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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

Band, D. L.↗
NASA NTRS2010

Autonomous Exploration for Gathering Increased Science

The Autonomous Exploration for Gathering Increased Science System (AEGIS) provides automated targeting for remote sensing instruments on the Mars Exploration Rover (MER) mission, which at the time of this reporting has had two rovers exploring the surface of Mars (see figure). Currently, targets for rover remote-sensing instruments must be selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. In particular, this technology will be used to automatically acquire sub-framed, high-resolution, targeted images taken with the MER panoramic cameras. This software provides: 1) Automatic detection of terrain features in rover camera images, 2) Feature extraction for detected terrain targets, 3) Prioritization of terrain targets based on a scientist target feature set, and 4) Automated re-targeting of rover remote-sensing instruments at the highest priority target.

Bornstein, Benjamin J.↗
NASA NTRS1990

Finding curvilinear features in speckled images

A method for finding curves in digital images with speckle noise is described. The solution method differs from standard linear convolutions followed by thresholds in that it explicitly allows curvature in the features. Maximum a posteriori (MAP) estimation is used, together with statistical models for the speckle noise and for the curve-generation process, to find the most probable estimate of the feature, given the image data. The estimation process is first described in general terms. Then, incorporation of the specific neighborhood system and a multiplicative noise model for speckle allows derivation of the solution, using dynamic programming, of the estimation problem. The detection of curvilinear features is considered separately. The detection results allow the determination of the minimal size of detectable feature. Finally, the estimation of linear features, followed by a detection step, is shown for computer-simulated images and for a SAR image of sea ice.

Samadani, Ramin↗
NASA NTRS2010

Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

neural network↗
NASA NTRS1984

Use of SIR-B multiincidence-angle imagery to study iceberg detectability and offshore ocean feature extraction

The Shuttle Imaging Radar B (SIR-B) iceberg experiment is described and expected results are outlined. The specific objectives are: (1) to study the L-band satellite SAR detectability of iceberg targets as a function of incidence angle and observed conditions; (2) to compare L-, X-, and C-band aircraft imagery, and to use this data as a basis for an appropriate simulation of satellite imagery and to compare with SIR-B images; and (3) to determine the possibility of comparive available surface or near surface currents with estimates of the range component of surface current derived from the SIR-B data (IOS experiment).

Gray, A. L.↗
NASA NTRS1984

Analysis of LANDSAT-4 TM Data for Lithologic and Image Mapping Purpose

Lithologic mapping techniques using the near infrared bands of the Thematic Mapper onboard the LANDSAT 4 satellite are investigated. These methods are coupled with digital masking to test the capability of mapping geologic materials. Data are examined under medium to low Sun angle illumination conditions to determine the detection limits of materials with absorption features. Several detection anomalies are observed and explained.

Podwysocki, M. H.↗
NASA NTRS1999

Adaptive Filtering and Edge Detection Using Range Data

It is typical in edge and corner detection applications to examine a single scale or to consider some space of scales in the image without knowing which scale is appropriate for each location in the image.

Image smoothing edge detection stereoscopy filteri↗
NASA NTRS1998

Ginga Gamma-Ray Burst Line Occurrence

The purpose of this project is the statistical evaluation of the occurrence of spectral lines in the gamma-ray burst spectra detected by the Ginga burst detector, and the comparison of the Ginga results to the BATSE observations. Two significant line features were detected in the Ginga bursts, but thus far none have been detected in the bursts BATSE detected. These line features may indicate the presence of strong magnetic fields in bursts, and therefore are important physical diagnostics of the conditions in the plasma which radiates the observed gamma-rays. The issue is whether there is a discrepancy between the Ginga and BATSE results; the potential discrepancy must be evaluated statistically. Even if BATSE line detections are announced, the statistical methodology we have developed can be used to estimate the rate at which different types of spectral features occur.

Band, David↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events, and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, enable detection of atmospheric features during daytime operations with a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS data. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS1983

Detection of silicate emission features in the 8- to 13-micron spectra of main belt asteroids

A presentation is given of 8.0-13.0 micron spectra (Delta lambda/lambda = 0.02-0.03) for six main belt asteroids, which range from 58 to 220 km in diameter and sample the five principal taxonomic classes (C, S, M, R and E). Narrow, well-defined silicate emission features are present on two of the asteroids, the C-type 19 Fortuna and the M-type 21 Lutetia. No comparable emission features are observed on the S-types 11 Parthenope and 14 Irene, the R-type 349 Dembowska or the E-type 64 Angelina.

Feierberg, M. A.↗
NASA NTRS1986

Airborne spectroradiometry: The application of AIS data to detecting subtle mineral absorption features

Analysis of Airborne Imaging Spectrometer (AIS) data acquired in Australia has revealed a number of operational problems. Horizontal striping in AIS imagery and spectral distortions due to order overlap were investigated. Horizontal striping, caused by grating position errors can be removed with little or no effect on spectral details. Order overlap remains a problem that seriously compromises identification of subtle mineral absorption features within AIS spectra. A spectrometric model of the AIS was developed to assist in identifying spurious spectral features, and will be used in efforts to restore the spectral integrity of the data.

Cocks, T. D.↗
NASA NTRS1992

The dust coma of Comet P/Giacobini-Zinner in the infrared

The paper presents 1-20-micron photometry of P/Giacobini-Zinner obtained at the NASA Infrared Telescope Facility, during 1985 June-September (r = 1.57-1.03 AU). A broad, weak 10-micron silicate emission feature was detected on August 26.6; a similar weak emission feature could have been hidden in the broadband photometry on other dates. The total scattering and emitting cross section of dust in the inner coma was similar to that in other short-period comets, but a factor of 10 (r = 1.56 AU) to 100 (r = 1.03 AU) lower than the amount of dust in Comet Halley. The thermal emission continuum can be fit with models weighted toward either small or large absorbing grains. The dust production rate near perihelion was about 100,000 g/s (small-grain model) to about 1,000,000 g/s (large-grain model). The corresponding dust/gas mass ratio on August 26 was about 0.1-1. A silicate-rich heterogeneous grain model with an excess of large particles is compatible with the observed spectrum of Giacobini-Zinner on August 26. Thus, weak or absent silicate emission does not necessarily imply an absence of silicates in the dust, although the abundance of silicate particles not greater than 1 micron radius must have been lower than in Comet Halley.

Hanner, M. S.↗
NASA NTRS1995

The soft gamma-ray spectrum of A0535+26: Detection of an absorption feature at 110 keV by OSSE

We present soft gamma-ray observations by the Oriented Scintillation Spectrometer Experiment (OSSE) on the Compton Gamma Ray Observatory (GRO) of the transient X-ray binary pulsar A0535+26. The observations were made 1994 February 8-17, immediately prior to the peak of a giant outburst. The phase averaged spectrum is complex and cannot be described by a single-component model. We find that structure in the spectrum above 100 keV can best be modeled by an absorption feature near 110 keV, which we interepret as the signature of cyclotron resonant scattering. Because of OSSE's 45 keV threshold, we are unable to make a definitive statement on the presence of a 55 keV absorption line; however, we can conclude that if this line does exist, it must have a smaller optical depth than the line at 110 keV. A first harmonic (=fundamental) cyclotron resonance at 110 keV corresponds to a magnetic field strength at the surface of the neutron star of approximately 1 x 10(exp 13) G (approximately 5 x 10(exp 12) G if the first harmonic is at 55 keV).

Grove, J. E.↗
NASA NTRS1998

Unraveling the 10 micron "silicate" feature of protostars: the detection of frozen interstellar ammonia

We present infrared spectra of four embedded protostars in the 750-1230 cm-1 (13.3-8.1 microns) range. For NGC 7538 IRS 9, a new band is reported at 1110 cm-1 (9.01 microns, and several others may be present near 785, 820, 900, 1030, and 1075 cm-1 (12.7, 12.2, 11.1, 9.71, and 9.30 microns). The band 1110 cm-1 is attributed to frozen NH3. Its position and width imply that the NH3 is frozen in a polar, H2O-rich interstellar ice component. The NH3/H2O ice ratio inferred for NGC 7538 IRS 9 is 0.1, making NH3 as important a component as CH3OH and CO2 in the polar ices along this line of sight. At these concentrations, hydrogen bonding between the NH3 and H2O can account for much of the enigmatic low-frequency wing on the 3240 cm-1 (3.09 microns) H2O interstellar ice band. The strength of the implied NH3 deformation fundamental at 1624 cm-1 (6.158 microns) can also account for the absorption at this position reported by ISO.

NASA Center ARC↗
NASA NTRS2005

Application of an Orbital GPR Model to Detecting Martian Polar Subsurface Features

There are numerous challenges in successfully implementing and interpreting planetary ground penetrating radar (GPR) measurements. Many are due to substantial uncertainties in the target ground parameters and the intervening medium (i.e., the ionosphere). These uncertainties generate a compelling need for meaningful quantitative simulation of the planetary GPR problem. An accurate numerical model would enable realistic numerical GPR simulations using parameter regimes much broader than are possible in laboratory or field experiments. Parameters such as source bandwidth and power, surface and subsurface features, and ionospheric profiles could be rapidly iterated to understand their impact on GPR performance and the reliable interpretation of GPR data.

Xu, Y.↗
NASA NTRS2014

Detecting Low-Contrast Features in the Cosmic Ray Albedo Proton Map of the Moon

High energy cosmic rays constantly bombard the lunar regolith, producing (via nuclear evaporation) secondary 'albedo' or 'splash' particles like protons and neutrons, some of which escape back to space. Lunar Prospector and the Lunar Reconnaissance Orbiter (LRO), have shown that the energy distribution of albedo neutrons is modulated by the elemental composition of the lunar regolith, and by ice deposits in permanently shadowed polar craters. Here we investigate an analogous phenomenon with high energy ((is) approximately 100 MeV) lunar albedo protons.

LOW - CONTRAST↗
NASA NTRS2021

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗
NASA NTRS1992

The 8-13 micron spectrum of the young stellar object WL 16

Spectroscopy of WL 16 in the 8-13-micron region reveals a spectrum dominated by the hydrocarbon emission features at 7.7, 8.6, 11.25, and 12.7 microns. The emission plateau between the latter two features is detected, the first such detection in a young stellar object. The expected silicate feature is badly masked by the strong emission bands. Combining our spectra with the infrared spectral energy distribution indicates that silicate absorption is probably present.

Hanner, M. S.↗