Engineering Commons^™

Dual Quaternions For Gravity Recovery Missions, Ryan Kinzie

Doctoral Dissertations and Master's Theses

A dual quaternion-based modeling, state estimation and control approach is introduced as a better alternative to the traditional methods which are currently utilized for gravity recovery missions. The proposed modeling and control approach was verified against and compared to the tangent bundle to Special Euclidean Group 3 through MATLAB simulations. The dual quaternion-based approach shows superior performance over traditional linearized and uncoupled methodologies, in both modeling accuracy of spacecraft translational position, and the ability to control the pose of a test mass relative to its host spacecraft. Utilizing data products from the Gravity Recovery and Climate Experiment Follow-On mission, a …

Developing Fixed-Bias Langmuir Probes For Multi-Point Constellation Deployments, Henry Carter Valentine

Doctoral Dissertations and Master's Theses

Since their initial development in the early 20th century, electrostatic Langmuir probes have proved invaluable in terrestrial and interplanetary ionospheric sounding applications. When deployed aboard rocket and satellite platforms, these probes yield high-cadence, in-situ measurements of key plasma parameters such as electron density, ion density, and electron temperature. Furthermore, the efficacy of Langmuir probes in characterizing the three-dimensional structure and dynamics of ionospheric plasmas can be augmented by the technique of multi-payload deployments. In this work, we discuss the design, development, and analysis of fixed-bias Langmuir probes constructed for two multi-point science campaigns: the Mars-bound, dual-satellite Escape and Plasma Acceleration …

Framework For Integrating Industry Knowledge Into A Large Language Model To Assist Construction Cost Estimation, Prashnna Ghimire

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

The construction industry generates a large amount of data across projects produced by digital devices, tools, and methods, and this volume is rapidly increasing. However, the industry lags behind in adopting data-driven technologies. On the other hand, the rapid advancement of generative AI (GenAI) in recent years, especially state-of-the-art large language models (LLMs), shows great potential and has been increasingly adopted in many industries; however, the construction industry is behind in adoption. While academic studies have proposed various machine learning applications for construction, industry implementation has lagged due to a disconnect between these proof-of-concept developments and practical industry needs. Also, …

Effects Of High Temperature Compaction On 3d Printed Carbon Fiber Reinforced Polymer Composites, John D. Barber

Mechanical & Aerospace Engineering Theses & Dissertations

The aim of this dissertation was to study the effects of hot temperature compaction (HTC) upon the polymorphism and the mechanical behavior of an additively manufactured (AM), carbon fiber reinforced polyamide (PA6). Different pressure and temperature levels during HTC were tested to determine the overall effect upon the mechanical behavior and the material crystalline composition. Treated, carbon fiber reinforced PA6 samples were analyzed using differential scanning calorimetry, x-ray diffraction, thermogravimetric analysis, scanning electron microscopy, double cantilever beam testing and three-point bending testing. When considered with respect to as-printed samples, an HTC temperature of 190°C combined with 80 psi pressure resulted …

From Image Enhancement To Model Protection Integrating Generative Ai And Secure Learning In Computer Vision, Mohammad Shahab Uddin

Electrical & Computer Engineering Theses & Dissertations

This dissertation aims to address critical challenges in the field of computer vision and machine learning, focusing on three key areas: image translation, denoising, and model security. The research encompasses novel methodologies and models that significantly advance existing techniques. This dissertation will not only provide valuable contributions to the academic community but also hold significant potential for practical applications in domains ranging from surveillance to autonomous systems.

Consequently, this dissertation proposes three goals. First, we present new approaches for converting optical videos to infrared videos using deep learning. To apply powerful deep learning based algorithms for object detection and classification …

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

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 …

Valorization Of Industrial Wastes To Low-Carbon Minerals, Nandan Nag

Civil & Environmental Engineering Theses & Dissertations

This study concentrates on developing methods for utilizing industrial waste materials such as red mud and phosphate slag. In this study, an electrochemical process was developed for recovering iron metal powder from red mud. Red mud is a waste material from aluminum refineries. Although red mud can be utilized as a construction and building material, its use is becoming controversial due to its high metal content, which contributes to its toxicity. However, the high iron oxide content in red mud makes it a good feedstock for producing iron. In this project, we employed an electrochemical technique for extracting iron powder …

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 …

A Formal Simulation Model For Discrete Rate Simulation, Thomas J. Tracey

Electrical & Computer Engineering Theses & Dissertations

Simulation is an essential tool for virtualizing systems by creating a representative model of real or hypothetical systems and observing how they change over time. Two predominant simulation paradigms include Discrete Event Simulation (DES) and Continuous Simulation, which both have their strengths and weaknesses. DES does not handle continuous state variables, while continuous simulation handles continuous state variables but encounters errors where these variables have discrete changes in their behavior. This difficulty between the two predominant simulation paradigms prompted the creation of a new simulation paradigm to cover this gap: Discrete Rate Simulation (DRS). DRS as a simulation paradigm focuses …

Low Dissolved Oxygen Operation Enhances Biological Phosphorus Removal In Wastewater Treatment, Haley Morgan

Civil & Environmental Engineering Theses & Dissertations

Biological phosphorus (bio-P) removal occurs when bacteria are exposed to anaerobic/aerobic conditions that stimulate polyphosphate-accumulating organisms (PAOs) to uptake phosphorus. In most treatment plants, dissolved oxygen (DO) concentrations in the aeration tanks are commonly at or above 2.0 mg O₂/L, which is often referred to as the most optimal aeration condition for bio-P. If bio-P can happen efficiently at lower DO (< 0.3 mg O₂/L), then the energy demand of aeration can be decreased appreciably. This research was conducted in a biological nutrient removal (BNR) pilot plant to assess the performance of bio-P at progressively lower DO operation. This …

Mechanochemical Reaction Kinetics—A Theoretical Modeling And Experimental Study, Maria Dolores Masso Ramirez

Mechanical & Aerospace Engineering Theses & Dissertations

Mechanochemical reactions represent a class of chemical processes triggered by mechanical energy, widely recognized for their efficiency, solvent-free nature, and environmental benefits. They are increasingly applied in synthetic chemistry, a branch of science focused on creating new chemical compounds through controlled laboratory reactions. However, the mechanisms underlying mechanical activation remain poorly understood, primarily due to the difficulty in quantifying energy transfer during milling, where solid reactants are ground together using a ball mill. This thesis aims to bridge the gap in understanding the relationship between mechanical parameters and reaction kinetics by developing and validating a theoretical framework based on dimensional …

On Generalization Across Environments In Multi-Objective Reinforcement Learning, Jayden Jing Xiang Teoh, Pradeep Varakantham, Peter Vamplew

Research Collection School Of Computing and Information Systems

No abstract provided.

Dual Operation Aggregation Graph Neural Networks For Solving Flexible Job-Shop Scheduling Problem With Reinforcement Learning, Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang

Research Collection School Of Computing and Information Systems

With the widespread adoption of Internet Protocol (IP) communication technology and web-based platforms, cloud manufacturing has become a significant hallmark of Industry 4.0. Integrating graph algorithms into these web-enabled environments is crucial as they facilitate the representation and analysis of complex relationships in manufacturing processes, enabling efficient decision-making and adaptability in dynamic environments. As a key scheduling problem in cloud manufacturing, the flexible job-shop scheduling problem (FJSP) finds extensive applications in real-world scenarios. However, traditional FJSP-solving methods struggle to meet the efficiency and adaptability demands of cloud manufacturing due to generalization issues and excessive computational time, while reinforcement learning-based methods …

Learning-Guided Bi-Objective Evolutionary Optimization For Green Municipal Waste Collection Vehicle Routing, Shubing Liao, Yixin Xu, Yunyun Niu, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Waste management has emerged as a critical issue in modern society, where vehicles are scheduled to visit multiple locations for waste collection and transport. This study focuses on a key problem in waste management: route optimization of waste collection vehicles, and formulate it as a bi-objective vehicle routing problem with stochastic demand (VRPSD), aiming to minimizing both total costs and carbon emissions. Although previous studies have significantly advanced our understanding of solving similar problems, the lack of real-world data and limited problem-solving capabilities still restrict the practical applicability of existing methods. To bridge this research gap, this study designed a …

Josephson Junctions: Fabrication And Applications For The Axion Dark Matter Experiment, Jonah M. Sachs

Senior Honors Papers / Undergraduate Theses

The observation of axions could revolutionize the world of physics. Through microwave frequency cavity readout of the photons associated with these axions, the ADMX project at WashU utilizes multiple different forms of the Josephson junction (JJ), a superconductive circuit element. The physics behind the JJ are essential to understanding its operation for resonant cavity readout in addition to parametric amplification. Parametric amplifiers produced using JJs can approach the signal-to-noise ratio set by quantum mechanics, and prove essential for the amplification chain used by the ADMX experiment for axionic detection. The limits of these amplifiers are set by the noise tuning …

Nanosecond Pulsed Electric Field And Plasma Jets For Cancer Therapy, Edwin Ayobami Oshin

Biomedical Engineering Theses & Dissertations

Nanosecond pulsed electric field (nsPEF) employs nanosecond-duration, high voltage pulses to induce oxidative stress, leading to temporary or permanent damage to cells or tissue (also known as reversible and irreversible electroporation), and has been considered a promising approach for cancer therapy. In parallel to this, nanosecond pulsed atmospheric pressure plasma jets (ns-APPJs) have also shown to be effective in inactivating cancer cells or increasing sensitivity of cells to electric fields. ns-APPJs are known to generate reactive chemical agents, including reactive oxygen and nitrogen species (RONS) which induces oxidative stress resulting in cell proliferation, apoptosis, and necrosis. In this dissertation, a …

Predicting Battery Efficiency: A Theoretical Approach, Haiti Schafers

SACAD: Scholarly Activities

The Kansas ElectroRally Races continuously demand faster electric vehicles (EV). When designing a vehicle one of the most important components is the battery. This study was designed to find the following; What is the optimal throttle percentage to run a 36V 20Ah (768Wh) LiFePO4 Rechargeable Battery Pack[1].

The optimal throttle percentage was found with methods detailed in a different section. Based on the performance of the older cars this study choose to focus on 50% and 70%, while obtaining other relevant data to further that inference.

Estimating Snow Coverage Percentage On Solar Panels Using Drone Imagery And Machine Learning For Enhanced Energy Efficiency, Ashraf Saleem, Ali Awad, Amna Mazen, Zoe Mazurkiewicz, Ana Dyreson

Michigan Tech Publications

Snow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a …

Painting Rich Six-Dimensional Pictures Using Polarized Fluorescence Microscopy, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

No abstract provided.

03.31.2025 Ored Connect, Liz Williamson

ORED Newsletter

Indirect Cost Rate Agreement

NIH Transition to Common Forms

NIH Implementation of New Initiatives and Policies

Automated Data Analysis For Concussion Patient Records: A Flutter-Based Desktop Application, Fhaheem Tadamarry

USF Tampa Graduate Theses and Dissertations

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The …

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …

Recent Advances In Non-Enzymatic Electrochemical Sensors For Theophylline Detection, Gustria Ernis, Yulia M T A Putri, Muhammad Iqbal Syauqi, Prastika Krisma Jiwanti, Yeni Wahyuni Hartati, Takeshi Kondo, Qonita Kurnia Anjani, Jarnuzi Gunlazuardi

Journal of Electrochemistry

Detection of target analytes at low concentrations is significant in various fields, including pharmaceuticals, healthcare, and environmental protection. Theophylline (TP), a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma, bronchitis, and emphysema, has a narrow therapeutic window with a safe plasma concentration ranging from 55.5–111.0 μmol·L^–1 in adults. Accurate monitoring of TP levels is essential because too low or too high can cause serious side effects. In this regard, non-enzymatic electrochemical sensors offer a practical solution with rapidity, portability, and high sensitivity. This article aims to provide a comprehensive review of the recent developments …

Study On Dopamine Electrochemical Sensing Based On Au@Mos2, Ning An, Ni Su, Xin-Ran Li, Jian-Yu Liu, Qi-Yan Wang

Journal of Electrochemistry

Dopamine (DA) is a vital neurotransmitter, and accurate detection of its concentration is critical for both clinical diagnostics and neuroscience research. Due to its electrochemical activity, DA is commonly detected using electrochemical methods, which are favored for their simplicity, fast response time, and suitability for in vivo analysis. In this work, a highly sensitive DA electrochemical sensor was developed using an Au@MoS₂ composite, created by modifying molybdenum disulfide (MoS₂) nanosheets with gold nanoparticles through HAuCl₄ reduction, and it was aimed at enhancing DA adsorption and improving detection performance. Scanning Electron Microscopy (SEM), transmission electron microscopy (TEM), …

Exploring Corona Discharge In Electrostatic Printing For Electronics And In Other Application, Zijian Weng

USF Tampa Graduate Theses and Dissertations

The field of printed electronics (PEs) is expanding rapidly, driven by increasing market demand. According to reports, the global market for PEs is expected to reach $23 billion by 2026, with a compound annual growth rate of 18.3% from 2023 to 2028. This growth necessitates the development of innovative printing technologies that can meet these demands efficiently, delivering high-quality, high-resolution PEs suitable for applications in health, environmental monitoring, sports, aerospace, and more.

Currently, PEs manufacturing relies on two main groups of printing technologies. Contact printing methods offer high-speed production but struggle with precision, while non-contact methods provide high resolution but …

Robust Spacecraft Autonomy For Deep Space Exploration In Special Euclidean Group Se(3), Matthew Wittal

Doctoral Dissertations and Master's Theses

Over the past half-century, humanity has gained extensive experience conducting manned spaceflight near Earth. Arguably, "near Earth" could even include the Moon — the most distant destination humans have reached. However, "near" in this work primarily refers low Earth orbit (LEO). One could argue that we have not truly left Earth since the Apollo, as spacecraft in some LEOs remain subject to atmospheric drag thus emphasizing their continued connection to Earth's immediate environment. Reflecting on this, it becomes clear that humanity has largely remained bound to Earth’s immediate vicinity since the Apollo missions reached the Moon. However, that is set …

In-Situ Electrodeposition Of Feco-Mof On Au Ultramicroelectrode For Highly Sensitive Detection Of Epinephrine, Yan Chen, Shang Jian, Si-Yu Wan, Xiao-Tong Cui, Zhong-Gang Liu, Zheng Guo

Journal of Electrochemistry

Metal-organic framework (MOF) nanostructures have emerged as a prominent class of materials in the advancement of electrochemical sensors. The rational design of bimetallic MOF-functionalized microelectrode is of importance for improving the electrochemical performance but still in great challenge. In this work, the bimetallic FeCo-MOF nanostructures were assembled onto a gold disk ultramicroelectrode (Au UME, 5.2 μm in diameter) via an in-situ electrodeposition method, which enhanced the sensitive detection of epinephrine (EP). The in-situ electrodeposited FeCo-MOF exhibited a characteristic nanoflower-like morphology and was uniformly dispersed on the Au UME. The FeCo-MOF/Au UME demonstrated excellent electrochemical performance on the detection of EP …

Assessing Groundwater Availability And Land Subsidence Risk Associated With Anthropogenic Activities In A Complex Aquifer System, Aya Bakr Attya Mohamed

LSU Doctoral Dissertations

The Southern Hills aquifer system (SHAS) is the primary source of water in the Capital area of Louisiana. However, the overexploitation of freshwater-bearing aquifers and the presence of two active faults, the BR fault and the DSS fault, have resulted in groundwater depletion, saltwater intrusion, and increased the risk of land surface subsidence. Such critical issues pose substantial threats to groundwater availability and exert detrimental effects on the environment and urban infrastructures. This dissertation presents a comprehensive investigation of the SHAS for recognizing the consequences of groundwater depletion and its potential long-term impacts on groundwater availability and land stability. A …

The Asset Management Optimization Engine: An Ai And Machine Learning Model Approach To Pavement Asset Management, Matt Versdahl

USF Tampa Graduate Theses and Dissertations

While state Departments of Transportation (DOT) face major funding challenges, the need to find optimal ways to preserve and maintain pavement assets remains. Asset management employs a lowest cost lifecycle method to analyze asset costs and determine the best investment strategies to preserve it throughout its lifecycle. As new technology emerges, so do opportunities to leverage it. DOTs collect a significant amount of performance data on pavement and use it to decide how to keep it in a state of good repair. The literature in this area focuses on engineering techniques applied to treatment strategies. This dissertation research focuses on …