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Air Force Institute of Technology

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Articles 271 - 300 of 3047

Full-Text Articles in Physical Sciences and Mathematics

Steady State Thermal Blooming With Convection: Modeling, Simulation And Analysis, Jeremiah S. Lane Sep 2023

Steady State Thermal Blooming With Convection: Modeling, Simulation And Analysis, Jeremiah S. Lane

Theses and Dissertations

The modeling, simulation, and analysis of high energy laser propagation is a research topic of significant interest to the defense community. A detailed understanding of the phenomenon of thermal blooming is crucial as it is detrimental to the propagation of lasers over long distances and in the presence of aerosols. The simulation of thermal blooming has historically relied on wave optics models and scaling laws for the fluid response to the laser. Since thermal blooming occurs in the presence of natural convection, however, there is a need for simulating this coupled fluid-beam effect using a first principles approach. In this …

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Numerical Simulation Of Nonlinear Wave Equations With Machine Learning, Kristina O. F. Williams Sep 2023

Numerical Simulation Of Nonlinear Wave Equations With Machine Learning, Kristina O. F. Williams

Theses and Dissertations

A machine learning procedure is proposed to create numerical schemes for solutions of certain types of nonlinear wave equations on coarse grids. This method trains stencil weights of a discretization of the equation, with the truncation error of the scheme as the objective function for training. A neural network is used as a model for the stencil weights. The method uses centered finite differences to initialize the optimization routine and a second-order implicit-explicit time solver as a framework. Symmetry conditions are enforced on the learned operator to ensure a stable method. The procedure is applied to the Korteweg - de …

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Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill Sep 2023

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

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Advanced Statistical Methodology For The Modern Probability Of Detection, Christine E. Knott Sep 2023

Advanced Statistical Methodology For The Modern Probability Of Detection, Christine E. Knott

Theses and Dissertations

Probability of detection (POD) is an invaluable part of the calculations used by the USAF to validate the capabilities of nondestructive inspection systems for detecting defects in critical structural components on aircraft. A POD study consists of a designed experiment, linear modeling, and a probability of detection verses defect size curve. This curve is useful for determining how often an aircraft should be re-inspected. Some POD studies are unsuccessful in creating realistic POD curves because the statistical modeling used has two common limitations: (1) a lack of convergence leading to no solution and, (2) violated assumptions leading to incorrect solutions. …

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Analysis Of Coronal Magnetic Field Parameters During X- And M-Class Solar Flares, Seth H. Garland Sep 2023

Analysis Of Coronal Magnetic Field Parameters During X- And M-Class Solar Flares, Seth H. Garland

Theses and Dissertations

Using Non-Linear Force Free Field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-minute cadence Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of one hour before through one hour after the start of 18 X-class flares and 12 M-class flares. Several magnetic field parameters were calculated from the modeled fields directly – as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis – for two different regions: areas with photospheric Bz ≥ 300 G (Active Region – AR) and areas above the photosphere with the …

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Thermal Blooming With Laser-Induced Convection: Radial Basis Function Simulation, Benjamin F. Akers, Steven T. Fiorino, Jonah A. Reeger Aug 2023

Thermal Blooming With Laser-Induced Convection: Radial Basis Function Simulation, Benjamin F. Akers, Steven T. Fiorino, Jonah A. Reeger

Faculty Publications

The propagation of a high energy laser through a nearly stagnant absorbing medium is studied. The absorption values and time scale of the problem are such that the laser induces convective heat currents transverse to the beam. These currents couple to the laser via the refractive index, causing time dependent thermal blooming. A numerical method is developed and applied to the model in [ J. Electromagn. Waves Appl. 33, 96 (2019) ], using radial basis functions for spatial differencing, which allows for irregular point spacings and a wide class of geometries. Both the beam and laser-induced fluid dynamics are …

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Anomaly Detection In The Molecular Structure Of Gallium Arsenide Using Convolutional Neural Networks, Timothy Roche, Aihua W. Wood, Philip Cho, Chancellor Johnstone Aug 2023

Anomaly Detection In The Molecular Structure Of Gallium Arsenide Using Convolutional Neural Networks, Timothy Roche, Aihua W. Wood, Philip Cho, Chancellor Johnstone

Faculty Publications

This paper concerns the development of a machine learning tool to detect anomalies in the molecular structure of Gallium Arsenide. We employ a combination of a CNN and a PCA reconstruction to create the model, using real images taken with an electron microscope in training and testing. The methodology developed allows for the creation of a defect detection model, without any labeled images of defects being required for training. The model performed well on all tests under the established assumptions, allowing for reliable anomaly detection. To the best of our knowledge, such methods are not currently available in the open …

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Spectral Broadening Effects On Pulsed-Source Digital Holography, Steven A. Owens, Mark F. Spencer, Glen P. Perram Aug 2023

Spectral Broadening Effects On Pulsed-Source Digital Holography, Steven A. Owens, Mark F. Spencer, Glen P. Perram

Faculty Publications

Using a pulsed configuration, a digital-holographic system is setup in the off-axis image plane recording geometry, and spectral broadening via pseudo-random bit sequence is used to degrade the temporal coherence of the master-oscillator laser. The associated effects on the signal-to-noise ratio are then measured in terms of the ambiguity and coherence efficiencies. It is found that the ambiguity efficiency, which is a function of signal-reference pulse overlap, is not affected by the effects of spectral broadening. The coherence efficiency, on the other hand, is affected. As a result, the coherence efficiency, which is a function of effective fringe visibility, is …

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Propagation Of Spatiotemporal Optical Vortex Beams In Linear, Second-Order Dispersive Media, Milo W. Hyde Iv, Miguel A. Porras Jul 2023

Propagation Of Spatiotemporal Optical Vortex Beams In Linear, Second-Order Dispersive Media, Milo W. Hyde Iv, Miguel A. Porras

Faculty Publications

In this paper, we study the behaviors of spatiotemporal optical vortex (STOV) beams propagating in linear dispersive media. Starting with the Fresnel diffraction integral, we derive a closed-form expression for the STOV field at any propagation distance z in a general second-order dispersive medium. We compare our general result to special cases published in the literature and examine the characteristics of higher-order STOV beams propagating in dispersive materials by varying parameters of the medium and source-plane STOV field. We validate our analysis by comparing theoretical predictions to numerical computations of a higher-order STOV beam propagating through fused silica, where we …

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Wave Optics Approach To Solar Cell Brdf Modeling With Experimental Results, Madilynn Compean, Todd V. Small, Milo W. Hyde Iv, Michael Marciniak Jul 2023

Wave Optics Approach To Solar Cell Brdf Modeling With Experimental Results, Madilynn Compean, Todd V. Small, Milo W. Hyde Iv, Michael Marciniak

Faculty Publications

Light curve analysis is often used to discern information about satellites in geosynchronous orbits. Solar panels, comprising a large part of the satellite’s body, contribute significantly to these light curves. Historically, theoretical bidirectional reflectance distribution functions (BRDFs) have failed to capture key features in the scattered light from solar panels. In recently published work, a new solar cell BRDF was developed by combining specular microfacet and “two-slit” diffraction terms to capture specular and periodic/array scattering, respectively. This BRDF was experimentally motivated and predicted many features of the solar cell scattered irradiance. However, the experiments that informed the BRDF were limited …

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A Hybrid Metaheuristic And Computer Vision Approach To Closed-Loop Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano, Shay V. Wallace, Benji Maruyama, Gilbert L. Peterson Jul 2023

A Hybrid Metaheuristic And Computer Vision Approach To Closed-Loop Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano, Shay V. Wallace, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Fused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing …

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Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti Jul 2023

Hyperspectral Point Cloud Projection For The Semantic Segmentation Of Multimodal Hyperspectral And Lidar Data With Point Convolution-Based Deep Fusion Neural Networks, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which …

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The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel Jul 2023

The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel

Faculty Publications

In the armed forces, successful digitalization is crucial to ensure effective operations. Much of the existing literature on project factors during the planning and execution phases of public IT projects do not focus specifically on military sector projects. Therefore, the paper aims to provide empirical insights into the characteristics of successful military IT projects. Data from such projects in NATO countries and agencies were collected through interviews and project documents. The findings relating to the main variable of interest, “delivery of client benefit,” supported previous findings on IT project performance. Medium-sized projects performed better than small and large projects, and …

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Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan, Clark N. Taylor, Scott L. Nykl Jul 2023

Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan, Clark N. Taylor, Scott L. Nykl

Faculty Publications

One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is …

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Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine Jul 2023

Cost Estimation Trends For Major Defense Acquisition Programs, Sammantha Jones, Edward D. White, Jonathan D. Ritschel, Shawn M. Valentine

Faculty Publications

The authors use both descriptive and inferential techniques to investigate average and standard deviation trends in cost estimates for major defense acquisition programs (MDAPs) grouped into decades from the 1970s to 2010s. For total program-cost-growth factors (CGFs), the 2010s exhibited lower CGFs compared to the 1990s. For the program-acquisition-unit cost (PAUC) CGFs, the 2010s appear lower than the 1990s and borderline lower than the 1970s. A statistically significant decreasing trend in the standard deviations of total program CGFs throughout the decades was identified. This lowering variability trend also appeared for PAUC CGFs from the 1980s onward. This finding appears to …

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Intrinsic Point Defects (Vacancies And Antisites) In Cdgep2 Crystals, Timothy D. Gustafson, Nancy C. Giles, Peter G. Schunemann, Kevin T. Zawilski, Kent L. Averett, Jonathan E. Slagle, Larry E. Halliburton Jun 2023

Intrinsic Point Defects (Vacancies And Antisites) In Cdgep2 Crystals, Timothy D. Gustafson, Nancy C. Giles, Peter G. Schunemann, Kevin T. Zawilski, Kent L. Averett, Jonathan E. Slagle, Larry E. Halliburton

Faculty Publications

Cadmium germanium diphosphide (CdGeP2) crystals, with versatile terahertz-generating properties, belong to the chalcopyrite family of nonlinear optical materials. Other widely investigated members of this family are ZnGeP2 and CdSiP2. The room-temperature absorption edge of CdGeP2 is near 1.72 eV (720 nm). Cadmium vacancies, phosphorous vacancies, and germanium-on-cadmium antisites are present in as-grown CdGeP2 crystals. These unintentional intrinsic point defects are best studied below room temperature with electron paramagnetic resonance (EPR) and optical absorption. Prior to exposure to light, the defects are in charge states that have no unpaired spins. Illuminating a CdGeP2 …

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A Comparison Of Quaternion Neural Network Backpropagation Algorithms, Jeremiah Bill, Bruce A. Cox, Lance Champaign Jun 2023

A Comparison Of Quaternion Neural Network Backpropagation Algorithms, Jeremiah Bill, Bruce A. Cox, Lance Champaign

Faculty Publications

This research paper focuses on quaternion neural networks (QNNs) - a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. Previous studies have shown that QNNs outperform real-valued neural networks in basic tasks and have potential in high-dimensional problem spaces. However, research on QNNs has been fragmented, with contributions from different mathematical and engineering domains leading to unintentional overlap in QNN literature. This work aims to unify existing research by evaluating four distinct QNN backpropagation algorithms, including the novel GHR-calculus backpropagation algorithm, and providing concise, scalable implementations of each algorithm using a …

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Optimal Estimation Inversion Of Ionospheric Electron Density From Gnss-Pod Limb Measurements: Part I-Algorithm And Morphology, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons, Tyler C. Summers, Robert Gardiner-Garden Jun 2023

Optimal Estimation Inversion Of Ionospheric Electron Density From Gnss-Pod Limb Measurements: Part I-Algorithm And Morphology, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons, Tyler C. Summers, Robert Gardiner-Garden

Faculty Publications

GNSS-LEO radio links from Precise Orbital Determination (POD) and Radio Occultation (RO) antennas have been used increasingly in characterizing the global 3D distribution and variability of ionospheric electron density (Ne). In this study, we developed an optimal estimation (OE) method to retrieve Ne profiles from the slant total electron content (hTEC) measurements acquired by the GNSS-POD links at negative elevation angles (ε < 0°). Although both OE and onion-peeling (OP) methods use the Abel weighting function in the Ne inversion, they are significantly different in terms of performance in the lower ionosphere. The new OE results can overcome the large Ne oscillations, sometimes negative values, seen in the OP retrievals in the E-region ionosphere. In the companion paper in this Special Issue, the HmF2 and NmF2 from the OE retrieval are validated against ground-based ionosondes and radar observations, showing generally good agreements in NmF2 from all sites. Nighttime hmF2 measurements tend to agree better than the daytime when the ionosonde heights tend to be slightly lower. The OE algorithm has been applied to all GNSS-POD data acquired from the COSMIC-1 (2006–2019), COSMIC-2 (2019–present), and Spire (2019–present) constellations, showing a consistent ionospheric Ne morphology. The unprecedented spatiotemporal sampling of the ionosphere from these constellations now allows a detailed analysis of the frequency–wavenumber spectra for the Ne variability at different heights. In the lower ionosphere (~150 km), we found significant spectral power in DE1, DW6, DW4, SW5, and SE4 wave components, in addition to well-known DW1, SW2, and DE3 waves. In the upper ionosphere (~450 km), additional wave components are still present, including DE4, DW4, DW6, SE4, and SW4. The co-existence of eastward- and westward-propagating wave4 components implies the presence of a stationary wave4 (SPW4), as suggested by other earlier studies. Further improvements to the OE method are proposed, including a tomographic inversion technique that leverages the asymmetric sampling about the tangent point associated with GNSS-LEO links.

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Numerical Simulation Of The Korteweg–De Vries Equation With Machine Learning, Kristina O. F. Williams, Benjamin F. Akers Jun 2023

Numerical Simulation Of The Korteweg–De Vries Equation With Machine Learning, Kristina O. F. Williams, Benjamin F. Akers

Faculty Publications

A machine learning procedure is proposed to create numerical schemes for solutions of nonlinear wave equations on coarse grids. This method trains stencil weights of a discretization of the equation, with the truncation error of the scheme as the objective function for training. The method uses centered finite differences to initialize the optimization routine and a second-order implicit-explicit time solver as a framework. Symmetry conditions are enforced on the learned operator to ensure a stable method. The procedure is applied to the Korteweg–de Vries equation. It is observed to be more accurate than finite difference or spectral methods on coarse …

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Machine Learning Data Feature Reduction And Model Optimization, Francisco P. Maturana, Phillip M. Lacasse Jun 2023

Machine Learning Data Feature Reduction And Model Optimization, Francisco P. Maturana, Phillip M. Lacasse

AFIT Patents

For machine learning data reduction and model optimization, a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from …

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A Bit-Parallel Tabu Search Algorithm For Finding Es2 -Optimal And Minimax-Optimal Supersaturated Designs, Luis B. Morales, Dursun A. Bulutoglu Jun 2023

A Bit-Parallel Tabu Search Algorithm For Finding Es2 -Optimal And Minimax-Optimal Supersaturated Designs, Luis B. Morales, Dursun A. Bulutoglu

Faculty Publications

We prove the equivalence of two-symbol supersaturated designs (SSDs) with N (even) rows, m columns, smax=4t+i, where i ∈ {0,2}, t ∈ Z≥0 and resolvable incomplete block designs (RIBDs) whose any two blocks intersect in at most (N+4t+i)/4 points. Using this equivalence, we formulate the search for two-symbol E(s2)-optimal and minimax-optimal SSDs with smax ∈ {2,4,6} as a search for RIBDs whose blocks intersect accordingly. This allows developing a bit-parallel tabu search (TS) algorithm. The TS algorithm found E(s2)-optimal and minimax-optimal SSDs achieving the sharpest known E(s2) lower bound with …

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Modeling Potential Damages Of Hurricanes At The Facility Scale: A Case Study Of Eglin Air Force Base, Alexander J. Baldwin, Christopher M. Chini, Steven J. Schuldt, Justin D. Delorit Jun 2023

Modeling Potential Damages Of Hurricanes At The Facility Scale: A Case Study Of Eglin Air Force Base, Alexander J. Baldwin, Christopher M. Chini, Steven J. Schuldt, Justin D. Delorit

Faculty Publications

Tropical cyclones have intensified and are expected to strengthen throughout the 21st century due to climate change impacts. Storm intensification poses a serious threat to coastal municipalities and infrastructure that are not designed to withstand future wind and storm surge potential. Coupled modeling software has been widely used to replicate extreme weather events and simulate climate change effects to understand the magnitude of vulnerability coastal regions face. However, these studies are often conducted at too coarse resolutions to provide decision makers with actionable information for facility-level adaptation. This study addresses this limitation using a coupled modeling approach by simulating climate-intensified …

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Failure Distributions For Parallel Dependent Identical Weibull Components, Gina S. Sigler Jun 2023

Failure Distributions For Parallel Dependent Identical Weibull Components, Gina S. Sigler

Theses and Dissertations

For a parallel system, when one component fails, the failure distribution of the remaining components will have an increased failure rate. This dissertation takes a novel approach to finding the associated failure distribution of the full system using ordinal statistic distributions for correlated Weibull components, allowing for unknown correlations between the dependent components. A Taylor series approximation is presented for the two component; system failure time distributions are also derived for two failures in a two component system, two failures in an n component system, three failures in a three component system, and k failures in an n component system. …

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Quantitative Modeling Of Text-Based Intelligence Source Uncertainty, Adam D. Nesmith Jun 2023

Quantitative Modeling Of Text-Based Intelligence Source Uncertainty, Adam D. Nesmith

Theses and Dissertations

An all-source intelligence analyst’s primary job is delivering timely, well-sourced assessments on relevant targets based on uncertain and incomplete information. Each assessment includes a likelihood that the assessment is true, and a confidence level based on the uncertainty of the sources used. Quantitative all-source intelligence analysis is not widely implemented despite the acknowledged limitations of qualitative intelligence assessments and the existence of proposed quantitative methods. This is due to the challenge of quantitatively representing uncertainty in text-based intelligence reporting (i.e., HUMINT, OSINT, SIGINT), which limits the effectiveness and usability of previously suggested methods. This research creates a novel framework for …

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Non-Degenerate Two-Photon Absorption And Excited State Line Shapes In Alkali Vapors, Timothy M. True Jun 2023

Non-Degenerate Two-Photon Absorption And Excited State Line Shapes In Alkali Vapors, Timothy M. True

Theses and Dissertations

The rubidium 5S-5D two-photon transitions were probed between 758-798 nm, where a six order of magnitude change in the cross section was observed and the polarization dependence was seen explicitly. Collisional rates are measured in cesium on the 6P-9S and 5D-10F transitions, with broadening rates as large as 710 MHz/Torr and shifting rates as large as 273 MHz/Torr. We observe Stark broadening on the cesium 5D-10F line shape, with Lorentzian width increasing from 200 MHz to 40 GHz. The influence of electron impact is discussed under these conditions and with 0-20 Torr of helium. Cascade amplified spontaneous emission is seen …

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Fate And Transport Of Per- And Polyfluoroalkyl Substances (Pfas) At Aqueous Film Forming Foam (Afff) Discharge Sites: A Review, Jeffery T. Mcgarr, Eric G. Mbonimpa, Drew C. Mcavoy, Mohamad R. Soltanian May 2023

Fate And Transport Of Per- And Polyfluoroalkyl Substances (Pfas) At Aqueous Film Forming Foam (Afff) Discharge Sites: A Review, Jeffery T. Mcgarr, Eric G. Mbonimpa, Drew C. Mcavoy, Mohamad R. Soltanian

Faculty Publications

Per- and polyfluorinated alkyl substances (PFAS) are an environmentally persistent group of chemicals that can pose an imminent threat to human health through groundwater and surface water contamination. In this review, we evaluate the subsurface behavior of a variety of PFAS chemicals with a focus on aqueous film forming foam (AFFF) discharge sites. AFFF is the primary PFAS contamination risk at sites such as airports and military bases due to use as a fire extinguisher. Understanding the fate and transport of PFAS in the subsurface environment is a multifaceted issue. This review focuses on the role of adsorbent, adsorbate, and …

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Method Of Evanescently Coupling Whispering Gallery Mode Optical Resonators Using Liquids, Hengky Chandrahalim, Kyle T. Bodily May 2023

Method Of Evanescently Coupling Whispering Gallery Mode Optical Resonators Using Liquids, Hengky Chandrahalim, Kyle T. Bodily

AFIT Patents

The present invention relates to evanescently coupling whispering gallery mode optical resonators having a liquid coupling as well as methods of making and using same. The aforementioned evanescently coupling whispering gallery mode optical resonators having a liquid couplings provide increased tunability and sensing selectivity over current same. The aforementioned. Applicants’ method of making evanescent-wave coupled optical resonators can be achieved while having coupling gap dimensions that can be fabricated using standard photolithography. Thus economic, rapid, and mass production of coupled WGM resonators-based lasers, sensors, and signal processors for a broad range of applications can be realized.

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Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks May 2023

Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks

Faculty Publications

Machine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider …

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Optical Fiber Tip Micro Anemometer, Jeremiah C. Williams, Hengky Chandrahalim Apr 2023

Optical Fiber Tip Micro Anemometer, Jeremiah C. Williams, Hengky Chandrahalim

AFIT Patents

A passive microscopic flow sensor includes a three-dimensional microscopic optical structure formed on a cleaved tip of an optical fiber. The three-dimensional microscopic optical structure includes a post attached off-center to and extending longitudinally from the cleaved tip of the optical fiber. A rotor of the three-dimensional microscopic optical structure is received for rotation on the post. The rotor has more than one blade. Each blade has a reflective undersurface that reflects a light signal back through the optical fiber when center aligned with the optical fiber, the blades of the rotor shaped to rotate at a rate related to …

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Filter-Based Air Sampler Capable Of Integration Into Small Unmanned Aerial Vehicles, Robert M. Eninger, Stepanie A. Ohms, Jeremy M. Slagley Apr 2023

Filter-Based Air Sampler Capable Of Integration Into Small Unmanned Aerial Vehicles, Robert M. Eninger, Stepanie A. Ohms, Jeremy M. Slagley

AFIT Patents

A filter-based air sampler, more specifically a filter-based air sampler capable of integration into small unmanned aerial systems is disclosed. The filter-based air sampler may include a filter assembly which has as its component parts: an open faced air intake component, a filter, and a filter support that has a central supporting grid. The filter assembly may joined to the housing of a fan, such as a centrifugal fan, with the supporting grid of the filter support being disposed over the air inlet of the fan.

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