Electrical and Computer Engineering Commons^™

The Equity Implications Of Pecuniary Externalities On An Electric Grid, Charles Sims, Gasser G. Ali, J Scott Holladay, Tim Roberson, Chien-Fei Chen, Islam H. El-Haddad

Civil Engineering Faculty Publications

The adoption of rooftop photovoltaic (PV) systems can create upward pressure on retail electricity rates as utilities are forced to spread their fixed costs of generation and transmission across a smaller customer base. Since high-income households are more likely to purchase PV systems, low-income households may be disproportionately impacted by these rate increases. Using a novel combination of agent-based computational economic modeling and a choice experiment of rooftop solar adoption, we show how this pecuniary externality between low- and high-income customers increases low-income electricity bills by 10% in an area with some of the highest poverty rates in the United …

A Low-Power Mixed-Signal Potentiostat System-On-Chip With Integrated Dual-Slope Adc, Seth Mcrobert

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

This thesis presents the design and characterization of a low-power mixed-signal potentiostat that was integrated with a 65 nm core in a SoC for low-power electrochemical sensing applications. The system integrates a low-noise transimpedance-based potentiostat front end with a 12-bit dual-slope analog-to-digital converter (ADC) for accurate current-to-digital conversion. The potentiostat core—comprising the control amplifier, current-mirror network, and transimpedance amplifier—consumes 38.2 µA from a 2.5 V supply (95.5 µW) and achieves an input-referred noise floor of 113 µVRMS over a 330 Hz bandwidth, while having an input current range from 1 nA to 20 µA and a noise-limited sensitivity of 56.4 …

Design Of A Novel Robust Adaptive Fractional-Order Model Predictive Controller For Boost Converter Using Grey Wolf Optimization Algorithm, Chao Peng, Seyyed Morteza [email protected] Ghamari, Hasan Mollaee, Omid Rezaei

Research outputs 2022 to 2026

Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise …

Machine Learning Assisted Quality Control In Metal Additive Manufacturing: A Review, Zeqi Hu, Changlin Huang, Lechun Xie, Lin Hua, Yujie Yuan, Lai Chang Zhang

Research outputs 2022 to 2026

Additive manufacturing (AM) promotes the production of metallic parts with significant design flexibility, yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance. Process variability often leads to defects, insufficient geometric accuracy and inadequate material properties, which are difficult to effectively manage due to limitations of traditional quality control methods in modeling high-dimensional nonlinear relationships and enabling adaptive control. Machine learning (ML) offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM. The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM. First, …

Next-Generation Underwater Localization: Artificial Intelligence-Based And Energy-Aware Approaches, Mainul Islam Chowdhury, Quoc Viet Phung, Iftekhar Ahmed, Walid K. Hasan, Daryoush Habibi

Research outputs 2022 to 2026

Designing accurate, reliable, and energy-efficient localization techniques for underwater acoustic networks is highly challenging due to factors such as large propagation delays, the absence of Global Positioning System (GPS), node mobility, and limited acoustic link capacity. In any underwater sensor network (UWSN) monitoring application, data collected by underwater nodes becomes more meaningful when accompanied by location information. However, traditional localization methods often rely on geometric models and statistical filters that are highly sensitive to sensor noise and communication constraints. Energy consumption is another primary concern in UWSNs, not only because replacing and recharging underwater batteries are challenging, but also due …

Feed Network Design For Passive Beamforming In Hemispherical Phased Arrays, Daniel Flores

Theses and Dissertations

This thesis presents the design, fabrication, and evaluation of a four-element microstrip patch antenna array with a passive 1 to 4 corporate feed network operating at 5 GHz. A single inset-fed patch was developed on Rogers DiClad 880 to ensure accurate tuning and mechanical flexibility, achieving a measured resonance of 5.009 GHz with excellent return loss. The corporate feed network, synthesized using T-junction dividers and quarter-wave transformers, demonstrated strong impedance matching, balanced amplitude distribution, and broadside realized gain near 10 dBi when integrated with the array.

Beam steering was examined through simulation-based phase control, revealing effective scanning up to approximately …

Optimal & Robust Control Of A Bidirectional Dc-Dc Converter In Ev Systems, Yasser Ayeva

Electrical Engineering and Computer Science Faculty Publications and Presentations

This paper analyzes the performance of PI, LQR, and H∞ controllers for the regulation of a bidirectional buck boost converter in electric vehicle systems. To get the system state space equations, a continuous conduction average model is linearized. For analysis a PI controller will be used as baseline, the LQR controller will be used to improve transient response, and the H∞ controller will be used for the system robustness and disturbance rejection. The simulation results show that the advanced controllers surpass the PI controller in terms of overshoot, settling time, and voltage ripple, with the H∞ controller offering the best …

Reinforcement Learning Based Security Schemes For Distributed Ai Systems, Ashan Chamath Gunawardena

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Distributed machine learning (DML) is a component of modern intelligent systems, enabling collaborative training across devices such as mobile clients, vehicles, and edge networks. However, the decentralized nature of these systems introduces vulnerabilities, particularly data poisoning attacks that compromise model integrity and degrade performance. Traditional defenses, such as statistical filtering, robust aggregation, and privacy-preserving techniques, often struggle to adapt to overwhelming adversaries or operate under strict privacy and real-time constraints. This dissertation proposes the use of reinforcement learning (RL) and deep reinforcement learning (DRL) based misbehavior detection schemes that dynamically identify poisoning attempts in distributed AI systems, including federated learning, …

Rapid State-Of-Health Estimation Of Batteries Using Machine Learning With Limited Eearly-Discharge Voltage Data, Mohammad Bakhtiari

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

The utilization of lithium-ion batteries has been rapidly expanding across diverse sectors, including electric transportation, stationary energy storage systems, and the built environment. Ensuring a high level of reliability in these applications is essential, as the performance and safety of such systems depend strongly on the accurate assessment of the battery’s State of Health (SOH). Conventional SOH estimation techniques—often based on complex electrochemical models or extensive laboratory testing—tend to require a large number of measurements, advanced instrumentation, and high computational cost. These factors make them impractical for large-scale deployment or real-time monitoring. This study introduces a simplified machine-learning-based approach for …

Series Resonant Converters With Medium Voltage Sic Mosfets For Electric Aircraft Applications, Xinyuan Du

Graduate Theses and Dissertations

To develop high-performance high- power-density MV power converters, the emerging silicon carbide (SiC) devices are more attractive than their silicon (Si) counterparts, since the fast switch frequency brought by the SiC can effectively reduce the volume and weight of the filter components and thus increase the converter power density. From the converter topology perspective, with the MV dc distribution, the single stage isolated dc/dc converter are suitable for next-generation electric aircraft system due to soft switching and high power density. In this work, comprehensive static and dynamic characterizations were conducted for the latest 6.5 kV silicon carbide (SiC) MOSFETs from …

Design Of Phase Shifter And True Time Delay Gan Mmics For High-Temperature Ku-Band Applications, Michael Lee Thompson

Graduate Theses and Dissertations

This thesis presents the design, implementation, and high-temperature characterization of Gallium Nitride (GaN) monolithic microwave integrated circuits (MMICs) developed for beamforming and phased-array systems operating in the Ku-band (12-14 GHz). Two integrated circuits were designed: a 3-bit digital phase shifter and a 3-bit true time delay (TTD), both optimized for high linearity, low insertion loss, low phase error, and stable operation at elevated temperatures up to 300°C. Developing both a phase shifter and a TTD enabled a direct comparison of their beamforming performance and the evaluation of beam squint effects over frequency, a critical factor in wideband array design. The …

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 …

Solar Powered Conservation: The Effects Of Solar Photovoltaic Facilities On Avian Community Composition, Michael Christopher Ferrara

Graduate Theses and Dissertations

The expansion of solar photovoltaic energy infrastructure is transforming landscapes worldwide. Increasingly, facilities are planted with native vegetation, yet how vegetation characteristics and landscape variables shape wildlife communities in solar facilities remains poorly understood. Because avian populations have been in decline and are sensitive to habitat changes, understanding how solar facilities influence avian communities is important. In Chapter 1, we used autonomous recording units, local vegetation measurements, and land use and land cover data to quantify how avian community occupancy is influenced by solar cover in solar facilities and comparable reference sites. We used a Bayesian multi-species occupancy model to …

A Study On Utilizing Coherently Coupled Orbital Angular Momentum Beams For Maritime Sensing And Communication, Evan Robertson

All Dissertations

A large portion of the world is covered in water which introduces a couple of key challenges in communication and sensing systems. The impact of particulates in the water will limit the ability to successfully transmit information through the water. The study of how the channel impacts specific frequencies and the ability to transmit more information at a single time can limit the impact of this environment in how it degrades an optical communication system. In sensing applications, it is important to detect information related to an object’s motion which will either be towards or away from a system, or …

Computational Methods For Complex Electromagnetic Geometries And Media, Edgar Bustamante

Open Access Theses & Dissertations

Additive manufacturing has enabled electromagnetic devices with increasingly complex geometries, but existing numerical tools remain limited in their ability to model and design such structures. This dissertation presents two major advancements that address these restrictions in the finite-difference frequency-domain method (FDFD) and the spatially-variant lattice algorithm (SVLA). These two numerical methods provide a foundation for future exploration in the simulation, optimization, and realization of next-generation electromagnetic devices. First, a general bianisotropic FDFD formulation based on the vector wave equation is presented that enables practical modeling of metamaterials using effective medium homogenized parameters rather than explicitly resolving subwavelength metamaterial features. The …

Further Insights Into The Network Link Outlier Factor's (Nlof) Light-Load Penalty, Sunday Oluwaleke Ogundele

Open Access Theses & Dissertations

This research investigates the performance of the Network Link Outlier Factor with Most Likely Links (NLOF:MLL), under varying network load conditions. Earlier studies reported that the NLOF:MLL algorithm experienced a noticeable drop in fault-localization accuracy when operating in lightly loaded networks. To further examine this limitation, 240 experiments were carried out to observe how the algorithm responds as overall network load increases. The evaluation focused on the classification performance metrics: precision, recall, and F1-score. The results show that NLOF:MLL’s effectiveness improves as network load increases but that the rate of improvement slows progressively, eventually stabilizing in a pattern consistent with …

Development And Optimization Of A Deep Level Transient Spectroscopy System, Samuel Ruiz

Open Access Theses & Dissertations

Deep-level transient spectroscopy (DLTS) remains one of the most widely used techniques for identifying electrically active defects that affect leakage current, carrier lifetime, and overall reliability in semiconductor devices. Legacy boxcar or capacitance meter implementations, however, often struggle with limited signal-to-noise ratio, labor-intensive data collection, and poor adaptability across diverse material systems. This thesis presents the design and optimization of a semi-automated, lock-in-amplifier-based DLTS platform. By pairing a Zurich HF2LI with PID-controlled cryogenic sweeps, precision signal generation, and MATLAB driven acquisition and processing scripts, the system will (i) enhance SNR through phase-sensitive detection, programmable low pass filtering, and parasitics calibration; …

Image And Signal Processing Methods For Clinical Decision Support: Reference-Based Lung Segmentation On Chest X-Rays And Ecg-Based Study Of Acute Hyperkalemia Using Machine Learning, Basavarajaiah Shanmukhayya Totada

Open Access Theses & Dissertations

ABSTRACT

Study-I: An Image Processing Pipeline for Reference Guided Lung Region Detection in Chest Radiographs with Shape Similarity Matching Accurate and reliable segmentation of the lung region in chest X-ray (CXR) images is essential for computer-aided diagnosis (CAD) systems, particularly in the early detection and monitoring of lung disorders. Traditional segmentation techniques often rely on manual annotations, limiting scalability and adaptability. The proposed reference-guided approach selects the most similar healthy CXRs dynamically, ensuring flexibility while benefiting from dataset-specific reference images. Building upon prior approaches that utilize shape similarity-based selection and SIFT-flow, this study introduces a fully automated segmentation pipeline that …

Data Center Composite Load Model Parameters’ Tuning, Mohammed Sleiman, Amirreza Sahami, Oluwatimilehin Adeosun, Nathaniel Rice, Katelyn Vance

Electrical and Computer Engineering Faculty Research & Creative Works

The vast expansion of data center campuses has created concentrated and highly dynamic electrical loads that challenge traditional transmission system planning and protection studies. This paper presents a novel approach to model the load of data centers and validate their dynamic performance in a composite-load-model (CMLD) framework.

The data center has three major modules: a static element representing the static devices and busway impedance; an electronic-based element constituting power converters such as rectifiers and inverters, associated power supplies, and a motor-based element encompassing the induction nature of chillers, fans and pumps. Each module represented equivalent algebraic and differential equations depicting …

Data Driven Design Of Ultra High Performance Concrete Prospects And Application, Bryan K. Aylas-Paredes, Taihao Han, Advaith Neithalath, Jie Huang, Ashutosh Goel, Aditya Kumar, Narayanan Neithalath

Electrical and Computer Engineering Faculty Research & Creative Works

Ultra-high-performance concrete (UHPC) is a specialized class of cementitious composites that is increasingly used in various applications, including bridge decks, connections between precast components, piers, columns, overlays, and the repair and strengthening of bridge elements. The mechanical and durability properties of UHPC are significantly influenced by factors such as low water-to-binder ratios, the inclusion of supplementary cementitious materials (SCMs), and fiber reinforcement. Machine learning (ML) has been employed to predict the performance of UHPC and optimize its mixture designs by using various raw materials. This study first provides a comprehensive review of ML applications in UHPC, focusing on predicting workability, …

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 …

Ai-Driven Electromagnetic Design And Performance Prediction Of Microstrip Antennas, Eduardo Javier Vazquez

Theses and Dissertations

This thesis investigates the application of Deep Learning to automate and accelerate microstrip antenna inverse design. The initial investigation was to predict microstrip antenna performance from its geometry parametric input, and found out that forward prediction with adopted geometric representation results in ill-posed scenario, high ambiguity and unstable mapping. The study later mostly focuses on the inverse prediction by machine learning from S11 parameter input to predict antenna patch geometry parameters instead.

A dataset of 5,000 ANSYS HFSS simulated antennas (later filtered to 4,136 valid samples) was generated using cubic spine -described geometry profiles. Multiple neural architectures were adopted for …

Volume Status Detection With Integral Pulse Frequency Modulation Model Of Peripheral Venous Pressure, Jeremiah Rhys Wimer

Graduate Theses and Dissertations

This thesis studies the effectiveness of utilizing a modified integral pulse frequency modulation (IPFM) algorithm to synthesize peripheral venous pressure (PVP) waveforms for the detection of several physiological conditions using logistic regression. PVP waveforms are collected from 18 human patients and 4 porcine subjects. Human data are used to train models for determining the hypovolemic status of the patient, while the porcine data are used to train models for determining level of anesthesia and detecting internal hemorrhaging. In the human dataset, the waveforms collected from the 18 patients are classified into two groups according to serum chloride levels: resuscitated (≥100 …

Extreme Bandgap Recessed-Gate Metal Oxide Semiconductor Heterostructure Field Effect Transistors With Drain Current 0.28 A Mm−1 And Threshold Voltage −1.5 V, Abdullah Al Mamun Mazumder, Abdullah Mamun, Kenneth Stephenson, Kamal Hussain, Tariq Jamil, Grigory Simin, Asif Khan

Faculty Publications

Herein, the first demonstration of hybrid high-k oxide (ZrO2-Al2O3) incorporation into extreme bandgap (EBG) Al_0.87Ga_0.13N/Al_0.64Ga_0.36N metal-oxide-semiconductor heterostructure field-effect transistors (MOSHFETs) is presented, with both planar and recessed-gate designs on the same AlN/sapphire template with a state-of-the-art low contact resistance of 1.4 Ω mm (contact resistivity, ρc ≈ 5.7 × 10−6 Ω cm2). The recessed-gate MOSHFETs achieve a threshold voltage shift of ΔVTH = 5.8 V, highlighting improved channel control. Static output measurements reveal a peak drain current (IDS) of 340 mA mm⁻¹ for the planar …

An Audio Data-Driven Roadway Digital Twin And Its Underlying Framework For A Digitized Transportation Construction Environment, Anisha Deria, Pedro J. Chacon Dominguez, Yong-Cheol Lee, Jin W. Choi

Michigan Tech Publications

The increasing need to build and maintain transportation systems has led project managers to manage multiple projects simultaneously. Roadway projects often entail several miles of job site, making it difficult to keep track of progress and maintenance activities. To improve the situation, this study proposes an audio data-driven roadway digital twin framework for real-time and remote monitoring of construction projects. The latent characteristics of a digital twin required for establishing a digitized work environment were investigated. As a primary method of seamlessly linking virtual and physical environments, audio data classified and analyzed by deep neural network (DNN) has been employed …

Enhancing Grid Integration Of High-Power Loads Through Modular Unfolding-Based Power Conversion With Decentralized Control, Sanat R. Poddar

All Graduate Theses and Dissertations, Fall 2023 to Present

The transition to electric vehicles (EVs) is a critical step toward reducing greenhouse gas emissions and creating a cleaner, more sustainable transportation sector. However, many drivers — particularly those operating larger vehicles such as trucks and buses — face significant challenges, including limited driving range and lengthy charging times. High-power, fast-charging stations, which can rapidly replenish EV batteries, are therefore essential for mitigating these barriers and making electric transportation practical across all vehicle classes.

Despite the promise of electric vehicles, deploying high-power fast-charging stations presents significant challenges. They require large amounts of electricity, making them expensive to install, operate, and …

Design, Control, And Optimization Of Unfolding-Based Ac-Dc Topologies With Three-Port Resonant Converters For Electric Vehicle Battery Charging Applications, Aditya Zade

All Graduate Theses and Dissertations, Fall 2023 to Present

The global effort to reduce greenhouse gas emissions and reliance on fossil fuels has made electric mobility a cornerstone of sustainable transportation. This transition is driving demand for advanced charging infrastructure and more efficient power conversion systems. Power converters play a central role, not only in enabling reliable EV charging but also in integrating renewable energy sources with the grid. Because of the large amount of power involved, these converters must operate efficiently, reliably, and at low cost. Conventional high-power chargers often use two stages of conversion, which are effective but limited by size and energy losses. To overcome these …

Complementary Color Laser Illumination For Perceptual Contrast Enhancement In Structurally Colored Samples, Tomoshree Dash

All Theses

Structural color, a phenomenon arising from nanoscale interaction between light and materials, has the potential to unveil biological, chemical, physical, and mechanical characteristics of the sample. It enables direct visualization of the sample by human users with high spatiotemporal resolution. However, its impact is often constrained by challenges in the perceptual differentiation of subtle color variations. This thesis introduces complementary color laser illumination (C²LI) as an imaging technique that enhances color perception of the Human Visual System (HVS) by accessing the psychophysical non-linearities in chromatic color perception. C²LI offers a platform optimized for the HVS by …

Editable 4d Gaussian Splatting: Scalable And Consistent Video Editing Via Uv-Texture Decomposition, Shuai Lyu

All Theses

Dynamic 3D videos are now widely used in VR, AR, and telepresence, where users often want to change the style or appearance of objects such as clothing over a whole sequence. However, editing such dynamic 3D content is still hard, because appearance, geometry, and motion are tightly coupled, so even a simple color change must stay consistent in all views and at all time steps. Existing editing methods based on 4D Gaussian Splatting often work in a frame-by-frame way, which is slow and makes it difficult to keep temporal and multi-view consistency.

This thesis proposes Editable-4DGS, a representation-centric framework that …

Lstm Network-Based Scheme For Automatic Characterization Of Power Quality Disturbances, Akram Elmitwally, Mohamed Nader

Mansoura Engineering Journal

Recognition of power quality (PQ) troubles is a critical task in the electrical power industry. Most previous works solve the classification problem using separate feature extraction phase and classification phase. Each phase has its own techniques, and consumes a computation time. This study proposes to utilize the long short-term memory (LSTM) network as a deep learning model to classify the PQ events in one shot. The LSTM network uses its particular processing to classify a PQ event signal directly by reading its time-sequence data. Then, a dedicated post-classification algorithm (PCA) extracts start time, end time, duration, amplitude, and total harmonic …