU (0–50% w/v). The sensors were fabricated using a reproducible workflow combining flame-brushing tapering, plasma surface preparation, and rotational magnetron sputtering, enabling azimuthally uniform gold coatings without the use of thiol or sulfur linker chemistries. Two sputtering durations (24 s and 30 s) were investigated to examine thickness-dependent plasmonic coupling and sensing performance. Optical measurements were conducted using a broadband supercontinuum source and compact spectrometer within a 20 µL microfluidic sensing chamber. Increasing glucose concentration produced a monotonic decrease in transmission …

Nanostructured Cathode Catalysts For Aem Electrolysis: From Catalyst Design To Degradation And Hydrogen Dynamics, Yamini Kumaran

Electronic Theses & Dissertations (2024 - present)

Anion exchange membrane water electrolysis (AEMWE) presents a promising pathway toward cost-effective and sustainable hydrogen production by integrating the chemical robustness of alkaline systems with the compact, zero-gap design of proton exchange membrane electrolyzers. However, the widespread implementation of AEMWE is limited by the availability of highly active and durable platinum-group-metal (PGM)-free catalysts and by an incomplete understanding of their degradation behavior under realistic operating conditions.

This dissertation focuses on the development, characterization, and mechanistic investigation of nanostructured MoNi₄–MoO₂-based electrodes for efficient and stable hydrogen generation under alkaline and membrane-integrated environments. MoNi₄–MoO₂ nanorods …

Learning - Augmented Stochastic Model Predictive Control For Adaptive Battery Energy Storage Operation Under Net Load Uncertainty, Muhammad Amad Asif

Electronic Theses & Dissertations (2024 - present)

Battery energy storage systems play a critical role in enabling reliable operation of power systems with high penetration of renewable energy. However, optimal control of BESS is challenging due to uncertainty in net load and electricity prices. Deterministic MPC, which relies on point forecasts, can perform suboptimally under forecast errors. SMPC addresses this limitation by incorporating uncertainty through scenario-based optimization. Nevertheless, its performance depends on fixed objective parameters, particularly the cycling penalty, which governs the trade-off between economic cost and battery utilization. This thesis develops a learning-augmented SMPC framework for battery control under net load uncertainty. The proposed approach integrates …

Agricultural Productivity Under Energy Development: Insights From California, Wai Yan Siu, Sherzod B. Akhundjanov

ODU Articles

This paper examines how agricultural productivity patterns in Kern County, California, a leading region for both agricultural production and oil and gas development, co-vary with the spatial and temporal expansion of hydraulic fracturing and associated energy infrastructure. Using parcel- and county-level analyses, we characterize how agricultural productivity differs across proximity to energy development and across spatial scales. The results reveal spatially heterogeneous and scale-dependent patterns: parcel-level evidence indicates lower Enhanced Vegetation Index-based vegetation productivity within the 20-mile proximity zone around fracking wells, while county-level results show heterogeneous crop-specific yield changes during the post-expansion period. Together, these findings highlight the importance …

Modern Potentiostat Architectures For Electrochemical Sensing: Design, Integration, And Future Directions, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Potentiostats are essential to electrochemical sensing, enabling precise control of electrode potentials and measurement of current responses. As demand grows for portable, wearable, and point-of-care systems, potentiostat design has evolved from benchtop instruments to compact, low-power, and wirelessly connected platforms. This review provides a comprehensive, system-level perspective on modern potentiostat architectures, covering operational principles, analog front-end design, signal generation and acquisition, communication protocols, and software integration. Unlike prior reviews that treat these aspects independently, this work integrates electrochemical theory with electronic design and data communication frameworks. Key components, including operational amplifiers, transimpedance amplifiers, DAC/ADC subsystems, and microcontroller-based control, are examined …

Cybersecurity Center For Offshore Wind Energy (Final Project Round), Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

This project establishes a Cybersecurity Center for Offshore Wind Energy with the objective of designing and operating a cyber-physical testbed for wind energy farms (WEFs) that enables comprehensive cybersecurity research. The testbed incorporates a Supervisory Control and Data Acquisition (SCADA) system connected to turbine models via industrial-grade programmable logic controllers (PLCs) and remote terminal units (RTUs). It supports side-channel data acquisition, implementation and analysis of various cyberattack scenarios, and development of attack detection, mitigation, and best-practice guidance tailored to wind energy systems. During the project, the team expanded the number and fidelity of mathematical turbine models (MTMs), integrated these models …