Electrical and Computer Engineering Commons^™

An Explainable Cs-Mitigation Triangular (Ecsmt) Framework To Secure Graph Neural Networks, Sabah Ettahri, Sergio Pallas Enguita, Chung-Hao Chen, Wen-Chao Yang

Electrical & Computer Engineering Faculty Publications

This research addresses cyber risk by defending against backdoor attacks on Graph Neural Networks (GNNs). We propose the Explainable Complex System-Mitigation Triangular (ECSMT) Framework, which integrates Robust Training, Graph Regularization, and Data Sanitization into a lightweight, hardware-efficient defense layer. To evaluate structural generalizability, we conducted empirical evaluations across three distinct benchmark domains (AIDS, MUTAG, and PROTEINS) using a Graph Isomorphism Network (GIN) backbone. Under a baseline 5% backdoor subgraph trigger injection ratio, ECSMT achieves excellent utility retention, securing a Clean Accuracy (CA) of 97.33% (±0.62%) while reducing the Attack Success Rate (ASR) from 97.00% down to 69.45% on the primary …

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. …

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 …

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 …

Comparative Analysis Of Ev-Charging Converters Using Silicon Carbide Mosfets And Silicon Igbt’S, Muhammad A. Kahn

Electrical Engineering Theses

The rapid expansion of electric vehicle (EV) charging infrastructure has increased the demand for efficient and reliable power electronic converters. Semiconductor device selection is critical to system performance, influencing efficiency, power density, and reliability. This thesis presents a comparative analysis of Silicon (Si) Insulated Gate Bipolar Transistors (IGBTs) and Silicon Carbide (SiC) MOSFETs in a representative EV charging converter, with emphasis on electrical performance and thermal behavior under identical operating conditions.

The research employs PLECS (Plexim) and electro-thermal simulation models to accurately characterize device and system interactions. The converter configuration consists of a grid-connected voltage source inverter (VSI) that maintains …

Representation Learning: From Computer Vision To Applications In Computational Biology, Parisa Boodaghimalidarreh

Computer Science and Engineering Dissertations

High-dimensional imaging data has become central to modern science, yet its scale and complexity make it difficult to model, compress, and interpret. This dissertation develops and applies machine learning methods that extract compact, informative, and predictive representations from such data, and demonstrates their reach across two very different domains: the reconstruction of natural images and the analysis of transcriptional dynamics during \textit{Drosophila} embryogenesis. Across three studies, a shared methodological thread---learning efficient representations of complex image data---connects advances in computer vision to the mechanistic study of gene regulation.

The first study addresses a core limitation of vector-quantized image models: poor codebook …

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 …

Swap And Manufacturing Challenges In Dews, Austin J. Ensign

Electrical Engineering Dissertations

Military advancements in cheaper, smaller, and more robust weapon systems have led to establishment of Directed Energy Weapons (DEWs). DEWs require enormous amounts of power, cooling, and therefore, power electronics and their supporting accessories. Current generations of DEWs have simple topologies for power distribution that have a large Size, Weight, and Power (SWaP) footprint. Many applications in the military have limited SWaP areas, so the current topology can lead to design integration issues. The control Printed Circuit Boards Assemblies (PCBAs) have hundreds of connections that monitor pressures, temperatures, flows, and more of the cooling system for DEW. This can lead …

Asymmetric-Function-Gated Utilization Of Environmental Heat Energy To Produce Electricity Without Requiring A Temperature Gradient: A Type-B Energy Technology, James Weifu Lee

Chemistry & Biochemistry Faculty Publications

This article highlights “Type-B energy technology” isothermally utilizing environmental heat energy to generate electricity, showing its: a) basic system designs, b) equations, c) calculations, d) experimental measurements, and e) live experimental demonstrations, for an asymmetric function-gated electricity generator system comprising an exceptionally low electron-work-function surface as an electron emitter and a space gap that permits the isothermally emitted electrons to travel ballistically to a collector. The equations, calculations, experimental measurements, and live experimental demonstrations showed that the type-B energy technology can utilize the limitless environmental heat energy to generate electricity without requiring any temperature gradient as long as the electron-work …

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 …

Renewable And Affordable Energy For Apartments. Inquiry Into Renewable And Affordable Energy For Apartments, Nishadi Ruwandima Weerasinghe Mudiyanselage, Asma Aziz

Research outputs 2022 to 2026

Recent literature identifies a persistent disparity between detached housing and apartments in accessing renewable and affordable electricity. While rooftop solar PV deployment in Australia  has expanded rapidly since 2017, access to these technologies in multi-unit dwellings (MUDs) remains limited, resulting in higher electricity costs for apartment residents compared with standalone houses. This inequity is increasingly significant given rising electricity prices and the growing share of Australians living in apartments.

A Universal Hybrid Model-Free Deep Quantum–Transfer Learning Controller Enhanced By Grey Wolf Optimization For Dc–Dc Boost Converters With Hardware-In-Loop Validation, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

This paper proposes a universal hybrid model-free quantum–transfer learning controller with enhanced online grey wolf optimization algorithm (GWO–QTL) for DC–DC boost converter. This system has the characteristics of non-minimum phase behavior, parasitic effects, and fractional-order dynamics because of high frequency operation. These characteristics make analytical modeling complicated and make it difficult to have a single traditional controller that will operate reliably over different converter types. This motivates the creation of a unified model-free control framework that is able to learn directly from the behavior of the converter without relying on the topology specific models. Reinforcement learning, where an agent interacts …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Drone Endurance In Hydrogen Fuel Cell Hybrid Technologies: Power Architectures And Design Strategies, Yaw Chong Tak, Tarek Abedin, Johnny Koh Siaw Paw, Jagadeesh Pasupuleti, Tan Jian Ding, Tiong Sieh Kiong, K. Kadirgama, F. Benedict, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

This study delivers an exhaustive exploration of novel and hybrid power systems for Unmanned Aerial Vehicles (UAVs) aimed at improving endurance, efficiency, and mission performance. In the wake of increasing requirements for long-endurance and high-performance UAVs, traditional battery systems are limited by their energy density and lifetime. To overcome this, the research compares four primary power sources: hydrogen fuel cells, lithium-based batteries, photovoltaic cells, and supercapacitors, with a focus on their hybrid architecture integration. The originality of this research is in the relative comparison of these power sources in multi-mode UAV operations, with an emphasis on their performance, energy management …

Covert Performance Of Star-Ris Aided Thz Communication System With Rsma And Phase Errors, Xiangbin Yu, Yue Zhou, Shihao Yan, Yun Rui, Xiaoyu Dang, Chau Yuen, Mohsen Guizani

Research outputs 2022 to 2026

In this paper, the covert performance of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided Terahertz (THz) communication systems with rate-splitting multiple access (RSMA) over the generalized α-μ fading channel is studied, where the discrete phase error and noise power uncertainty are considered. By means of the Gaussian approximation and Gamma distribution, the closed-form outage probability (OP) of the legitimate users is firstly derived. Then, the covert performance of the system is analyzed, and average detection error probability (ADEP) is deduced for the warden, and resultant closed-form ADEP is obtained. By minimizing the ADEP, the optimal detection threshold …

Design Of Artificial Interference Signal Waveforms For Covert Communication Aided By Multiple Friendly Nodes, Xuyang Zhao, Wei Guo, Yongchao Wang, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider a covert communication scenario with multiple friendly interference nodes. The goal is to hide a legitimate communication link from a transmitter to a receiver under a warden’s surveillance. Firstly, we propose a novel strategy for generating artificial noise (AN) signals and formulate a corresponding design problem, aiming to minimize the adverse effects of AN on the legitimate receiver while enhancing communication covertness. Specifically, we optimize the basis matrix for AN signal space using statistical information of the involved channel coefficients, when precise channel state information are unavailable. Secondly, we analyze the geometric structure of the …

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 Deep Transfer Learning-Based Adaptive Cascade Pi Controller Enhanced By Reinforcement Learning Algorithm And Snake Optimization For Robust Speed Regulation Of Brushless Dc Motors, Seyyed Morteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) are common in electric cars, industrial automation, and robotics because of their high efficiency, high torque control, and compact size. Nevertheless, strong speed and current regulation is not easily attained because of system variation, load variations and the shortcomings of traditional fixed-gain proportional-integral (PI) controllers. In this paper, a new snake optimization-assisted deep transfer learning-based reinforcement learning (SOA-DTL-RL)-based adaptive cascade PI controller is proposed that combines transfer learning with fast adaptation, Reinforcement learning with real-time optimization, and snake optimization with optimal initial gain selection to guarantee the robust speed and current regulation in BLDC motors. The proposed …

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 …

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 …

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 …

A Portable Potentiostat Integrated With A Pt/Zno/Lig Electrode For Non-Enzymatic Glucose Detection, Reagan Aviha, Gymama Slaughter

Center for Bioelectronics Publications

Continuous glucose monitoring is critical for effective diabetes management; however, conventional benchtop potentiostats are bulky, costly, and unsuitable for decentralized point-of-care (PoC) applications. To address these limitations, this work presents a miniaturized, low-cost electrochemical sensing platform integrating a non-enzymatic glucose sensor with a portable potentiostat. The sensing electrode is based on laser-induced graphene modified with zinc oxide and platinum nanostructures via electrodeposition to enable sensitive glucose detection under physiological conditions. A custom-designed portable potentiostat was developed to control electrode potentials and perform electrochemical measurements, and its performance was experimentally validated against a commercial Metrohm system. Glucose detection was evaluated using …

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 …

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 …

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 …

Next-Generation Computing Hardware: Advancements In Tantalum Oxide Reram For Ai And Neuromorphic Applications, Rajas Ravindra Mathkari

Electronic Theses & Dissertations (2024 - present)

The rapid development of artificial intelligence, machine learning, and data-intensive computing has exposed the fundamental limitations of conventional von Neumann architectures, in which energy and time are continuously lost transferring data between physically separate memory and processing units. In contrast, the human brain performs complex computations directly at the point of memory storage through billions of parallel synaptic connections, a paradigm known as in-memory computing. Realizing this in hardware requires memory devices that are fast, energy-efficient, non-volatile, and capable of storing multiple resistance levels in an analog manner. Resistive Random Access Memory (ReRAM) based on tantalum oxide (TaOx) is one …