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Learning To Dogfight: Proximal Policy Optimization Vs. Double Deep Q Network For 2v2 Air Combat With Directed Energy Weapons In Afsim, Caden W. Wilson Mar 2025

Learning To Dogfight: Proximal Policy Optimization Vs. Double Deep Q Network For 2v2 Air Combat With Directed Energy Weapons In Afsim, Caden W. Wilson

Theses and Dissertations

This research utilizes reinforcement learning (RL) to train two blue agents each imbued with a directed energy weapon (DEW) in a 2v2 within visual range air combat maneuvering problem. A phased solution approach is employed to repeatedly tune and train several RL algorithm implementations: Proximal Policy Optimization (PPO) and Double Deep Q Network (DDQN). Phase I of training includes reward shaping for basic flight elements such as altitude, airspeed, and target proximity. Phase II of training builds off policies developed in Phase I, but rewards emphasize winning the aerial engagement by any means necessary. DDQN significantly outperforms PPO in Phase …

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Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson Mar 2025

Numerical Studies Of Semiclassical Light Storage Using The Coherent Atomic Transfer Function, Zachary T. Johnson

Theses and Dissertations

Quantum communication through photons relies on photonic storage to preserve quantum states. However, when photons interact with matter, quantum information becomes distorted. A recently developed semi-classical analytical model predicts output light pulses from an electromagnetically induced transparency (EIT) system. Using the predictions, the model known as the Coherent Atomic Transfer (CAT) Function, is capable of predicting the stored pulse or reconstructing the original pulse. Using numerical convolution and deconvolution with the CAT function as an analog of the point spread function of Fourier optics can provide insights on the effects of EIT storage on the retrieved pulse. Blind deconvolution is …

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Correlating Aerosol Morphology And Optical Properties With Image Blur: An Investigation Of Aerosol-Induced Image Degradation, Patricia A. Byrd Mar 2025

Correlating Aerosol Morphology And Optical Properties With Image Blur: An Investigation Of Aerosol-Induced Image Degradation, Patricia A. Byrd

Theses and Dissertations

Small-angle scattering by relatively large cloud/fog droplets and ice crystals is known to degrade imagery. However, it is not well investigated how much of an impact aerosols, especially the prevalent anthropogenic fine/ultra-fine/coarse particles, have in image degradation. This thesis explores the contribution of aerosols to image blur but focuses on collecting field data with a relatively large variety of ambient aerosol characterization and optical instrumentation. Field experiments were conducted over six days, correlating aerosol measurements (particle counters and nephelometer) with image quality from a visible camera along a 450m path. Image blur was quantified using the Modulation Transfer Function (MTF), …

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Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson Mar 2025

Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson

Theses and Dissertations

Event-based cameras excel in dynamic environments, and do not face challenges like washout and motion blur, like a frame-based camera. This work describes the process used to collect the first EBS data collect for use in AAR, and develops an event simulator to generate synthetic training data for evaluating CNN architectures on asynchronous data. The three models compared are a traditional CNN, a YOLO-based CNN, and an asynchronous sparse CNN. The YOLO-based model achieved the best accuracy, while the sparse CNN, despite being less optimized, maintained an average IoU of 0.9. These results highlight the potential of asynchronous approaches for …

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Grassmannian Codes From Stratified Frames, William J. Brinkley Mar 2025

Grassmannian Codes From Stratified Frames, William J. Brinkley

Theses and Dissertations

An equichordal tight fusion frame (ECTFF) is a finite sequence of equi-dimensional subspaces of a Euclidean space that achieves equality in Conway, Hardin and Sloane's simplex bound. Every ECTFF is an optimal Grassmannian code with respect to the chordal distance. We introduce a method for constructing an ECTFF from any finite sequence of unit norm tight frames that happen to be stratified in a certain sense. We moreover show how to construct stratified unit norm tight frames from a difference family for a finite abelian group, as well as from a suitable combination of a resolvable balanced incomplete block design …

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Heterogeneous Boid Swarm Performance Under Environmental And Neighbor Communication Link Variability, Jonathan C. Oxborrow Mar 2025

Heterogeneous Boid Swarm Performance Under Environmental And Neighbor Communication Link Variability, Jonathan C. Oxborrow

Theses and Dissertations

Artificial swarms are of growing interest in numerous fields and use cases. As their utilization increases drones and robots with different capabilities will be required to coordinate for task completion thus creating heterogeneous swarms. Swarm individuals generally communicate with all neighbors inside their sensor range generating a significant amount of message traffic. Previous research of a heterogeneous group in a non-physical environment has shown that restricting communication to only one neighbor of each different capability maintained performance. This work applies that finding to a heterogeneous boid swarm with the addition of varied environmental conditions. The swarm is comprised of three …

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Quantum Ghost Imaging Through An Atmospheric Turbulence Simulator, Mikaela R. Morris Mar 2025

Quantum Ghost Imaging Through An Atmospheric Turbulence Simulator, Mikaela R. Morris

Theses and Dissertations

The use of single photons and photon pairs has become a cornerstone for new developments in the sensing and imaging community for their ability to improve image resolution and signal to noise readings in low light environments and large standoff distances. Quantum imaging techniques have an ability to overcome the fundamental limits of classical imaging, like diffraction. Quantum ghost imaging, a specific type of quantum imaging, uses a bi-photon pair produced during the process of spontaneous parametric down conversion to produce an image. The pair is split using a beam splitter, the signal photon is transmitted through an aperture and …

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Adiabatic And Overall Effectiveness Superposition Theory For Upstream Phantom Cooling On A Film Cooled Leading Edge, Nathaniel J. Stout Mar 2025

Adiabatic And Overall Effectiveness Superposition Theory For Upstream Phantom Cooling On A Film Cooled Leading Edge, Nathaniel J. Stout

Theses and Dissertations

Film cooling experimentation aims to improve the cooling performance while minimizing the amount of coolant flow redirected from the compressor. The coolant flow that is used for film cooling reduces the maximum thrust any gas turbine engine can produce. Finding creative ways to improve cooling performance without increasing the amount of coolant used, like phantom cooling, is essential for the gas turbine industry to improve engine performance while increasing turbine components’ lifespan. Phantom cooling refers to any secondary cooling effect that propagates downstream from its original cooling application to cool subsequent turbine components. Phantom cooling is actively gaining more attention …

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Approximating Three-Body Trajectories With A Knot Theory Inspired Model For Orbit Generation And Determination, Mason R. Mill Mar 2025

Approximating Three-Body Trajectories With A Knot Theory Inspired Model For Orbit Generation And Determination, Mason R. Mill

Theses and Dissertations

This thesis explores a novel approach to approximating three-body trajectories using a knot theory-inspired model for orbit generation and determination. Traditional methods for solving the Circular Restricted Three-Body Problem (CR3BP) rely on numerical integration and correction schemes to generate trajectories, often requiring iterative refinements. This research investigates the application of knot theory principles—such as torus knots, Alexander polynomials, and Reidemeister moves—to categorize and model complex orbital trajectories in the CR3BP. By leveraging these mathematical tools, the study aims to enhance trajectory prediction, orbit determination, and mission planning for spacecraft operating in the cislunar environment. The results demonstrate that knot theory …

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Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson Mar 2025

Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson

Theses and Dissertations

This research investigates the effect of misalignment on the near-field scattering of cylinders in the 550-700 GHz frequency band. A Type-1 calibration is performed on previously collected data, using a near-field physical optics solution to simulate scattering at various positions and orientations. The alignment of the cylinders at the time of measurement is predicted by comparing the range profiles of the theoretical and calibrated responses. The data with the most similar range profiles had a mean calibration difference metric of -2.78 dB and a standard deviation of -0.57 dB, demonstrating the presence of sources of error that are dominant over …

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An Analytical Approach In Solving A Two-On-One Pursuit Evasion Differential Game With Fast Evader And No-Point Capture, Nathan T. Morrow Mar 2025

An Analytical Approach In Solving A Two-On-One Pursuit Evasion Differential Game With Fast Evader And No-Point Capture, Nathan T. Morrow

Theses and Dissertations

This paper is concerned with a co-planar pursuit-evasion scenario where two Pursuers (P) are after an Evader (E). The players are holonomic/can turn on a dime and their speeds, VP and VE, are constant, but the evader is faster than the pursuers, that is, the speed ratio parameter μ =  VE/VP > 1. The Pursuers are endowed with a circular capture disc whose radius l > 0. A differential game (DG) with three states and one parameter is addressed through geometric and analytical methods where a partial solution is outlined and visualized. The game is split …

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Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith Mar 2025

Analyzing Lf/Vlf Lightning Waveforms To Estimate D-Region Electron Density Profiles: Process, Application, And Comparison, William R. Smith

Theses and Dissertations

Lightning waveforms in the low frequency (LF; 30-300 kHz) and the very low frequency (VLF; 3-30 kHz) can be exploited to produce data-driven ionospheric Dregion electron density profile (EDP) estimates with significantly higher spatial and temporal coverage than previously available. The lightning waveforms used in this paper are signals detected in the LF/VLF of negative cloud-to-ground lightning by the Earth Networks Total Lightning Detection Network. Each waveform contains a ground wave and a time-delayed ionospheric reflection. The time delay between the ground wave and ionospheric reflection has previously been used to estimate a single specular reflection altitude, where LF/VLF emissions …

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Cognitive-Affective Intercultural Sensitivity (Cais): A Self-Transformative Conception And Its Methodological Challenges, Paraskevi Simou, Valentin El Sayed, Julien Teyssier, Patrick Denoux Mar 2025

Cognitive-Affective Intercultural Sensitivity (Cais): A Self-Transformative Conception And Its Methodological Challenges, Paraskevi Simou, Valentin El Sayed, Julien Teyssier, Patrick Denoux

Online Readings in Psychology and Culture

In today's rapidly evolving global landscape, individuals are increasingly required to navigate diverse cultural interactions, prompting extensive research into intercultural competence. This paper examines the multifaceted concept of intercultural sensitivity, drawing from cross-cultural psychology, intercultural communication, and emerging intercultural psychology. Despite the lack of a universally accepted definition, previous studies have highlighted the importance of an affective component within intercultural competence which usually refers to intercultural sensitivity. While acknowledging the contributions and limitations of seminal models such as the Developmental Model of Intercultural Sensitivity (DMIS) and Chen's affective intercultural sensitivity model, we propose a novel conceptualization termed cognitive-affective intercultural sensitivity …

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Renewable Electricity Management Cloud System For Smart Communities Using Advanced Machine Learning, Yukta Mehta, Vincent Lo, Vijen Mehta, Kunal Agrawal, Charan Teja Madabathula, Eugene Chang, Jerry Gao Mar 2025

Renewable Electricity Management Cloud System For Smart Communities Using Advanced Machine Learning, Yukta Mehta, Vincent Lo, Vijen Mehta, Kunal Agrawal, Charan Teja Madabathula, Eugene Chang, Jerry Gao

Faculty Research, Scholarly, and Creative Activity

Based on the renewable energy assessment in 2023, it was found that only 21% of total electricity is generated using renewable sources. As the global demand for electricity rises in the AI world, the need for electricity management will increase and must be optimized. Based on research, many companies are working on green AI electricity management, but few companies are working on predicting shortages. To identify the rising electricity demand, predict the shortage, and to bring attention to consumption, this study focuses on the optimization of solar electricity generation, tracking its consumption, and forecasting the electricity shortages well in advance. …

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The Effect Of Compilation On The Identification Of Obfuscated Malware, Eli T. Fulkerson Mar 2025

The Effect Of Compilation On The Identification Of Obfuscated Malware, Eli T. Fulkerson

Masters Theses & Doctoral Dissertations

Automated identification of malicious JavaScript is a core problem within modern malware analysis. Code obfuscation is a common tactic used in order to evade detection. Obfuscation hinders both manual and automated detection methods, including neural network techniques. In order for these methods to effectively classify malware, it is beneficial to reduce the effects of obfuscation as well as to optimize the configuration and structure of the neural network to be well suited for the task. To overcome these challenges, a new framework is presented: “PyRHOH”, a metalearning framework that implements Bayesian optimization. This framework adds structure and rigor to the …

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Exploring The Dynamics Of Lotka-Volterra Systems: Efficiency, Extinction Order, And Predictive Machine Learning, Sepideh Vafaie, Deepak Bal, Michael A.S. Thorne, Eric Forgoston Mar 2025

Exploring The Dynamics Of Lotka-Volterra Systems: Efficiency, Extinction Order, And Predictive Machine Learning, Sepideh Vafaie, Deepak Bal, Michael A.S. Thorne, Eric Forgoston

School of Computing Faculty Scholarship and Creative Works

For years, a main focus of ecological research has been to better understand the complex dynamical interactions between species that comprise food webs. Using the connectance properties of a widely explored synthetic food web called the cascade model, we explore the behavior of dynamics on Lotka-Volterra ecological systems. We show how trophic efficiency, a staple assumption in mathematical ecology, affects species extinction. With clustering analysis, we show how straightforward inequalities of the summed values of birth, death, self-regulation, and interaction strengths provide insight into which food webs are more enduring or stable. Through these simplified summed values, we develop a …

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Leveraging Artificial Intelligence And Machine Learning To Accelerate Discovery Of Disease-Modifying Therapies In Type 1 Diabetes., Melanie R. Shapiro, Erin M. Tallon, Matthew E. Brown, Amanda L. Posgai, Mark A. Clements, Todd M. Brusko Mar 2025

Leveraging Artificial Intelligence And Machine Learning To Accelerate Discovery Of Disease-Modifying Therapies In Type 1 Diabetes., Melanie R. Shapiro, Erin M. Tallon, Matthew E. Brown, Amanda L. Posgai, Mark A. Clements, Todd M. Brusko

Manuscripts, Articles, Book Chapters and Other Papers

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the 'gold standard' of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning …

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Evaluation Of Enzyme Activity Predictions For Variants Of Unknown Significance In Arylsulfatase A, Shantanu Jain, Marena Trinidad, Thanh Binh Nguyen, Kaiya Jones, Santiago Diaz Neto, Fang Ge, Ailin Glagovsky, Cameron Jones, Giankaleb Moran, Boqi Wang, Kobra Rahimi, Sümeyra Zeynep Çalıcı, Luis R Cedillo, Silvia Berardelli, Buse Özden, Ken Chen, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Sadhna Rana, Swatantra Pradhan, Rajgopal Srinivasan, Rakshanda Sajeed, Dinesh Joshi, Eshel Faraggi, Robert Jernigan, Andrzej Kloczkowski, Jierui Xu, Zigang Song, Selen Özkan, Natàlia Padilla, Xavier De La Cruz, Rocio Acuna-Hidalgo, Andrea Grafmüller, Laura T Jiménez Barrón, Matteo Manfredi, Castrense Savojardo, Giulia Babbi, Pier Luigi Martelli, Rita Casadio, Yuanfei Sun, Shaowen Zhu, Yang Shen, Fabrizio Pucci, Marianne Rooman, Gabriel Cia, Daniele Raimondi, Pauline Hermans, Sofia Kwee, Ella Chen, Courtney Astore, Akash Kamandula, Vikas Pejaver, Rashika Ramola, Michelle Velyunskiy, Daniel Zeiberg, Reet Mishra, Teague Sterling, Jennifer L Goldstein, Jose Lugo-Martinez, Sufyan Kazi, Sindy Li, Kinsey Long, Steven E Brenner, Constantina Bakolitsa, Predrag Radivojac, Dean Suhr, Teryn Suhr, Wyatt T Clark Mar 2025

Evaluation Of Enzyme Activity Predictions For Variants Of Unknown Significance In Arylsulfatase A, Shantanu Jain, Marena Trinidad, Thanh Binh Nguyen, Kaiya Jones, Santiago Diaz Neto, Fang Ge, Ailin Glagovsky, Cameron Jones, Giankaleb Moran, Boqi Wang, Kobra Rahimi, Sümeyra Zeynep Çalıcı, Luis R Cedillo, Silvia Berardelli, Buse Özden, Ken Chen, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Sadhna Rana, Swatantra Pradhan, Rajgopal Srinivasan, Rakshanda Sajeed, Dinesh Joshi, Eshel Faraggi, Robert Jernigan, Andrzej Kloczkowski, Jierui Xu, Zigang Song, Selen Özkan, Natàlia Padilla, Xavier De La Cruz, Rocio Acuna-Hidalgo, Andrea Grafmüller, Laura T Jiménez Barrón, Matteo Manfredi, Castrense Savojardo, Giulia Babbi, Pier Luigi Martelli, Rita Casadio, Yuanfei Sun, Shaowen Zhu, Yang Shen, Fabrizio Pucci, Marianne Rooman, Gabriel Cia, Daniele Raimondi, Pauline Hermans, Sofia Kwee, Ella Chen, Courtney Astore, Akash Kamandula, Vikas Pejaver, Rashika Ramola, Michelle Velyunskiy, Daniel Zeiberg, Reet Mishra, Teague Sterling, Jennifer L Goldstein, Jose Lugo-Martinez, Sufyan Kazi, Sindy Li, Kinsey Long, Steven E Brenner, Constantina Bakolitsa, Predrag Radivojac, Dean Suhr, Teryn Suhr, Wyatt T Clark

Faculty, Staff and Students Publications

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfatase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning …

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Intelligent Soccer Event Detection And Highlights Generation With Broadcast Cues Integration, Anirudh Narayanan, Sergei Chuprov, Leon Reznik, Raman Zatsarenko, Dmitrii Korobeinikov Mar 2025

Intelligent Soccer Event Detection And Highlights Generation With Broadcast Cues Integration, Anirudh Narayanan, Sergei Chuprov, Leon Reznik, Raman Zatsarenko, Dmitrii Korobeinikov

Computer Science Faculty Publications

In this paper, we present an innovative approach to automate key event detection and highlights generation from soccer match videostreams that allows to improve accuracy and reliability, as well as to reduce data consumption and training time. Our method segments the videostream into distinct frames based on camera angles and activities, and integrates intelligent video analytics with additional visual information provided by broadcasters. As our major novelty in comparison to other intelligent soccer video analysis approaches, we deploy a Multi-Class Image Classifier to segment the video into wide-angle overviews, close-ups, and in-game replays, which allows us to improve the event …

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On The Exploration Of Crystallographic Anisotropy And Defects In Shock Loading Using Molecular Dynamics, Benjamin P. Helman Mar 2025

On The Exploration Of Crystallographic Anisotropy And Defects In Shock Loading Using Molecular Dynamics, Benjamin P. Helman

Theses and Dissertations

The impact of crystallographic orientation, grain boundaries, and vacancies on the shock behavior of aluminum was investigated using molecular dynamics simulations. Shock loading in the [001], [011], and [111] directions was explored, revealing anisotropic behavior in shock speed, melting, dislocation density, and unique phase changes. The Hugoniot elastic limit in the [100], [110], and [111] directions was calculated as 23.2 GPa, 24 GPa, and 18.4 GPa respectively. These results were found to be an order of magnitude larger than the compressive yield strength computed at equilibrium. Additionally, metastable melting in the [011] and [111] directions occurred roughly 1000 K below …

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Abolition By Algorithm, Peter N. Salib Mar 2025

Abolition By Algorithm, Peter N. Salib

Michigan Law Review

In one sense, America’s newest abolitionist movement—advocating the elimination of policing and prison—has been a success. Following the 2020 Black Lives Matter protests, a small group of self-described radicals convinced a wide swath of ordinary liberals to accept a sweeping claim: Mere reforms cannot meaningfully reduce prison and policing’s serious harms. Only elimination can. On the other hand, abolitionists have failed to secure lasting policy change. The difficulty is crime. In 2021, following a nationwide uptick in homicides, liberal support for abolitionist proposals collapsed. Despite being newly “abolition curious,” left-leaning voters consistently rejected concrete abolitionist policies. Faced with the difficult …

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Irreducible Three-Loop Vacuum-Polarization Correction In Muonic Bound Systems, Gregory S. Adkins, Ulrich D. Jentschura Mar 2025

Irreducible Three-Loop Vacuum-Polarization Correction In Muonic Bound Systems, Gregory S. Adkins, Ulrich D. Jentschura

Physics Faculty Research & Creative Works

Three-loop electronic vacuum-polarization corrections due to irreducible diagrams are evaluated for two-body muonic ions with nuclear charge numbers 1≤Z≤6. The corrections are of order α3(Zα)2mr, where α is the fine-structure constant and mr is the reduced mass. Numerically, the energy corrections are found to be of the same order of magnitude as the largest of the order α2(Zα)6mr corrections, and are thus phenomenologically interesting. Our method of calculation eliminates numerical uncertainty encountered in other approaches.

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Advances In Theory And Computational Methods For Next-Generation Thermoelectric Materials, Junsoo Park, Alex M. Ganose, Yi Xia Mar 2025

Advances In Theory And Computational Methods For Next-Generation Thermoelectric Materials, Junsoo Park, Alex M. Ganose, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

This is a review of theoretical and methodological development over the past decade pertaining to computational characterization of thermoelectric materials from first principles. Primary focus is on electronic and thermal transport in solids. Particular attention is given to the relationships between the various methods in terms of the theoretical hierarchy as well as the tradeoff of physical accuracy and computational efficiency of each. Further covered are up-and-coming methods for modeling defect formation and dopability, keys to realizing a material's thermoelectric potential. We present and discuss all these methods in close connection with parallel developments in high-throughput infrastructure and code implementation …

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Impact Of Roughness Spanwise Wavelength On Secondary Flow Rearrangement, Jianzhi Yang, Xiang I. A. Yang, Luoqin Liu, Jian Zhu, Xiaowei Zhu Mar 2025

Impact Of Roughness Spanwise Wavelength On Secondary Flow Rearrangement, Jianzhi Yang, Xiang I. A. Yang, Luoqin Liu, Jian Zhu, Xiaowei Zhu

Mechanical and Materials Engineering Faculty Publications and Presentations

Spanwise heterogeneity in surface roughness generates secondary mean flows in a rough-wall turbulent boundary layer. This study investigates the influence of roughness spanwise wavelength on the arrangement of these secondary flows using direct numerical simulation (DNS). We systematically vary the spanwise wavelength, δS/δ⁠, from ππ/16 to π2π⁠, while maintaining constant roughness height and surface coverage density. Here, S represents the roughness spanwise wavelength, and δδ denotes the outer length scale, which in this case is the half-channel height. Secondary flows are observed in all DNS cases, but their configurations depend on the spanwise wavelength. Specifically, small wavelengths result in low-momentum …

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Should Oral Examination Be Reimagined In The Era Of Ai?, Prashanti Eachempati, Ramnarayan Komattil, Arathi Arakala Mar 2025

Should Oral Examination Be Reimagined In The Era Of Ai?, Prashanti Eachempati, Ramnarayan Komattil, Arathi Arakala

Peninsula Dental School

As artificial intelligence (AI) continues to reshape education, concerns about the authenticity of student work have escalated, particularly in relation to written assignments influenced by AI-powered tools. This article explores the role of the oral examination as a valuable method for assessing true student understanding and considers its potential for broader use across various educational levels. We propose that oral exams could be effectively integrated into undergraduate courses, providing a means to verify student comprehension in an era of AI-generated content. By adopting oral assessments, educators can enhance academic integrity while fostering essential skills such as critical thinking and effective …

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Applying Image Processing And Computer Vision For Damage Detection In Photovoltaic Panels, Montaser Abdelsattar, Ahmed Abdelmoety, Ahmed Emad-Eldeen Mar 2025

Applying Image Processing And Computer Vision For Damage Detection In Photovoltaic Panels, Montaser Abdelsattar, Ahmed Abdelmoety, Ahmed Emad-Eldeen

Mansoura Engineering Journal

As the use of Photovoltaic (PV) panels as a sustainable energy source grows, there is a need for effective and precise techniques to monitor and manage these systems. Conventional techniques for identifying faults in PV panels, such as manual inspections, require a significant amount of effort and are susceptible to mistakes made by humans. This study introduces an innovative automated method that utilizes image processing techniques implemented using the OpenCV library to identify panel faults, namely hotspots, which are important indications of possible failures. The study approach combines grayscale conversion, histogram analysis, and adaptive thresholding to accurately detect and evaluate …

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Ground-Based Reconnaissance Observations Of 21 Exoplanet Atmospheres With The Exoplanet Transmission Spectroscopy Imager, Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, Darren L. Depoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone Mar 2025

Ground-Based Reconnaissance Observations Of 21 Exoplanet Atmospheres With The Exoplanet Transmission Spectroscopy Imager, Ryan J. Oelkers, Luke M. Schmidt, Erika Cook, Mary Anne Limbach, Darren L. Depoy, J. L. Marshall, Jimmy Ardoin, Mitchell Barry, Evan Batteas, Alexandra Boone

Physics & Astronomy Faculty Publications

One of the most prolific methods of studying exoplanet atmospheres is transmission spectroscopy, which measures the difference between the depth of an exoplanet's transit signal at various wavelengths and attempts to correlate the depth changes to potential features in the exoplanet's atmosphere. Here we present reconnaissance observations of 21 exoplanet atmospheres measured with the Exoplanet Transmission Spectroscopy Imager (ETSI), a recently deployed spectrophotometer on the McDonald Observatory Otto Struve 2.1 m telescope. ETSI measurements are mostly free of systematics through the use of a novel observing technique called common-path multiband imaging (CMI), which has been shown to achieve photometric color …

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The Effect Of Vera C. Rubin Observatory Cadence Selections On Kilonova Detectability, Cristina Andrade, Raiyah Alserkal, Luis Salazar Manzano, Emma Martin, Igor Andreoni, Michael W. Coughlin, Nidhal Guessoum, Liliana Rivera Sandoval Mar 2025

The Effect Of Vera C. Rubin Observatory Cadence Selections On Kilonova Detectability, Cristina Andrade, Raiyah Alserkal, Luis Salazar Manzano, Emma Martin, Igor Andreoni, Michael W. Coughlin, Nidhal Guessoum, Liliana Rivera Sandoval

Physics & Astronomy Faculty Publications

The discovery of the optical/infra-red counterpart (AT2017gfo) to the binary neutron star gravitational-wave detection (GW170817), which was followed by a short gamma-ray burst (GRB 170817), marked a groundbreaking moment in multi-messenger astronomy. To date, it remains the only confirmed joint detection of its kind. However, many experiments are actively searching for similar fast-fading electromagnetic counterparts, known as kilonovae. Fortunately, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) provides excellent prospects for identifying kilonova candidates either from, or independent of, gravitational-wave and GRB triggers. Cadence choices for LSST surveys are especially important for maximising the likelihood of …

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Generation Of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under A Bayesian Framework For Electroanatomical Mapping, Sunil Mathew, Jasbir Sra, Daniel B. Rowe Mar 2025

Generation Of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under A Bayesian Framework For Electroanatomical Mapping, Sunil Mathew, Jasbir Sra, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

Electroanatomical mapping is a technique used in cardiology to create a detailed 3D map of the electrical activity in the heart. It is useful for diagnosis, treatment planning and real time guidance in cardiac ablation procedures to treat arrhythmias like atrial fibrillation. A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping to generate a patient-specific 3D model of the chamber being mapped. The use of probabilistic machine learning models under a Bayesian framework provides a way to quantify uncertainty in results and provide a natural framework of interpretability …

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Exploring The Potential Of Strongly Coupled Lagrangian Data Assimilation In An Ocean–Atmosphere System, Luyu Sun, Amit Apte, Laura Slivinski, Elaine T. Spiller Mar 2025

Exploring The Potential Of Strongly Coupled Lagrangian Data Assimilation In An Ocean–Atmosphere System, Luyu Sun, Amit Apte, Laura Slivinski, Elaine T. Spiller

Mathematical and Statistical Science Faculty Research and Publications

Precise measurements of ocean surface flow velocities are essential for refining forecasts in a coupled ocean–atmosphere system. While oceanic data are generally sparse, surface drifters present an opportunity by providing detailed and frequently observed sea surface currents, which are a critical component in the dynamics at air–sea interface. Such observations could potentially address the usual data gaps in a coupled ocean–atmosphere assimilation system. In this study, we investigate the implications of assimilating drifter data within a coupled system with intermediate complexity based on a quasigeostrophic model—Modular Arbitrary-Order Ocean–Atmosphere Model (MAOOAM)—using observing system simulation experiments (OSSEs). Two main strategies for assimilating …

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