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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Analog In-Memory Computing for the Synthetic Aperture Radar Polar Format Algorithm

As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.

Analog computing↗
NASA NTRS2008

Image Compression Algorithm Altered to Improve Stereo Ranging

A report discusses a modification of the ICER image-data-compression algorithm to increase the accuracy of ranging computations performed on compressed stereoscopic image pairs captured by cameras aboard the Mars Exploration Rovers. (ICER and variants thereof were discussed in several prior NASA Tech Briefs articles.) Like many image compressors, ICER was designed to minimize a mean-square-error measure of distortion in reconstructed images as a function of the compressed data volume. The present modification of ICER was preceded by formulation of an alternative error measure, an image-quality metric that focuses on stereoscopic-ranging quality and takes account of image-processing steps in the stereoscopic-ranging process. This metric was used in empirical evaluation of bit planes of wavelet-transform subbands that are generated in ICER. The present modification, which is a change in a bit-plane prioritization rule in ICER, was adopted on the basis of this evaluation. This modification changes the order in which image data are encoded, such that when ICER is used for lossy compression, better stereoscopic-ranging results are obtained as a function of the compressed data volume.

Kiely, Aaron↗
DOE OSTI2020

An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction

Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously demonstrated performance in image recognition tasks across several disciplines (e.g. materials science, medical imaging, facial recognition). Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system. Prediction of material processing history based on image data (classification), calculation of area fraction of phases present in the micrographs (segmentation), and kinetic modeling from segmentation results were performed. Results indicate that convolutional neural networks represent microstructure image data well, and segmentation using the k-means clustering algorithm yields results that agree well with manually annotated images. Classification accuracies of original and segmented images are both 94% for a 5-class classification problem. Kinetic modeling results agree well with previously reported data using manual thresholding. The image quantification and kinetic modeling approach developed and presented here aims to reduce researcher bias introduced into the characterization process, and allows for leveraging information in limited image data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

All-optical phase conjugation using diffractive wavefront processing

Abstract Optical phase conjugation (OPC) is a nonlinear technique used for counteracting wavefront distortions, with applications ranging from imaging to beam focusing. Here, we present a diffractive wavefront processor to approximate all-optical phase conjugation. Leveraging deep learning, a set of diffractive layers was optimized to all-optically process an arbitrary phase-aberrated input field, producing an output field with a phase distribution that is the conjugate of the input wave. We experimentally validated this wavefront processor by 3D-fabricating diffractive layers and performing OPC on phase distortions never seen during training. Employing terahertz radiation, our diffractive processor successfully performed OPC through a shallow volume that axially spans tens of wavelengths. We also created a diffractive phase-conjugate mirror by combining deep learning-optimized diffractive layers with a standard mirror. Given its compact, passive and multi-wavelength nature, this diffractive wavefront processor can be used for various applications, e.g., turbidity suppression and aberration correction across different spectral bands.

42 ENGINEERING↗
DOE OSTI2022

Improving Single-Stage Object Detectors for Nighttime Pedestrian Detection

We report Improving the reliability of nighttime pedestrian detection is a crucial challenge towards the design of robust autonomous systems. Not surprisingly, most pedestrian fatalities occur in low-illumination settings, thus emphasizing the need for new algorithmic advances. This work presents a novel pedestrian detection approach that makes a number of crucial modifications to the state-of-the-art YOLOV5-PANet architecture, in order to improve the reliability of features extracted from nighttime images. More specifically, the proposed architecture systematically incorporates powerful shuffle attention mechanisms and a transformer module to improve the feature learning pipeline. Instead of advocating the use of other sensing modalities that are better suited for nighttime detection, our approach relies only on conventional RGB cameras and is hence broadly applicable. Our empirical studies with nighttime pedestrian detection benchmarks show that with only minimal increase in model complexity, our approach provides significant improvements in detection efficacy over existing solutions. Finally, we explore the impact of post-hoc network pruning on the speed-accuracy trade-off of our approach and demonstrate that it is well suited for reduced memory/compute requirements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Masks for Hadamard transform optics, and weighing designs

This paper gives a brief survey of the design of masks for Hadamard spectrometers and image scanners. Three different criteria are described for judging a mask, as well as techniques for choosing masks that are not too far from the optimum.

Sloane, N. J. A.↗
NASA NTRS1980

Middle infrared multispectral aircraft scanner data - Analysis for geological applications

Multispectral middle IR (8-13 microns) data were acquired with an aircraft scanner over Utah. Because these digital image data were dominated by temperature, all six channels were highly correlated. Extensive processing was required to allow geologic photointerpretation based on subtle variations in spectral emittance between rock types. After preliminary processing, ratio images were produced and color ratio composites created from these. Sensor calibration and an atmospheric model allowed determination of surface brightness, temperature, emittance, and color composite emittance images. The best separation of major rock types was achieved with a principal component transformation, followed by a Gaussian stretch, followed by an inverse transformation to the original axes.

Kahle, A. B.↗
NASA NTRS1987

First results on quiet and magnetic granulation from SOUP

The flight of Solar Optical Universal Polarimeter (SOUP) on Spacelab 2 allowed the collection of time sequences of diffraction limited (0.5 arc sec) granulation images with excellent pointing (0.003 arc sec) and completely free of the distortion that plagues groundbased images. The p-mode oscillations are clearly seen in the data. Using Fourier transforms in the temporal and spatial domain, it was shown that the p-modes dominate the autocorrelation lifetime in magnetic regions. When these oscillations are removed the autocorrelation lifetime is found to be 500 sec in quiet and 950 sec in magnetic regions. In quiet areas exploding granules are seen to be common. It is speculated that a significant fraction of granule lifetimes are terminated by nearby explosions. Using local correlation tracking techniques it was able to measure horizontal displacements, and thus transverse velocities, in the magnetic field. In quiet sun it is possible to detect both super and mesogranulation. Horizontal velocities are as great as 1000 m/s and the average velocity is 400 m/s. In magnetic regions horizontal velocities are much less, about 100 m/s.

Title, A. M.↗
NASA NTRS1990

Initial results in the development of a synthetic aperture microwave radiometer

A radiometer that measures the complex correlation of the voltage from pairs of antennas at many different baselines is being developed. Each baseline produces a sample point in the Fourier transform of the scene, and a map of the scene is obtained after all measurements have been made by inverting the transform. A substantial reduction in the antenna collecting area required compared to a conventional imaging radiometer can be obtained in this manner. An aircraft prototype being developed is a hybrid which uses real aperture antennas to obtain resolution along-track (stick antennas) and uses aperture synthesis to obtain resolution across-track. The prototype was flight-tested aboard the NASA P-3 in June 1988. During this flight a map was made of the Delmarva Peninsula south of NASA's Wallops Flight Facility. This initial map shows the major land/water features and compares very favorably with a Landsat image of the area, suggesting a bright outlook for the development of this technique in the future.

Le Vine, David M.↗
NASA NTRS1993

An astrophysics data program investigation of spatial structure of supernova remnants

The final report on astrophysics data program investigation of spatial structure of supernova remnants for the period 1 Aug. 1989 to 31 Jul. 1991 is presented. The goal of the project was the study of the spatial structure of supernova remnants (SNR's) as observed in the x-ray band. A number of software tools were developed for the analysis: (1) a program to fit various geometric models to high resolution x-ray data, and (2) programs for Fourier Transform analysis of clumping in SNR's. These programs were applied to high resolution imager (HRI) data on the young galactic SNR's Tycho and Kepler with some success.

Hughes, John P.↗
NASA NTRS1996

Experimental Studies on a Compact Storage Scheme for Wavelet-based Multiresolution Subregion Retrieval

Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images effectively. In order to use them for image library systems, a compact storage scheme for quantized coefficient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.

Poulakidas, A.↗
NASA NTRS2002

Frequency Analyzed Laser Light Scattering (Falls)

A simple and robust light scattering technique for spray diagnostics was developed. The technique provides a spray's intact length and breakup frequency. Scattered light is collected from a laser sheet that illuminates a portion of the spray. The scattered light is imaged on a fast photodiode whose output is sampled at 20 kHz. Fast Fourier transform (FFT) analysis is performed on the sampled signal. The shape of the FFT produced can be correlated to one of three distinct spray regimes: intact sheet, ligaments, or droplets. Strobe and laser illuminated photographs are also shown for reference.

Rhys, Noah O.↗
NASA NTRS1992

The rhodopsins: structure and function. Introduction

Nature makes use of the propensity of retinal for light-dependent double-bond isomerization in a number of systems and in a variety of ways. The common theme for light receptors based on this kind of chemistry is that (1) the retinal is bound in most cases to a small membrane protein via a protonated lysine-retinal Schiff base, (2) the absorption maximum in the visible is tuned to a suitable wavelength largely by electrostatic interaction with polar protein residues, and (3) the light-induced bond rotations and strains in the retinal set off reaction chains during which at least part of the excess free energy acquired is transferred to the protein and causes pK shifts of acidic residues and/or backbone conformational changes. The physiological consequence of the process initiated by absorption of light is either the activation of an information transfer chain (sensory and visual rhodopsins) or energy transduction which drives the electrogenic movement of ions across the membrane (ion-motive rhodopsins). Rhodopsins with these functions occur in bacteria and in higher organisms; from an evolutionary standpoint they are not related to one another. Nevertheless, all of these proteins are remarkably similar and form a distinct family.

NASA Discipline Number 52-30↗
NASA NTRS2011

Wavenumber Imaging For Damage Detection and Measurement

This paper presents a method for analyzing ultrasonic wavefield data using the Continuous Wavelet Transform (CWT) applied in the spatial domain. Unlike data obtained by sparse arrays of transducers, full wavefield data contains information local to the structure and can be used to obtain more detailed measurements of damage type, location, size, etc. By calculating the CWT of the wavefield in the spatial domain, the wavenumber spectrum is determined for the inspected locations. Because wavenumber is affected by the local geometry and material properties of the structure through which Lamb waves propagate, the wavenumber spectrum can be analyzed to assess the location, severity, and size of damage. The technique is first applied to experimental wavefield data obtained using a laser Doppler vibrometer and automated positioning stage. The out-of-plane velocity along the length of a composite stringer was measured to detect the presence of delaminations within the composite overwrap. Next, simulated corrosion is detected and measured within an aluminum plate using the two dimensional CWT. The experimental results show the usefulness of the technique for vehicle structure inspection applications.

Rogge, Matthew D.↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Nanoscale Magnetic Domains in Polycrystalline Mn 3 Sn Films Imaged by a Scanning Single-Spin Magnetometer

Noncollinear antiferromagnets with novel magnetic orders, vanishingly small net magnetization, and exotic spin related properties hold enormous promise for developing next-generation, transformative spintronic applications. A major ongoing research focus of this community is to explore, control, and harness unconventional magnetic phases of this emergent material system to deliver state-of-the-art functionalities for modern microelectronics. Here we report direct imaging of magnetic domains of polycrystalline Mn 3 Sn films, a prototypical noncollinear antiferromagnet, using nitrogen-vacancy-based single-spin scanning microscopy. Nanoscale evolution of local stray field patterns of Mn 3 Sn samples are systematically investigated in response to external driving forces, revealing the characteristic “heterogeneous” magnetic switching behaviors in polycrystalline textured Mn 3 Sn films. Furthermore, our results contribute to a comprehensive understanding of inhomogeneous magnetic orders of noncollinear antiferromagnets, highlighting the potential of nitrogen-vacancy centers to study microscopic spin properties of a broad range of emergent condensed matter systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Boosting Noise2Inverse via enhanced model selection for denoising computed tomography data

Synchrotron-based x-ray tomographic imaging enables the examination of the internal structure of materials at high spatial and temporal resolution. Experimental constraints can impose dose and time limits on the measurements, introducing a higher level of noise and artifacts in the reconstructed images. Deep learning has emerged as a powerful tool to remove noise from reconstructed images. Recently, the Noise2Inverse method was designed specifically for denoising reconstructed images without requiring paired noisy and clean images. This method creates multiple statistically independent reconstructions used to pair the data in which training involves transforming one reconstruction into the other, and vice versa. Originally designed to be used after a fixed number of epochs, we see in practice that this approach may not produce the optimal model and may unnecessarily waste computational resources. Therefore, we propose an alternative method of identifying the best model during training that aligns with the Noise2Inverse method. During validation, we compare the model output of the multiple reconstructions among each other. We hypothesize that the best model is the one that produces images with the highest similarity, implying a convergence in the predicted material properties and absorption values. To compare model outputs, we consider the absolute error, square error, structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and cosine similarity. We evaluate our method on two simulated tomography datasets and two, real-world, low-contrast, high-energy x-ray tomography datasets. We show our approach is more effective at determining the best model, up to an increase of 12.50% and 12.53% in SSIM and PSNR, respectively, while only requiring a fifth of the training time compared to the original approach.

CT↗