Electrical and Computer Engineering Commons^™

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 …

Investigation Of Fine-Grain Cu And Cu Alloys For Low-Temperature Hybrid Bonding Applications, Sarabjot Singh

Electronic Theses & Dissertations (2024 - present)

Hybrid bonding has emerged as a key enabler for next-generation three-dimensional (3D) integration, offering fine-pitch interconnects and improved electrical performance. However, conventional Cu–Cu hybrid bonding typically requires elevated temperatures to achieve sufficient diffusion and interface quality, posing challenges for temperature-sensitive device integration and process compatibility. This work investigates materials engineering approaches to enable low-temperature Cu–Cu bonding through both microstructure design and alloying strategies.

This work begins by examining grain refinement in Cu as a pathway to enhance diffusion through increased grain boundary density, providing efficient atomic transport without introducing additional elements. Three Cu-based systems Cu–Co, Cu–Ag, and Cu–Al were systematically …

Development Of Alternative Plasma Etching Techniques For The Selective Removal Of Tan With Respect To Sioch Dielectric Materials To Enable Future Back-End-Of-The-Line Scaling, Ivo Otto Iv

Electronic Theses & Dissertations (2024 - present)

Transistor scaling has continued according to Moore’s Law for over fifty years. As transistor size decreases, adequate power delivery is required to enable transistor scaling without performance loss. Power delivery is provided by a metal interconnect network with insulating dielectric that connects the transistor level to the power source, the signal speed within this metal line network limiting transistor level switching speeds. Reduction of signal delay has moved from primarily dimension-based improvement towards adoption of conductor and dielectric materials with lower resistivity and a reduced dielectric constant value, respectively: transitioning from Al/SiO₂ to Cu/low-κ SiOCH. With this transition comes …

On Distribution Matching For Probabilistic Constellation Shaping, Dimitrie C. Popescu, Cameron Kowski

Electrical & Computer Engineering Faculty Publications

Distribution matching (DM) transforms a stream of equally likely bits into digital symbols that correspond to a specific digital modulation scheme with probabilities of occurrence following a specified statistical distribution. This technique is referred to as probabilistic constellation shaping (PCS), and using the Maxwell–Boltzmann (MB) distribution improves spectral and energy efficiency of digital modulation schemes by enabling more frequent transmissions of lower energy digital symbols. The background behind PCS is presented in the paper and the two main DM methods widely used in practice are discussed, outlining scenarios in which one DM method may be preferable over the other. For …

Neural Network Transceiver For Ltv Mimo Channels, Iresha Amarasekara

Electronic Theses & Dissertations (2024 - present)

Eigenfunctions are commonly employed to characterize kernels in various data-driven analyses. In machine learning, eigenfunction decomposition typically relies on Mercer's theorem, which assumes kernel symmetry. However, this condition is often unmet in communication systems, where channel kernels are asymmetric due to differences in downlink and uplink propagation environments. The High-Order Generalized Mercer's Theorem (HOGMT) provides a systematic approach for decomposing multidimensional asymmetric kernels into eigenfunctions. To address the complexity of eigen-decomposition, this work introduces a baseline neural network (NN) framework HNET. The HNET framework is further enhanced by incorporating the augmented Lagrangian method (ALM) to explicitly enforce orthogonality constraints. This …

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 …

Estimating Sediment Properties Using A New Source Level Function For Wind-Driven Underwater Sound Derived From Long-Term Archival Data, S Bruce Martin, Martin Siderius

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-driven breaking waves generate the background sound throughout the ocean. An accurate source level for wind-driven breaking waves is needed for estimating the ambient sound levels needed for sound exposure modeling, environmental assessments, and assessing the detection performance of sonars. Previous models applied a constant roll-off of sound levels at -16 dB/decade at all wind speeds, and these models' source levels were flat at frequencies below ∼1000 Hz due to a lack of measurements. Here, we analyzed 16 long-term archival datasets with limited anthropogenic sound sources to estimate the wind-driven source level down to 100 Hz. We estimated the site-specific …

Deep Reinforcement Learning-Enhanced Model Predictive Control Using Dc-Link Energy Modulation For Inter-Area Oscillation Damping In Pmsg-Based Power Systems, Emad M. Elhaji, Shichao Liu, Hicham Chaoui, Osarodion Emmanuel Egbomwan

Electrical & Computer Engineering Faculty Publications

Low-frequency oscillations remain a major concern in modern power systems with increasing penetration of renewable energy sources. Conventional damping methods, such as power system stabilizers and fixed-gain controllers, may not provide sufficient damping for converter-interfaced wind generators under changing operating conditions and disturbances. To address these limitations, this paper proposes a deep reinforcement-learning-enhanced model predictive control (DRL-MPC) framework for damping inter-area oscillations in multi-machine power systems incorporating permanent magnet synchronous generator (PMSG)-based wind turbines. The proposed approach leverages the energy stored in the DC-link capacitor as a fast dynamic buffer to enable supplementary active-power modulation for oscillation damping. A twin-delayed …

Co-Sputtered Cuni Heteroatomic Electrocatalyst For Enhanced 5-Hydroxymethylfurfural Selective Electrochemical Conversion, Moumita Dikshit, Baleeswaraiah Muchharla, Luz Vazquez Rivera, Kapil Kumar, Sunita Sanwaria, Kishor Kumar Sadasivuni, Abdennaceur Karoui, Sandeep Kumar, Adetayo Adedeji, Bijandra Kumar

Civil & Environmental Engineering Faculty Publications

The electrochemical conversion of biomass-derived 5-hydroxymethylfurfural (HMF) represents a promising, economically viable, and environmentally sustainable approach for producing value-added chemicals using renewable energy and in situ hydrogen generated through water electrolysis. However, the electrochemical hydrogenation (ECH) of HMF remains challenging due to the inherently low catalytic activity and selectivity of the electrodes, compounded by competition with the kinetically favored hydrogen evolution reaction (HER) in aqueous electrolytes. In this work, we demonstrate that Cu_xNi_100-x heteroatomic thin films, fabricated via direct current (DC) magnetron co-sputtering, achieve a more than one order of magnitude increase in the HMF to 2,5-Bis-hydroxymethylfuran …

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 …

Stem-Fit And Soil-Fit: Integrated Plant And Soil Nitrogen-Hormone Sensing With Machine Learning-Based Forecasting For Next-Generation Precision Agriculture, Nafize I. Hossain, Mohammad Solaiman, A.K.M. Ahsanul Habib, Md Al Mahmud Hossain Al Hadi, Shawana Tabassum

Electrical Engineering Faculty Publications and Presentations

Inefficient fertilizer application in agriculture leads to reduced crop productivity, nutrient losses, and reduced crop resilience, highlighting the urgent need for real-time monitoring of plant–soil nutrient and stress dynamics. This research aims to develop and validate a multiplexed sensing platform for real-time, in-situ measurement of key soil nutrients (Soil-FIT) and plant phytohormones (Stem-FIT) involved in nitrogen signaling and stress regulation. The proposed sensor suite integrates 3D-printed modules for continuous monitoring of nitrate, ammonium, and pH in both soil and plant sap, along with salicylic acid (SA), indole-3-acetic acid (IAA), methyl jasmonate (MeJA), and ethylene (ET) in plant sap. The sensors, …

Development Of Feature Tokenizer Deep Learning Model For Fault Diagnosis In Marine Propulsion System, Pratik Anand Deshpande, J. Preetha Roselyn, Prabha Sundaravadivel

Electrical Engineering Faculty Publications and Presentations

The fault diagnostics in Brushless Direct Current (BLDC) motor drive system is critical for operational safety and system lifespan in propulsion system applications. However, signature parameters such as currents, voltages, speed, and torque have provided nonlinear behavior, which limits the usefulness of traditional model-based approaches. This research provides a deep learning based intelligent system to monitor the failures in marine propulsion system. Each signal feature is represented as a structured token, with a specific class token used to collect global contextual information. The proposed model captures both local temporal dynamics and global inter-feature interdependence multi-layer self-attention processes, allowing for the …

Decentralized Linear Solvers: Communication Cost And Privacy, Nelson G. Brasil, Vinay A. Vaishampayan

Publications and Research

We consider the problem of multiple parties iteratively solving a system of linear equations in a decentralized manner. Specifically, we solve for $\negr{x} \in \R^n$, the $n \times n$ system of linear equations $M\negr{x} = \negr{b}$ when each party only knows their row of the matrix $M$ and a single component of $\negr{b} \in \R^n$. Our objective is to determine the tradeoff between the accuracy of the solution and the total communication cost measured in bits. A fully connected, reliable mesh network is assumed to connect the different parties. We develop a general formulation that applies to a large class …

Adaptive Multi-Grade Deep Learning For Highly Oscillatory Fredholm Integral Equations Of The Second Kind, Jie Jiang, Yuesheng Xu

Mathematics & Statistics Faculty Publications

This paper studies the use of Multi-Grade Deep Learning (MGDL) for solving highly oscillatory Fredholm integral equations of the second kind. We provide rigorous error analyses of continuous and discrete MGDL models, showing that the discrete model retains the convergence and stability of its continuous counterpart under sufficiently small quadrature error. We identify the DNN training error as the primary source of approximation error, motivating a novel adaptive MGDL algorithm that selects the network grade based on training performance. Numerical experiments with highly oscillatory (including wavenumber 500) and singular solutions confirm the accuracy, effectiveness and robustness of the proposed approach.

High Temperature Mold Gap Measurement Using Optical Fiber Interferometry, Jie Huang, Ronald O'Malley, Dinesh Reddy Alla, Deva Prasaad Neelakandan, Muhammad Roman

Patents

A temperature insensitive fiber optic Fabry-Perot interferometer based mold gap measurement system that can be employed in foundry and continuous casting facilities for both non-ferrous and ferrous production applications. For steel continuous casting, the sensor is also capable of detecting crystallite formation in the mold flux layer within the mold gap by detecting reflections from both the flux layer and the steel shell to facilitate direct mold lubrication monitoring for continuous casting. These interferometers can also be easily multiplexed to perform interface shape measurements or monitor gap variations at various positions within a complex mold design. The ability to measure …

Transverse Magnetic Mode Split Post Dielectric Resonator, Donghyun Kim, Xiaoning Ye, Chaofeng Li

Patents

A device for measuring a property of a substrate comprises an enclosure,two resonators,and two probes.The substrate has a top surface, and the enclosure defines a cavity for placing the substrate. The resonators extend into the cavity of the enclosure. The probes extend into the cavity, and each of the probes comprises a loop that extends about an axis that is substantially parallel to the top surface of the substrate so that the probes are operable to excite a transverse magnetic mode signal in the cavity.

The Operation And Control Strategy Of Microgrids: A Brief Review, Mohammad Alam, Salsabila Zaman

Electrical Engineering Student Publications

A microgrid is an effective approach for integrating various distributed energy resources to meet local energy demand. It possesses the capability to operate independently or in conjunction with the main utility grid. Typically, a microgrid is a small-scale power system (usually several megawatts or less) comprising three primary components: the ability to function in both grid-connected and islanded modes, distributed power generation sources, and autonomous control centers. Microgrids enhance system resilience by ensuring uninterrupted operation during grid disturbances. Additionally, they are environmentally sustainable and contribute to improved power quality. As a crucial element of modern power systems, microgrids can effectively …

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 …

Optimizing Ev Battery Charging Using Fuzzy Logic In The Presence Of Uncertainties And Unknown Parameters, Minhaz Uddin Ahmed, Md Ohirul Qays, Stefan Lachowicz, Parvez Mahmud

Research outputs 2022 to 2026

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system …

Optically-Pumped Semiconductor Optical Amplifiers, Dhruvkumar Desai

Graduate Studies Theses and Dissertations 2026

Semiconductor optical amplifiers (SOAs) offer a low-cost, compact solution for power amplification needs in an optical communication system compared with the dominant erbium-doped fiber amplifiers (EDFAs). They can also provide a wide gain bandwidth. However, conventional electrically-pumped SOAs suffer from larger noise figures, low saturation power, and polarization dependence, in comparison with EDFAs. We propose an optically-pumped SOA (OP-SOA) that will maintain the benefits of conventional SOAs while closing the gaps in other amplifier performance metrics. The underlying reasons for optical pumping are twofold. First, optical pumping allows higher carrier injection and thus "population inversion." Second, optical pumping decouples carrier …

Integrated Corridor Management Framework For Severe Freeway Incidents, Sanjida Afroz Iqra

Graduate Studies Theses and Dissertations 2026

Traffic incidents are a major source of non-recurrent congestion on urban freeways, generating substantial mobility, safety, and economic impacts. Severe incidents that block multiple or all travel lanes are particularly disruptive because they degrade freeway operations and propagate congestion onto surrounding arterials. Effective Integrated Corridor Management (ICM) requires the ability to identify severe incidents, estimate their network-wide impacts, and anticipate the traffic conditions and driver behaviors that contribute to instability. This dissertation develops a data-driven ICM framework to address these challenges using real-world incident, crash, detector, and connected vehicle data from major Central Florida corridors, including I-4 and SR-417. The …

Advancing Cyber-Physical Security And Resilience Of Modern Power Systems: Intelligent Monitoring, Secure Operation, And Resilient Recovery, Md Moshiur Rahman

Graduate Studies Theses and Dissertations 2026

Modern power distribution systems are rapidly evolving into cyber-physical, DER-rich, and data-driven networks that rely on extensive sensing, communication, automation, grid-edge intelligence, and operator decision support. While this transformation improves flexibility, observability and controllability, it also expands the cyber-attack surface and increases the risk that cyber intrusions can propagate into physical disturbances, compromised DER operation, degraded situational awareness, and interrupted service continuity. This dissertation advances the cyber-physical security and resilience of modern distribution systems by developing a high-fidelity real-time cyber-physical hardware-in-the-loop testbed using OPAL-RT, EXata CPS, industrial relays, SCADA/RTAC, HMI, and grid-edge devices to emulate realistic DER-integrated distribution grid operation. …

Detection And Discrimination Of Targets In Infrared Imagery, Adam T. Cuellar

Graduate Studies Theses and Dissertations 2026

Automated infrared (IR) imagery analysis is essential for persistent surveillance, defense, and security, yet it remains difficult when sensors move and when unknown objects appear. This dissertation addresses two fundamental problems: (1) detecting small moving targets amid platform-motion induced parallax and (2) distinguishing between known stationary targets and out-of-distribution (OOD) objects.   For detecting moving targets while the platform itself is in motion, auxiliary Global Positioning System and Inertial Navigation System data are combined with image analysis. Direction Cosine Matrices from calibrated inertial measurements enable sub-pixel frame alignment unattainable with purely image-based registration. The stabilized sequence is processed by a Reed–Xiaoli …

Investigation Of Bulk And Periodically Stacked X(2) And X(3) Materials Using Mid-Infrared Femtosecond Pulses, Woraprach Kusolthossakul

Graduate Studies Theses and Dissertations 2026

Mid-infrared (MIR) spectral region represents a vital part of the electromagnetic spectrum, offering applications in spectroscopy, biomedicine, and remote sensing. However, there are no direct broadband MIR laser sources. Nonlinear frequency conversion techniques using second-order nonlinear materials, such as optical parametric oscillator (OPO) and intra-pulse difference frequency generator (IDFG), are promising methods for generating a broadband MIR spectrum. To achieve an efficient conversion, the phase matching condition between the interacting waves of the nonlinear process must be satisfied. Quasi-phase matching (QPM) is a special technique that enables efficient nonlinear processes in materials that cannot be conventionally phase matched using birefringence. …

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 …

Abig4l: A Semantic Reasoning Engine For Verilog Via Abstract Interpretation, Alicia Thoney

Graduate Studies Theses and Dissertations 2026

Modern Electronic Design Automation (EDA) flows treat structural and semantic reasoning as separate concerns. Synthesis structurally optimizes RTL, while dedicated verification teams validate functional behavior through testbenches and formal methods. This organizational divide means designers have no automatic, sound way to understand what their RTL does at the moment they are writing it. This thesis introduces AbIG4L (Abstract Interpretation of Generic 4-valued Logic), a static analysis framework that bridges this gap by delivering sound semantic reasoning automatically, directly within the hardware design loop.

The framework operates on a language-agnostic intermediate representation to compute sound over-approximations of hardware behavior across 0, …

Bowtie Antenna-Coupled Bi₂Te₃–Sb₂Te₃ Thermoelectric Detector For Millimeter-Wave Radiation, Rumana Zahir

Graduate Studies Theses and Dissertations 2026

We explore the development of antenna-coupled thermoelectric detectors for long-wavelength radiation using sputtered bismuth telluride (Bi₂Te₃) and antimony telluride (Sb₂Te₃) thin films. Detection in the infrared, millimeter-wave, and terahertz regimes is critical for sensing, imaging, and energy applications; however, many existing detectors rely on cryogenic operation, exhibit narrow spectral response, or require complex material systems. In this work, we investigate the feasibility of reliable, room-temperature long-wavelength detection using microfabricated thermoelectric thin films integrated with antenna structures. A systematic full-factorial experimental approach was employed to determine how sputtering parameters and post-deposition annealing influence …

Voltage Stability Enhancement Of Large Load Interconnections Using Syncronous Condensers, Muhammad Ibrahim Abbas

Graduate Studies Theses and Dissertations 2026

The rapid integration of hyperscale data centers as large, concentrated loads presents a growing voltage stability challenge in grids weakened by synchronous generator retirement and increasing inverter-based resource penetration. This work proposes a Jacobian-based sensitivity framework for systematically identifying voltage-critical buses and optimally siting synchronous condensers as voltage support resources. A voltage-weighted sensitivity index, extracted from the full Jacobian inverse, is introduced to combine network-wide reactive coupling strength with observed voltage drops into a single deployable placement criterion. Validation on IEEE 14-bus and 30-bus test systems under multiple loading scenarios demonstrates that the proposed criterion consistently identifies the correct placement …