Electrical and Computer Engineering Commons^™

Twitter-Based Osint For Cyber Event Analytics, Dakota S. Dale, Kylie Mcclanahan, W. Sky Elder, Qinghua Li

Electrical Engineering and Computer Science Faculty Publications and Presentations

Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system's applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Transplant Surgeon Fuzzy Associative Memory (Tsfam): Model For Capturing Surgeon Perspective, Rachel Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

AI-driven healthcare decision-making is multi-faceted, requiring complex logic to adapt to evolving policies and societal demands. Effective change implementation by healthcare providers and multidisciplinary organ transplant teams depends on adaptive decision-making. The proposed Transplant Surgeon Fuzzy Associative Memory (TSFAM) model introduces a novel approach to Human-AI Teaming, keeping human expertise central while dynamically adjusting to changing requirements. TSFAM employs fuzzy logic to manage imperfect data and human ambiguity, integrating the transplant surgeon perspective with the AI deep learning decision-making tool, creating a resilient solution in this critical domain. By embedding adaptive capabilities into the architecture, TSFAM exemplifies the adaptability of …

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Grid Services Demonstration Using Service-Oriented Derms, Midrar Adham, Sean Keene, Tylor E. Slay, Jaime T. Kolln, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This work demonstrates four grid service use cases using a service-oriented DER Management System within an Energy Grid of Things network. Imposed by a set of rules referred to as the Energy Service Interface, the DER Management System uses IEEE 2030.5 as its primary communication protocol. To simulate the efforts of Grid Operators, Grid Service Providers, and consumers to provide grid services, a co-simulation environment that leverages the GridAPPS-D simulation platform was developed. The results show that a service-oriented DER Management System provides a means of achieving reliable and impactful grid services while protecting customers’ privacy and ensuring their agency …

Electro-Vascular Dynamics During Auditory Processing: Variation Along The Schizotypy Continuum, John P. Mclinden

Open Access Dissertations

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are complementary noninvasive neuroimaging modalities that have contributed greatly to our understanding of the mechanisms of auditory processing. As an emerging technology, fNIRS has produced promising insights into the mechanisms of auditory processing, although considerable work remains to characterize hemodynamic responses in auditory tasks. In addition, the complementary nature of EEG and fNIRS and the relationships between the signals they record have been underexplored. In particular, nonlinear interactions between these signals remain under-characterized, despite the potential benefit to future multimodal neuroimaging studies. A challenge in understanding these interactions is the influence of systemic …

A Compact Frequency Comb Generator For Large-Format Superconducting Microwave Kinetic Inductance Detectors, Sungyoun Seo

Open Access Master's Theses

Microwave Kinetic Inductance Detectors (MKIDs) are promising superconducting detectors that achieve superior sensitivity to silicon-based photo detectors by leveraging a small bandgap of superconductors. They are advantageous for sensitive imaging instruments, particularly space telescopes. However, a large-format array with several hundreds of thousands of detectors is required for large-scale observations in space, and it encounters multiple challenges. The biggest challenge is the absence of compact, energy-efficient, and radiation-tolerant readout solutions. Of multiple functions in the MKID readout, the new frequency comb generator is presented in this thesis. It features a low on-chip memory requirement while promising the relaxed frequency resolution …

Minimalistic Communication-Based Swarm Slam, Felix B. Koch

Open Access Master's Theses

This study presents and validates Range of Communication Swarm SLAM(RCS-SLAM), a novel SLAM approach designed for multi-robot systems using minimal hardware. Unlike traditional methods that rely on range or bearing measurements, RCS-SLAM only uses the identifiers of nearby robots within communication range. In the front-end, each robot constructs an individual pose graph based on its own odometry, while recording communication events with neighboring robots. The key novelty lies in the use of one-hop communication, where robots not only detect nearby peers but also relay received identifiers, effectively acting as transmitters themselves. This mechanism extends the communication graph and enables the …

Comparative Study Of Physiological Signals Between The Embraceplus And The Shimmer3 Gsr+, Cassidy Considine

Open Access Master's Theses

Understanding team dynamics and their physiological impact is essential across high-stakes industries such as healthcare, aviation, and defense. In stressful environments, optimizing team performance requires not only assessing communication but also monitoring physiological indicators of stress. Electrodermal activity (EDA) and heart rate variability (HRV) are commonly used to capture autonomic responses, offering insight into emotional arousal and cardiovascular regulation.

This pilot study compares a commercial wearable device, the EmbracePlus, with a research-grade device, the Shimmer3 GSR+, to evaluate differences in physiological signal acquisition. Twelve participants completed a five-phase experimental protocol while wearing both devices simultaneously on the non-dominant wrist/forearm. EDA …

Heart Rate And Electrodermal Activity And Their Relationship With Performance In High Fatigue Environments, Brian Dunbar

Open Access Master's Theses

Confidential material has been removed.

Hardware Implementation Of An Efficient Frequency Comb Generator For Microwave Kinetic Inductance Detectors, Frank Atobrah Danso

Open Access Master's Theses

The frequency comb generator is an essential component in the readout of superconducting detectors. This component is responsible for generating the resonant frequencies necessary to detect photon interaction with the detectors. This component however has not received the much needed attention in terms of readout systems optimization. Conventional implementations rely on either inverse fast Fourier transform (IFFT) engines, which scale poorly in power with increasing frequency resolution requirement but allow flexible tone updates, or lookup tables (LUTs), which demand large memory for high frequency resolution.

Microwave Kinetic Inductance Detectors (MKIDs) are superconducting detectors which have gained significant interest for cryogenic …

Optimal Subspace Estimation For Linear Nested Arrays: Applications And Performance Metrics, Brendan Dunn

Open Access Master's Theses

OSE (Optimal Subspace Estimation) is an algorithm that obtains an estimated subspace from structured data observed in noise. While OSE is able to obtain an accurate subspace estimate with a small number of snapshots, it has not been demonstrated how this affects the performance of many applications that leverage OSE. One such application is beamforming; Using an OSE-based beamformer, this thesis will apply common performance metrics to measure how much of an advantage an accurate subspace estimate provides. This algorithm will be compared against other widely used beamformers such as MPDR and DMR to benchmark its efficacy. Mismatch is then …

Global Spectral Clustering Of Temporally Stable Pixels For Epics Identification, Validation, And Hyperspectral Assignment For Satellite Calibration, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

No abstract provided.

Refinement Of Trend-To-Trend Cross Calibration Total Uncertainties Utilizing Extended Pseudo Invariant Calibration Sites (Epics) Global Temporally Stable Target, Minura Samaranayake

Electronic Theses and Dissertations

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band …

A Multiscale Ai Framework For Forest And Agriculture Health Monitoring: Drone-Based Object Recognition And Segmentation For Automated Ecological Assessment, Sruthi Keerthi Valicharla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forest and agricultural ecosystems are increasingly at risk due to invasive species, pests, and diseases, necessitating scalable, automated, and intelligent monitoring solutions. Traditional field based forest and agriculture health assessments are limited by cost, time, and spatial coverage. This dissertation presents a multiscale deep learning framework that automates forest and agriculture health monitoring using drone imagery and computer vision techniques. The system operates across three spatial levels: forest level, tree level, and leaf level, combining object detection, segmentation, and classification models to support large scale ecological assessment.

At the forest level, high-altitude drone imagery is processed using object detection and …

An Investigation Into Energy Consumption And V2g Potential Of An Electric School Bus Fleet, Rupesh Dahal

Graduate Theses, Dissertations, and Problem Reports (ETD)

The transition towards sustainable public transportation demands detailed evaluation of alternative fuel technologies. This thesis presents a comprehensive energy assessment and comparative analysis of diesel and electric school buses (ESBs) operating within the Monongalia County school district, its Vehicle-to-Grid (V2G) potential, and efficiency analysis of charging system. Utilizing real-world operational data collected from buses running identical routes, the study quantifies and compares their mileage specific energy consumption. The analysis reveals an average fuel economy of approximately 6.17 miles per gallon (MPG) for the conventional diesel school bus, contrasted with an average energy consumption rate of 0.46 miles per kilowatt-hour (miles/kWh) …

Design Of An Active-Control Pressure Regulator For A Rocket, Seth Arkwright

Williams Honors College, Honors Research Projects

An electronically actuated pressure-regulation valve and controller are necessary for maintaining pressure levels in a pressure-fed liquid-fueled rocket engine. Mechanical regulators are too heavy and have unacceptably high-pressure variation throughout the operation of a dynamic system like a rocket. Developing an electronic pressure regulator using a PID controller is a method by which a reliable, accurate, and lightweight flow regulator can be built for use on a rocket vehicle. This work will outline the development of flow simulations, PID controller, physical valve hardware, and initial testing.

Dc To Dc Buck Converter, Matthew Bildstein

Williams Honors College, Honors Research Projects

This research project focuses on the design, implementation, and optimization of a high-efficiency DC-to-DC buck converter that steps down 12V to 5V for applications such as USB-powered devices, embedded systems, and automotive electronics. Building on my Associate’s degree project, where a 555 timer-based was replace with a LM2576 switching regulator IC.

The research involves circuit simulation in Multisim, PCB layout optimization in Ultiboard, and prototype testing to assess voltage stability, efficiency, and thermal performance. The project will incorporate best practices in power electronics design, including proper grounding techniques and thermal management strategies.

Expected outcomes …

Voice Operated Automatic Chessboard, Logan Pursley, Rafael Soler, Jason Roser, Tyler Hoxworth

Williams Honors College, Honors Research Projects

Chess is a board game played at a variety of skill levels worldwide. Over 1000 years, chess has always required players to move the pieces manually. Unfortunately, anyone who lacks dexterity in their hands or arms cannot manually move the chess pieces on their own. Consequently, a chess board that can be played without hands or arms must be created to accommodate a broader audience. This is the motivation behind the Automatic Chessboard. This design will allow a player to engage in a dynamic and physical game of chess using only their voice. A 3-axis control system will allow for …

Trace Flight Computer, Zachary Stratton, Vandad Mossavand Varkaneh, Nicholas Ely

Williams Honors College, Honors Research Projects

In the world of collegiate rocketry, there are currently no commercial-off-the-shelf flight computers capable of being fitted to liquid engine rockets with thrust vectoring control. Currently, amateur rocketeers utilize unreliable Arduino-based systems or expensive drone computers. The objective of this project is to design a flight computer capable of data collection and filtering, telemetry transmission, and real-time controls of critical safety systems using readily available commercial-off-the-shelf components to allow rocketeers to complete their designs within a reasonable budget. The system will collect data from two IMUs, a barometric altimeter, and a magnetometer, then filter the collected data using custom Kalman …

All-In-One Security System, Nathaniel Rohr, Elizabeth M. Daniels, Kyle H. Sturgin, Jonathan R. Carozza

Williams Honors College, Honors Research Projects

The proposed All-in-One Security system is a security system capable of automatically or manually arming and disarming the building it is installed in. When armed, the system rolls down the window shades and locks the front door. When disarmed, the system unlocks the front door and rolls up the window shades. The system will have a keypad by the front door for arming and disarming the system. Additionally, there will be a smartphone app that can arm and disarm the system via bluetooth.

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski

Williams Honors College, Honors Research Projects

NASA's Artemis program requires precise navigation capabilities to establish the first sustained presence on the lunar surface. However, as launches bring necessary orbital infrastructure, the Artemis program will face a critical period during which reliable lunar navigation is not possible. To address this challenge, the V.E.C.T.O.R. system tracks assets, such as rovers and astronauts, as User Terminals relative to a pre-existing cell tower, or Base Station. To do so, the system leverages existing Base Station hardware to calculate the location of User Terminals in conjunction with existing communications infrastructure.

Multitec: A Data-Driven Multimodal Short Video Detection Framework For Healthcare Misinformation On Tiktok, Lanyu Shang, Yang Zhang, Yawen Deng, Dong Wang

Computer Science Faculty Works

With the prevalence of social media and short video sharing platforms (e.g., TikTok, YouTube Shorts), the proliferation of healthcare misinformation has become a widespread and concerning issue that threatens public health and undermines trust in mass media. This paper focuses on an important problem of detecting multimodal healthcare misinformation in short videos on TikTok. Our objective is to accurately identify misleading healthcare information that is jointly conveyed by the visual, audio, and textual content within the TikTok short videos. Three critical challenges exist in solving our problem: i) how to effectively extract information from distractive and manipulated visual content in …

Computational Assessment Of Vitrimers Self-Healing For Renewable Energy And Aerospace Structures, Walaaeldin Mohamed Ahmed Derbala

Browse all Theses and Dissertations

Self-healing polymers, particularly vitrimers, are emerging as promising candidates in the development of advanced materials for renewable energy and aerospace structures. These materials exhibit dynamic covalent bond exchange mechanisms that enable reprocess ability, damage repair, and extended operational lifetime under harsh conditions. This study presents a density functional theory (DFT)-based computational investigation of the mechanistic pathways and energetics of bond exchange reactions in model vitrimer systems. We explore transition states, energy barriers, and thermodynamic features corresponding to associative and dissociative self-healing reactions in vitrimers. The study focuses on Diaminodiphenyl disulfide (AFD), a bifunctional molecule composed of two para-substituted aminophenyl rings …

Reinforcement Learning For Adversarial Environments: Multi-Agent Hide And Seek With Multi-Modal Sensing, Christian Alejandro Carrizales

Browse all Theses and Dissertations

The development of intelligent and competitive agents in AI versus AI adversarial environments was explored through the utilization of reinforcement learning techniques with sensing modalities. A Hide-and-Seek simulation environment was developed using the Unity game engine along with the ML-Agents Toolkit. An engagement test campaign with a set of performance metrics was designed. Four AI versus AI adversarial scenarios were considered using the Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) multi-agent reinforcement learning algorithms. Furthermore, the impact of sensing modalities on competing agents’ learning performance was investigated by varying the sensing capabilities of the hider and seeker, respectively. Experiments …

Hydrogen Storage Density And Adsorption Energy Barriers On Li-Decorated Bc3 Nanosheet, Sri Venkat Pavan Upasi

Browse all Theses and Dissertations

Hydrogen is considered an emerging carrier of clean energy with renewable capabilities. Widespread hydrogen energy utilization necessitates efficient storage strategies. Functionalized nanomaterials, such as Li-decorated BC3 nanosheets, are among the primary candidate materials for hydrogen storage. This research investigates the feasibility of hydrogen storage by estimating energy barriers and their dependence on storage density on Li-decorated BC3 nanosheet. Density functional theory (DFT) simulations provide estimates of adsorption energies and saddle points for hydrogen storage and compare corresponding reaction rates. The results are expected to help us understand the advantages and possible shortcomings of hydrogen storage on such nanomaterials.

Mobile Weather Satellite Receiver, Luke Datsko, Sam Watts, Jason Do, Adam Bechtler

Williams Honors College, Honors Research Projects

The "Mobile Weather Satellite Receiver" project aims to create a portable, user-friendly device that receives and displays weather information from geostationary satellites, addressing the limitations of traditional weather sources like the Internet and weather radio, particularly in remote areas. This device will collect and demodulate satellite data, including imagery and Emergency Managers Weather Information Network (EMWIN) forecasts, to provide users with detailed local forecasts and real-time alerts.

Designed with a user-centric approach, the system includes a satellite dish, Software Defined Radio (SDR), a Raspberry Pi, and a custom software interface for ease of use. Its portability and ability to function …

A Novel Real-Time Threshold Algorithm For Closed-Loop Epilepsy Detection And Stimulation System, Liang Hung Wang, Zhen Nan Zhang, Chao Xin Xie, Hao Jiang, Tao Yang, Qi Peng Ran, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Jian Bo Chen, Tsung Yi Chen, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once …