Digital Commons Network^™

Power Flow Control Of The Triple Active Bridge Converter, Lauryn Reece Morris

Masters Theses

With the rise of renewable energies, electric vehicles, and microgrids, the development of power electronic topologies that can seamlessly integrate these systems into the grid is crucial. The triple active bridge is an extension of the dual active bridge topology and is capable of energy storage or renewable energy integration into a power electronic converter and thus the power grid. Consisting of three H-Bridge converters, the triple active bridge topology is an expanding area of research to meet these growing demands. With the addition of the third bridge, the system has additional nonlinear characteristics which introduce complexity in solving for …

Critical Behavior Ofthedilutedquasi-Onedimensionalquantum Ising Model, Logan Bradley Sowadski

Masters Theses

We explore the phase transition in the diluted quasi-one-dimensional quantum Ising model. We begin by giving an introduction to the Ising model followed by derivations of the properties and observables. A brief overview of Monte-Carlo simulations is also given focusing on two algorithms that make simulations for physically realizable systems possible, the Metropolis and Wolff algorithms. We finally discuss the concept of random disorder in the system and the effects this can have on the bulk of the system.

These concepts are then directly applied to a quasi-one-dimensional Ising model. Motivated by recent experiments on the spin-chain material cobalt niobate, …

Development Of Stalling Detection Algorithms And Operator Behavior Models For Evaluating Hydraulic Excavator Performance, Mateo Fernando Montenegro Defaz

Masters Theses

This study aims to develop a reliable algorithm for stalling detection and investigate how operator behaviors impact the truck-loading process, focusing on swing and bucket rotations. While the study of excavator digging conditions and equipment performance is well understood, describing stalling from telemetry data and the influence of operators’ behavior on overall system efficiency remain underexplored. This study seeks to: (i) develop a reliable algorithm for stall detection by analyzing key operational variables such as velocity and angles; (ii) implement statistical and machine learning techniques, specifically a Support Vector Machine (SVM) classification algorithm, to differentiate between ideal and non-ideal digging …

Design Of A Spacecraft Power System Experiment, Jay Bakulkumar Kamdar

Masters Theses

This research delves into the design, implementation, and analysis of a controlled experimental framework for photovoltaic cell characterization and its application in spacecraft power systems. Such a system was simulated and validated by benchmarking characteristics of a photovoltaic module and implementing them in a system design simulation yielding performance data for comparative analysis. A hardware setup was developed to perform IV characterization tests under steady state thermal conditions. Experiments conducted across various thermal regulation profiles helped quantify the effects of temperature on the photovoltaic performance parameters. Active cooling reduced the steady state cell temperature by 17.9% and improved the cell …

Overall Tropospheric Noise Over The St. Louis Using Isce And Fringe Processing, Maryann Lee

Masters Theses

Interferometric Synthetic Aperture Radar (InSAR) is a satellite-based technique that is used to measure various aspects such as ground deformation, earthquakes, volcanic activity, and motions of glaciers. Although the technique has proved very useful, measurements errors such as ionospheric and tropospheric noise, and deformation often cause problems with making precise measurements. InSAR is a tool used in geodesy that is used to measure the change of surface deformation over time through a series of repeated SAR acquisitions. These errors in the acquisitions occur from factors of signal decorrelation, atmospheric delays, and phase unwrapping errors that affect the accuracy and precision …

Evaluating Tropospheric Delay Modeling For Enhanced Geospatial Accuracy, Adeyinka Olaoluwa Olaseinde

Masters Theses

Interferometric Synthetic Aperture Radar (InSAR) is a vital technique for monitoring ground surface deformation across large spatial scales. However, variability in atmospheric water vapor, pressure, and temperature introduces tropospheric delays that can significantly affect measurement accuracy. These delays are often corrected using outputs from numerical weather prediction (NWP) models. RAiDER, an open-source Python package, facilitates this by estimating tropospheric delays using models such as the High-Resolution Rapid Refresh (HRRR). Previous studies have shown that weather model–based corrections can substantially reduce atmospheric noise in InSAR data but also highlight persistent residual errors, especially at spatial scales smaller than the model grid …

Extraction And Separation Of Metal Cations From Synthetic Acidic Solutions Using Emulsion Liquid Membrane, Nouhaila Filali

Masters Theses

The work is organized into two main papers supported by experimental and comparative studies using Emulsion Liquid Membrane (ELM) systems. The first paper explores the individual extraction of heavy metals (Cd, Cr, As, Ni) from synthetic phosphoric acid using Alamine 336 and D2EHPA as carriers. Alamine 336 achieved the highest extraction efficiency (>90%) and stability, offering a promising route to reduce toxic impurities in fertilizer-grade acid. The second paper examines the selective separation of Ni2+ and Co2+ from various acidic solutions using the ELM configuration. Systematic variation of carriers, stripping agent and feed acidity, clarified transport mechanisms and stability, …

Testing The Life-History Dependence Of Dietary Restriction Effects On Oxidative Molecular Damage In Hornworms, Fahime Taheri

Masters Theses

DR and ambient temperature are two environmental factors widely implicated in shaping organismal maintenance and longevity through effects on metabolic rate and oxidative damage. This study tests whether DR and reduced body temperature independently or interactively reduce oxidative protein damage in the gut of tobacco hornworm larvae, a short-lived ectotherm whose life history may constrain typical DR responses. Larvae hatched from eggs were allocated to two feeding regimes (AL and DR) and to 20 °C and 30 °C. At a defined endpoint, guts dissected and homogenized, and analyzed for protein carbonyl content and total protein. Protein damage was expressed as …

The Organization Of Serotonergic Fibers In The Pacific Angelshark Brain: Neuroanatomical And Supercomputing Analyses, Skirmantas Janušonis, Ralf Metzler, Thomas Vojta

Physics Faculty Research & Creative Works

Serotonergic axons (fibers) are a universal feature of all vertebrate brains. They form meshworks, typically quantified with regional density measurements, and appear to support neuroplasticity. The self-organization of this system remains poorly understood, partly because of the strong stochasticity of individual fiber trajectories. In an extension to our previous analyses of the mouse brain, serotonergic fibers were investigated in the brain of the Pacific angel shark (Squatina californica), a representative of a unique (ray-like) lineage of the squalomorph sharks. First, the fundamental cytoarchitecture of the angel shark brain was examined, including the expression of ionized calcium-binding adapter molecule 1 (Iba1, …

Arc-Jet And Free-Flight Evaluation Of Laminar And Turbulent Diffusion Models On Ablation, Kyle Worden, Andrew Heider, Serhat Hosder

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper investigates the effect of diffusion models on carbon ablation with a parametric study on a slug-calorimeter geometry in arc-jet flow at two enthalpy conditions using two arc-jet flow modeling approaches, and at corresponding enthalpy-matched free-flight conditions using RANS CFD simulations. Additionally, a sphere-cone geometry is modeled at the arc-jet enthalpy matched free-flight conditions and at a high Reynolds number free-flight condition. The approximate-corrected form of Fick's law and the Stefan-Maxwell diffusion models are investigated for equilibrium and finite-rate carbon ablation in laminar and turbulent flows, which is simulated with the Menter-SST turbulence model. The turbulent cases include the …

Improving Neural Network Efficiency With Multi-Fidelity And Dimensionality Reduction Techniques, Vignesh Sella, Thomas O'Leary-Roseberry, Xiaosong Du, Mengwu Guo, Joaquim R.R.A. Martins, Omar Ghattas, Karen Willcox, Anirban Chaudhuri

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Design problems in aerospace engineering often require numerous evaluations of expensive to-evaluate high-fidelity models, resulting in prohibitive computational costs. One way to address the computational cost is through building surrogates, such as deep neural networks (DNNs). However, DNNs may only be an effective surrogate when sufficient evaluations of the high-fidelity model are required such that the up-front training cost is amortized, or in situations that require real-time responses (such as interactive visualizations). Typically, the data requirements for adequately accurate training of DNNs are often impractical for engineering applications. To alleviate this issue, the proposed work utilizes output dimensionality reduction along …

Correction: Advancing Cislunar Space Domain Awareness Through Robust Optimization Framework For Optical Sensors-Based Autonomous Satellite Systems (American Institute Of Aeronautics And Astronautics Inc, Aiaa), Smriti Nandan Paul, Siwei Fan, Igor Panfil

Mechanical and Aerospace Engineering Faculty Research & Creative Works

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High-Order Dynamic Mode Decomposition For Multidimensional Harmonic Retrieval, Yanming Zhang, Steven Gao, Lijun Jiang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Estimating channel parameters such as azimuth, elevation, Doppler shift, and delay is a key challenge in wireless communication, often formulated as a multidimensional harmonic retrieval (MHR) problem. To address this, we propose a high-order dynamic mode decomposition (HODMD) framework for robust frequency estimation from high-dimensional signals in noisy environments. The HODMD approach combines high-order singular value decomposition (HOSVD) to decompose tensor data into a core tensor and mode matrices, with dynamic mode decomposition (DMD) to extract frequencies from the imaginary parts of the DMD eigenvalues. Simulation examples validate the effectiveness of the proposed method, demonstrating its efficiency in solving MHR …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Enhanced Multidimensional Harmonic Retrieval In Mimo Wireless Channel Sounding, Yanming Zhang, Wenchao Xu, A. Long Jin, Tianquan Tang, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a two-dimensional matrix format along the r-th dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the r-th dimension, respectively. Furthermore, recursive …

Performance Evaluation And Multiphysics Process Modeling Of Carbon Fiber Reinforced Thermoset Composites Using Microwave And Autoclave, Nayan Pundhir, Patrick Schwartzkopf, K. Chandrashekhara, Logan Wilcox, Kristen M. Donnell, Jim Lua, Rui Li

Mechanical and Aerospace Engineering Faculty Research & Creative Works

In this study, IM7/Cycom 5320-1 unidirectional prepreg has been utilized to manufacture 16-layer laminated composites: a symmetric cross-ply ([0°/90°]4s) and a quasi-isotropic ([45°/90°/−45°/0°]2s) configuration. Microwave and autoclave curing processes have been employed to manufacture the laminated composites. The manufactured composite cure was assessed using differential scanning calorimetry (DSC). The quality and porosity of the microwave-cured parts were juxtaposed to those of autoclave-cured parts through optical microscopy and micro-computed tomography (micro-CT) scanning. Mechanical characterization of the microwave-cured panels was conducted using uniaxial tensile and flexural tests, with results juxtaposed to autoclave-cured samples. Experimental characterization revealed that the microwave-cured parts exhibited nearly …

Design And Testing Of An Intrusive Arm For Supersonic Flow Sampling, Caleb Roberts, Davide Viganò

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Intrusive probes are critical tools in supersonic wind tunnel testing, enabling direct measurement of flow properties such as pressure and heat flux. To support these measurements, a single-axis intrusive arm was designed and implemented in the Missouri S&T Supersonic Wind Tunnel. The system is designed to maintains precise probe positioning, while withstanding the aerodynamic loads, and preserving a dynamic seal between the test section and ambient environment. Its modular design accommodates a range of probe types for various research applications. This paper presents the design methodology, including structural, mechanical, and aerodynamic considerations, and details the integration process. System performance was …

Few-Shot Learning-Enhanced Tiered Path Planning For Mars Rover Navigation, Ziyi Wang, Di Yu, Mina Khalilzadeh Fathi, Chaoying Pei

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Path planning for Mars rovers presents significant challenges due to the diverse terrain, ranging from easily navigable areas to hazardous zones. Traditional methods typically classify terrain simply as passable or impassable, failing to account for the nuances of more moderately challenging areas. In this paper, we introduce a tiered terrain-aware path planning strategy, employing few-shot learning to classify and segment Martian terrain into levels of difficulty. The few-shot learning model, trained on Earth, is sent to the rover, enabling real-time processing of images from satellites or helicopters. The flexibility of few-shot learning, which requires minimal data and training time, enables …

A Low-Frequency-Stable Higher-Order Isogeometric Discretization Of The Augmented Electric Field Integral Equation, Maximilian Nolte, Riccardo Torchio, Sebastian Schöps, Jürgen Dölz, Felix Wolf, Albert E. Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

This contribution investigates the connection between Iso geometric analysis (IGA) and integral equation (IE) methods for full-wave electromagnetic problems up to the low-frequency limit. The proposed spline-based IE method allows for an exact representation of the model geometry described in terms of nonuniform rational B-splines (NURBS) without meshing. This is particularly useful when high accuracy is required or when meshing is cumbersome, for instance, during the optimization of electric components. The augmented electric field IE (EFIE) is adopted, and the deflation method is applied, so the low-frequency breakdown is avoided. The extension to higher-order basis functions is analyzed and the …

Active And Reactive Power Flow Control Of The Dual Active Bridge Converter, Lauryn Morris, Thomas W. Francois, Jonathan Saelens, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present work introduces a combined feedback/feed-forward current control system, utilizing the calculated and measured converter currents translated into the dq reference frame, to control the output power. The system was simulated in PLECS to demonstrate the control algorithm's ability to track the dq currents and provide the necessary output power.

Cognitive Fatigue Detection Using Photoplethysmography (Ppg) And Reaction Time Data, Anhar Sami Mohammed, Prajoy Podder, Maciej Jan Zawodniok, Cihan Dagli

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a framework for real-time cognitive fatigue detection among shift workers using an integrated approach that combines photoplethysmography (PPG) data and reaction time analysis with advanced deep learning models, including Long Short-Term Memory (LSTM) networks and Feedforward Neural Networks (FNNs). The system leverages heart rate variability (HRV) and reaction time data to identify fatigue indicators. The results demonstrate significant performance, with the first FNN model achieving a test accuracy of 98.94% and a loss of 0.2928, while the second FNN model achieved the same accuracy with a slightly higher loss of 0.3089. The LSTM model, designed for sequential …

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT) which is subsequently spatiotemporally imaged with an infrared (IR) camera. As all antennas have spatial variation in their radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT. This nonuniform heating causes uncertainty in defect detection and has the potential to lead to false positives and/or negatives. To this end, thermographic signal reconstruction (TSR), a well-established thermographic signal …

Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT). The inspection surface of the SUT is imaged with an infrared (IR) camera over the inspection time. As the thermal excitation originates from a spatially varying radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT that is directly related to this power density. This nonuniform heating causes uncertainty in defect detection and has the potential to lead …

Enhancing Fiber Optic Interferometric Sensing With Microwave Photonics-Based Dispersion Fourier Transform And Integrated Magnitude–Phase Analysis, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber optic inline interferometers are widely used for high-precision sensing due to their sensitivity, compactness, and immunity to electromagnetic interference. Traditional optical spectral analysis methods suffer from limited dynamic range due to free spectral range (FSR) constraints, while microwave photonic filtering (MPF) techniques based on dispersion Fourier transform (DFT) provide an alternative by mapping optical signals into the radio frequency (RF) domain. However, conventional passband frequency tracking in MPF systems has limited sensitivity, and the recently demonstrated phase-based methods, though highly sensitive, are constrained by phase wrapping beyond 2π. In this work, we propose and experimentally demonstrate an integrated magnitude–phase …

A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario …

A Hybrid Method For Source Direction Finding With Radio Frequency Interference And Gaussian White Noise, Yanming Zhang, Wenchao Xu, Antonios Argyriou, A. Long Jin, Tianquan Tang, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise …

Ibis Model Simulation Accuracy Improvement With Slew Rate Correction, Yifan Ding, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The accuracy of Power-Supply-Induced Jitter (PSIJ) simulation in Input/Output Buffer Information Specification (IBIS) models is critical for ensuring robust high-speed signal integrity analysis, but it lacks accuracy in predicting the PSIJ when the pre-driver exists in the model. Previous studies have proposed methods to improve IBIS PSIJ simulation accuracy with pre-driver effect included in the IBIS switching coefficients modification process. However, these methods fail to accurately model the output waveform slew rate change with varied power noise. In this work, an improved modification method was proposed to incorporate power-aware characteristics into the modified IBIS model, thereby improving the accuracy of …

A Non-Destructive And Simple Setup Method For Dielectric Liquid Characterization In A Wide Frequency Range With Djordjevic-Sarkar Model, Reza Vahdani, Reza Asadi, Seyedmehdi Mousavi, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a non-destructive and simple setup method for characterizing dielectric liquids over a broad frequency range (up to 30 GHz) using the Djordjevic-Sarkar model. By employing a differential microstrip line and comparing scattering parameters in air-filled and liquid-immersed scenarios, the proposed method achieves precise dielectric constant (DK) and dissipation factor (Df) extraction. 2 Liquid samples (PAO4 and DC-15) were tested using this method. Validation against the cavity resonance method demonstrates a strong agreement for the extracted DK values, with a relative error of less than 1.5 %, indicating high accuracy. However, the method is less sensitive to Df, …

Impact Of Voltage Regulator Modules On Power Distribution Network Impedance, Hanyu Zhang, Zhiping Yang, Alvis Hsu, Ryan Hou, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The voltage regulator module (VRM) impacts the power distribution network (PDN) impedance in the low frequency range. The effect of VRM is essential for reliable PDN analysis and simulation. However, the existing VRM models for power integrity (PI) simulation lack an in-depth understanding of the effect of the feedback control in the VRM. In this paper, the impact of the VRM control loop on the PDN impedance is investigated. The relationship between the VRM output impedance and the PDN impedance at the IC input pin is derived. The output impedance of a VRM is analyzed using small signal analysis. Eventually, …