Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons™

Open Access. Powered by Scholars. Published by Universities.®

Air Force Institute of Technology

2022

Discipline
Keyword
Publication
Publication Type

Articles 121 - 131 of 131

Full-Text Articles in Physical Sciences and Mathematics

Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Joshua Larson, Clark N. Taylor, Thomas Wischgoll Feb 2022

Delaunay Walk For Fast Nearest Neighbor: Accelerating Correspondence Matching For Icp, James D. Anderson, Ryan M. Raettig, Joshua Larson, Clark N. Taylor, Thomas Wischgoll

Faculty Publications

Point set registration algorithms such as Iterative Closest Point (ICP) are commonly utilized in time-constrained environments like robotics. Finding the nearest neighbor of a point in a reference 3D point set is a common operation in ICP and frequently consumes at least 90% of the computation time. We introduce a novel approach to performing the distance-based nearest neighbor step based on Delaunay triangulation. This greedy algorithm finds the nearest neighbor of a query point by traversing the edges of the Delaunay triangulation created from a reference 3D point set. Our work integrates the Delaunay traversal into the correspondences search of …

Go to article

Utilization And Efficient Computation Of Polarization Factor Q For Fast, Accurate Brdf Modeling, Samuel D. Butler, Michael A. Marciniak Feb 2022

Utilization And Efficient Computation Of Polarization Factor Q For Fast, Accurate Brdf Modeling, Samuel D. Butler, Michael A. Marciniak

Faculty Publications

The Bidirectional Reflectance Distribution Function (BRDF) is of substantial use in remote sensing, scene generation, and computer graphics, to describe optical scatter off realistic surfaces. This paper begins by summarizing our prior work in relating wave optics and geometric optics models, culminating with the Modified Cook-Torrance (MCT) model. The MCT model is evaluated here against aluminum, Infragold, and silver paint at various wavelengths in the IR. In each case, the MCT model is shown to outperform a standard microfacet model. Then, this paper shows a non-trivial method of computing the primary new term, the polarization factor Q. This optimization …

Go to article

Cu2+ And Cu3+ Acceptors In Β-Ga2O3 Crystals: A Magnetic Resonance And Optical Absorption Study, Timothy D. Gustafson, Nancy C. Giles, Brian C. Holloway, Christopher A. Lenyk, J. Jesenovec, J. S. Mccloy, M. D. Mccluskey, Larry E. Halliburton Feb 2022

Cu2+ And Cu3+ Acceptors In Β-Ga2O3 Crystals: A Magnetic Resonance And Optical Absorption Study, Timothy D. Gustafson, Nancy C. Giles, Brian C. Holloway, Christopher A. Lenyk, J. Jesenovec, J. S. Mccloy, M. D. Mccluskey, Larry E. Halliburton

Faculty Publications

Electron paramagnetic resonance (EPR) and optical absorption are used to characterize Cu2+ (3d9) and Cu3+ (3d8) ions in Cu-doped β-Ga2O3. These Cu ions are singly ionized acceptors and neutral acceptors, respectively (in semiconductor notation, they are Cu− and Cu0 acceptors). Two distinct Cu2+ EPR spectra are observed in the as-grown crystals. We refer to them as Cu2+(A) and Cu2+(B). Spin-Hamiltonian parameters (a g matrix and a 63,65Cu hyperfine matrix) are obtained from the angular dependence of each spectrum. Additional electron-nuclear double resonance …

Go to article

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons Jan 2022

A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons

Faculty Publications

Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …

Go to article

Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor Jan 2022

Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor

Faculty Publications

This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement …

Go to article

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik Jan 2022

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

Go to article

Electromagnetic Multi–Gaussian Speckle, Milo W. Hyde Iv, Olga Korotkova Jan 2022

Electromagnetic Multi–Gaussian Speckle, Milo W. Hyde Iv, Olga Korotkova

Faculty Publications

Generalizing our prior work on scalar multi-Gaussian (MG) distributed optical fields, we introduce the two-dimensional instantaneous electric-field vector whose components are jointly MG distributed. We then derive the single-point Stokes parameter probability density functions (PDFs) of MG-distributed light having an arbitrary degree and state of polarization. We show, in particular, that the intensity contrast of such a field can be tuned to values smaller or larger than unity. We validate our analysis by generating an example partially polarized MG field with a specified single-point polarization matrix using two different Monte Carlo simulation methods. We then compute the joint PDFs of …

Go to article

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Effect Of Connection State & Transport/Application Protocol On The Machine Learning Outlier Detection Of Network Intrusions, George Yuchi [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

The majority of cyber infiltration & exfiltration intrusions leave a network footprint, and due to the multi-faceted nature of detecting network intrusions, it is often difficult to detect. In this work a Zeek-processed PCAP dataset containing the metadata of 36,667 network packets was modeled with several machine learning algorithms to classify normal vs. anomalous network activity. Principal component analysis with a 10% contamination factor was used to identify anomalous behavior. Models were created using recursive feature elimination on logistic regression and XGBClassifier algorithms, and also using Bayesian and bandit optimization of neural network hyperparameters. These models were trained on a …

Go to article

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …

Go to article

Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals Jan 2022

Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio [*], Torrey J. Wagner, Paul Auclair, Brent T. Langhals

Faculty Publications

Conducting effective quality control of weather observations in real time is vital to the 14th Weather Squadron’s mission of providing authoritative climate data. This study explored automated quality control of weather observations by applying multiple machine learning techniques to 43,487 surface weather observations from 5 years of data at a single location. Temperature predictors were evaluated using recursive feature elimination on linear regression and XGBoost algorithms, as well as using a neural network hyperparameter sweep. Modeling was repeated after calculating trigonometric transforms of temporal variables to give the models insight into the diurnal heating cycle of the Earth. All models …

Go to article

Studying The Conditions For Magnetic Reconnection In Solar Flares With And Without Precursor Flares, Seth H. Garland, Daniel J. Emmons, Robert D. Loper Jan 2022

Studying The Conditions For Magnetic Reconnection In Solar Flares With And Without Precursor Flares, Seth H. Garland, Daniel J. Emmons, Robert D. Loper

Faculty Publications

Forecasting of solar flares remains a challenge due to the limited understanding of the triggering mechanisms associated with magnetic reconnection, the primary physical phenomenon connected to these events. Studies have indicated that changes to the photospheric magnetic fields associated with magnetic reconnection – particularly in relation to the field helicity – occur during solar flare events. This study utilized data from the Solar Dynamics Observatory (SDO) Helioseismic and Magnetic Imager (HMI) and SpaceWeather HMI Active Region Patches (SHARPs) to analyze full vector-field component data of the photospheric magnetic field during solar flare events within a near decade long HMI dataset. …

Go to article

First Prev