Digital Commons Network^™

High-Efficiency And Compact Virtual Reality Displays, Zhenyi Luo

Graduate Studies Theses and Dissertations 2026

Virtual reality (VR) and augmented reality (AR) technologies are revolutionizing the way humans interact with digital information. However, current near-eye display systems still face critical challenges regarding optical efficiency, image quality, and device weight. This dissertation focuses on addressing these limitations through innovative liquid-crystal (LC) optics and system-level design strategies to achieve high-efficiency and compact VR displays.

First, to solve the longstanding problem of severe chromatic aberrations in ultrathin LC optics, I proposed an achromatic diffractive LC device. By stacking three LC layers with specifically designed spectral responses and polarization selectivity, I successfully reduced the chromatic aberrations. This idea enables …

Chronofy: A Temporal-Logical Decay Architecture For Information Validity In Time-Aware Retrieval-Augmented Generation, Muntaser Syed, Marius Silaghi, Sheikh Abujar, Sharun Akter

Electrical Engineering and Computer Science Student Publications

Retrieval-Augmented Generation (RAG) systems retrieve and integrate external knowledge to ground large language model (LLM) outputs. However, current RAG architectures treat all retrieved facts as equally valid regardless of temporal provenance, leading to temporal hallucination, where plausible but obsolete facts corrupt the output. A clinical lab reading from yesterday is actionable; the same reading from six months ago is noise. We present Chronofy, a three-layer neuro-symbolic framework implementing the Temporal-Logical Decay Architecture (TLDA) that embeds temporal validity directly into the representation, retrieval, and reasoning layers of RAG systems. Layer 1 reserves a dedicated temporal subspace within Matryoshka embeddings to make …

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 …

Automated Pill Dispenser, Ryan Oderkirk, Connor Beaven, Rachelle Labrie, Josue Panchana

Williams Honors College, Honors Research Projects

The project we propose is an automated system for dispensing dosages of medication throughout the day. It will be able to alert a user when their pills need to be taken and give them the correct dosages of up to four different medications. These dosages are configurable as well as the scheduled time they are to be taken. In addition, the pill dispenser will alert users when they are low on medications and need to refill the machine.

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 …

Ontological Runtime Monitoring For Mission Engineering, Alexander R. Will

Theses and Dissertations

The advent of the Urban Air Mobility (UAM) concept will bring low-altitude aviation to civilians through passenger and cargo transport. However, the prospective vehicles in UAM studies are predominantly autonomous, raising questions about their efficacy and stability in densely populated urban areas. At the same time, extensive work has been performed to define a new branch of systems engineering that focuses on runtime behavior, synchronization, communication, and task allocation.This field is known as "mission engineering". Mission engineering has been deployed in military scenarios to model human-autonomy cooperation. By applying the concepts from this field to UAM, full systems-of-systems can be …

Nanomagnet Based Straintronic Devices For Unconventional Computing: Simulation And Performance Analysis, Rahnuma Rahman

Theses and Dissertations

Nanomagnetic devices are of great interest in digital hardware because of their non-volatility and dynamic ability to change magnetization but suffer from high switching error rates and temperature sensitivity. Magnetostrictive nanomagnets that utilize strain to switch between stable magnetization states encoding bit information are of interest since they are extremely energy efficient as piezoelectric layers can be used to rotate magnetization that have switching energies in the range of attojoules. Their stochasticity can also be useful in probabilistic, analog, neuromorphic, and collective computing systems, where occasional switching errors are not devastating. The dissertation extends spintronics beyond conventional computing schemes by …

Texels: A Programmable Textile Interface For Replicating Textures, Maya E. Eusebio

Honors Undergraduate Theses

Self-moving fabric interfaces have massive potential for applications in fields ranging from art to haptic feedback to deployable space structures. However, current systems of implementation face the impracticalities of bulkiness, burnout, and energy consumption on top of limiting designs that can only contract uniformly or create one pre-programmed shape. For this technology to bring the change that it promises, we must break the barriers of usability and sustainability to make it a practical choice. This thesis aims to develop a scalable model of fabric that designers, programmers, and anyone else can acquire or create with accessible materials, integrate into design …

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 …

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 …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

Timing And Stability Of Uav Operations In Smart City Environments: Experimental Analysis And Design Implications, Rabia Ipek Yasar

Theses and Dissertations

This thesis investigates the deployment and performance of unmanned aerial vehicles (UAVs) within the Virginia Commonwealth University Open Cyber City (OCC) testbed. The study focuses on evaluating real-time indoor positioning performance using the Crazyflie drone platform. High-precision position measurements are obtained using the Vicon motion capture system, enabling analysis of the latency between the drone’s actual position and the system-reported position. In a closed-loop control system, the time difference between the position measurement and the application of the control command is called the system delay. Both stationary (hovering) and trajectory-following experiments are conducted to evaluate system performance. Communication delays in …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Robust Broadband Characterization Of Flexible Absorbers, Sheet Material, And Liquids Using A Coaxial Structure, Joseph Christopher Stecher

Masters Theses

Modern high-frequency measurement systems require reliable calibration and sample positioning to ensure measurement fidelity. This thesis presents three studies addressing practical limitations in broadband material parameter extraction and instrumentation.

The first study introduces a modified Nicolson–Ross–Weir (NRW) technique for flexible, compression-sensitive materials from 100 MHz to 18 GHz. Rigid 3D-printed spacers ensure precise sample positioning, and a T-matrix–based de-embedding procedure removes spacer effects. Validation using microstrip measurements and full-wave simulation confirms accurate permittivity extraction across compression levels.

The second study extends NRW to sheet materials enabling accurate material characterization. Independent validation using toroidal inductors with leakage correction and parallel-plate capacitors …

Bridging Language And Game Worlds: Semantic Representations And Text-Driven Terrain Generation For Procedural Content, Zhongyu Xie

Electronic Theses and Dissertations

Procedural Content Generation (PCG) systems produce vast quantities of game levels, terrain, and environments, but lack semantic interfaces: no shared vocabulary exists between natural language, designer intent, and the structured representations generators operate on. This thesis addresses the language-content grounding gap in PCG through two complementary studies spanning semantic analysis and semantic synthesis. The first study introduces a group-supervised contrastive learning framework for semantic representation of symbolic PCG maps under many-to-one semantics, where visually distinct maps may share the same design intent. The framework combines parameter-guided semantic grouping, LLM-based caption augmentation, and a multi-positive contrastive objective that aligns language with …

Super-Resolution Learning Inspired Spectral-Spatial Correlation Network For Hyperspectral Target Detection, Jiaping Zhong, Yunsong Li, Jianxin Li, Yanzi Shi, Weiying Xie, Paolo Gamba

Research outputs 2022 to 2026

Hyperspectral target detection (HTD) aims at extracting targets from complex backgrounds while overcoming noise interference. Existing deep learning models for HTD usually suffer from low spatial resolution and unitary representation, especially in space-borne platforms. Super-resolution, as a critical technology to enhance the spatial details, could effectively address the aforementioned issue. To make super-resolution absolutely pose positive effects on target detection, this paper proposes an end-to-end novel super-resolution learning inspired spectral-spatial correlation network for hyperspectral target detection (SR-HTD) from the perspective of spatial and spectral regularization to achieve high-precision detection. Specifically, we designed a Spatial Correlation Aggregation (SCA) module inspired by …

Multi-Scale Sensor-Aware Variational Autoencoders For Adaptive Iot Security: Integrating Drift Calibration And Cyberattack Detection, Md Kamal Hossain, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Existing approaches to Industrial Internet of Things (IoT) anomaly detection treat sensor drift calibration and cyberattack detection as separate problems, overlooking their strong interdependence and the compound failure modes that arise when drift dynamics are exploited by stealthy adversaries. Industrial Internet of IoT sensor networks therefore require a unified framework that jointly models both phenomena. This paper proposes the first unified multi-scale sensor-aware variational autoencoder (MS-VAE) framework that jointly models sensor drift and malicious activity within shared latent representations, enabling integrated calibration and security monitoring. The framework introduces three key components: (i) a multi-scale latent architecture that captures short-term anomalies …

Sensing-Then-Beamforming: Robust Transmission Design For Ris-Empowered Integrated Sensing And Covert Communication, Xingyu Zhao, Min Li, Ming Min Zhao, Shihao Yan, Min Jian Zhao

Research outputs 2022 to 2026

Traditional covert communication often relies on the knowledge of the warden's channel state information, which is inherently challenging to obtain due to the non-cooperative nature and potential mobility of the warden. The integration of sensing and communication technology provides a promising solution by enabling the legitimate transmitter to sense and track the warden, thereby enhancing transmission covertness. In this paper, we develop a framework for sensing-then-beamforming in reconfigurable intelligent surface (RIS)-empowered integrated sensing and covert communication (ISACC) systems, where the transmitter (Alice) estimates and tracks the mobile aerial warden's channel using sensing echo signals while simultaneously sending covert information to …

A Novel Hybrid Robust Transfer Learning-Based Adaptive Fractional-Order Super-Twisting Sliding Mode Controller Enhanced For Speed Regulation Of Brushless Dc Motor, Seyyed Morteza Ghamari, Asma Aziz, Daryoush Habibi

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are widely used in applications that are highly-efficient, reliable, and compact, such as electric vehicles, robotics, and medical devices. However, the inherent nonlinearities and load sensitivity of BLDC motors require a robust and adaptive control strategy to ensure satisfactory performance under various operating conditions. Sliding mode control (SMC) has been widely used for the BLDC drives. However, because of its simplicity and robustness, the control effectiveness of the control is limited by the sensitivity to the disturbances and the chattering phenomenon. To remedy this, super-twisting (ST) technique has been proposed to achieve smoother response and better …

Unified Bayesian And Machine Learning-Based State Of Charge Estimation In A Pv Battery System, Showrov Rahman

Open Access Dissertations

The state of charge (SOC) of a battery indicates the remaining charge in the battery relative to its nominal maximum charge capacity. The accurate estimation of SOC is of utmost importance for efficient energy management as it indicates the level of energy stored in the battery.

Bayesian and machine learning (ML) approaches represent two distinct estimation strategies for battery state estimation. Bayesian filters, such as the Kalman filter (KF) and particle filter (PF), operate sequentially by incorporating physical models of battery dynamics. In contrast, ML-based methods are data-driven, and their performance largely depends on the quantity and quality of training …

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 …

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 …

Data Augmentation And The Reliability Of Conformal Prediction For Uncertainty Quantification In Medical Imaging, Rizwan Ahamed

Graduate Theses, Dissertations, and Problem Reports (ETD)

The safe clinical deployment of deep learning models for high-stakes medical imaging tasks requires more than high average accuracy; it requires demonstrable, per-case reliability. Uncertainty quantification (UQ) provides the missing signal that tells a clinician when a model prediction can be trusted and when a case should be escalated for expert review. Among UQ approaches, conformal prediction (CP) is especially attractive because it produces prediction sets that are guaranteed, under the assumption of exchangeability, to contain the true label with a user chosen probability, and it does so without assumptions about the model or the data distribution. This report first …

Frequency And Phase Synchronization Of Antenna Arrays With Phase Incoherent Microcontrollers, Harley W. Byrd

University of Kentucky Master's Theses

The construction of phased array antennas has traditionally been an expensive and complex task. It has recently been claimed that it is possible to construct high-performance Wi Fi antenna arrays using inexpensive consumer components. Motivated by some limited data available from online presentations of such devices, this thesis covers an attempt to construct a phased-array Wi-Fi antenna using several ESP32 chips, which have native Wi-Fi modulation and demodulation capabilities. While there are many positive aspects associated with utilizing ESP32 chips for this purpose, a key challenge is the inherent phase incoherence of their internal Phase Locked Loops (PLLs). The approach …

Architecting A Complex Adaptive System Model For Selecting Policies To Reduce Kidney Discard, Lirim Ashiku

Doctoral Dissertations

"The kidney allocation system is a complex, evolving system involving multiple heterogeneous agents. Each agent exhibits emergent behavior that may not fully align with the complex system goals. Therefore, there is a need for a transdisciplinary systems approach to visualize the interdependency among agents and understand the dominant patterns that shape the kidney allocation system.

First, this research presented an incremental hierarchical system engineering approach in identifying the agents’ needs and behaviors toward the complex systems’ goal of maximizing deceased donor kidney utilization and reducing kidney discard. The hierarchical systems approach linked with model-based system engineering aided in eliciting agents’ …

H2 Matrix Compression For Fast Magnetic Field Post-Processing, Calvin M. Demps

University of Kentucky Master's Theses

Large-scale computational electromagnetic simulations frequently require the assembly of dense system matrices, whose computation time and memory cost grow rapidly with problem size and can render high-resolution simulations impractical. This thesis presents a benchmark of the H² hierarchical matrix method applied to the construction of a coupling matrix that maps magnetization current in a structure to the magnetic field at observation points external to the structure. The underlying field interaction is derived from Maxwell's equations and discretized using a Nyström approach, producing a dense matrix.

The method is benchmarked across various geometries of increasing complexity and for increasing mesh …

Formalizing Asymmetric Control-Telemetry Separation In Distributed Industrial Control Systems, Andrew Manison

College of Graduate Studies: Theses & Dissertations

Distributed industrial control systems often place control and telemetry traffic on the same communication substrate even though the two workloads impose different requirements. Control paths need bounded request-response latency and predictable acknowledgement semantics, whereas telemetry paths benefit from scalable publish-subscribe fanout and tolerance for consumer-side delay. This thesis argues that, for the tested class of mixed workloads on shared commodity infrastructure, these communication roles should be separated architecturally rather than forced through a single protocol. To evaluate that claim, the thesis formalizes an asymmetric control- telemetry pattern and instantiates it in the Asymtra framework using gRPC for synchronous control and …