Digital Commons Network^™

Automated Generation Of Malware Metadata Signatures, Joel Schott

Masters Theses

In advanced, targeted malware attacks, the custom software tools used to package and send malicious files and messages can lead to distinctive metadata values that facilitate creation of a malware metadata signature. Manual creation of these signatures requires expert domain knowledge and is time-consuming and error-prone. Our goal is to automate this process. We created several methods of automatically generating malware metadata signatures for ZIP files and emails. We evaluated these methods by comparing signatures generated with these methods to existing expert-created signatures. We found automated methods for ZIP files and emails that are capable of generating metadata signatures that …

Fetal Acidosis Prediction Using Attention Enhanced Convolutional Neural Networks, Anusha Adhikari

Masters Theses

This study explores the integration of spectral mixtures of fetal heart rate (FHR) and uterine contraction (UC) signals to enhance the prediction of fetal acidosis, utilizing the CTU-CHB dataset. Several classification models were trained using two distinct oversampling techniques and inputs, demonstrating that models incorporating spectral mixtures significantly outperform those using raw signals. These models, particularly when combined with convolutional neural networks (CNNs) and attention mechanisms, achieved a notable F1-score of 0.98, with the highest model achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.95. The research employs a variety of techniques including short-time Fourier transform and …

Emi Mitigation, Material Characterization, Transformer Equivalent Circuit Modeling, Reza Vahdani

Masters Theses

Modern high-frequency electronic systems demand precise characterization and modeling techniques to ensure signal integrity and electromagnetic compatibility. This thesis presents three core studies focused on real-world challenges in high-speed and power electronics: EMI mitigation using 3D printed absorbers, wideband liquid dielectric characterization, and accurate transformer modeling.

The first study demonstrates a targeted approach to mitigating electromagnetic interference (EMI) in a commercial router. By using holography imaging to identify radiation hotspots, custom absorber structures were designed with commercially available materials, fabricated via 3D printing, and applied directly to emission sources. Radiated emission tests in a reverberation chamber showed up to 9 …

Ground Testing Of Cryogenic Environments In Vacuum Conditions, Lucas A. Scott

Masters Theses

The environment in the vacuum of space is unforgiving and ever-changing. Not only facing the lack of a protective atmosphere, but powerful radiation and extreme temperature ranges as well. Equipment destined for space must be hardened to withstand them. Just launching satellites that are theoretically able to survive these conditions is unfeasibly costly in the event of failure. Because of this, spacecraft must be tested in a simulated environment that can match the harsh climate in space. Therefore, it is the goal of the Gas and Plasma Dynamics Lab's (GPDL) to test and prove specific methods of recreating these conditions. …

Performance Of Standard Medical Mllms On Ecg Image Data, Prisha Anil

Masters Theses

This work presents a structured benchmarking study of multimodal large language models (MLLMs) applied to electrocardiogram (ECG) interpretation tasks. We evaluate three representative architectures: MedGemma, HuatuoGPT-Vision, and LLaVA-Med, across progressive experimental stages involving text-only structured prompt normalization, text–image fusion with ECG plots, and full multimodal fusion incorporating time-series signals. A standardized five-section cardiology prompt was designed to enforce consistent output structure and SCP-code alignment, enabling reproducible metric computation across models. Quantitative evaluation using BERTScore, token-level F1, and diagnostic accuracy demonstrates that HuatuoGPT-Vision achieves the highest semantic and diagnostic alignment, while MedGemma exhibits superior formatting stability and reproducibility. In contrast, LLaVA-Med …

Development Of Dual-Scan Nuclear Magnetic Resonance (Nmr) Pulse Programs For Spin-Lattice Relaxation Measurements, Zachary Mayes

Doctoral Dissertations

This research develops and refines the Split Inversion Pulse and Recovery (SIP R) methodology for spin-lattice relaxation (T₁) measurements in NMR spectroscopy. SIP R introduces a two-scan difference technique using a split inversion pulse sequence, where the 180° pulse is split into two 90° pulses phase-shifted with respect to each other. This approach simplifies data fitting, requiring only two parameters to extract T₁ values, compared to the traditional inversion-recovery method, which needs three. The SIP-R-DS and SIP-R-S adaptations extend this framework by incorporating selective NMR resonance excitations, enhancing its applicability to cross-polarization experiments and enabling unobstructed NOE measurements. These adaptations …

Mid-Field Shock And Impulse Estimation Methods For Blast Loading On Tall Targets, Ethan Allan Steward

Doctoral Dissertations

Blast resistant structural design continues to be a major research area for governments around the world due to explosive threats from both state and non-state actors. Many of the typical targets of explosive attacks, such as government buildings, commercial high rise office buildings, and apartment complexes are mid to high-occupancy buildings that present a tall profile relative to the charge size and are often clad in curtain walls. In blast resistant structural design, the origin of a shock wave is typically assumed to be in the far-field, creating a wave that is nearly planar and parallel to at least one …

Advancing Thermodynamic Modeling In Materials Design: From Phase Stability In Metallic Systems To Ferroelectric Property Prediction In Functional Oxide, Kyaw Hla Saing Chak

Doctoral Dissertations

This dissertation advances the CALPHAD (CALculation of PHase Diagrams) approach for thermodynamic modeling which often lacks sufficient description of crystal lattices for critical phases in multicomponent system. Additionally, CALPHAD does not consider the structural features and its connection with functional properties for functional materials. To address these issues, a novel dual-ordered sublattice model for the κ-phase in Fe-Al-C system, (Fe,Al)3(Fe,Al)1(C,Va)1(C,Va)3, is introduced that improves predictions of equilibrium compositions and phase stability by accounting for both substitutional and interstitial ordering. For the Fe-B-C system, new sublattice formulations for FCC [(Fe)1(C,B,Va)1] and BCC [(Fe,B)1(C,B,Va)3], phases enhance boron solubility predictions and reveal insights …

Ai-Powered Image-Based Assessment Of Pressure Injuries Using You Only Look Once (Yolo) Version 8 Models, Mehedi Hasan Tusar, Fateme Fayyazbakhsh, Niloofar Zendehdel, Eduard Mochalin, Igor Melnychuk, Lisa Gould, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Objective: The primary objective of this study is to enhance the detection and staging of pressure injuries using machine learning capabilities for precise image analysis. This study explores the application of the You Only Look Once version 8 (YOLOv8) deep learning model for pressure injury staging. Approach: We prepared a high-quality, publicly available dataset to evaluate different variants of YOLOv8 (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and five optimizers (Adam, AdamW, NAdam, RAdam, and stochastic gradient descent) to determine the most effective configuration. We followed a simulation-based research approach, which is an extension of the Consolidated Standards of Reporting Trials …

Hands-Free Uav Control: Real-Time Eye Movement Detection Using Eog And Lstm Networks, Niloofar Zendehdel, Khosro Ghorbani Zadeh, Haodong Chen, Yun Seong Song, Ming C. Leu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Industry 4.0 has created a growing need for effective human-robot collaboration (HRC). As robots and humans work more closely together, efficient communication becomes essential for coordinating their actions seamlessly. While speech may seem like the obvious choice for communication, noisy factory environments can render it impractical. Additionally, workers often have their hands occupied with assembly tasks, making hand-controlled interfaces less practical for controlling robots. To address these challenges, this paper presents a novel, hands-free method for robot control using electrooculography (EOG) signals–specifically, eye movements and blinks–with unmanned aerial vehicles (UAVs) used as the demonstration platform. We developed a real-time system …

Deep Reinforcement Learning-Based Optimal Takeoff Trajectory Design Of An Evtol Drone, Nathan M. Roberts, Bingling Huang, Xiaosong Du

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The continuing development of electric vertical take-off and landing (eVTOL) aircraft presents promising opportunities to alleviate transportation congestion. However, designing and executing takeoff trajectories that optimally balance energy usage, passenger comfort, and aircraft constraints remains challenging. Conventional design optimization provides a solution for pre-defined flight conditions and environments but is not ideal in real-world applications. In contrast, deep reinforcement learning (DRL) implements optimal policy making real-time decisions with no assumption of mathematical models. Seeing the lack of literature on DRL-based takeoff trajectory design of eVTOL aircraft, we implement and conduct DRL-based optimal takeoff trajectory designs for the Airbus³ Vahana …

An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells, Amir Hasan Babanezhad, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a multiphase high step-up interleaved converter is introduced, which ensures low-input current ripple, low-component currents, and voltage stresses. The proposed circuit guarantees ZVS operation for power switches and ZCS operation for power diodes, which significantly reduce converter switching losses and EMI emission and improve its efficiency. Therefore, its passive components volume can be reduced by using high switching frequencies. In addition, the interleaved technique reduces input current ripple and input filter volume and provides high-power density. High-voltage gain and low-voltage stresses on the components are also achieved due to the integration of the converter structure with the …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Phase-Variation Microwave Resonator For Highly Sensitive Dynamic Sensing, Chen Zhu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

High-precision dynamic sensing is critical in fields, such as industrial automation, structural health monitoring, and environmental sensing, where real-time responses to minuscule changes can prevent system failures or optimize performance. In this work, we introduce and demonstrate a phase-variation coaxial cable resonator (CCR) as a highly sensitive sensor for dynamic sensing applications. As a proof of concept, a prototype device based on a custom-designed CCR is thoroughly investigated for dynamic displacement measurements, as displacement is a fundamental quantity essential to numerous applications. The sensor consists of two components: a static CCR device and a movable conducting plate. As the conducting …

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …

Advancing Temperature Monitoring Of The Bottom Anode In A Direct Current Electric Arc Furnace Operations With Distributed Optical Fiber Sensors., Ogbole Collins Inalegwu, Rony Kumer Saha, Yeshwanth Reddy Mekala, Farhan Mumtaz, Nicholas Dionise, Zane Voss, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The bottom anode in the Direct Current Electric Arc Furnace (DC EAF) is critical for completing the electrical circuit necessary for sustaining the arc within the furnace. For pin-type bottom anodes, monitoring of the temperature of select pins instrumented with thermocouples is performed to track bottom wear in the EAF and inform the operator when the furnace should be removed from service. This work presents the results from a plant trial using distributed temperature monitoring of bottom anode pins in a 165-ton DC EAF over a two-month service period utilizing two optical fiber sensing techniques: fiber Bragg grating (FBG) and …

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Comparison Of Cnn And Lstm Networks On Human Intention Prediction In Physical Human-Robot Interactions, Khosro Ghorbani Zadeh, Niloofar Zendehdel, George L. Holmes, Keyri Moreno Bonnett, Amy Costa, Devin Michael Burns, Ming-Chuan Leu, Yun Seong Song

Psychological Science Faculty Research & Creative Works

Advancements in robotics and AI have increased the demand for interactive robots in healthcare and assistive applications. However, ensuring safe and effective physical human-robot interactions (pHRIs) remains challenging due to the sophistication of human motor communication and intent recognition. Traditional physics-based models struggle to capture the dynamic nature of human force interactions, limiting robot adaptability. To address these limitations, neural networks (NNs) have been explored for force-movement intention prediction. While multi-layer perceptron (MLP) networks show potential, they struggle with temporal dependencies and generalization. Long Short-Term Memory (LSTM) networks effectively model sequential dependencies, while Convolutional Neural Networks (CNNs) enhance spatial feature …

Empirical Study Of Far-Field Radio Frequency Wireless Power Transfer Testbed For Underground Mines Based On 1.8 Ghz, Anabi Hilary Kelechi, Samuel Frimpong

Mining Engineering Faculty Research & Creative Works

A novel 1.8 GHz far-field wireless power transfer (WPT) testbed was designed to achieve long-distance energy harvesting in underground mine (UM). Prior experimental studies confirm that underground mines experience 50 dB radio frequency (RF) signal attenuation compared to a typical wireless environment. Due to this severe signal attenuation, the currently proposed radio frequency energy harvesting (RF-EH) testbed cannot be deployed in underground mines. In this study, we proposed, designed and performed an empirical study with a military-grade class A radio frequency wireless power transfer (RF-WPT) testbed. The various digital modulation schemes experimented with included: BPSK, DBPSK, QPSK, OQPSK, Pi4DQPSK, 8PSK, …

Predictive Modeling For Optimal Gel Treatment Design In Brownfields Using Ensemble Machine Learning And Data Upsampling Via Generative Ai, Munqith Aldhaheri, Baojun Bai, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Efficiently designed gel treatments play a vital role in extending the lifespan of brownfields through rejuvenating oil production. Recently, a three-mode mathematical methodology named the VCR approach has been proposed for designing effective treatments. To optimize this approach, it is crucial to determine the appropriate design mode systematically rather than relying solely on the intuitive judgments of field operators. This study introduces an advanced methodology for predicting the optimal design type of gel treatments using 12 reservoir and production variables. The methodology integrates ensemble machine-learning (EML) models with historical data from 65 field projects across 11 countries (1985-2020). The Random …

Characterization And Leaching Feasibility Studies Of Copper Flue Dust For The Recovery Of Main And Trace Metals, Fardis Nakhaei, Marek Locmelis, Lana Alagha, Michael S. Moats, Carlos Eyzaguirre, Cory Smith

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Although copper smelter dust (CSD) is classified as hazardous waste, it also serves as a valuable secondary resource, offering potential for the recovery of critical elements. In this study, CSD samples were collected from the waste heat boiler (WHB) and electrostatic precipitator (ESP) units connected to the flash smelting furnace at a copper smelter. The representative samples were extensively characterized by particle size, and chemical and mineralogical analyses. Following sample characterization, leaching experiments were performed to evaluate the potential extraction of Cu, Fe, As, Zn, Pb, In, Ga, and Ge from the flue dusts. Distilled water, H2SO4, and HCl were …

Two-Component Droplet Phases And Their Spectral Stability In One Dimension, E. G. Charalampidis, S. I. Mistakidis

Physics Faculty Research & Creative Works

We unravel the existence and stability properties of one-dimensional droplets arising in genuine two-component particle-imbalanced bosonic mixtures under the influence of a weak harmonic confinement. A plethora of miscible droplet phases is found with the majority component atoms in the vicinity of the minority ones assembling in a droplet configuration, while the remaining excess atoms are in the gas state. Our computations identify a transition from a bound to a trapped gas many-body state for increasing total atom number and fixed interactions, with the bound character being prolonged for stronger intercomponent attractions. The spectral stability of these two-component droplets is …

Students’ Perceptions Of Self And Peers Predict Self-Reports Of Cheating, Amber M. Henslee, Luke Settles, Sara E. Johnson, Gayla R. Olbricht

Psychological Science Faculty Research & Creative Works

Academic dishonesty and how to address it are common concerns across higher education disciplines, but engineering students admit to higher rates of academic dishonesty than other students. However, first-year students may be particularly receptive to prevention efforts. Considering self-perception, social norming, and behavioral choice theories, we hypothesized that 1.) Students who perceived themself as ethical and more knowledgeable of the consequences for misconduct would be less likely to self-report cheating and 2.) Students who perceived cheating and plagiarism to be common would be more likely to self-report cheating. For this study, freshmen engineering students (N=703) reported their self-perception, perception of …

How Do Human And Ai Gender Bias Interact In Hiring Decisions?, Eyuel Getahun, Daniel Burton Shank, Casey I. Canfield, Jessica L. Cundiff, Jenny L. Davis, Celia Freed

Psychological Science Faculty Research & Creative Works

The hiring process is crucial for organizational success but has long been troubled by human biases. Many organizations now include AI in their hiring protocols to mitigate these biases and increase efficiency. However, AI itself can have biases baked-in. Human biases and AI biases are distinct but related; here, we examine how human and AI biases interact to affect hiring outcomes. Through an online experiment, we examine this question in the context of gendered hiring for a male-dominated leadership position in electrical engineering. The study tests how elevated and depressed AI recommendations for male and female job candidates affect participant …

Elucidating The Role Of Hydrogen At C-In2o3/A-In2o3−X Interface, Julia E. Medvedeva, Mariana I. Bertoni

Physics Faculty Research & Creative Works

Ab initio molecular dynamics simulations and hybrid functional electronic structure calculations are employed to determine the formation, the structural and electronic properties, and the dynamics of covalent (In-OH) and ionic (In-H-In) hydrogen defects at crystalline-In2O3/amorphous-In2O3−x interface. This comprehensive computational study considers (i) various interstitial and substitutional hydrogen site locations within the crystalline, amorphous, and interfacial regions; (ii) several oxygen-to-hydrogen ratios; and (iii) possible defect charged states. The results reveal hydrogen's inability to fully passivate the undercoordinated under-shared in atoms in amorphous highly substoichiometric oxide, giving rise to the formation of deep electron traps even in net-charge neutral structures. These trap …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …

Development, Characterization And Testing Of Traditonal And Advanced Nuclear Fuel Cladding Materials, Joshua Eddy Rittenhouse

Doctoral Dissertations

Kanthal D and FeCrAl alloys in general, are prospective candidates as accident tolerant nuclear fuel cladding materials. The work presented herein focuses on applying two techniques of severe plastic deformation, equal channel angular pressing (ECAP) and high-pressure torsion (HPT), as means of grain refinement to improve irradiation resistance. Samples of as-received, ECAP, and HPT processed Kanthal D were exposed to neutron irradiation to a dose of 2 DPA at two different temperatures, 300 °C and 500 °C. Detailed characterization was performed including mechanical and microstructural, and several positive improvements with regards to irradiation resistance were identified in the ECAP and …

Nmr Relaxometry Approach For The Detection Of Asphalt Binder Performance Properties, Rebecca Herndon

Doctoral Dissertations

"Asphalt is a necessary material for transportation infrastructure. Understanding the material properties of asphalt binders is critical for improving infrastructure and enhancing the lifetime of pavements. While the physical properties of asphalt binders are well-defined by standard procedures and parameters, the chemical environment lacks definition. Physical parameters like viscosity and stiffness are traditionally used to differentiate asphalt binders, describe aging, and indicate the impact of additives. However, these tests have limited insight into the material's chemical environment. Therefore, a nondestructive analytical technique, nuclear magnetic resonance (NMR) relaxometry, was employed to investigate the chemical environment of asphalt binders. Although NMR relaxometry …