11115 research outputs found
Sort by
Twisted Spatiotemporal Optical Vortex Beams in Dispersive Media
Get PDFWe derive a closed-form expression for the mutual coherence function (MCF) of a twisted spatiotemporal optical vortex (STOV) beam after propagating a distance in a linear dispersive medium. A twisted STOV beam is a partially coherent optical field that possesses a coherent STOV and a stochastic twist coupling its space and time dimensions. These beams belong to a special class of space–time-coupled light fields that carry transverse (to the direction of propagation) orbital angular momentum, making them potentially useful in numerous applications including quantum optics, optical manipulation, and optical communications. After presenting the derivation, we validate our new general MCF by showing that it simplifies to the MCFs for two example beams from the literature. Lastly, we present and analyze space–time beam profiles and complex arguments (phases) of the MCF at multiple and differing amounts of material dispersion, field correlation or coherence, and beam twist to gain insight into how twisted STOV beams evolve in dispersive media. We conclude with a brief summary
GNSS Software Defined Radio: History, Current Developments, and Standardization Efforts
Get PDFTaking the work conducted by the global navigation satellite system (GNSS) software-defined radio (SDR) working group during the last decade as a seed, this contribution summarizes, for the first time, the history of GNSS SDR development. This report highlights selected SDR implementations and achievements that are available to the public or that influenced the general development of SDR. Aspects related to the standardization process of intermediate-frequency sample data and metadata are discussed, and an update of the Institute of Navigation SDR Standard is proposed. This work focuses on GNSS SDR implementations in general-purpose processors and leaves aside developments conducted on field programmable gate array and application-specific integrated circuit platforms. Data collection systems (i.e., front-ends) have always been of paramount importance for GNSS SDRs and are thus partly covered in this work. This report represents the knowledge of the authors but is not meant as a complete description of SDR history
An Analysis of Precision: Occlusion and Perspective Geometry’s Role in 6D Pose Estimation
Get PDFAchieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective distortion with a pinhole camera. Additionally, we highlight the crucial role of knowledge about occluded features. Training the CNN with such knowledge slightly reduces pixel precision, but enables the prediction of 3 times more features, including those that are not initially visible, resulting in an overall better performing 6D system. Notably, we reveal that the data augmentation technique of scale can interfere with pixel precision when used during training. These findings are crucial for the entire system, which leverages the Solve Perspective-N-Point (Solve-PnP) algorithm, achieving 6D pose accuracy within 1° and 7 cm at distances ranging from 7.5 to 35 m from the camera. Moreover, this solution operates in real-time, achieving sub-10ms processing times on a desktop PC
Garbage In ≠ Garbage Out: Exploring GAN Resilience to Image Training Set Degradations
Get PDFGenerative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance (FID) of images generated by a conditional deep convolutional GAN (cDCGAN). We find that at the α=0.05 level, brightening, darkening, and blurring are statistically significantly more detrimental to the resulting GAN image quality than removing the degraded data completely, while other degradations are typically safe to keep in training datasets. Additionally, we find that in the case of randomized partial occlusion, the FID of the resulting GAN images approaches that of the degraded training set for increasing levels of occlusion, with the surprising result that GAN FID performance is equal to that of the training set at 75% degradation
Steady State Thermal Blooming with Convection: Modeling, Simulation and Analysis
Get PDFThe 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 work, we introduce a coupled method to solve the steady-state Boussinesq Navier–Stokes equations for fluid behavior with the paraxial equation for beam propagation. We introduce four distinct methods for solving the forced Boussinesq equations in stream function-vorticity variables in two dimensions: a fixed-point approach, a perturbation series expansion, a functional Pad´e approximant, and a composite Pad´e–Newton method. The fluid is coupled to the laser propagation via the refractive index. We prove both the existence of steady solutions for small laser intensities through the fixed-point method as well as the parametric analyticity of solutions in laser intensity. The steady fluid solvers are then used to simulate thermal blooming within an experimental chamber at a laser wavelength with high absorption. The results of this simulation are directly compared to experimental results, where we conclude that an off-centered beam propagating within a finite enclosure will experience horizontal asymmetries in the irradiance pattern as a result of the induced asymmetric temperature fluctuations
Propagation of Spatiotemporal Optical Vortex Beams in Linear, Second-order Dispersive Media
Get PDFIn 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 model the index of refraction with the corresponding Sellmeier equation
Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks
Get PDFThe 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 enables ingestion and exploitation with point cloud processing neural network methods. Additionally, we present a composite fusion-style, point convolution-based neural network architecture for the semantic segmentation of HSPC and lidar point cloud data. We investigate the effects of the proposed HSPC representation for both unimodal and multimodal networks ingesting a variety of hyperspectral and lidar data representations. Finally, we compare the performance of these networks against each other and previous approaches. This study paves the way for innovative approaches to multimodal remote sensing data fusion, unlocking new possibilities for enhanced data analysis and interpretation
Intentional Technology For Teaching Practice
Get PDFIn today’s era, where educational technology is in a near-constant state of evolution, the imperative is not just to adopt technology, but to do so with a defined purpose and strategy. As educators within military education there is a growing need to discern which technological tools and practices align best with our mission and the goals we set for our students. Teaching is more than just transferring knowledge—it’s about fostering environments conducive to growth, critical thinking, and lifelong learning. This e-book contains collective insights, experiences, and reflections from faculty participating in a Faculty Learning Community (FLC) a yearlong, structured, community of practice, engaged in the thoughtful exploration of educational technology topics during the academic year of 2022-2023 at the Air Force Institute of Technology. Whether by leveraging social annotation tools to engage students in reading, formulating effective methods to produce and utilize educational content, innovating with game-based learning, or seamlessly integrating multiple applications for meaningful classroom experiences, our aim is to provide you with insights and actionable guidance for use within your own classrooms
Statistical Reliability Estimation of Satellites Operating from 1991-2020
No full textReliability analysis using satellite failure data for satellites launched in the years 1991-2020 is presented. The analysis is conducted using nonparametric as well as parametric methods. In order to derive a nonparametric reliability estimate from the raw failure data, the Kaplan-Meier estimator is utilized. The Weibull distribution is then utilized to attempt and parameterize the behaviors seen in the nonparametric results. To better interpret the data, data is split into groups with respect to launch dates. Results from this analysis show a general increase in overall satellite reliability over the previous 30 years
Risks from Spacecraft Breakup Events in Near Rectilinear Halo Orbits
Get PDFSpacecraft breakup events were simulated in the vicinity of the Lunar Gateway’s Near Rectilinear Halo Orbit to evaluate the risk of debris collision with the station. The Monte Carlo simulations model the breakup of an object shortly after deployment from the Gateway, and using the propagated trajectories of over a million particles across 5,000 random breakup events, the likelihood of collision with the Gateway is statistically evaluated. Approaches within 10 km were observed for only 2.4% of the breakup events, suggesting a low risk of collision, but approaches at larger distances that might still generate a debris avoidance maneuver were relatively likely. Changes in velocity at deployment of above 4 m/s appeared to lower the risk of collision. The risk was highest at about 1.57 days after the breakup but dropped rapidly as the vast majority of objects departed the Near Rectilinear Halo Orbit within weeks. After one year, about 20% of the fragments had impacted the Moon, 61% had escaped the Earth-Moon system, and 19% remained elsewhere in cislunar space. Few objects would intersect useful near-Earth orbits and there were very few Earth impacts. The results of this study provide quantifiable insight into the risk of debris collision with the Gateway that can be used to develop operational plans to mitigate this risk