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Air Force Institute of Technology

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Articles 1 - 30 of 3047

Full-Text Articles in Physical Sciences and Mathematics

Effective Visibility In The Infrared Bands, Peter L. Dean-Erlander, Steven T. Fiorino, Ronald G. Driggers Aug 2026

Effective Visibility In The Infrared Bands, Peter L. Dean-Erlander, Steven T. Fiorino, Ronald G. Driggers

Faculty Publications

Visibility is an atmospheric metric for the comparison of terrestrial imaging conditions and locations. A limitation of visibility is that it is defined for the visible spectrum only, and there is no simple infrared equivalent. This study compares three reflective infrared wavebands: near-IR (NIR), shortwave IR (SWIR), and extended shortwave IR (eSWIR), to the visible band across a global set of cities to develop three rule-of-thumb functions for IR effective visibility. To accomplish this, a radiometric sensor model is combined with the Laser Environmental Effects Definition and Reference (LEEDR) software package to calculate the visibility of a black-and-white contrast target …

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The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin Aug 2026

The Use Of Machine Learning Models For Predicting The Dielectric Strength Of Gases, Matthew Mileski, Paul W. Groth, Timothy S. Wolfe, Adib J. Samin

Faculty Publications

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to …

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An Atmospheric Optical Turbulence Structure Parameter Measurements System Based On Direct Ri Sensing Using High-Resolution Fiber-Optic Sensors, Matej Njegovec, Simon Pevec, Vedran Budinski, Boris Macuh, Melissa K. Beason, Denis Onlagic Aug 2026

An Atmospheric Optical Turbulence Structure Parameter Measurements System Based On Direct Ri Sensing Using High-Resolution Fiber-Optic Sensors, Matej Njegovec, Simon Pevec, Vedran Budinski, Boris Macuh, Melissa K. Beason, Denis Onlagic

Faculty Publications

The paper presents a method to characterize the refractive index structure parameter (Cn2) associated with optical turbulence directly. The characterization system is based on miniature, high‑resolution, all‑fiber refractive index (RI) sensors. The refractive index sensors employ open‑path, low‑finesse Fabry–Perot interferometers that are approximately 8 mm long and 200 μm in diameter. The active part of the interferometers is made of ultra‑low‑expansion glass, which eliminates the influence of thermal expansion on the refractive index measurements. The proposed refractive index sensor, operating in a differential configuration, achieved a resolution of 2×10-9 RIU using a custom‑designed spectral interrogation system …

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Modeling Formation Of Turbulent Sporadic-E Clouds Using Realistic Wind Data, Aaron M. Schinder, Kenneth S. Obenberger, Jorge L. Chau, Juan M. Urco, Matthias Clahsen, Benjamin F. Akers, Daniel J. Emmons Aug 2026

Modeling Formation Of Turbulent Sporadic-E Clouds Using Realistic Wind Data, Aaron M. Schinder, Kenneth S. Obenberger, Jorge L. Chau, Juan M. Urco, Matthias Clahsen, Benjamin F. Akers, Daniel J. Emmons

Faculty Publications

A high resolution two-dimensional multi-fluid model of sporadic-E layers was developed and driven with physically realistic mesosphere, lower thermosphere (MLT) winds measured over Albuquerque, New Mexico. The realistic E-region winds are produced by the HYdrodynamic Point-wise Environment Reconstructor (HYPER) model that ingests meteor derived wind observations from a Spread-spectrum Interferometric Multistatic meteor radar Observing Network (SIMONe) system combined with the Navier-Stokes equations to provide high resolution three-dimensional wind fields over time. Sporadic-E dynamics are simulated using both realistic winds from HYPER as well as idealized hyperbolic tangent windshears to compare and contrast. Overall, the model shows greater inhomogeneity and irregularity …

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Observations On Recurrent Loss In The Neural Network Model Of A Partial Differential Equation: The Advection–Diffusion Equation, Jonah A. Reeger Jul 2026

Observations On Recurrent Loss In The Neural Network Model Of A Partial Differential Equation: The Advection–Diffusion Equation, Jonah A. Reeger

Faculty Publications

A growing body of literature has been leveraging techniques of machine learning (ML) to build novel approaches to approximating the solutions to partial differential equations. Noticeably absent from the literature is a systematic exploration of the stability of the solutions generated by these ML approaches. Here, a recurrent network is introduced that matches precisely the evaluation of a multi-step method paired with a collocation method for approximating spatial derivatives in the advection–diffusion equation. This allows for two things: (1) the use of traditional tools for analyzing the stability of a numerical method for solving PDEs and (2) bringing to bear …

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Generation Of Khz-Rate Complex-Structured Liquid Targets For Relativistic Laser–Plasma Interactions, Michael L. Dexter, Stephen J. Hageman, Gregory Ngirmang, Kyle D. Frische, Joseph Snyder, John T. Morrison, Enam A. Chowdury, Anil K. Patnaik Jun 2026

Generation Of Khz-Rate Complex-Structured Liquid Targets For Relativistic Laser–Plasma Interactions, Michael L. Dexter, Stephen J. Hageman, Gregory Ngirmang, Kyle D. Frische, Joseph Snyder, John T. Morrison, Enam A. Chowdury, Anil K. Patnaik

Faculty Publications

With the rise of high repetition rate ultra-intense laser systems, there is a need for solid density targets to study relativistic laser–plasma interactions that can operate at the same repetition rate. Flowing liquid targets are attractive because they are self-replenished, debris free, cost effective and easy to use. Liquid targets have been used for high-repetition rate (up to kHz rate) generation of electrons, protons, x rays, and neutrons by our group and elsewhere. In this Letter, we demonstrate a kHz-rate generation of a variety of dynamically shaped complex-structured targets from the interaction of a 1016 W/cm2 focused laser …

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Analysis And Machine Learning Adaptation Of A Cognitive Model For Human Memory, Trevor Cross, Aihua W. Wood Jun 2026

Analysis And Machine Learning Adaptation Of A Cognitive Model For Human Memory, Trevor Cross, Aihua W. Wood

Faculty Publications

In this paper, we use the Duolingo SLAM dataset to analyze several cognitive models of second language acquisition and develop new approaches for enhanced performance. In particular, we consider the Predictive Performance Equation and some of its underlying power laws. Leveraging insights from machine learning, we develop simple one-feature models as building blocks for combined models that match or in certain cases outperform the existing models at much reduced computational cost. In addition, a neural network with one fully connected hidden layer is constructed that outperforms all other models on sufficiently large datasets.

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Active Learning Of Constraint Boundaries Using Expected Magnitude Of Incorrectness And Neural Networks, Atticus Beachy, Ramana V. Grandhi Jun 2026

Active Learning Of Constraint Boundaries Using Expected Magnitude Of Incorrectness And Neural Networks, Atticus Beachy, Ramana V. Grandhi

Faculty Publications

This research proposes an acquisition function for constraint boundary identification, with applications to hypersonic air vehicles. Hypersonic vehicles endure extreme thermal loads caused by aerodynamic heating, resulting in a strong coupling between structural performance and aerothermodynamics. However, modeling coupled system behaviors requires simultaneous consideration of both aerodynamic and structural design variables, increasing the dimensionality of the design trade space and the difficulty of accurately modeling the constraints. Several active learning schemes have been proposed to accelerate identification of the composite feasible region that satisfies all constraints. Some of these require integrating the surrogate model over the entire design space with …

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Matching Two Long Interferometric Pathlengths Using Low Temporal Coherence Light For Finding Hong–Ou–Mandel Dip, Keith A. Wyman, Noah S. Everett, Anil K. Patnaik Jun 2026

Matching Two Long Interferometric Pathlengths Using Low Temporal Coherence Light For Finding Hong–Ou–Mandel Dip, Keith A. Wyman, Noah S. Everett, Anil K. Patnaik

Faculty Publications

Hong–Ou–Mandel (HOM) dip from a biphoton source in a two-photon interferometer provides a myriad of quantum tools for quantum communication and sensing. But the stringent requirements for spatial coherence between the photon pair makes it prohibitively difficult to observe high-fidelity HOM dip in long-distance free-space implementations, e.g., for the photon pairs involved in quantum communication need to match the two path lengths within a few 10 s of micron because of the short coherence width of the two-photon wave-packet. While many techniques for the pathlength balancing of two interferometric arms have been studied and applied extensively, such balancing is further …

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Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz Jun 2026

Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …

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Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee May 2026

Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee

Faculty Publications

Background: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. Methods: Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient’s history. Hyperparameter-tuned …

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Inconsistencies Emerge Between Regional And Local-Scale Water Security Metrics At Military Installations, Abigail Birnbaum, Michael L. Berg, Caitlin Grady, Daniel Weeks, Christopher M. Chini May 2026

Inconsistencies Emerge Between Regional And Local-Scale Water Security Metrics At Military Installations, Abigail Birnbaum, Michael L. Berg, Caitlin Grady, Daniel Weeks, Christopher M. Chini

Faculty Publications

Recent United States federal policy for military installations has emphasized the importance of developing a standardized approach for water security assessment to monitor changes in water resources and the ability for an installation to meet both its civilian and mission needs. For military installations in the United States, these assessments must consider demands both inside and outside the installation’s fence line, as regional resources are required to meet mission readiness. Focusing on physical water scarcity, this study compares four water security metrics with unique formulations and spatial resolutions, including an installation-scale metric and multiple regional metrics defined for either baseline …

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Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji May 2026

Measurements And Scaling Of Ion Propulsion Impulse During Driven Magnetic Reconnection, Fatima Ebrahimi, Nicholas A. O'Gorman, Kush Maheshwari, Jongsoo Yoo, Alexandre Sainterme, Hantao Ji

Faculty Publications

Impulse scaling during magnetic reconnection, the magnetic energy conversion to kinetic energy, via direct Mach probe measurements in Magnetic Reconnection Experiment is examined. Ion exhaust velocity and impulse scalings with reconnecting magnetic field during the push phase of driven reconnection are presented. The outflows and impulse measurements are compared with global MHD simulations. Both measurements and simulations reveal a favorable scaling, greater than linear, of impulse with reconnecting field. These scaling results establish that magnetic reconnection could be utilized for plasma propulsion.

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Sheets Of Spectral Data Of Stokes Waves In Weakly Nonlinear Models, Benjamin F. Akers, Ryan Creedon Apr 2026

Sheets Of Spectral Data Of Stokes Waves In Weakly Nonlinear Models, Benjamin F. Akers, Ryan Creedon

Faculty Publications

We study the spectral stability of small-amplitude Stokes waves in a family of weakly nonlinear, unidirectional models of the form ut + Lu + (u2)x = 0. We introduce a perturbation method to expand the spectral data in wave amplitude near flat-state eigenvalue collisions, with the ratio of the colliding modes as a free parameter. This yields sheets of spectral data whose slices at fixed amplitude give isolas of instability. The same perturbation framework treats both high-frequency and Benjamin--Feir instabilities, extends to discontinuous dispersion relations (including the Akers--Milewski equation), and, for the first time, provides an …

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Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley Apr 2026

Impact Of Collodion Thickness On Fission Track Generation Within Lexan Employed For Nuclear Forensic Analyses, Ryan K. Chapman, Zachary P. Meisel, Abigail A. Bickley

Faculty Publications

We report results from a computational study of fission track damage within a Lexan substrate caused by 235U suspended within a collodion film of varied thickness. MCNP6.3 was used to model energy deposition and displacements per atom within a Lexan film coated by a film of collodion, located within the air-filled 6 inch dry tube of a TRIGA reactor. The collodion layer was varied between 20 and 100 μm thickness, resulting in an 8.8% difference in energy deposition and 12% difference in displacements-per-atom within the Lexan substrate. These findings indicate that collodion adhesive thickness has a moderate impact on …

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Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter Apr 2026

Comparison Of Anomaly Detection Methods On Event-Based Vision Sensor Data In A High Noise Environment, Will Johnston, Anthony L. Franz, Shannon R. Young, Rachel Oliver, Zachary Theis, Brian Mcreynolds, Michael L. Dexter

Faculty Publications

Event-based vision sensors (EVSs) provide unique frequency analysis opportunities due to their event data output and high temporal resolution. Anomaly detection methods used in hyperspectral analysis can be used on the event frequency spectra to detect targets. However, the introduction of a strong, flickering interfering source can reduce the EVS sensitivity and obscure targets of interest. Previous work presented a method showing that targets could still be detected through an overwhelming source using frequency analysis, background suppression, and statistical filtering. This paper extends that research and compares the ability of five different eigenanalysis anomaly detection methods (principal component background suppression …

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Radiation Protection Factor Research: The Keystone For Conventional-Nuclear Integration (Cni), Andrew W. Decker Apr 2026

Radiation Protection Factor Research: The Keystone For Conventional-Nuclear Integration (Cni), Andrew W. Decker

Faculty Publications

This article briefly describes Defense Threat Reduction Agency (DTRA) vehicle Radiation Protection Factor (RPF) research and explains its critical role in enabling future Operations in a Nuclear Environment (ONE) and Conventional-Nuclear Integration (CNI) on behalf of the US Department of War (DoW).1 As such, the background and practical utility of RPF values is discussed, as well as the justification for renewed Army and DoW investment into RPF research to enhance Joint military planning, survivability, and lethality on tomorrow’s nuclear battlefields. Advances in DTRA RPF research directly strengthen US strategic and extended deterrence efforts and support all Agencies and Departments responsible …

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Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming Mar 2026

Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming

Faculty Publications

Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the …

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Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain Mar 2026

Characterizing Atmospheric Turbulence With The Lunar Step Response Method, Patrick D. Carattini, Caleb J. Stilp, Katelyn M. Atkinson, Stephen C. Cain

Faculty Publications

Most methods that astronomers use to characterize the strength of atmospheric turbulence in and around their observatories use differential image motion monitors observing a star to provide the necessary data for the measurement. With the Moon becoming a greater national priority, the need to characterize atmospheric paths between observatories on Earth and the Moon is potentially going to grow in the future. To this end, the differential image motion monitor is not an ideal instrument for characterizing turbulence along paths between observatories and the Moon as the bright Moon makes it difficult to detect and locate stars in its vicinity. …

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Transfer Learning Neural Networks For Nuclear Forensic Image Morphology Using Image Splitting Techniques, Niko A. Petrocelli, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley Mar 2026

Transfer Learning Neural Networks For Nuclear Forensic Image Morphology Using Image Splitting Techniques, Niko A. Petrocelli, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley

Faculty Publications

Manual morphological analysis of actinide particles from scanning electron microscope (SEM) imagery is a critical component of nuclear forensics but is prone to significant inter-analyst variability. To address this challenge, this work develops and evaluates an automated classification method using deep learning. We introduce a methodology based on partitioning 1906 SEM images, representing 13 classes of uranium compounds, into smaller patches for analysis. Three convolutional neural network (CNN) architectures of increasing complexity were compared: a custom baseline CNN, a simple transfer learning model using ResNet50v1, and a complex model featuring hierarchical feature extraction and a spatial attention mechanism built upon …

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Dynamic Magnetic Null Behavior In Planar Ion Diodes: Particle-In-Cell Analysis Of Field Oscillations And Ion Beam Dynamics, Jesse C. Foster, Stephen B. Swanekamp, Paul F. Ottinger Mar 2026

Dynamic Magnetic Null Behavior In Planar Ion Diodes: Particle-In-Cell Analysis Of Field Oscillations And Ion Beam Dynamics, Jesse C. Foster, Stephen B. Swanekamp, Paul F. Ottinger

Faculty Publications

Particle-in-cell simulations of a 1.75 MV, 375 kA, and 50 ns planar pinched-beam diode reveal that the strongest gigahertz-frequency oscillations in electric field and ion current arise from the dynamic motion of the magnetic null near the anode tip. These oscillations, which appear when the ion transit time becomes comparable to the local field-variation timescale, periodically expand the effective anode–cathode gap and generate bursts of over-accelerated ions. The resulting ion energy spectrum broadens substantially near the null while maintaining excellent beam uniformity along the anode. The simulations, therefore, demonstrate a direct physical linkage between ion transit time instability and magnetic …

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Experiments Towards A Neutron Target For Measurements In Inverse Kinematics, S. F. Dellmann, Caroline M. Harrington, O. R. Cantrell, A. L. Cooper, A. Couture, D, V, Gorelov, I Knapová, S. M. Mosby, R. Reifarth, A. Alvarez, A. Aprahamian, J. Butz, I. J. Bos, Michael T. Febbraro, T. Hankins, B. M. Harvey, T. Heftrich, M. Le, Juan J. Manfredi, A. B. Mcintosh, K. V. Manukyan, M. Matney, S. Regener, D. Robertson, A. Simon, D. Sokolovic, E. Stech, G. Tabacaru, W. Tan, M. Wiescher, S. Yennello Feb 2026

Experiments Towards A Neutron Target For Measurements In Inverse Kinematics, S. F. Dellmann, Caroline M. Harrington, O. R. Cantrell, A. L. Cooper, A. Couture, D, V, Gorelov, I Knapová, S. M. Mosby, R. Reifarth, A. Alvarez, A. Aprahamian, J. Butz, I. J. Bos, Michael T. Febbraro, T. Hankins, B. M. Harvey, T. Heftrich, M. Le, Juan J. Manfredi, A. B. Mcintosh, K. V. Manukyan, M. Matney, S. Regener, D. Robertson, A. Simon, D. Sokolovic, E. Stech, G. Tabacaru, W. Tan, M. Wiescher, S. Yennello

Faculty Publications

Neutron-induced reactions play an important role in fundamental nuclear physics, nuclear astrophysics, and applications. In the case of reactions on rare isotopes, there are limited options for direct experimental measurements. The Neutron Target Demonstrator project at Los Alamos National Laboratory seeks to test the feasibility of moderating spallation neutrons within a 1 m3graphite cube to create a standing neutron target for neutron-induced reaction measurements in inverse kinematics. This paper presents the results of experimental neutron flux distribution tests using neutron sources (ranging from 1 keV to 50 MeV) created by accelerators at the University of Notre Dame and …

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Discovery Of Dynamical Structures Mapping Chaotic Transport Pathways In The Earth–Moon Cr3bp, Tyler J. Kapolka, Christina E. Paljug, Robert A. Bettinger, Rachel Oliver, Bruce A. Cox, Jeremiah A. Specht Feb 2026

Discovery Of Dynamical Structures Mapping Chaotic Transport Pathways In The Earth–Moon Cr3bp, Tyler J. Kapolka, Christina E. Paljug, Robert A. Bettinger, Rachel Oliver, Bruce A. Cox, Jeremiah A. Specht

Faculty Publications

Chaos; Deterministic chaos; Earth–Moon system; Poincaré map; Quasi-chaotic; Surface of section /// For the Circular Restricted 3-Body Problem (CR3BP), the topologies present within a Poincaré map enable the extraction of useful information regarding periodic, quasi-periodic, and chaotic trajectory behavior. Aside from the prominent topologies that follow distinct concentric patterns around fixed points, indicative of the periodic and quasi-periodic motion that is often the central focus of CR3BP research, there are also many “dusty” regions on the Poincaré map that appear random without an apparent structure and are indicative of chaotic motion. This paper, for the first time in literature, identifies …

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Uranium Chemical Compound Classification Using Sub-Images And Statistical Machine Learning For Nuclear Forensics, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley Feb 2026

Uranium Chemical Compound Classification Using Sub-Images And Statistical Machine Learning For Nuclear Forensics, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley

Faculty Publications

Uranium particle analysis from Scanning Electron Microscope (SEM) imagery is a crucial tool in nuclear forensics. The particle morphology lexicon proposed by Tamasi et al. in J Radioanal Nucl Chem 307, 1611–1619 (2016) follows a standardized, manual identification process to identify particle morphology features. The present work seeks to mirror this methodology using computer feature selection from the scikit-image Python library rather than human classification. Using a random forest classifier, a 56% overall uranium true positive classification accuracy (a 39.6% balanced classification accuracy) was achieved on a test set outperforming a naïve (chance) model by 48%. The methodology introduced splits …

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Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz Jan 2026

Proposed Methodology For Correcting Fourier-Transform Infrared Spectroscopy Field-Of-View Scene-Change Artifacts, Kody A. Wilson, Michael L. Dexter, Benjamin F. Akers, Anthony L. Franz

Faculty Publications

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the …

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A Comparison Of Modeled Daytime E Regions From E-Probed And Pyiri With Ionosonde Observations, Daniel J. Emmons, Cornelius C. J. H. Salinas, Dong L. Wu, Nimalan Swarnalingam, Eugene V. Dao, Jorge L. Chau, Yosuke Yamazaki, Kyle E. Fitch, Victoriya V. Forsythe Jan 2026

A Comparison Of Modeled Daytime E Regions From E-Probed And Pyiri With Ionosonde Observations, Daniel J. Emmons, Cornelius C. J. H. Salinas, Dong L. Wu, Nimalan Swarnalingam, Eugene V. Dao, Jorge L. Chau, Yosuke Yamazaki, Kyle E. Fitch, Victoriya V. Forsythe

Faculty Publications

While the F region is the primary focus of many ionospheric models because it contains the peak electron density, the E region is an important region for ionospheric conductivities and high-frequency radio propagation. This study analyzes modeled E regions from the newly developed PyIRI and E-PROBED models. A long-term comparison of E region predictions from E-PROBED and PyIRI with ionosonde observations is performed for three sites spanning low- (Fortaleza, Brazil), mid- (El Arenosillo, Spain), and high-latitudes (Gakona, Alaska). Modeled foE and hmE trends are compared against a combination of manually-scaled and automatically-scaled ionograms using ARTIST-5 for the period 2009–2024 for …

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Amp: Single-Shot Ultra-Wide Fisheye-To-Cubemap Pnp Pose Estimation, Ryan M. Raettig, Richard R. Nyquist, Scott L. Nykl, Clark N. Taylor, Christine M. Schubert Kabban Dec 2025

Amp: Single-Shot Ultra-Wide Fisheye-To-Cubemap Pnp Pose Estimation, Ryan M. Raettig, Richard R. Nyquist, Scott L. Nykl, Clark N. Taylor, Christine M. Schubert Kabban

Faculty Publications

Estimating the position and orientation of a rigid object from an image is critical for situational awareness in robotics and autonomous systems. This study explores relative pose estimation using an ultra-wide fisheye camera for unmanned aircraft inspection vehicles. Ultra-wide fisheye lenses introduce radial distortion and capture features beyond the rectilinear image plane, rendering rectilinear Perspective-n-Point (PnP) algorithms inadequate. Designing a bespoke ultra-wide fisheye localization algorithm requires consideration of both the feature detection method and the pose estimator itself. This study proposes a novel method that combines (1) a fisheye-to-cubemap reprojection, (2) a You Only Look Once (YOLO) convolutional neural network …

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Statistical Overview Of Long-Lived Active Regions Observed Across Multiple Carrington Rotations, Emily I. Mason, Kara L. Kniezewski Dec 2025

Statistical Overview Of Long-Lived Active Regions Observed Across Multiple Carrington Rotations, Emily I. Mason, Kara L. Kniezewski

Student Publications

The study of solar active regions (ARs) is of central importance to a range of fundamental science, as well as the practical applications of space weather. Active region emergence and life cycles are two areas of particular interest, yet the lack of consistent full-Sun observations has made long-term studies of active regions difficult. Here, we present results from a study to identify and characterize long-lived active regions (LLARs), defined as those which were observed during at least two consecutive Carrington rotations and which did not undergo significant successive flux emergence once the decay phase began. Such active regions accounted for …

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Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Gaber, Mohamed Abdelraheem Dec 2025

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Gaber, Mohamed Abdelraheem

Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

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Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik Oct 2025

Applying Machine Learning Methods To Laser Acceleration Of Protons: Synthetic Data For Exploring The High Repetition Rate Regime, John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith, Alona Kryshchenko, Christopher M. Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

Advances in ultra‐intense laser technology have increased repetition rates and average power for chirped‐pulse laser systems, which offer a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser‐plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large ∼1.5 million data point synthetic data set for proton acceleration using a physics‐informed analytic model that we …

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