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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Non-NASA Center↗
NASA NTRS1975

Low-cost data analysis systems for processing multispectral scanner data

A research-oriented data analysis system was developed which is used for evaluating complex remote sensor systems and for development of techniques for application of remotely sensed data. Some modular hardware components were developed which may be added to one's existing facilities to establish a low-cost data analysis system for processing multispectral scanner data. Software modules which are compatible with small general purpose digital computers process and analyze remote sensor data, and convert it to information needed by users. The software modules are written in FORTRAN IV language for ease of transfer to other computer systems. The basic hardware and software system requirements are defined for some low-cost data analysis systems consisting of an image display system, a small general purpose digital computer, and an output recording device. The hardware modules consist of: a LANDSAT MSS data reformatting program; a series of spectral pattern recognition programs required to generate surface classification maps and tabular information; programs to convert computer generated maps from image space to a geographically referenced base; programs to extract data and irregularly shaped areas and to produce thematic maps of the designated areas; and programs to tabulate acreages of selected classification categories. Some off-the-shelf, inexpensive digital image display systems are described.

Whitley, S. L.↗
NASA NTRS1972

Magnetic coercivity and ferromagnetic species in lunar materials

Lunar samples have reduced coercive force, high values of R sub H (ratio of remanent coercive force to coercive force), and constriction in their magnetic hysteresis loops due to the presence of superparamagnetic and multidomain iron grains. The high R sub H values are also attributable to the magnetic shape effects of the iron grains. Spheres, cubes, and needles, as well as more irregular metal grains were observed. The coercive force values are quite meaningless unless the size and shape distributions are determined. The R sub H and the ratio of saturation remanence to saturation magnetization values can be considered characteristic of the size and shape modes of the ferromagnetic grains in a natural sample, and a classification of natural materials based on their hysteresis characteristics is presented with special reference to lunar samples.

Wasilewski, P.↗
NASA NTRS1989

Methods and decision making on a Mars rover for identification of fossils

A system for automated fusion and interpretation of image data from multiple sensors, including multispectral data from an imaging spectrometer is being developed. Classical artificial intelligence techniques and artificial neural networks are employed to make real time decision based on current input and known scientific goals. Emphasis is placed on identifying minerals which could indicate past life activity or an environment supportive of life. Multispectral data can be used for geological analysis because different minerals have characteristic spectral reflectance in the visible and near infrared range. Classification of each spectrum into a broad class, based on overall spectral shape and locations of absorption bands is possible in real time using artificial neural networks. The goal of the system is twofold: multisensor and multispectral data must be interpreted in real time so that potentially interesting sites can be flagged and investigated in more detail while the rover is near those sites; and the sensed data must be reduced to the most compact form possible without loss of crucial information. Autonomous decision making will allow a rover to achieve maximum scientific benefit from a mission. Both a classical rule based approach and a decision neural network for making real time choices are being considered. Neural nets may work well for adaptive decision making. A neural net can be trained to work in two steps. First, the actual input state is mapped to the closest of a number of memorized states. After weighing the importance of various input parameters, the net produces an output decision based on the matched memory state. Real time, autonomous image data analysis and decision making capabilities are required for achieving maximum scientific benefit from a rover mission. The system under development will enhance the chances of identifying fossils or environments capable of supporting life on Mars

Eberlein, Susan↗
NASA NTRS2020

150 Shades of Green: Using the Full Spectrum of Remote Sensing Reflectance to Elucidate Color Shifts in the Ocean

This article proposes a simple and intuitive classification system by which to define full spectral remote sensing reflectance (Rrs(λ)) data with a quantitative output that enables a more manageable handling of spectral information for aquatic science applications. The weighted harmonic mean of the Rrs(λ) wavelengths outputs an Apparent Visible Wavelength (in units of nanometers), representing a one-dimensional geophysical metric of color that is inherently correlated to spectral shape. This dimensionality reduction of spectral information combined with the output along a continuum of wavelength values offers a robust and user-friendly means to describe and analyze spectral Rrs(λ) in terms of spatial and temporal trends and variability. The uncertainty in the algorithm's estimation of spectral shape is demonstrated on a global scale, in addition to the utility of the algorithm to discern spectral-spatial-temporal trends in the ocean, on a per-pixel basis for the entire 22 year continuous ocean color (SeaWiFS and MODIS-Aqua) time-series. This technique can be applied to datasets of varying multi- and hyper-spectral resolutions, providing continuity between heritage and future satellite sensors, and further enabling an effective means of elucidating similarities or differences in complex spectral signatures within the constraints of two dimensions. This straightforward means of conceptualizing multi-dimensional variability can help maximize the potential of the spectral information embedded in remote sensing data.

ocean color↗
NASA NTRS2024

A Multi‐Probe Automated Classification of Ice Crystal Habits During the IMPACTS Campaign

Although all ice crystals are unique, many can be grouped together by shape or habit, with members of a habit class sharing similar representations of properties such as fall velocity and growth rate. A decision tree algorithm designed to be adaptable to any particle imaging probe, thus enabling the creation of habit size distributions over a size range larger than that of any probe on its own, is used to classify ice crystals imaged by three airborne cloud probes in mid-latitude winter cyclones during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Crystals are sorted into seven habit classes based on their morphological properties: sphere, column/needle, plate, graupel, dendrite, aggregate, and irregular. Although adaptability was its primary goal, the algorithm was found to be moderately skillful for identifying idealized habit images. Quantitative tests of the algorithm’s adaptability displayed mixed results, as Two-Dimensional Stereo Probe (2DS) classifications showed moderate correlation with Particle Habit Imaging and Polar Scattering Probe (PHIPS) classifications, but only weak correlation with High Volume Precipitation Spectrometer (HVPS) classifications. The algorithm was applied to random sets of images from each probe in a case study of a mesoscale snow band sampled on 7 February 2020. In the case study, qualitative analysis of particle images revealed general agreement on classifications among the probes, supporting the algorithm’s applicability to multiple cloud probes. Most classifications appeared correct upon manual inspection, suggesting that in practical use, the algorithm is reasonably able to classify non-idealized images.

Julian Schima↗
NASA NTRS1977

Ground truth management system to support multispectral scanner /MSS/ digital analysis

A computerized geographic information system for management of ground truth has been designed and implemented to relate MSS classification results to in situ observations. The ground truth system transforms, generalizes and rectifies ground observations to conform to the pixel size and shape of high resolution MSS aircraft data. These observations can then be aggregated for comparison to lower resolution sensor data. Construction of a digital ground truth array allows direct pixel by pixel comparison between classification results of MSS data and ground truth. By making comparisons, analysts can identify spatial distribution of error within the MSS data as well as usual figures of merit for the classifications. Use of the ground truth system permits investigators to compare a variety of environmental or anthropogenic data, such as soil color or tillage patterns, with classification results and allows direct inclusion of such data into classification operations. To illustrate the system, examples from classification of simulated Thematic Mapper data for agricultural test sites in North Dakota and Kansas are provided.

Coiner, J. C.↗
NASA NTRS2008

NASA Tech Briefs, December 2008

Topics covered include: Crew Activity Analyzer; Distributing Data to Hand-Held Devices in a Wireless Network; Reducing Surface Clutter in Cloud Profiling Radar Data; MODIS Atmospheric Data Handler; Multibeam Altimeter Navigation Update Using Faceted Shape Model; Spaceborne Hybrid-FPGA System for Processing FTIR Data; FPGA Coprocessor for Accelerated Classification of Images; SiC JFET Transistor Circuit Model for Extreme Temperature Range; TDR Using Autocorrelation and Varying-Duration Pulses; Update on Development of SiC Multi-Chip Power Modules; Radio Ranging System for Guidance of Approaching Spacecraft; Electromagnetically Clean Solar Arrays; Improved Short-Circuit Protection for Power Cells in Series; Electromagnetically Clean Solar Arrays; Logic Gates Made of N-Channel JFETs and Epitaxial Resistors; Improved Short-Circuit Protection for Power Cells in Series; Communication Limits Due to Photon-Detector Jitter; System for Removing Pollutants from Incinerator Exhaust; Sealing and External Sterilization of a Sample Container; Converting EOS Data from HDF-EOS to netCDF; HDF-EOS 2 and HDF-EOS 5 Compatibility Library; HDF-EOS Web Server; HDF-EOS 5 Validator; XML DTD and Schemas for HDF-EOS; Converting from XML to HDF-EOS; Simulating Attitudes and Trajectories of Multiple Spacecraft; Specialized Color Function for Display of Signed Data; Delivering Alert Messages to Members of a Work Force; Delivering Images for Mars Rover Science Planning; Oxide Fiber Cathode Materials for Rechargeable Lithium Cells; Electrocatalytic Reduction of Carbon Dioxide to Methane; Heterogeneous Superconducting Low-Noise Sensing Coils; Progress toward Making Epoxy/Carbon-Nanotube Composites; Predicting Properties of Unidirectional-Nanofiber Composites; Deployable Crew Quarters; Nonventing, Regenerable, Lightweight Heat Absorber; Miniature High-Force, Long-Stroke SMA Linear Actuators; "Bootstrap" Configuration for Multistage Pulse-Tube Coolers; Reducing Liquid Loss during Ullage Venting in Microgravity; Ka-Band Transponder for Deep-Space Radio Science; Replication of Space-Shuttle Computers in FPGAs and ASICs; Demisable Reaction-Wheel Assembly; Spatial and Temporal Low-Dimensional Models for Fluid Flow; Advanced Land Imager Assessment System; Range Imaging without Moving Parts.

Source record↗
NASA NTRS1990

Classification of auroral precipitation fluxes by characteristic parameters and their effects on the coupling of the precipitation to the ambient ionosphere

The spectral shapes of the precipitating auroral electron flux spectra are quantified by an automated fitting procedure which represents each flux spectrum as a superposition of Maxwellian and Gaussian partial fluxes. This makes it possible to represent each spectrum by a set of characteristic parameters which describe the shape of that spectrum. A set of inverted-V events observed by the Low-Energy Plasma Instrument on Dynamics Explorer 2 near the fall 1981 equinox, has been analyzed. The distribution of the peak inverted-V energies in magnetic local time and invariant latitude (IL) has been obtained, and it is shown that by far the highest peak energies occur in the range of IL between 65 and 70 and MLT between 18 and 24 hrs. It is also shown how the difference in spectral characteristics of the precipitation fluxes between the cusp and the nighttime auroral zone determines the thermal coupling of the precipitation to the ambient ionosphere.

Fontheim, E. G.↗
NASA NTRS1971

Lunar rilles: A catalog and method of classification

A representative sample of lunar rilles observed on Lunar Orbiter 4 and 5 photographs is presented. A method of classification of rilles is described which will provide a means of quantitatively grouping rilles of similar planemetric shape. The method is based on a finite Fourier approximation of the edge of the rille. It provides an adequate approximation to all planemetric characteristics of the rille except rille recurvature.

Oberbeck, V. R.↗
NASA NTRS1991

Stochastic robustness

To carry out stochastic robustness analysis, an expected probability distribution is assigned to each uncertain parameter in the system. The Monte Carlo analysis proceeds by repeatedly assigning shaped random values to each plant parameter, evaluating the stability of performance metric, and performing the binary classification (stable/unstable, etc.). If the system is stable, the state response to a unit disturbance impulse can be propagated to establish whether the response would violate settling time envelopes and whether peak actuator use would violate predetermined maximums. The final estimates of the probability of each form of unacceptable behavior are found by dividing the number of cases in which the overall system had that form of unacceptability by the number of cases run. Stability robustness can be portrayed graphically using the stochastic root locus and by using histograms of parameter values found in the unacceptable cases.

Marrison, C.↗
NASA NTRS1987

Refining image segmentation by polygon skeletonization

A skeletonization algorithm was encoded and applied to a test data set of land-use polygons taken from a USGS digital land use dataset at 1:250,000. The distance transform produced by this method was instrumental in the description of the shape, size, and level of generalization of the outlines of the polygons. A comparison of the topology of skeletons for forested wetlands and lakes indicated that some distinction based solely upon the shape properties of the areas is possible, and may be of use in an intelligent automated land cover classification system.

Clarke, Keith C.↗
NASA NTRSDate not supplied

Convolutional Neural Networks for Image Classification in Metal Selective Laser Meting Additive Manufacturing

Selective laser melting (SLM) is a metal additive manufacturing process that has several advantages such as the large range of metal materials that can be accommodated, 3D printing of complex shape components, the ability to adjust material properties, and cost reduction as expensive production equipment may not be required. Therefore, process monitoring is crucial in different stages of the component building. In this work, convolutional neural networks (CNNs) are investigated as a suitable technique for post-inspection of builds. The monitoring of manufactured parts was conducted by collecting computed tomography (CT) images and identifying defects. Five CNN models were implemented and tested for the classification of the CT images. The models were based on NASNetMobile and DenseNet121, and a custom built CNN model. The results of this work show that CNNs can be feasible and reliable for rapid monitoring and classification of defects in CT images from build fabrication using SLM.

Rodolfo Ledesma↗
NASA NTRS1994

A proposition for the classification of carbonaceous chondritic micrometeorites

Classification of interplanetary dust particles (IDP's) should be unambiguous and, if possible, provide an opportunity to interrelate these ultrafine IDP's with the matrices of undifferentiated meteorites. I prefer a scheme of chemical groupings and petrologic classes that is based on primary IDP properties that can be determined without prejudice by individual investigators. For IDP's of 2-50 microns these properties are bulk elemental chemistry, morphology, shape, and optical properties. The two major chemical groups are readily determined by energy dispersive spectroscopic analysis using the scanning or analytical electron microscope. Refinement of chondritic IDP classification is possible using the dominant mineral species, e.g. olivine, pyroxene, and layer silicates, and is readily inferred from FTIR, and automated chemical analysis. Petrographic analysis of phyllosilicate-rich IDP's will identify smectite-rich and serpentine-rich particles. Chondritic IDP's are also classified according to morphology, viz., CP and CF IDP's are aggregate particles that differ significantly in porosity, while the dense CS IDP's have a smooth surface. The CP IDP's are characterized by an anhydrous silicate mineralogy, but small amounts of layer silicates may be present. Distinction between the CP and CF IDP's is somewhat ambiguous, but the unique CP IDP's are fluffy, or porous, ultrafine-grained aggregates. The CP IDP's, which may contain silicate whiskers, are the most carbon-rich extraterrestrial material presently known. The CF IDP's are much less porous that CP IDP's. Using particle type definitions, CP IDP's in the NASA JSC Cosmic Dust Catalogs are approx. 15 percent of all IDP's that include nonchondritic spheres. Most aggregate particles are of the CF type.

Rietmeijer, Frans J. M.↗
NASA NTRS1999

Classification of ion mobility spectra by functional groups using neural networks

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Non-NASA Center↗
NASA NTRS2007

Initial Verification of GEOS-4 Aerosols Using CALIPSO and MODIS: Scene Classification

A-train sensors such as MODIS and MISR provide column aerosol properties, and in the process a means of estimating aerosol type (e.g. smoke vs. dust). Correct classification of aerosol type is important because retrievals are often dependent upon selection of the right aerosol model. In addition, aerosol scene classification helps place the retrieved products in context for comparisons and analysis with aerosol transport models. The recent addition of CALIPSO to the A-train now provides a means of classifying aerosol distribution with altitude. CALIPSO level 1 products include profiles of attenuated backscatter at 532 and 1064 nm, and depolarization at 532 nm. Backscatter intensity, wavelength ratio, and depolarization provide information on the vertical profile of aerosol concentration, size, and shape. Thus similar estimates of aerosol type using MODIS or MISR are possible with CALIPSO, and the combination of data from all sensors provides a means of 3D aerosol scene classification. The NASA Goddard Earth Observing System general circulation model and data assimilation system (GEOS-4) provides global 3D aerosol mass for sulfate, sea salt, dust, and black and organic carbon. A GEOS-4 aerosol scene classification algorithm has been developed to provide estimates of aerosol mixtures along the flight track for NASA's Geoscience Laser Altimeter System (GLAS) satellite lidar. GLAS launched in 2003 and did not have the benefit of depolarization measurements or other sensors from the A-train. Aerosol typing from GLAS data alone was not possible, and the GEOS-4 aerosol classifier has been used to identify aerosol type and improve the retrieval of GLAS products. Here we compare 3D aerosol scene classification using CALIPSO and MODIS with the GEOS-4 aerosol classifier. Dust, smoke, and pollution examples will be discussed in the context of providing an initial verification of the 3D GEOS-4 aerosol products. Prior model verification has only been attempted with surface mass comparisons and column optical depth from AERONET and MODIS.

Welton, Ellsworth J.↗
NASA NTRS1974

Craters on Earth, Moon, and Mars - Multivariate classification and mode of origin

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian calderas, and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components.

Pike, R. J.↗