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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

A review of current procedures for normalizing aircraft flyover noise data to reference meteorological conditions

Procedures that are currently used to normalize raw aircraft noise data to reference weather conditions are reviewed. These procedures sometimes result in calculated values of molecular absorption which differ from measured values, especially at higher frequencies. An explanation is offered for this discrepancy, and its effect on normalized sound levels and on calculations of effective perceived noise level is examined.

Deloach, R.↗
NASA NTRS1981

Digital image data sets for remote sensing instruction

Several sets of LANDSAT image segments and a set of simple image processing programs operating in batch mode are being used to teach an introductory course in digital image analysis and classification. The image data sets contain only a small number of pixels and can be run quickly on most university computing systems. A number of image data sets derived from the raw spectral bands (smoothed data sets, band ratios and texture data sets) allows the student to examine and include these extracted variables in the classification process. Another data set consists of three registered LANDSAT image segments for three different dates so that the student may experiment with multitemporal data processing. The three LANDSAT scenes used in the class are described and the image data sets available for class exercises are listed.

Eyton, J. R.↗
NASA NTRS1984

Intraband Radiometric Performance of the LANDSAT-4 Thematic Mapper

Radiometric characteristics of the LANDSAT 4 Thematic Mapper (TM) that can be established without absolute calibration of spectral data are examined. The analysis is based on radiometrically raw (B type) data of three daytime and two nighttime scenes. A set of 512 lines are examined on an individual detector basis. Subscenes selected for uniform-radiance are used to characterize subtle radiometric differences and noise problems. Virtually all nonideal performance is incorporated into the fully processed (P type) images, but disguised by the geometric resampling technique.

Kieffer, H. H.↗
NASA NTRS1988

A high speed data acquisition and analysis system for transonic velocity, density, and total temperature fluctuations

The high speed Dynamic Data Acquisition System (DDAS) is described which provides the capability for the simultaneous measurement of velocity, density, and total temperature fluctuations. The system of hardware and software is described in context of the wind tunnel environment. The DDAS replaces both a recording mechanism and a separate data processing system. The data acquisition and data reduction process has been combined within DDAS. DDAS receives input from hot wires and anemometers, amplifies and filters the signals with computer controlled modules, and converts the analog signals to digital with real-time simultaneous digitization followed by digital recording on disk or tape. Automatic acquisition (either from a computer link to an existing wind tunnel acquisition system, or from data acquisition facilities within DDAS) collects necessary calibration and environment data. The generation of hot wire sensitivities is done in DDAS, as is the application of sensitivities to the hot wire data to generate turbulence quantities. The presentation of the raw and processed data, in terms of root mean square values of velocity, density and temperature, and the processing of the spectral data is accomplished on demand in near-real-time- with DDAS. A comprehensive description of the interface to the DDAS and of the internal mechanisms will be prosented. A summary of operations relevant to the use of the DDAS will be provided.

Clukey, Steven J.↗
NASA NTRS2019

Real-Time Background Oriented Schlieren: Catching Up With Knife Edge Schlieren

Background Oriented Schlieren (BOS) is a widely used technique that provides density gradient information in flow fields of interest, without imposing stringent optical quality requirements on the facility/experiment windows and/or optics used in the BOS setup. Typically, the BOS reference image is acquired before the test begins (flow off) and then the "live" image data are acquired during the actual testing/experiment (flow on). The raw BOS image data, while displayed in real-time as they are acquired from the camera, unfortunately provide little if any visual indication of the density gradients in the flow. Generally, the "live" images must be processed off-line after the testing is completed, providing no indication of the success of the BOS setup and no feedback on the operational success of the test. Advances in computer processing hardware enables the implementation of real-time processing and display of the BOS image data. Two different approaches to implementing the real-time BOS (RT-BOS) processing capability are described herein. First, a traditional multi-core Central Processing Unit (CPU) based approach using scheduled parallel threads is used to build a RT-BOS processing engine. In the second approach, a Graphical Processing Unit (GPU) approach is used to costruct a RT-BOS processing engine. Generally, high core count CPU processors can provide a useful processing rate for RT-BOS. However, the GPU based approach exceeds the processing capability of the CPU approach, at a fraction of the cost. The GPU approach places no restrictions on the Host PC processing capability, except that it be capable of acquiring the BOS image data from the camera in real-time.

Wernet, Mark P.↗
NASA NTRS2014

Physical Science Informatics: Providing Open Science Access to Microheater Array Boiling Experiment Data

The Physical Science Informatics (PSI) system is the next step in this an effort to make NASA sponsored flight data available to the scientific and engineering community, along with the general public. The experimental data, from six overall disciplines, Combustion Science, Fluid Physics, Complex Fluids, Fundamental Physics, and Materials Science, will present some unique challenges. Besides data in textual or numerical format, large portions of both the raw and analyzed data for many of these experiments are digital images and video, requiring large data storage requirements. In addition, the accessible data will include experiment design and engineering data (including applicable drawings), any analytical or numerical models, publications, reports, and patents, and any commercial products developed as a result of the research. This objective of paper includes the following: Present the preliminary layout (Figure 2) of MABE data within the PSI database. Obtain feedback on the layout. Present the procedure to obtain access to this database.

nucleate boiling↗
NASA NTRS1987

Measurement-based reliability/performability models

Measurement-based models based on real error-data collected on a multiprocessor system are described. Model development from the raw error-data to the estimation of cumulative reward is also described. A workload/reliability model is developed based on low-level error and resource usage data collected on an IBM 3081 system during its normal operation in order to evaluate the resource usage/error/recovery process in a large mainframe system. Thus, both normal and erroneous behavior of the system are modeled. The results provide an understanding of the different types of errors and recovery processes. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A sensitivity analysis is performed to investigate the significance of using a semi-Markov process, as opposed to a Markov process, to model the measured system.

Hsueh, Mei-Chen↗
NASA NTRS1987

A measurement-based performability model for a multiprocessor system

A measurement-based performability model based on real error-data collected on a multiprocessor system is described. Model development from the raw errror-data to the estimation of cumulative reward is described. Both normal and failure behavior of the system are characterized. The measured data show that the holding times in key operational and failure states are not simple exponential and that semi-Markov process is necessary to model the system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of different failure types and recovery procedures.

Ilsueh, M. C.↗
NASA NTRS2000

Level 1 Processing of MODIS Direct Broadcast Data From Terra

In February 2000, an effort was begun to adapt the Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1 production software to process direct broadcast data. Three Level 1 algorithms have been adapted and packaged for release: Level 1A converts raw (level 0) data into Hierarchical Data Format (HDF), unpacking packets into scans; Geolocation computes geographic information for the data points in the Level 1A; and the Level 1B computes geolocated, calibrated radiances from the Level 1A and Geolocation products. One useful aspect of adapting the production software is the ability to incorporate enhancements contributed by the MODIS Science Team. We have therefore tried to limit changes to the software. However, in order to process the data immediately on receipt, we have taken advantage of a branch in the geolocation software that reads orbit and altitude information from the packets themselves, rather than external ancillary files used in standard production. We have also verified that the algorithms can be run with smaller time increments (2.5 minutes) than the five-minute increments used in production. To make the code easier to build and run, we have simplified directories and build scripts. Also, dependencies on a commercial numerics library have been replaced by public domain software. A version of the adapted code has been released for Silicon Graphics machines running lrix. Perhaps owing to its origin in production, the software is rather CPU-intensive. Consequently, a port to Linux is underway, followed by a version to run on PC clusters, with an eventual goal of running in near-real-time (i.e., process a ten-minute pass in ten minutes).

Lynnes, Christopher↗
NASA NTRS1997

MOLA II Laser Transmitter Calibration and Performance

The goal of the document is to explain the algorithm for determining the laser output energy from the telemetry data within the return packets from MOLA II. A simple algorithm is developed to convert the raw start detector data into laser energy, measured in millijoules. This conversion is dependent on three variables, start detector counts, array heat sink temperature and start detector temperature. All these values are contained within the return packets. The conversion is applied to the GSFC Thermal Vacuum data as well as the in-space data to date and shows good correlation.

Afzal, Robert S.↗
NASA NTRS1984

Landsat/4/Global Positioning System navigation results

An experimental GPSPAC flown on the Landsat-4 spacecraft was the first spaceborne navigation system to use the NAVSTAR Global Position System (GPS). In order to validate the accuracy and reliability of GPSPAC orbit solutions, definitive Landsat-4 ephemerides, derived from ground based tracking data, were generated and compared with GPSPAC estimates. In addition, Landsat-4 orbital solutions were reconstructed from raw GPS measurement data with a GPSPAC navigation simulator program using different Kalman filter constants. Ephemeris comparisons and simulator results are presented, as well as recommendations for the navigation filter.

Heuberger, H.↗
NASA NTRS2019

Quantitative Comparison of Proprietary and Open-Source Georeferencing Tools for Use with Astronaut Photography

The Crew Earth Observations (CEO) Facility within the Earth Science and Remote Sensing Unit at NASA’s Johnson Space Center supports the acquisition, analysis, and curation of astronaut photography of Earth’s surface and atmosphere. Astronauts on the International Space Station (ISS) respond to requests from CEO to acquire imagery of scientific and education targets, to include high profile targets in response to activations from the International Charter for Space & Major Disasters (also known as the International Disaster Charter, or IDC) and NASA’s Disasters Program. CEO facilitates the acquisition of astronaut photography in response to IDC events and delivers georeferenced data products to the United States Geological Survey (USGS) for distribution to the disaster community. Using GeoRef, an internal web-based tool developed in collaboration with NASA’s Ames Research Center, CEO generates data packages of georeferenced imagery, uncertainty images for assessing control and tie point accuracy, and metadata documenting raw and processed data. Operational experience with the Georef software identified vulnerabilities to internal code and server errors that can significantly increase time of data production. As such, CEO developed a backup procedure in case the GeoRef software experiences front-end or back-end errors. A system using OSGEO’s open-source QGIS software combined with a semi-automated pipeline using the object-oriented Python language and the Geospatial Abstract Library for generating metadata is quantitatively compared to GeoRef’s data package for quality and productivity. Root Mean Square Error (RMSE) provides a standard measurement of data quality as it relates to ground error. Assessing RMSE measurements generated from georeferenced astronaut photographs acquired with different obliquity and focal length offers a comprehensive accuracy assessment of the software’s transformation algorithms. This assessment will indicate the software's ability to produce data products with the least ground-error or highest data quality regarding ground accuracy. In addition, a comparison of the software’s efficiency in generating a data package that includes georeferenced images, metadata, and uncertainty images for measuring tie/ground point error was performed. Initial results, based on the comparison of three nadir-facing astronaut photographs acquired with a 95mm focal length, reveal the QGIS-based system's average RMSE is 2.36 (pixels) suggesting its georectification system produces data products that meet and perhaps improve upon Georef solution's average RMSE of 32.99 (pixels). However, the QGIS system was unable to reproduce two unique Georef data products, uncertainty images for measuring tie and control point errors and a translated unwrapped image. In addition, the Georef software is designed to accept handheld camera pose information from a hardware component (Geosens) scheduled for deployment on the ISS in late 2018; this information is intended to provide increased accuracy and auto-registration capability for astronaut photographs. Future work is expected to determine the QGIS-based georectification system’s potential as an open-source alternative (and operational backup) to Georef for georeferencing the full range of resolutions and viewing angles unique to handheld digital camera imagery in support of ISS disaster response activities.

Jagge, Amy M.↗
NASA NTRS2010

Kepler Data Release 3 Notes

This describes the collection of data and the processing done on it so when researchers around the world get the Kepler data sets (which are a set of pixels from the telescope of a particular target (star, galaxy or whatever) over a 3 month period) they can adjust their algorithms fro things that were done (like subtracting all of one particular wavelength for example). This is used to calibrate their own algorithms so that they know what it is they are starting with. It is posted so that whoever is accessing the publicly available data (not all of it is made public) can understand it .. (most of the Kepler data is under restriction for 1 - 4 years and is not available, but the handbook is for everyone (public and restricted) The Data Analysis Working Group have released long and short cadence materials, including FFls and Dropped Targets for the Public. The Kepler Science Office considers Data Release 3 to provide "browse quality" data. These notes have been prepared to give Kepler users of the Multimission Archive at STScl (MAST) a summary of how the data were collected and prepared, and how well the data processing pipeline is functioning on flight data. They will be updated for each release of data to the public archive and placed on MAST along with other Kepler documentation, at http:// archive.stsci.edu/kepler/documents.html .Data release 3 is meant to give users the opportunity to examine the data for possibly interesting science and to involve the users in improving the pipeline for future data releases. To perform the latter service, users are encouraged to notice and document artifacts, either in the raw or processed data, and report them to the Science Office.

Cleve, Jeffrey E.↗
NASA NTRS2010

Kepler Data Release 4 Notes

The Data Analysis Working Group have released long and short cadence materials, including FFIs and Dropped Targets for the Public. The Kepler Science Office considers Data Release 4 to provide "browse quality" data. These notes have been prepared to give Kepler users of the Multimission Archive at STScl (MAST) a summary of how the data were collected and prepared, and how well the data processing pipeline is functioning on flight data. They will be updated for each release of data to the public archive and placed on MAST along with other Kepler documentation, at http://archive.stsci.edu/kepler/documents.html. Data release 3 is meant to give users the opportunity to examine the data for possibly interesting science and to involve the users in improving the pipeline for future data releases. To perform the latter service, users are encouraged to notice and document artifacts, either in the raw or processed data, and report them to the Science Office.

Van Cleve, Jeffrey↗
NASA NTRS2021

ICESat-2 Atmospheric Channel Description, Data Processing and First Results

The Advanced Topographic Laser Altimeter System (ATLAS) was launched aboard the Ice Cloud and land-Elevation Satellite-2 (ICESat-2) satellite in September 2018. ATLAS is a single wavelength (532 nm) lidar system designed to acquire high resolution measurements of the earth's surface while also obtaining atmospheric backscatter from molecules, clouds, and aerosols. Because ATLAS is optimized for altimetry, the atmospheric data acquired is unique in many respects and requires non-standard analysis techniques. For example, the high repetition rate laser limits the vertical extent of the profiles to just 14 km and causes atmospheric scattering from above 15 km to be added to the scattering in the lower 0–14 km profile. In addition, the limited vertical range of the acquired profiles renders it difficult to compute the magnitude of the solar background and hinders the application of standard calibration techniques. Despite these limitations, methods have been developed to successfully produce data products that have value to the atmospheric community for cloud and aerosol research and are currently available at the National Snow and Ice Data Center (NSIDC). In this paper we describe the ICESat-2 atmospheric channel and the methods used to process the ATLAS raw photon count data to obtain calibrated backscatter and higher level products such as layer heights and type, blowing snow, column optical depth and apparent surface reflectance.

Stephen P Palm↗
NASA NTRS2012

NLSI Focus Group on Recovery of Missing ALSEP Data: Status Update for 2012 NLSI Science Forum

On the six Apollo lunar landed missions, the Astronauts deployed the Apollo Lunar Surface Experiments Package (ALSEP) science stations which measured active and passive seismic events, magnetic fields, charged particles, solar wind, heat flow, the diffuse atmosphere, meteorites and their ejecta, lunar dust, etc. Today s investigators are able to extract new information and make new discoveries from the old ALSEP data utilizing recent advances in computer capabilities and new analysis techniques. However, current-day investigators are encountering problems in trying to use the ALSEP data. The data were in formats often not well described in the published reports and contained rerecording anomalies which required tape experts to resolve. To solve these problems the DPS Lunar Data Node was established at NASA Goddard Space Flight Center (GSFC) NASA Space Science Data Center (NSSDC) in 2008 and is currently in the process of making the existing archived ALSEP data available to current-day investigators in easily useable forms. However, current estimates by NSSDC archivists are that only about 60 percent of the PI processed ALSEP data and less than 30 percent of the raw experiment ALSEP data-of-interest to current lunar science investigators are currently in the NSSDC archives.

Lewis, Lyach R.↗
NASA NTRS2019

Microphone Phased Array NetCDF/HDF5 Archival Files: Application Program Interface Reference

An application program interface (API) has been developed for the creation and access of structured data files generated by microphone phased arrays utilized in aeroacoustics research. Two structured binary file formats are supported, namely NetCDF (Network Common Data Form) and HDF5 (Hierarchical Data Format) files. The API consists of a library of routines callable from C, Fortran or Matlab, with native versions of the API provided for each language. The libraries are divided into categories for file handling, file definition and initialization, data writing, data recovery, and error handling. The API is intended to provide a mechanism for generating self-describing binary files for long-term archiving of raw and processed data generated by phased array systems.

Humphreys, William M., Jr.↗
NASA NTRS1984

An adaptive interpolator algorithm for area-array fine guiance sensors

The present paper provides a description of recent and continuing work related to the development of tracking systems possessing artificial intelligence. It is pointed out that artificial intelligence is being used to provide trackers with the abilities to sense environmental changes which can affect tracker performance, to optimize tracker performance in a changing environment, and to report on the accuracy and stability of the pointing data. The implementation of artificial intelligence in tracking systems is based on the use of adaptive algorithms which correct for tracking errors by applying polynomial corrections to raw tracking output data. The basic operation of a centroid tracking system is discussed, taking into account the limitations on accuracy and stability which exist in the absence of intelligence. Attention is given to the correction method and the implementation of artificial intelligence to improve tracking.

Glavich, T. A.↗