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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Analysis of airborne imaging spectrometer data for the Ruby Mountains, Montana, by use of absorption-band-depth images

Airborne Imaging Spectrometer-1 (AIS-1) data were obtained for an area of amphibolite grade metamorphic rocks that have moderate rangeland vegetation cover. Although rock exposures are sparse and patchy at this site, soils are visible through the vegetation and typically comprise 20 to 30 percent of the surface area. Channel averaged low band depth images for diagnostic soil rock absorption bands. Sets of three such images were combined to produce color composite band depth images. This relative simple approach did not require extensive calibration efforts and was effective for discerning a number of spectrally distinctive rocks and soils, including soils having high talc concentrations. The results show that the high spectral and spatial resolution of AIS-1 and future sensors hold considerable promise for mapping mineral variations in soil, even in moderately vegetated areas.

Brickey, David W.↗
NASA NTRS1991

Distributed memory compiler design for sparse problems

A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.

Wu, Janet↗
NASA NTRS1993

Automatic detection of mass-resolved ion conics

A processing algorithm to automatically detect a specific type of ion distribution (called the ion conic distribution) in data obtained from a space-based mass spectrometer has been devised. Automation of this task is necessary due to the sparseness of conic events within the very large databases typical of space plasma instruments. This paper reports on the algorithm used to perform this automated analysis, along with a description of the methods used to verify the algorithm and a summary of initial results on the characterization of the near-earth space plasma.

Doherty, Mark F.↗
NASA NTRS2000

The Origin and Distribution of Heavy Elements in HCG 62

We present recent data on the compact group HCG 62 taken with AXAF CCD Imaging Spectrometer-S (ACIS-S) on Chandra. The sparseness of groups and their relatively simple dynamical history allow the properties of the Intergalatic Medium (IGM) to be more directly related to galaxy evolution than may be possible in clusters, and their lower gas temperatures produce strong lines from a broader range of elements than is the case in hotter clusters. This observation exploits the high X-ray brightness of HCG 62 to determine accurately the abundances of heavy elements as a function of position in the group, to test whether abundance variations are associated with individual galaxies, and to trace the origin of the enrichment.

Vrtilek, Jan↗
NASA NTRS2006

Quantifying Global Uncertainties in a Simple Microwave Rainfall Algorithm

While a large number of methods exist in the literature for retrieving rainfall from passive microwave brightness temperatures, little has been written about the quantitative assessment of the expected uncertainties in these rainfall products at various time and space scales. The latter is the result of two factors: sparse validation sites over most of the world's oceans, and algorithm sensitivities to rainfall regimes that cause inconsistencies against validation data collected at different locations. To make progress in this area, a simple probabilistic algorithm is developed. The algorithm uses an a priori database constructed from the Tropical Rainfall Measuring Mission (TRMM) radar data coupled with radiative transfer computations. Unlike efforts designed to improve rainfall products, this algorithm takes a step backward in order to focus on uncertainties. In addition to inversion uncertainties, the construction of the algorithm allows errors resulting from incorrect databases, incomplete databases, and time- and space-varying databases to be examined. These are quantified. Results show that the simple algorithm reduces errors introduced by imperfect knowledge of precipitation radar (PR) rain by a factor of 4 relative to an algorithm that is tuned to the PR rainfall. Database completeness does not introduce any additional uncertainty at the global scale, while climatologically distinct space/time domains add approximately 25% uncertainty that cannot be detected by a radiometer alone. Of this value, 20% is attributed to changes in cloud morphology and microphysics, while 5% is a result of changes in the rain/no-rain thresholds. All but 2%-3% of this variability can be accounted for by considering the implicit assumptions in the algorithm. Additional uncertainties introduced by the details of the algorithm formulation are not quantified in this study because of the need for independent measurements that are beyond the scope of this paper. A validation strategy for these errors is outlined.

Kummerow, Christian↗
NASA NTRS2010

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Riggs, George A.↗
NASA NTRS2012

JESTR: Jupiter Exploration Science in the Time Regime

Solar system objects are inherently time-varying with changes that occur on timescales ranging from seconds to years. For all planets other than the Earth, temporal coverage of atmospheric phenomena is limited and sparse. Many important atmospheric phenomena, especially those related to atmospheric dynamics, can be studied in only very limited ways with current data. JESTR is a mission concept that would remedy this gap in our exploration of the solar system by ncar-continuous imaging and spectral monitoring of Jupiter over a multi-year mission lifetime.

Noll, Keith S.↗
NASA NTRS1986

Algorithms versus architectures for computational chemistry

The algorithms employed are computationally intensive and, as a result, increased performance (both algorithmic and architectural) is required to improve accuracy and to treat larger molecular systems. Several benchmark quantum chemistry codes are examined on a variety of architectures. While these codes are only a small portion of a typical quantum chemistry library, they illustrate many of the computationally intensive kernels and data manipulation requirements of some applications. Furthermore, understanding the performance of the existing algorithm on present and proposed supercomputers serves as a guide for future programs and algorithm development. The algorithms investigated are: (1) a sparse symmetric matrix vector product; (2) a four index integral transformation; and (3) the calculation of diatomic two electron Slater integrals. The vectorization strategies are examined for these algorithms for both the Cyber 205 and Cray XMP. In addition, multiprocessor implementations of the algorithms are looked at on the Cray XMP and on the MIT static data flow machine proposed by DENNIS.

Partridge, H.↗
DOE OSTI2024

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Automatic Image Point Matching

Sparse Image Point Matching (SIPM) is a foundational technology for photo triangulation, structure from motion (SfM), Simultaneous Location and Mapping (SLAM), and data fusion. The goal of the matching is to automatically generate sets of image coordinates that identify the same feature across images. Ideally, the process should be robust to lighting, scale, perspective, and modality changes. The scope of the image matching topic in the field of remote sensing (RS) is enormous because of the variety of collection platforms, modalities, sensor types, applications, and subjects. In this work, we report the history of and assess the state of the art of visible-spectrum (panchromatic and color) image matching of the Earth’s surface. Work specific to large-format images (LFI) (e.g., metric aerial cameras and Earth-observing satellites) will be highlighted. However, the state of the art in this century will mostly be traced through machine vision research and benchmarks because research specific to LFI is rare.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Computer architectures for computational physics work done by Computational Research and Technology Branch and Advanced Computational Concepts Group

Slides are reproduced that describe the importance of having high performance number crunching and graphics capability. They also indicate the types of research and development underway at Ames Research Center to ensure that, in the near term, Ames is a smart buyer and user, and in the long-term that Ames knows the best possible solutions for number crunching and graphics needs. The drivers for this research are real computational physics applications of interest to Ames and NASA. They are concerned with how to map the applications, and how to maximize the physics learned from the results of the calculations. The computer graphics activities are aimed at getting maximum information from the three-dimensional calculations by using the real time manipulation of three-dimensional data on the Silicon Graphics workstation. Work is underway on new algorithms that will permit the display of experimental results that are sparse and random, the same way that the dense and regular computed results are displayed.

Source record↗
NASA NTRS2010

Fast Solution in Sparse LDA for Binary Classification

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic form along with the inherent sequential nature of greedy search itself. Together this enables the use of highly-efficient partitioned-matrix-inverse techniques that result in large speedups of computation in both the forward-selection and backward-elimination stages of greedy algorithms in general.

Moghaddam, Baback↗
NASA NTRS2013

The Utility of the Real-Time NASA Land Information System Data for Drought Monitoring Applications

Measurements of soil moisture are a crucial component for the proper monitoring of drought conditions. The large spatial variability of soil moisture complicates the problem. Unfortunately, in situ soil moisture observing networks typically consist of sparse point observations, and conventional numerical model analyses of soil moisture used to diagnose drought are of coarse spatial resolution. Decision support systems such as the U.S. Drought Monitor contain drought impact resolution on sub-county scales, which may not be supported by the existing soil moisture networks or analyses. The NASA Land Information System, which is run with 3 km grid spacing over the eastern United States, has demonstrated utility for monitoring soil moisture. Some of the more useful output fields from the Land Information System are volumetric soil moisture in the 0-10 cm and 40-100 cm layers, column-integrated relative soil moisture, and the real-time green vegetation fraction derived from MODIS (Moderate Resolution Imaging Spectroradiometer) swath data that are run within the Land Information System in place of the monthly climatological vegetation fraction. While these and other variables have primarily been used in local weather models and other operational forecasting applications at National Weather Service offices, the use of the Land Information System for drought monitoring has demonstrated utility for feedback to the Drought Monitor. Output from the Land Information System is currently being used at NWS Huntsville to assess soil moisture, and to provide input to the Drought Monitor. Since feedback to the Drought Monitor takes place on a weekly basis, weekly difference plots of column-integrated relative soil moisture are being produced by the NASA Short-term Prediction Research and Transition Center and analyzed to facilitate the process. In addition to the Drought Monitor, these data are used to assess drought conditions for monthly feedback to the Alabama Drought Monitoring and Impact Group and the Tennessee Drought Task Force, which are comprised of federal, state, and local agencies and other water resources professionals.

White, Kristopher D.↗
DOE OSTI2025

Randomized Algorithms for Symmetric Nonnegative Matrix Factorization

Symmetric Nonnegative Matrix Factorization (SymNMF) is a technique in data analysis and machine learning that approximates a matrix with a product of a nonnegative, low-rank matrix and it transpose. To design faster and more scalable algorithms for SymNMF we develop two randomized algorithms for its computation. The first method uses randomized matrix sketching to compute an initial low-rank approximation to the input matrix and proceeds to uses this as a low-rank input to rapidly compute a SymNMF. The second methods uses randomized leverage score sampling to approximately solve constrained least squares problems. Many successful methods for SymNMF rely on (approximately) solving sequences of constrained least squares problems. Here, we prove theoretically that leverage score sampling can approximately solve constrained least squares problems to e-accuracy. Finally we demonstrate both methods work in practice by applying them to graph clustering tasks on large real world data sets. These experiments show that our methods approximately maintain solution quality and achieve significant speed ups for both large dense and large sparse problems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Bidirectional reflectances of three soil surfaces and their characterization through model inversion

Spectral bidirectional reflectances were measured over three natural soil sites using a specially designed radiometer called the Parabola. Two of the sites were bare soils, and the third had a sparse cover of desert scrub. The reflectances were strongly non-Lambertian for all three surfaces, but with markedly different patterns. The measured data were fitted with a quasi-physical reflectance model in which the surface backscattering and forwardscattering are separately formulated. A soil reflectance characterization was obtained by assessing the contributions of the forward, backward, and Lambertian components. This three-parameter characterization produced a satisfactory fit to the measured reflectances and appears promising as a basis for soils categorization.

Deering, D. W.↗
NASA NTRS2000

Photochemistry of Methane in Model Atmospheres of Jupiter and Titan

The two central findings were 1) hydrogen atoms and hydrogen molecules photodissociated from methane are relatively richer in H than in D in other words deuterium atoms have a greater probability of remaining attached to the carbon atom. Titan, a moon of Saturn has an atmosphere which is largely nitrogen but also contains about 3% methane as well as smaller amounts of C2 and C3 hydrocarbons. If all these hydrocarbons are of biological origin, the isotopic scrambling occurring in living organisms would result in equal D atom abundances. On the other hand, if the higher hydrocarbons are derived from methane by photodissociation of methane, they should be richer in D than methane. Precise values for the enrichment were derived from our photochemical data. 2) When methane is dissociated by vuv light, methylene is produced in a singlet state. This explains why the higher hydrocarbons are sparse on Jupiter but relatively rich on Titan.

Bersohn, Richard↗
NASA NTRS2008

High-Resolution N-Band Observations of the Nova Rs Ophiuchi: First Science with the Keck Interferometer Nuller

We report new observations of the nova RS Ophiuchi using the Keck Interferometer Nulling Instrument (KIN), approximately 3.8 days following the most recent outburst that occurred on 2006 February 12. These observations represent the first scientific results from the KIN, which operates in N-band from 8 to 12.5 microns. The nulling technique is the sparse aperture equivalent of the conventional coronagraphic technique used in filled aperture telescopes. By fitting the unique KIN inner and outer spatial regime data, we have obtained an angular size of the mid-infrared continuum of 6.2,4.0, or 5.4 mas for a disk profile, Gaussian profile, and shell profile respectively. The data show evidence of enhanced neutral atomic hydrogen emission and atomic metals including silicon located in the inner spatial regime near the white dwarf relative to the outer regime. There are also nebular emission lines and evidence of hot silicate dust in the outer spatial region, centered at approximately 2.5E14 cm from the WD, that are not found in the inner regime. The nova flash in the outer spatial regime evidently excited these features before the blast wave reached these regions. These identifications support the following interpretation. The dust appears to be present between outbursts and was not created during the outburst event. We further discuss the present results in terms of a unifying model of the system that includes an increase in density in the plane of the orbit of the two stars created by a spiral shock wave caused by the motion of the stars through the cool wind of the red giant star. These data show the power and potential of the nulling technique which has been developed for the detection of Earthlike planets around nearby stars for the Terrestrial Planet Finder Mission and Darwin missions.

Danchi, William↗