Electrical and Computer Engineering Commons^™

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

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. …

Consensus And Controversies Of International Guidelines For The Diagnosis, Surveillance, And Management Of Fetal Growth Restriction: An Updated Comparison, Daniele Diane Mascio, Suneet P. Chauhan, Tullio Ghi, Asma Khalil, Juliana G. Martins, Sara Sorrenti, Tamara Stampalija, Fabrizio Zullo, Francesc Figueras

Department of Obstetrics & Gynecology Faculty Publications

OBJECTIVE: To compare areas of consensus and disagreements across contemporary international and national guidelines on the diagnosis, surveillance, and management of fetal growth restriction (FGR).

DATA SOURCES: Electronic searches of MEDLINE from database inception up to March 2026 using MeSH terms and keywords related to FGR and guidelines. STUDY ELIGIBILITY CRITERIA: Critical, structured comparison of national or international guidelines on FGR published since 2010. Final inclusion required unanimous agreement from all authors.

STUDY APPRAISAL AND SYNTHESIS METHODS: Pre-specified extraction across domains: definition; prediction/prevention; surveillance tools and frequency; delivery timing and mode; and labor induction methods. Dual data …

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 …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Flood-Level Estimation Using Aerial Imagery, Yisen Zhang

Electrical & Computer Engineering Projects for D. Eng. Degree

Reliable flood-level estimation using aerial UAV (Unmanned aerial vehicle) imagery is essential for effective post-disaster assessment and rapid emergency response. This study utilizes a UAV-based dataset, referred to as the UVA dataset, which integrates multiple public datasets and manually labeled UAV images, together with a multi-stage vehicle-centric framework for flood-depth estimation. The UVA dataset integrates images from multiple public sources, including Unmanned Drone Water Assessment (UDWA), Unmanned Aerial Vehicle Detection and Tracking (UAVDT), Car Parking Lot (CARPK), and additional UAV-view images collected from the internet, followed by manual annotation for water depth, viewing angle, and altitude labels. In the proposed …

Machine Learning For Anomaly Detection In Neural Network Security And Srf Cavities, Hal Ferguson

Electrical & Computer Engineering Theses & Dissertations

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications.

First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves …

Enhancing The Design Of Strained Superlattice Gallium Arsenide Based Photocathodes With Distributed Bragg Reflector, Adam D. A. Masters

Electrical & Computer Engineering Theses & Dissertations

Particle accelerators play a crucial role in our understanding of matter and the universe and have numerous practical applications in various fields. These devices enable scientists to examine the smallest components of matter, study the forces that govern their interactions, and probe conditions from the early universe. Moreover, accelerators are valuable in medicine, industry, and research, enhancing imaging methods, cancer therapies, and manufacturing techniques. As the experiments conducted at these facilities evolve and require higher precision, improved particle sources must continue to advance to keep up with their requirements. To do that, we enhanced the design of spin polarized electron …

Computational Modeling For Automatic Superconducting Cavity Fault Prediction And Classification Using Time Series Signals, Md Monibor Rahman

Electrical & Computer Engineering Theses & Dissertations

Processing multivariate time series signals collected from sensor networks is challenging because of complex temporal dependencies and non-stationarity. With the advent of artificial intelligence (AI) like machine learning and deep learning, it has become possible to process sensor-driven time series data more effectively than traditional statistical methods.

This dissertation aims to develop machine learning and deep learning models to address machine fault diagnosis using multivariate time series signals collected from the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. The first goal of the proposed work is to develop deep learning–based classification models and an unsupervised fault clustering approach …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Multimedia Forensics: Identification And Verification Of Source Camera, Vehicle Speed Estimation, And Deepfakes Detection, Jiajun Jiang

Electrical & Computer Engineering Theses & Dissertations

This dissertation advances multimedia forensics by addressing three critical research areas that enhance the authenticity verification and analysis of digital media. Multimedia forensics, which encompasses techniques for examining images, videos, audio, and text, faces increasing challenges due to sophisticated editing tools and massive data volumes. In the first study, a fast source camera identification and verification method based on PRNU analysis is proposed for video forensic investigations. By integrating camera rolling and I-frame analysis, this approach achieves a processing speed improvement of at least 15 times over conventional frame-by-frame methods while reducing false positives. The second study focuses on vehicular …

Ac Electric Fields Manipulate And Concentrate Dna Molecules On Electrodes, Akila Wijesinghe, Dharmakeerthi Nawarathna

Graduate Student Government Association Research Conference

Point-of-care (POC) electric field-based biosensors have emerged as a promising tool to detect early cancer biomarkers such as circulating tumor DNA (ctDNA), microRNA (miRNA), and proteins. To be effective in screening in clinical settings, these biosensors must be simple and easy to use. In this study, we have studied the manipulation of short DNA molecules suspended in a sessile drop to achieve this goal. Alternative current (AC) electric fields were used to polarize DNA molecules and produce dielectrophoretic (DEP) force on DNA molecules. DEP force is used to manipulate polarized DNA molecules towards the higher electric field gradients (toward the …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

31 - Shaped Adversarial Patches, Huong Quach

Undergraduate Research Symposium

In recent years, the development and deployment of computer vision models have become widespread, with applications ranging from autonomous vehicles to security systems. Among these, object detection algorithms like YOLO are particularly significant due to their real-time performance and accuracy in identifying and localizing objects within an image. However, the robustness of these models is increasingly challenged by adversarial attacks, which are deliberate manipulations designed to deceive the model's predictions.

In this paper, I present an approach to advancing the deception capabilities of adversarial patches, specifically targeting YOLO-based person detectors. The objective is to design and implement shaped adversarial patches …

Streamer Discharge Simulation For Plasma-Assisted Combustion, Stuart Jairo Reyes

Electrical & Computer Engineering Theses & Dissertations

A common and successful method to achieve atmospheric pressure fuel-air plasma-assisted combustion is through repetitive ns pulsed discharges and dielectric-barrier discharge. The transient phase in these discharges is dominated by transport influenced by strong space charges produced by ionization fronts, this can be best represented by the streamer model. The function of non-thermal plasma in these discharges is to excite the species in the fuel-air mixture to produce radicals which accelerate the chemical conversion reactions which directly lead to temperature rise, ultimately culminating in ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to developing a …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Development Of Laser-Induced Graphene-Based Bioanode Incorporating Thylakoid For Harvesting Energy, Amit Sarode

Biomedical Engineering Theses & Dissertations

Thylakoid-Based Biofuel Cells (TBFCs) present significant potential as renewable power sources; however, their development is impeded by challenges including delicate thylakoid membranes, limited electron transport efficiency, stability and dependence on expensive mediators. This study aimed to address these challenges by fabricating a novel photo-driven bioanode through the integration of Laser-Induced Graphene (LIG), Nb₄C₃T_x MXene, and thylakoid membranes. The fabrication process involved laser engraving to generate porous LIG electrodes, followed by MXene drop casting and thylakoid immobilization to enhance electrochemical performance and surface area. Morphological characterization supported that MXene incorporation increased active sites and surface roughness, …

The Effect Of Liquid Conductivity On A Helium Plasma Jet And The Hydrogen Peroxide Production, Edwin A. Oshin, M. Ziaur Rahman, Chunqi Jiang

Bioelectrics Publications

Understanding plasma-liquid interactions and the impact of the electrical conductivity (σ) of liquid on plasma is important for plasma applications in water treatment and medicine. Ultrapure water, phosphate-buffered saline (PBS) solution, biological media, and sodium chloride (NaCl) solutions at various concentrations were used in this study to provide a broad range of conductivity values ranging from 0.055 μS cm⁻¹ to 222 mS cm⁻¹. A helium atmospheric pressure plasma jet (APPJ) impinging on liquid was generated in ambient air using a hollow-needle electrode that was powered by 200 ns, 7 kV pulses at a pulse repetition frequency of …

Determining The Transient Electric Field And The Effect Of Pulse Repetition Frequency On The Field In A Repetitive 10-Ns Pulsed Discharge In A Quiescent Ch₄-Air Mixture, Chunqi Jiang

Bioelectrics Publications

The goal of this research is to understand the underlying physics enabled by nanosecond pulsed power in a discharge initiation and the following physicochemical processes that favor lean-fuel plasma ignition for combustion. The hypothesis of this project is that pulsed power waveforms such as the pulse repetition frequency (PRF), voltage amplitude, and dielectric surface alter the reduced electric field during the initiation of the discharge, resulting in different plasma properties, which will vary the physicochemical processes for efficient and selective radical productions. This is especially important for lean-burn combustion and reducing emission.

Optimizing Material Selection And Operational Conditions For Xhv Systems: Lessons From Aisi 1020 And 316l Comparative Studies, Aiman H. Al-Allaq, Md Abdullah Mamun, Matt Poelker, Abdelmageed Elmustafa

Mechanical & Aerospace Engineering Faculty Publications

In this study, AISI 1020 low-carbon steel was investigated as a cost-effective alternative to SS316L stainless steel for reaching extreme high vacuum (XHV) conditions. After being baked at 400°C, a vacuum chamber made of low-carbon steel material exhibited an outgassing rate approximately 2000 times smaller than a similar chamber made of stainless steel. Its activation energy for hydrogen diffusion (27 kJ/mol) is less than half that of stainless steel (60.3 kJ/mol), indicating more efficient hydrogen removal during bakeout. MolFlow+ simulations supported the experimental data and demonstrated the importance of system geometry optimization and minimizing stainless steel content for achieving optimal …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

A Comprehensive Review Of Piezoelectric Pvdf Polymer Fabrications And Characteristics, Nadia Ahbab, Sidra Naz, Tian-Bing Xu, Shihai Zhang

Mechanical & Aerospace Engineering Faculty Publications

Polyvinylidene fluoride (PVDF) polymer films, renowned for their exceptional piezoelectric, pyroelectric, and ferroelectric properties, offer a versatile platform for the development of cutting-edge micro-scale functional devices, enabling innovative applications ranging from energy harvesting and sensing to medical diagnostics and actuation. This paper presents an in-depth review of the material properties, fabrication methodologies, and characterization of PVDF films. Initially, a comprehensive description of the physical, mechanical, chemical, thermal, electrical, and electromechanical properties is provided. The unique combination of piezoelectric, pyroelectric, and ferroelectric properties, coupled with its excellent chemical resistance and mechanical strength, makes PVDF a highly valuable material for a wide …

Nanomaterial-Enabled Enhancements In Thylakoid-Based Biofuel Cells, Amit Sarode, Gymama Slaughter

Center for Bioelectronics Publications

Thylakoid-based photosynthetic biofuel cells (TBFCs) harness the inherent light-driven electron transfer pathways of photosynthesis to enable sustainable solar-to-electrical energy conversion. While TBFCs offer a unique route toward biohybrid energy systems, their practical deployment is hindered by sluggish electron transfer kinetics, unstable redox mediators, and inefficient interfacing between biological and electrode components. This review critically examines recent advances in TBFCs, with a focus on three key surface engineering strategies: (i) incorporation of nanostructured materials to enhance electrode conductivity and surface area; (ii) application of redox mediators to facilitate charge transfer between photosynthetic proteins and electrodes; and (iii) functional exploitation of individual …

Solid Loading Effects On The Assembly Of Alumina Particles In Aqueous Suspensions Due To The Dielectrophoretic Forces, Sivakumar Chithamallu, Rohan Kiran Parai, Dipankar Ghosh

Mechanical & Aerospace Engineering Faculty Publications

Current work in situ investigated the mechanisms of the interparticle interactions that evolve in dilute aqueous alumina suspensions subjected to alternating current (AC) electric field and the effects of solid loading of suspensions. The interactions were investigated for alumina suspension compositions in the 0.005‒0.04 vol.% solid loading range. Field‐induced interactions evolved via particle motion and dynamic assembly, chain formation parallel to the direction of the applied field and chain growth, chain cross‐linking, and chain thickening. The evolution time of each of those events was rapidly accelerated with solid loading. While chain cross‐linking was negligible in low solid loading suspensions, a …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

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 …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Rheology Of Alumina Suspensions Subjected To Alternating Current Electric Fields For Freeze-Casting, Sivakumar Chithamallu, Ruksana Baby, Jacob L. Jones, Dipankar Ghosh

Mechanical & Aerospace Engineering Faculty Publications

Alternating current (AC) electric field can extrinsically tune freeze‐cast microstructure, originating from field‐induced increase in viscosity of ceramic suspensions. However, the changes that occur in a ceramic suspension and rheological behavior, ultimately affecting freeze‐cast microstructure, are not well understood. Moreover, the effects of AC electrokinetic forces and temperature on viscosity need to be decoupled. The viscosity and temperature of ceramic suspensions subjected to AC field and direct heating were measured, revealing that the increase in viscosity is due to AC dielectrophoretic forces rather than field‐induced heating of suspension. The shear thinning behavior of suspensions characterized using a power‐law model reveals …