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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Synthetic aperture radar imaging from an inclined geosynchronous orbit

Images of earth can be produced with an assumed synthetic aperture radar (SAR) on a satellite platform undergoing a nutating relative motion from geosynchronous altitude. From a 50 deg inclined circular orbit, the contiguous United States can be imaged in about 3 h of segmented operation at 100-m resolution with 4-azimuth-look averaging. The 2450-MHz transmitter radiates 1312 W of average power from a steerable 15-m-diam antenna. The SAR can image daily an area bounded longitudinally and latitudinally.

Tomiyasu, K.↗
NASA NTRS1988

PIPS: A Procedure for Interactive Pyramid Segmentation

The Procedure for Interactive Pyramid Segementation was designed to identify regions of spatially connected and spectrally homogeneous pixels in multispectral image data, and to allow these regions to be interactively manipulated without the use of processing parameters. The objective is to provide the user with the capability to easily extract and identify regions corresponding to target objects of interest. The approach is to segment a multispectral image into a set of regions and to allow the analyst to interactively refine the segmentation by direct manipulation. The user can elect to: interactively display maps of the spatial distribution of regions for any designated image subset; display the statistics of a given region; and label, merge, or split regions.

Wharton, S. W.↗
NASA NTRS2018

Interferometric Characterization of Keck Segment Edge Errors

The Keck telescope segments were manufactured by stressed mirror polishing of large circular pieces of Zerodur that were then cut into hexagons and finished by Ion Beam Figuring (IBF). It has long been believed that this process results in segments with little or no edge effects. As a result, this same general approach is planned for segment manufacturing for the Thirty Meter Telescope (TMT) and the European Extremely Large Telescope (E-ELT). However, recent measurements at the Keck telescope suggest that at least some of the Keck segments have significant aberrations within 60 mm of the edge. These aberrations impact the telescope phasing and the overall telescope image quality. We present interferometric measurements of multiple Keck segments, characterizing the surface errors near the edges over spatial periods from 5 cm down to 1 mm. We show that the largest phasing and image quality effects are due to plateaus of unremoved material, left behind after IBF as a result of obscuration by the IBF supports. Apart from these plateaus, the edge quality is relatively good, though not as good as in the segment interiors. Some residual phasing and image quality effects remain, and these are not currently understood.

Martinek, Stephen J.↗
DOE OSTI2025

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗
NASA NTRS2022

Microstructure Segmentation with Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures, including VGG, Inception, and ResNet, were trained on over 100,000 labelled microscopy images from 54 classes. These pre-trained encoders were then embedded into multiple segmentation architectures including U-Net and DeepLabV3+ to evaluate segmentation performance on newly created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2 percent reduction in relative segmentation error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRS1989

Image understanding and the man-machine interface II; Proceedings of the Meeting, Los Angeles, CA, Jan. 17, 18, 1989

Image understanding concepts and models, image understanding systems and applications, advanced digital processors and software tools, and advanced man-machine interfaces are among the topics discussed. Particular papers are presented on such topics as neural networks for computer vision, object-based segmentation and color recognition in multispectral images, the application of image algebra to image measurement and feature extraction, and the integration of modeling and graphics to create an infrared signal processing test bed.

Barrett, Eamon B.↗
DOE OSTI2026

Gaia: segmented germanium detector for high-energy X-ray fluorescence and spectroscopic imaging

We present Gaia, a monolithic array of 96 high-purity germanium pixel detectors integrated with a custom low-noise application-specific integrated circuit (ASIC) and a field-programmable gate array (FPGA)-based data acquisition system. The sensor operates at ∼100 K using a commercial closed-cycle cryocooler, with the in-vacuum electronics thermally isolated from the cold finger to ensure thermal stability. The system demonstrates an average energy resolution of 711 eV at 122 keV, measured using a 57 Co source, and 253 eV at 5.89 keV, measured with 55 Fe across all channels. The readout architecture incorporates a high-performance FPGA paired with a dual-core ARM processor, forming a complete embedded Linux-based computing platform. Communication between the processor and FPGA is handled via memory-mapped I/O, and data are streamed over high-speed gigabit Ethernet. A full-scale 384-pixel Gaia detector, based on this 96-element module, is currently under fabrication.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Exoplanet Yield Sensitivity for the Hybrid Lyot Coronagraph from end-to-end modeling for LUVOIR-A

NASA's future direct imaging flagship mission will require telescope mirror segmentation to survey a statistically significant population of nearby stellar systems by increasing aperture size beyond that achieved by traditional space-borne monolithic apertures thus far. Segmentation is a path for the size expansion, but it entails a challenge due to the introduction of temporal pupil discontinuities in terms of both phase and amplitude. Modeling of the integrated performance of the coronagraph with the expected dynamical profile of the telescope is an important step toward realistic estimates of the mission's science yields. We present results of a study using the hybrid Lyot coronagraph with a 15-m on-axis, segmented optical telescope assembly (OTA), akin to that of the Large UV / Optical /Infrared (LUVOIR) mission. We have developed a high-fidelity, end-to-end model as part of the Segmented Coronagraph Design and Analysis (SCDA) study that includes diffractive optical propagations at the 1e-10 contrast level across the optical train including the OTA and intermediate re-imaging optics inside the coronagraph instrument. We perform a sensitivity analysis to assess the contrast sensitivity to wavefront perturbations for a set of key telescope terms including segment-level low-order and mid-spatial frequency aberrations and evaluate expected exoplanet yields obtained in the presence of these sets of aberrations. Industry-provided segment-level wavefront aberration datasets involving the following mechanisms are input to the end-to-end model: (1) thermal gradients and accelerations, and (2) telescope dynamics including line-of-sight and segment jitter. Dynamically averaged PSFs in the presence of aberrations are fed into AYO (Altruistic Yield Optimization), a mission design reference simulator, that then assesses the scientific yield sensitivities.

high-contrast imaging↗
NASA NTRS2010

Wavefront Control and Image Restoration with Less Computing

PseudoDiversity is a method of recovering the wavefront in a sparse- or segmented- aperture optical system typified by an interferometer or a telescope equipped with an adaptive primary mirror consisting of controllably slightly moveable segments. (PseudoDiversity should not be confused with a radio-antenna-arraying method called pseudodiversity.) As in the cases of other wavefront- recovery methods, the streams of wavefront data generated by means of PseudoDiversity are used as feedback signals for controlling electromechanical actuators of the various segments so as to correct wavefront errors and thereby, for example, obtain a clearer, steadier image of a distant object in the presence of atmospheric turbulence. There are numerous potential applications in astronomy, remote sensing from aircraft and spacecraft, targeting missiles, sighting military targets, and medical imaging (including microscopy) through such intervening media as cells or water. In comparison with prior wavefront-recovery methods used in adaptive optics, PseudoDiversity involves considerably simpler equipment and procedures and less computation. For PseudoDiversity, there is no need to install separate metrological equipment or to use any optomechanical components beyond those that are already parts of the optical system to which the method is applied. In Pseudo- Diversity, the actuators of a subset of the segments or subapertures are driven to make the segments dither in the piston, tilt, and tip degrees of freedom. Each aperture is dithered at a unique frequency at an amplitude of a half wavelength of light. During the dithering, images on the focal plane are detected and digitized at a rate of at least four samples per dither period. In the processing of the image samples, the use of different dither frequencies makes it possible to determine the separate effects of the various dithered segments or apertures. The digitized image-detector outputs are processed in the spatial-frequency (Fourier-transform) domain to obtain measures of the piston, tip, and tilt errors over each segment or subaperture. Once these measures are known, they are fed back to the actuators to correct the errors. In addition, measures of errors that remain after correction by use of the actuators are further utilized in an algorithm in which the image is phase-corrected in the spatial-frequency domain and then transformed back to the spatial domain at each time step and summed with the images from all previous time steps to obtain a final image having a greater signal-to-noise ratio (and, hence, a visual quality) higher than would otherwise be attainable.

Lyon, Richard G.↗
NASA NTRSDate not supplied

Design and performance analysis of a PIAACMC coronagraph on a segmented aperture

The Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC) offers high throughput and small inner working angle (IWA) for direct imaging of exoplanets. PIAACMC is also compatible with segmented apertures, which is important for the success of future large telescopes, such as the Large UV / Optical/ Infrared (LUVOIR) concept currently under way to inform NASA's2020 decadal survey. We present a design of PIAACMC for one of the segmented apertures considered for LUVOIR and simulate its performance with wavefront control, in monochromatic as well as broadband light. We characterize its performance in terms of throughput, inner working angle, contrast, area of the dark zone, and sensitivity to low order aberrations, in particular tip/tilt. In addition, we study the resilience to large stellar angular sizes and polarization. Finally, we compute the "performance map", which is a recently introduced figure of merit for coronagraphs

Exoplanets↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRS1998

Analytical Tools for Cloudscope Ice Measurement

The cloudscope is a ground or aircraft instrument for viewing ice crystals impacted on a sapphire window. It is essentially a simple optical microscope with an attached compact CCD video camera whose output is recorded on a Hi-8 mm video cassette recorder equipped with digital time and date recording capability. In aircraft operation the window is at a stagnation point of the flow so adiabatic compression heats the window to sublimate the ice crystals so that later impacting crystals can be imaged as well. A film heater is used for ground based operation to provide sublimation, and it can also be used to provide extra heat for aircraft operation. The compact video camera can be focused manually by the operator, and a beam splitter - miniature bulb combination provide illumination for night operation. Several shutter speeds are available to accommodate daytime illumination conditions by direct sunlight. The video images can be directly used to qualitatively assess the crystal content of cirrus clouds and contrails. Quantitative size spectra are obtained with the tools described in this report. Selected portions of the video images are digitized using a PCI bus frame grabber to form a short movie segment or stack using NIH (National Institute of Health) Image software with custom macros developed at DRI. The stack can be Fourier transform filtered with custom, easy to design filters to reduce most objectionable video artifacts. Particle quantification of each slice of the stack is performed using digital image analysis. Data recorded for each particle include particle number and centroid, frame number in the stack, particle area, perimeter, equivalent ellipse maximum and minimum radii, ellipse angle, and pixel number. Each valid particle in the stack is stamped with a unique number. This output can be used to obtain a semiquantitative appreciation of the crystal content. The particle information becomes the raw input for a subsequent program (FORTRAN) that synthesizes each slice and separates the new from the sublimating particles. The new particle information is used to generate quantitative particle concentration, area, and mass size spectra along with total concentration, solar extinction coefficient, and ice water content. This program directly creates output in html format for viewing with a web browser.

Arnott, W. Patrick↗
NASA NTRS1979

Low-level processing for real-time image analysis

A system that detects object outlines in television images in real time is described. A high-speed pipeline processor transforms the raw image into an edge map and a microprocessor, which is integrated into the system, clusters the edges, and represents them as chain codes. Image statistics, useful for higher level tasks such as pattern recognition, are computed by the microprocessor. Peak intensity and peak gradient values are extracted within a programmable window and are used for iris and focus control. The algorithms implemented in hardware and the pipeline processor architecture are described. The strategy for partitioning functions in the pipeline was chosen to make the implementation modular. The microprocessor interface allows flexible and adaptive control of the feature extraction process. The software algorithms for clustering edge segments, creating chain codes, and computing image statistics are also discussed. A strategy for real time image analysis that uses this system is given.

Eskenazi, R.↗
NASA NTRS2000

Qualitative and quantitative effects of harmonic echocardiographic imaging on endocardial edge definition and side-lobe artifacts

Harmonic imaging is a new ultrasonographic technique that is designed to improve image quality by exploiting the spontaneous generation of higher frequencies as ultrasound propagates through tissue. We studied 51 difficult-to-image patients with blinded side-by-side cineloop evaluation of endocardial border definition by harmonic versus fundamental imaging. In addition, quantitative intensities from cavity versus wall were compared for harmonic versus fundamental imaging. Harmonic imaging improved left ventricular endocardial border delineation over fundamental imaging (superior: harmonic = 71.1%, fundamental = 18.7%; similar: 10.2%; P <.001). Quantitative analysis of 100 wall/cavity combinations demonstrated brighter wall segments and more strikingly darker cavities during harmonic imaging (cavity intensity on a 0 to 255 scale: fundamental = 15.6 +/- 8.6; harmonic = 6.0 +/- 5.3; P <.0001), which led to enhanced contrast between the wall and cavity (1.89 versus 1.19, P <.0001). Harmonic imaging reduces side-lobe artifacts, resulting in a darker cavity and brighter walls, thereby improving image contrast and endocardial delineation.

NASA Discipline Cardiopulmonary↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
NASA NTRS1973

Digital processing of stereoscopic image pairs.

The problem under consideration is concerned with scene analysis during robot navigation on the surface of Mars. In this mode, the world model of the robot must be continuously updated to include sightings of new obstacles and scientific samples. In order to describe the content of a particular scene, it is first necessary to segment it into known objects. One technique for accomplishing this segmentation is by analyzing the pair of images produced by the stereoscopic cameras mounted on the robot. A heuristic method is presented for determining the range for each point in the two-dimensional scene under consideration. The method is conceptually based on a comparison of corresponding points in the left and right images of the stereo pair. However, various heuristics which are adaptive in nature are used to make the algorithm both efficient and accurate. Examples are given of the use of this so-called range picture for the purpose of scene segmentation.

Levine, M. D.↗