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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Accelerating Satellite Image Based Large-Scale Settlement Detection with GPU

Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases\---- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high performance computing (HPC) architecture capable of processing petascale image data. We exploit the parallel processing capability of GPUs to present a GPU-friendly algorithm for robust and efficient detection of settlements from large-scale high-resolution satellite imagery. Feature descriptor generation is an expensive, but a key step in automated scene analysis. To address this challenge, we present GPU implementations for three different feature descriptors\-- multiscale Historgram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM) Contrast and local pixel intensity statistics. We perform extensive experimental evaluations of our implementation using diverse and large image datasets. Our GPU implementation of the feature descriptor algorithms results in speedups of 220 times compared to the CPU version. We present an highly efficient settlement detection system running on a multiGPU architecture capable of extracting human settlement regions from a city-scale sub-meter spatial resolution aerial imagery spanning roughly 1200 sq. kilometers in just 56 seconds with detection accuracy close to 90\%. This remarkable speedup gained by our vision algorithm maintaining high detection accuracy clearly demonstrates that such computational advancements clearly hold the solution for petascale image analysis challenges.

Patlolla, Dilip Reddy↗
NASA NTRS2005

Quantitative Analysis of Venus Radar Backscatter Data in ArcGIS

Ongoing mapping of the Ganiki Planitia (V14) quadrangle of Venus and definition of material units has involved an integrated but qualitative analysis of Magellan radar backscatter images and topography using standard geomorphological mapping techniques. However, such analyses do not take full advantage of the quantitative information contained within the images. Analysis of the backscatter coefficient allows a much more rigorous statistical comparison between mapped units, permitting first order selfsimilarity tests of geographically separated materials assigned identical geomorphological labels. Such analyses cannot be performed directly on pixel (DN) values from Magellan backscatter images, because the pixels are scaled to the Muhleman law for radar echoes on Venus and are not corrected for latitudinal variations in incidence angle. Therefore, DN values must be converted based on pixel latitude back to their backscatter coefficient values before accurate statistical analysis can occur. Here we present a method for performing the conversions and analysis of Magellan backscatter data using commonly available ArcGIS software and illustrate the advantages of the process for geological mapping.

Long, S. M.↗
DOE OSTI2025

FY25 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and image analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and cracks in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or, in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, the identification of potential cracks was prioritized for the past several years at the request of program leadership.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Spatial characterization of heterogeneous nanopore surfaces from XCT scans of Niobrara shale

The multiphase fluid flow through natural and geo-architected nanopore structures is fundamentally controlled by the geometry of such nanopore structures, pore wettability and interfacial tension. Furthermore, multiphase fluid flow through the nanopore structures is compromised when the pore surfaces are heterogeneous, having surface regions of different composition, polarity, and wetting characteristics. In this regard, compositional analysis of the pore channel surface has been a big challenge in the past, since 3D analysis is required. Currently, due to advances in nano X-ray CT, with a voxel resolution of 20 nm, it is possible to identify nanopore network structures and quantify their surface composition in 3D. In this paper, the development of procedures for detailed analysis to quantify the composition of the pore channel surface regions, including the corresponding area, is described using nanoCT images. Image analysis procedures, including preprocessing of the 3D tomographic images, correction for the partial volume effect (PVE), and the use of a new algorithm for determination of pore channel surface composition and area are reported on. Videos of the nanopore structure have been provided as supplementary information to further describe the heterogeneity of the nanopore surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An automated, high-throughput method for standardizing image color profiles to improve image-based plant phenotyping

High-throughput phenotyping has emerged as a powerful method for studying plant biology. Large image-based datasets are generated and analyzed with automated image analysis pipelines. A major challenge associated with these analyses is variation in image quality that can inadvertently bias results. Images are made up of tuples of data called pixels, which consist of R, G, and B values, arranged in a grid. Many factors, for example image brightness, can influence the quality of the image that is captured. These factors alter the values of the pixels within images and consequently can bias the data and downstream analyses. Here, we provide an automated method to adjust an image-based dataset so that brightness, contrast, and color profile is standardized. The correction method is a collection of linear models that adjusts pixel tuples based on a reference panel of colors. We apply this technique to a set of images taken in a high-throughput imaging facility and successfully detect variance within the image dataset. In this case, variation resulted from temperature-dependent light intensity throughout the experiment. Using this correction method, we were able to standardize images throughout the dataset, and we show that this correction enhanced our ability to accurately quantify morphological measurements within each image. We implement this technique in a high-throughput pipeline available with this paper, and it is also implemented in PlantCV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Quantifying cellulose accessibility during enzyme-mediated deconstruction using 2 fluorescence-tagged carbohydrate-binding modules

Two fluorescence-tagged carbohydrate-binding modules (CBMs), which specifically bind to crystalline (CBM2a-RRedX) and paracrystalline (CBM17-FITC) cellulose, were used to differentiate the supramolecular cellulose structures in bleached softwood Kraft fibers during enzyme-mediated hydrolysis. Differences in CBM adsorption were elucidated using confocal laser scanning microscopy (CLSM), and the structural changes occurring during enzyme-mediated deconstruction were quantified via the relative fluorescence intensities of the respective probes. It was apparent that a high degree of order (i.e., crystalline cellulose) occurred at the cellulose fiber surface, which was interspersed by zones of lower structural organization and increased cellulose accessibility. Quantitative image analysis, supported by 13 C NMR, scanning electron microscopy (SEM) imaging, and fiber length distribution analysis, showed that enzymatic degradation predominates at these zones during the initial phase of the reaction, resulting in rapid fiber fragmentation and an increase in cellulose surface crystallinity. By applying this method to elucidate the differences in the enzyme-mediated deconstruction mechanisms, this work further demonstrated that drying decreased the accessibility of enzymes to these disorganized zones, resulting in a delayed onset of degradation and fragmentation. The use of fluorescence-tagged CBMs with specific recognition sites provided a quantitative way to elucidate supramolecular substructures of cellulose and their impact on enzyme accessibility. By designing a quantitative method to analyze the cellulose ultrastructure and accessibility, this study gives insights into the degradation mechanism of cellulosic substrates.

09 BIOMASS FUELS↗
DOE OSTI2017

Statistical Inference for Porous Materials using Persistent Homology.

We propose a porous materials analysis pipeline using persistent homology. We rst compute persistent homology of binarized 3D images of sampled material subvolumes. For each image we compute sets of homology intervals, which are represented as summary graphics called persistence diagrams. We convert persistence diagrams into image vectors in order to analyze the similarity of the homology of the material images using the mature tools for image analysis. Each image is treated as a vector and we compute its principal components to extract features. We t a statistical model using the loadings of principal components to estimate material porosity, permeability, anisotropy, and tortuosity. We also propose an adaptive version of the structural similarity index (SSIM), a similarity metric for images, as a measure to determine the statistical representative elementary volumes (sREV) for persistence homology. Thus we provide a capability for making a statistical inference of the uid ow and transport properties of porous materials based on their geometry and connectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

In situ high-temperature X-ray diffraction study of Sc-doped titanium oxide nanocrystallites

Titanium dioxide is an inexpensive wide-gap highly ionic semiconductor with striking photocatalytic capabilities in several heterogeneous photoredox reactions. A small crystal size is desirable to maximize the surface area, since photocatalytic reactions occur at the surface of a photocatalyst. Presented in this work are the synthesis and microstructural characterization of 4 at.% Sc-doped TiO 2 (4SDT) prepared by water-based co-precipitation. The crystal structure of 4SDT was examined via in situ high-temperature powder X-ray diffraction experiments from 25 to 1200°C. Rietveld analysis revealed single-phase anatase up to 875°C, while at 900°C the anatase-to-rutile phase transformation occurred and at higher temperatures additional reflections of Sc-rich phases (Sc 2 TiO 5 from 975°C and Ti 3 Sc 4 O 12 or Sc 2 O 3 at 1200°C) were observed. Debye function analysis (DFA) was applied to model the total scattering pattern directly in reciprocal space, allowing the reconstruction of Ti vacancies. Both Rietveld and DFA methods were applied to estimate the nanocrystallite size and shape with consistent growth in crystallite size with temperature: an ellipsoid shape with equatorial ~4.7 nm / axial (001) ~6.9 nm at 25°C to equatorial ~27.9 nm / axial (001) ~39.6 nm at 900°C refined by Rietveld analysis, versus a cylinder shape with D a , b = 4.3 nm and size dispersion σ ab = 1.5 nm, L c = 4.9 nm and σ c = 2.3 nm at 25°C to D a , b = 21.4 nm, σ ab = 8.3 nm, L c = 23.9 and σ c = 10.9 nm at 900°C estimated by DFA. The microstructural changes obtained by Rietveld and DFA methods were supported by high-resolution transmission electron microscopy image analysis, as well as by the less direct nitrogen sorption techniques that provide information on the size of non-agglomerated and dense particles. The Ti site-occupancy factor showed a linear increase from 0.6–0.8 at 25°C to unity at 900°C for anatase, and from ~0.7 at 900°C to unity at 1200°C for rutile, via Rietveld analysis and DFA.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microstructural characterization of SiO2/Ni nanocomposites electrodeposited from a sulphate bath modified by PEI

Highlights: • SiO{sub 2}/Ni + PEI nanocomposites are deposited from the sulphate bath containing PEI. • PEI allows incorporation of higher SiO{sub 2} vol% in SiO{sub 2}/Ni + PEI nanocomposites. • PEI in the form of a thin layer surrounds spherical Ni nanograins and SiO{sub 2} clusters. • PEI action ensures high hardness, tribological properties and corrosion resistance. - Abstract: SiO{sub 2}/Ni nanocomposite coatings containing well-dispersed SiO{sub 2} nanoparticles were electrodeposited from a sulphate bath with and without the addition of polyethylenimine (PEI) as a cationic surfactant. The surface morphology and microstructure in the cross-sections of the deposited nanocomposites were examined by scanning electron microscopy. More detailed microstructure characterization was performed using transmission, scanning-transmission and high resolution transmission electron microscopy. It was determined that the addition of 0.05 g/L and 0.1 g/L of PEI allows the incorporation of SiO{sub 2} nanoparticles in the nickel matrix to be increased in comparison with the nanocomposite deposited without a surfactant. Based on high spatial resolution microanalysis, performed using high count rate energy dispersive spectroscopy of characteristic X-rays in the scanning-transmission mode, the distribution of SiO{sub 2} nanoparticles in the nanocomposite was visualized on elemental maps. Image analysis of the electron microscopy images enabled the mean equivalent circle diameters of grains in the nickel matrix to be determined, together with the volume fraction of SiO{sub 2} nanoparticles in the nanocomposites. The volume fraction of SiO{sub 2} clusters in the SiO{sub 2}/Ni + PEI nanocomposites with the addition of 0.05 g/L and 0.1 g/L of PEI is equal to 18.4% and 13.3%, respectively. Furthermore, the PEI addition allows the nickel grain size in the matrix to be reduced. The combination of different imaging techniques confirmed that PEI is not only present as a surfactant in the electrolytic bath, but also incorporates into the microstructure of the nanocomposite. Nanocomposites electrodeposited in the presence of 0.05 g/L of PEI exhibit higher hardness, good tribological properties and improved corrosion resistance. Although increasing the PEI concentration in the bath up to 0.1 g/L strongly promotes grain size reduction and increase of the SiO{sub 2} volume fraction, it also causes an increase in the PEI fraction embedded in the microstructure, which results in deterioration of the properties of the SiO{sub 2}/Ni + PEI nanocomposites.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Genome evolution and nitrogen fixation in bacterial ectosymbionts of a protist inhabiting wood-feeding cockroaches

By combining genomics and isotope imaging analysis using high-resolution secondary ion mass spectrometry (NanoSIMS), we examined the function and evolution of Bacteroidales ectosymbionts of the protist Barbulanympha from the hindguts of the wood-eating cockroach Cryptocercus punctulatus . In particular, we investigated the structure of ectosymbiont genomes, which, in contrast to those of endosymbionts, has been little studied to date, and tested the hypothesis that these ectosymbionts fix nitrogen. Unlike with most obligate endosymbionts, genome reduction has not played a major role in the evolution of the Barbulanympha ectosymbionts. Instead, interaction with the external environment has remained important for this symbiont as genes for synthesis of transporters, outer membrane proteins, lipopolysaccharides, and lipoproteins have been retained. The ectosymbiont genome carried two complete operons for nitrogen fixation, a urea transporter, and a urease, indicating the availability of nitrogen as a driving force behind the symbiosis. NanoSIMS analysis of C. punctulatus hindgut symbionts exposed in vivo to 15 N 2 supports the hypothesis that Barbulanympha ectosymbionts are capable of nitrogen fixation. This genomic and in vivo functional investigation of protist ectosymbionts highlights the diversity of evolutionary forces and trajectories that shape symbiotic interactions. The ecological and evolutionary importance of symbioses is increasingly clear, but the overall diversity of symbiotic interactions remains poorly explored. Here in this study, we investigated the evolution and nitrogen fixation capabilities of ectosymbionts attached to the protist Barbulanympha from the hindgut of the wood-eating cockroach Cryptocercus punctulatus . In addressing genome evolution of protist ectosymbionts, our data suggest that the ecological pressures influencing the evolution of extracellular symbionts clearly differ from intracellular symbionts and organelles. Using NanoSIMS analysis, we also obtained direct imaging evidence of a specific hindgut microbe playing a role in nitrogen fixation. These results demonstrate the power of combining NanoSIMS and genomics tools for investigating the biology of uncultivable microbes. This investigation paves the way for a more precise understanding of microbial interactions in the hindguts of wood-eating insects and further exploration of the diversity and ecological significance of symbiosis between microbes.

09 BIOMASS FUELS↗
DOE OSTI2020

Toward Large-Scale Image Segmentation on Summit

Semantic segmentation of images is an important computer vision task that emerges in a variety of application domains such as medical imaging, robotic vision and autonomous vehicles to name a few. While these domain-specific image analysis tasks involve relatively small image sizes (~ 10 2 × 10 2 ), there are many applications that need to train machine learning models on image data with extents that are orders of magnitude larger (~10 4 × 10 4 ). Training deep neural network (DNN) models on large extent images is extremely memory-intensive and often exceeds the memory limitations of a single graphical processing unit, a hardware accelerator of choice for computer vision workloads. Here, an efficient, sample parallel approach to train U-Net models on large extent image data sets is presented. Its advantages and limitations are analyzed and near-linear strong-scaling speedup demonstrated on 256 nodes (1536 GPUs) of the Summit supercomputer. Using a single node of the Summit supercomputer, an early evaluation of a recently released model parallel framework called GPipe is demonstrated to deliver ~ 2X speedup in executing a U-Net model with an order of magnitude larger number of trainable parameters than reported before. Performance bottlenecks for pipelined training of U-Net models are identified and mitigation strategies to improve the speedups are discussed. Together, these results open up the possibility of combining both approaches into a unified scalable pipelined and data parallel algorithm to efficiently train U-Net models with very large receptive fields on data sets of ultra-large extent images.

Seal, Sudip↗
DOE OSTI2025

Editorial: Applications of spectroscopy and chemometrics in nuclear materials analysis

Optical analysis techniques, including spectroscopy and image analysis, have many advantages when applied to the study of nuclear materials. They require small sample sizes, can be performed remotely, and can be proceduralized through consistent practice. Most importantly, they provide a wealth of information by generating multivariate data. For example, ultraviolet–visible–near-infrared absorbance spectroscopy of actinides in aqueous and organic solutions is dependent on the oxidation state, anionic complexation, and temperature. These variables are important for solution-based separation processes, and sensitivity to these factors, combined with online monitoring, can drive the efficiency and control of these processes. The morphology and chemical composition of actinide particles can also provide a vital clue to the mechanisms by which the particles were formed, providing forensic information on the origins of the particles.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1992

Recognition and characterization of hierarchical interstellar structure. II - Structure tree statistics

A new method of image analysis is described, in which images partitioned into 'clouds' are represented by simplified skeleton images, called structure trees, that preserve the spatial relations of the component clouds while disregarding information concerning their sizes and shapes. The method can be used to discriminate between images of projected hierarchical (multiply nested) and random three-dimensional simulated collections of clouds constructed on the basis of observed interstellar properties, and even intermediate systems formed by combining random and hierarchical simulations. For a given structure type, the method can distinguish between different subclasses of models with different parameters and reliably estimate their hierarchical parameters: average number of children per parent, scale reduction factor per level of hierarchy, density contrast, and number of resolved levels. An application to a column density image of the Taurus complex constructed from IRAS data is given. Moderately strong evidence for a hierarchical structural component is found, and parameters of the hierarchy, as well as the average volume filling factor and mass efficiency of fragmentation per level of hierarchy, are estimated. The existence of nested structure contradicts models in which large molecular clouds are supposed to fragment, in a single stage, into roughly stellar-mass cores.

Houlahan, Padraig↗
NASA NTRS1995

Machine learning for a Toolkit for Image Mining

A prototype user environment is described that enables a user with very limited computer skills to collaborate with a computer algorithm to develop search tools (agents) that can be used for image analysis, creating metadata for tagging images, searching for images in an image database on the basis of image content, or as a component of computer vision algorithms. Agents are learned in an ongoing, two-way dialogue between the user and the algorithm. The user points to mistakes made in classification. The algorithm, in response, attempts to discover which image attributes are discriminating between objects of interest and clutter. It then builds a candidate agent and applies it to an input image, producing an 'interest' image highlighting features that are consistent with the set of objects and clutter indicated by the user. The dialogue repeats until the user is satisfied. The prototype environment, called the Toolkit for Image Mining (TIM) is currently capable of learning spectral and textural patterns. Learning exhibits rapid convergence to reasonable levels of performance and, when thoroughly trained, Fo appears to be competitive in discrimination accuracy with other classification techniques.

Delanoy, Richard L.↗
NASA NTRS2007

The Crew Earth Observations Experiment: Earth System Science from the ISS

This viewgraph presentation reviews the use of Astronaut Photography (AP) as taken from the International Space Station (ISS) in Earth System Science (ESS). Included are slides showing basic remote sensing theory, data characteristics of astronaut photography, astronaut training and operations, crew Earth observations group, targeting sites and acquisition, cataloging and database, analysis and applications for ESS, image analysis of particular interest urban areas, megafans, deltas, coral reefs. There are examples of the photographs and the analysis.

Stefanov, William L.↗
NASA NTRS2016

Multidimensional Processing and Visual Rendering of Complex 3D Biomedical Images

The proposed technology uses advanced image analysis techniques to maximize the resolution and utility of medical imaging methods being used during spaceflight. We utilize COTS technology for medical imaging, but our applications require higher resolution assessment of the medical images than is routinely applied with nominal system software. By leveraging advanced data reduction and multidimensional imaging techniques utilized in analysis of Planetary Sciences and Cell Biology imaging, it is possible to significantly increase the information extracted from the onboard biomedical imaging systems. Year 1 focused on application of these techniques to the ocular images collected on ground test subjects and ISS crewmembers. Focus was on the choroidal vasculature and the structure of the optic disc. Methods allowed for increased resolution and quantitation of structural changes enabling detailed assessment of progression over time. These techniques enhance the monitoring and evaluation of crew vision issues during space flight.

Sams, Clarence F.↗
DOE OSTI2018

Effect of Slag Composition on the Crystallization Kinetics of Synthetic CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO Slags

The crystallization kinetics of CaO-SiO{sub 2}-Al{sub 2}O{sub 3}-MgO (CSAM) slags was studied with the aid of single hot thermocouple technique (SHTT). Kinetic parameters such as the Avrami exponent (n), rate coefficient (K), and effective activation energy of crystallization (E{sub A}) were obtained by kinetic analysis of data obtained from in situ observation of glassy to crystalline transformation and image analysis. Also, the dependence of nucleation and growth rates of crystalline phases were quantified as a function of time, temperature, and slag basicity. Together with the observations of crystallization front, they facilitated establishing the dominant mechanisms of crystallization. In an attempt to predict crystallization rate under non-isothermal conditions, a mathematical model was developed that employs the rate data of isothermal transformation. The model was validated by reproducing an experimental continuous cooling transformation diagram purely from isothermal data.

36 MATERIALS SCIENCE↗