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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 397 records · Page 22
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NASA NTRS2001

Heat-Treatment of MgSiO Smokes of Astrophysical Interest: Possible Implications for Olivine-Pyroxene-Silica Assemblages in Chondritic Aggregate IDPs

Anhydrous silicates in coarse-grained ferromagnesiosilica principal components (PCs) formed during atmospheric entry flash-heating also constrain the astromineralogy of astrophysical dust. This is because of the unique closed-system behavior of these PCs in chondritic aggregate interplanetary dust particles (IDPs). Additional information is contained in the original extended abstract.

Rietmeijer, F. J. M.↗
NASA NTRS2001

Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

Guild, Liane S.↗
NASA NTRS2001

Non-seasonal Gravitational Effects of Interannual Meteorological Oscillations in Atmosphere and Ocean

Via mass transport, the interannual meteorological oscillations (ENSO, NAO, etc.) produce slight variations in the global gravitational field, which can be observed by satellite laser ranging (SLR) and anticipated space missions such as GRACE. The meteorological oscillation modes are identified using empirical orthogonal function and principal component decomposition using de-seasoned surface fields produced from the 40-years of NCEP reanalysis. This decompositional analysis requires an accounting for issues associated with area-weighting and non-zero mean. The atmospheric contributions to gravitational variation, mode by mode and in combinations, are then computed via regional integration. To address the corresponding oceanic contributions, we examine the output of the numerical POCM ocean circulation model (1992-1998) in two ways: ocean bottom pressure field and ocean surface topography minus steric effect. The resulting lowest-degree Stokes coefficients for both NCEP reanalysis and POCM are then compared with space geodetic SLR observations, to identify the importance of each meteorological oscillations in gravitational variation signals.

Chao, Benjamin F.↗
NASA NTRS1995

Spectroscopic Measurement of Leaf Water Status

A leaf drying experiment was carried out in the laboratory in which simultaneous spectral reflectance in the 350-2450 nm region, and leaf weights, were measured at 10 second intervals over a 40 minute period. As the leaf water weight dropped from approximately 60 to 38%. a nearly-linear rise in reflectance at all wavelengths beyond 1000 nm was observed. A principal components analysis of the time series of spectra in the 2000-2500 nm wavelength region showed that over 99% of the variance in the spectra, that were individually scaled to have a sum equal to that of the mean spectrum and subsequently mean corrected, was in the first component. This result shows that it is feasible to determine leaf water content remotely with an imaging spectrometer independent of the surface irradiance effects caused by topography.

Goetz, Alexander F. H.↗
NASA NTRS2000

Solar Spectral Radiative Forcing Due to Dust Aerosol During the Puerto Rico Dust Experiment

During the Puerto Rico Dust Experiment (PRIDE) upwelling and downwelling solar spectral irradiance was measured on board the SPAWAR Navajo and downwelling solar spectral flux was measured at a surface site using the NASA Ames Solar Spectral Flux Radiometer. These data will be used to determine the net solar radiative forcing of dust aerosol and to quantify the solar spectral radiative energy budget in the presence of elevated aerosol loading. We will assess the variability in spectral irradiance using formal principal component analysis procedures and relate the radiative variability to aerosol microphysical properties. Finally, we will characterize the sea surface reflectance to improve aerosol optical depth retrievals from the AVHRR satellite and to validate SeaWiFS ocean color products.

Pilewskie, P.↗
NASA NTRS2001

Geostationary Imaging FTS (GIFTS) Data Processing: Measurement Simulation and Compression

GIFTS (Geostationary Imaging Fourier Transform Spectrometer), a forerunner of next generation geostationary satellite weather observing systems, will be built to fly on the NASA EO-3 geostationary orbit mission in 2004 to demonstrate the use of large area detector arrays and readouts. Timely high spatial resolution images and quantitative soundings of clouds, water vapor, temperature, and pollutants of the atmosphere for weather prediction and air quality monitoring will be achieved. GIFTS is novel in terms of providing many scientific returns that traditionally can only be achieved by separate advanced imaging and sounding systems. GIFTS' ability to obtain half-hourly high vertical density wind over the full earth disk is revolutionary. However, these new technologies bring forth many challenges for data transmission, archiving, and geophysical data processing. In this paper, we will focus on the aspect of data volume and downlink issues by conducting a GIFTS data compression experiment. We will discuss the scenario of using principal component analysis as a foundation for atmospheric data retrieval and compression of uncalibrated and un-normalized interferograms. The effects of compression on the degradation of the signal and noise reduction in interferogram and spectral domains will be highlighted. A simulation system developed to model the GIFTS instrument measurements is described in detail.

Huang, Hung-Lung↗
NASA NTRS2001

Simultaneous Retrieval of Temperature, Water Vapor and Ozone Atmospheric Profiles from IASI: Compression, De-noising, First Guess Retrieval and Inversion Algorithms

A fast temperature water vapor and ozone atmospheric profile retrieval algorithm is developed for the high spectral resolution Infrared Atmospheric Sounding Interferometer (IASI) space-borne instrument. Compression and de-noising of IASI observations are performed using Principal Component Analysis. This preprocessing methodology also allows, for a fast pattern recognition in a climatological data set to obtain a first guess. Then, a neural network using first guess information is developed to retrieve simultaneously temperature, water vapor and ozone atmospheric profiles. The performance of the resulting fast and accurate inverse model is evaluated with a large diversified data set of radiosondes atmospheres including rare events.

Aires, F.↗
NASA NTRS2001

Gravitational Signal of Mass Redistribution Due to Interannual Meteorological Oscillations in Atmosphere and Ocean

Interannual meteorological oscillations (ENSO, QBO, NAO, etc.) have demonstrable influences on Earth's rotation. Here we study their effects on global gravitational field, whose temporal variations are being studied using SLR (satellite laser ranging) data and in anticipation of the new space mission GRACE. The meteorological oscillation modes are identified using the EOF (empirical orthogonal function)/PC (principal component) decomposition of surface fields (in which we take care of issues associated with the area-weighting and non-zero mean). We examine two fields, one for the global surface pressure field for the atmosphere obtained from the NCEP reanalysis (for the past 40 years), one for the surface topography field for the ocean from the Topex/Poseidon (T/P) data (for the past 8 years). We use monthly maps, and remove the mean-monthly ("climatology") values from each grid point, hence focusing only on non-seasonal signals. The T/P data were first subject to a steric correction where the steric contribution to the ocean surface topography was removed according to output of the numerical POCM model. The respective atmospheric and oceanic contributions to the gravitational variation, in terms of harmonic Stokes coefficients, are then combined mode-by-mode. Since the T/P data already contain the oceanic response to overlying atmospheric pressure, no regards to the inverted-barometer behavior for the ocean need be considered. Results for the lowest-degree Stokes coefficients can then be compared with space geodetic observations including the Earth's rotation and the SLR data mentioned above, to identify the importance of each meteorological oscillations in gravitational variation signals.

Chao, B. F.↗
NASA NTRS1995

Flight Crew Sleep in Long-Haul Aircraft Bunk Facilities: Survey Results

Modem long-haul aircraft can fly up to 16 continuous hours and provide a 24-hour, global capability. Extra (augmented) flight crew are available on long flights to allow planned rest periods, on a rotating basis, away from the flight deck in onboard crew rest facilities (2 bunks). A NASA/FAA study is under-way to examine the quantity and quality of sleep obtained in long-haul aircraft bunks and the factors that promote or interfere with that sleep. The first phase of the study involved a retrospective survey, followed by a second phase field study to collect standard polysomnographic data during inflight bunk sleep periods. A summary of the Phase I survey results are reported here. A multi-part 54-question retrospective survey was completed by 1,404 flight crew (37% return rate) at three different major US air carriers flying B747-100, 200, 400, and MD- 11 long-haul aircraft. The questions examined demographics, quantity and quality of sleep at home and in onboard bunks, factors that promote or interfere with sleep, and effects on subsequent performance and alertness. Flight crew reported a mean bunk sleep latency of 39.4 mins (SD=28.3 mins) (n=1,276) and a mean total sleep time of 2.2 hrs (SD=1.3 hrs) (n=603). (Different flight lengths could affect overall time available for sleep.) Crew rated 25 factors for their interference or promotion of bunk sleep. Figure I portrays the average ratings for each factor across all three carriers. A principal components analysis of the 25 factors revealed three areas that promoted bunk sleep: physiological (e.g., readiness for sleep), physical environment (e.g., bunk size, privacy), and personal comfort (e.g., blankets, pillows). Five areas were identified that interfered with sleep: environmental disturbance (e.g., background noise, turbulence), luminosity (e.g., lighting), personal disturbances (e.g., bathroom trips, random thoughts), environmental discomfort (e.g., low humidity, cold), and interpersonal disturbances (e.g., bunk partner).

Rosekind, Mark R.↗
NASA NTRS2001

Oleoresin, Chemistry and Spectral Reflectance in "Stressed" Lodgepole and White Bark Pine, Mammoth Mountain, California

Development of methods to identify the physical and chemical character of materials on the earth's surface is one of the foci of hyperspectral remote sensing activities. Enhancing the ability to elucidate changes in foliar chemistry that relate to the health of a plant is a benefit to plant physiologists, foresters, and plant ecologists, as well as geologist and environmental scientists. Vegetation covers the landscape throughout the temperate and tropical regions of the earth. The existence of vegetation in these areas presents special problems to remote sensing systems since geologic bedrock and alteration zones are masked. At times, however, alterations in the soil/sediment geochemical environment result in foliar chemical changes that are detectable via remote sensing. Examples include monitoring of chlorophyll reflectance/fluorescence and equivalent water thickness indices as indicators of drought-induced plant stress. Another processing and interpretation approach used with hyperspectral data has been principal components analysis (PCA). Rowan et al. used PCA to identify absorption feature patterns obtained from vegetated areas with discrete bedrock geology or mineralization as the substrate. Many researchers highlight the need to advance our ability for hyperspectral imaging in vegetated areas as a near-term priority.

Hickey, James C.↗
NASA NTRS2001

Low-Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

Northeast Yellowstone National Park (YNP) has a diversity of forest, range, and wetland cover types. Several remote sensing studies have recently been done in this area, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project conducted by Yellowstone Ecosystems Studies (YES) on the use of hyperspectral imaging for assessing riparian and in-stream habitats. In 1999, YES and NASA's Commercial Remote Sensing Program Office began collaborative study of this area, assessing the potential of synergistic use of hyperspectral, synthetic aperture radar (SAR), and multiband thermal data for mapping forest, range, and wetland land cover. Since the beginning, a quality 'reference' land cover map has been desired as a tool for developing and validating other land cover maps produced during the project. This paper recounts an effort to produce such a reference land cover map using low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and unsupervised classification techniques. The main objective of this study is to assess ISODATA classification for mapping land cover in Northeast YNP using select bands of low-altitude AVIRIS data. A secondary, more long-term objective is to assess the potential for improving ISODATA-based classification of land cover through use of principal components analysis and minimum noise fraction (MNF) techniques. This paper will primarily report on work regarding the primary research objective. This study focuses on an AVIRIS cube acquired on July 23, 1999, by the confluence of Soda Butte Creek with the Lamar River. Range and wetland habitats dominate the image with forested habitats being a comparatively minor component of the scene. The scene generally tracks from southwest to northeast. Most of the scene is valley bottom with some lower side slopes occurring on the western portion. Elevations within the AVIRIS scene range from approximately 1998 to 2165 m above sea level, based on US Geological Survey (USGS) 30-m digital elevation model (DEM) data. Despain and the National Park Service (NPS) provide additional description of the study area.

Spruce, Joseph P.↗
NASA NTRS1997

Learnability of Red-Green Opponency

Lennie, Haake, and Williams found that in the lateral geniculate nucleus (LGN), parvocellular unit responses are consistent with the hypothesis that their input connectivity is blind to the difference between middle-wavelength-sensitive (MWS) and long-wavelength-sensitive (LWS) cones. Most of their cells have a total MWS input weight opposite in sign and similar in magnitude to their total LWS input weight. If these weights are exactly balanced, the construction of a red-green opponent system from such units is simple: such units need only be aligned so the signs of their outputs agree. Ahumada and Mulligan described an associative learning process which can accomplish this alignment. If the units are not balanced (carry some luminance information), the strong overlap between the MWS and LWS spectral responses can cause units to agree more on the basis of luminance, and the associative process fails to produce red-green opponency. The learning theory requires that the LGN units be nearly balanced (more strongly encode chromaticity than luminance) and quantitatively expresses the requirement: the principal component of the LGN outputs must be in the red-green rather than the luminance direction. We show that the cone weights of the monkey LGN cells measured by Derrington, Krauskopf, and Lennie can satisfy this learnability criterion even if the MWS spectral response is close to the LWS spectral response, simulating anomalous trichromacy. The learnability theory provides a source of visual system variation for explaining why different anomalous trichromats may make the same average anomaloscope match (same pigments), but have either narrow (good opponent learning) or wide (poor learning) ranges of acceptable matches.

Ahumada, Albert J., Jr.↗
NASA NTRS2001

Input Decimated Ensembles

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Tumer, Kagan↗
NASA NTRS2002

General Aviation Interior Noise: Source/Path Identification - Part 1

There were two primary objectives of the research effort reported herein. The first objective was to identify and evaluate noise source/path identification technology applicable to single engine propeller driven aircraft that can be used to identify interior noise sources originating from structure-borne engine/propeller vibration, airborne propeller transmission, airborne engine exhaust noise, and engine case radiation. The approach taken to identify the contributions of each of these possible sources was first to conduct a Principal Component Analysis (PCA) of an in-flight noise and vibration database acquired on a Cessna Model 182E aircraft. The second objective was to develop and evaluate advanced technology for noise source ranking of interior panel groups such as the aircraft windshield, instrument panel, firewall, and door/window panels within the cabin of a single engine propeller driven aircraft. The technology employed was that of Acoustic Holography (AH). AH was applied to the test aircraft by acquiring a series of in-flight microphone array measurements within the aircraft cabin and correlating the measurements via PCA. The source contributions of the various panel groups leading to the array measurements were then synthesized by solving the inverse problem using the boundary element model.

Unruh, James F.↗
NASA NTRS2002

Spectral Data Reduction via Wavelet Decomposition

The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

Kaewpijit, S.↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS2002

Data Analysis for the SOLIS Vector Spectromagnetograph

The National Solar Observatory's SOLIS Vector Spectromagnetograph (VSM), which will produce three or more full-disk maps of the Sun's photospheric vector magnetic field every day for at least one solar magnetic cycle, is in the final stages of assembly. Initial observations, including cross-calibration with the current NASA/NSO spectromagnetograph (SPM) will soon be carried out at a test site in Tucson. This paper discusses data analysis techniques for reducing the raw data, calculation of line-of-sight magnetograms and both quick-look and high-precision inference of vector fields from Stokes spectral profiles. Existing SPM algorithms, suitably modified to accomodate the cameras, scanning pattern, and polarization calibration optics for the VSM, will be used to "clean" the raw data and to process line-of-sight, magnetograms. A recent. version of the High Altitude Observatory Milne-Eddington (HAO-ME) inversion code (Skumanich and Lites; 1987, 11)J 322, p. 473) will he used for high-precision vector fields since the algorithm has been extensively tested, is well understood, and is fast enough to complete data analysis within 24 hours of data acquisition. The simplified inversion algorithm of Auer, Heasley. arid House (1977, Sol. Phys. 55, p. 47) forms the initial guess for this version of the HAO-ME code and will be used for quick-look vector analysis of VSM data since its performance on simulated Stokes profiles is better than other candidate methods. Improvements (e.g., principal components analysis or neural networks) are under consideration and will be straightforward to implement. However, current resources are sufficient to store the original Stokes profiles only long enough for high-precision analysis. Retrospective reduction of Stokes data with improved methods will not be possible, and modifications will only be introduced when the advantages of doing so are compelling enough to justify discontinuity in the long-term data stream.

Jones, Harrison P.↗
NASA NTRS2002

NASA Materials Related Lessons Learned

Lessons Learned have been the basis for our accomplishments throughout the ages. They have been passed down from father to son, mother to daughter, teacher to pupil, and older to younger worker. Lessons Learned have also been the basis for the nation's accomplishments for more than 200 years. Both government and industry have long recognized the need to systematically document and utilize the knowledge gained from past experiences in order to avoid the repetition of failures and mishaps. Through the knowledge captured and recorded in Lessons Learned from more than 80 years of flight in the Earth's atmosphere, NASA's materials researchers are constantly working to develop stronger, lighter, and more durable materials that can withstand the challenges of space. The Agency's talented materials engineers and scientists continue to build on that rich tradition by using the knowledge and wisdom gained from past experiences to create futurist materials and technologies that will be used in the next generation of advanced spacecraft and satellites that may one day enable mankind to land men on another planet or explore our nearest star. These same materials may also have application here on Earth to make commercial aircraft more economical to build and fly. With the explosion in technical accomplishments over the last decade, the ability to capture knowledge and have the capability to rapidly communicate this knowledge at lightning speed throughout an organization like NASA has become critical. Use of Lessons Learned is a principal component of an organizational culture committed to continuous improvement.

Garcia, Danny↗