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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

TPSAS-NF1676L-33992-DND

The CERES Science Team integrates and fuses observations from 6 CERES instruments aboard the Terra, Aqua, S-NPP, and NOAA-20 missions with data from more than 20 other unique data sources. Following the November 2017 launch of CERES Flight Model 6 (FM6) onboard NOAA20, CERES has now amassed over 80 instrument-years of valuable Earth radiation budget data. The rapidly growing volume of CERES data coupled with the introduction of new data products alongside improvements to existing science algorithms fosters the requirement for faster, more flexible, and scalable data production and orchestration. New virtualized, cloud-centric compute hardware hosted by the NASA Langley Research Center’s (LaRC) Atmospheric Sciences Data Center (ASDC) provides an ideal environment for these ever-increasing data production demands for CERES. This poster discusses updates to the implementation of the CERES Data Management Team’s (DMT) CERES AuTomAted job Loading sYSTem (CATALYST), a custom data processing workflow engine for CERES, to use on-demand computing resources to perform automated CERES data production processing in a Linux-based container environment. Linux containers provide CERES the flexibility to build multiple production environments in containers tailored for specific workloads and allow effortless provisioning of resources based on the CERES Science Team’s data production requirements.

Thomas N. Hillyer↗
NASA NTRS2011

Graphical Language for Data Processing

A graphical language for processing data allows processing elements to be connected with virtual wires that represent data flows between processing modules. The processing of complex data, such as lidar data, requires many different algorithms to be applied. The purpose of this innovation is to automate the processing of complex data, such as LIDAR, without the need for complex scripting and programming languages. The system consists of a set of user-interface components that allow the user to drag and drop various algorithmic and processing components onto a process graph. By working graphically, the user can completely visualize the process flow and create complex diagrams. This innovation supports the nesting of graphs, such that a graph can be included in another graph as a single step for processing. In addition to the user interface components, the system includes a set of .NET classes that represent the graph internally. These classes provide the internal system representation of the graphical user interface. The system includes a graph execution component that reads the internal representation of the graph (as described above) and executes that graph. The execution of the graph follows the interpreted model of execution in that each node is traversed and executed from the original internal representation. In addition, there are components that allow external code elements, such as algorithms, to be easily integrated into the system, thus making the system infinitely expandable.

Alphonso, Keith↗
NASA NTRS1989

Artificial intelligence issues related to automated computing operations

Large data processing installations represent target systems for effective applications of artificial intelligence (AI) constructs. The system organization of a large data processing facility at the NASA Marshall Space Flight Center is presented. The methodology and the issues which are related to AI application to automated operations within a large-scale computing facility are described. Problems to be addressed and initial goals are outlined.

Hornfeck, William A.↗
NASA NTRS1995

The Atmospheric Data Acquisition And Interpolation Process For Center-TRACON Automation System

The Center-TRACON Automation System (CTAS), an advanced new air traffic automation program, requires knowledge of spatial and temporal atmospheric conditions such as the wind speed and direction, the temperature and the pressure in order to accurately predict aircraft trajectories. Real-time atmospheric data is available in a grid format so that CTAS must interpolate between the grid points to estimate the atmospheric parameter values. The atmospheric data grid is generally not in the same coordinate system as that used by CTAS so that coordinate conversions are required. Both the interpolation and coordinate conversion processes can introduce errors into the atmospheric data and reduce interpolation accuracy. More accurate algorithms may be computationally expensive or may require a prohibitively large amount of data storage capacity so that trade-offs must be made between accuracy and the available computational and data storage resources. The atmospheric data acquisition and processing employed by CTAS will be outlined in this report. The effects of atmospheric data processing on CTAS trajectory prediction will also be analyzed, and several examples of the trajectory prediction process will be given.

Jardin, M. R.↗
NASA NTRS1996

Study and Analysis of The Robot-Operated Material Processing Systems (ROMPS)

This is a report presenting the progress of a research grant funded by NASA for work performed during 1 Oct. 1994 - 31 Sep. 1995. The report deals with the development and investigation of potential use of software for data processing for the Robot Operated Material Processing System (ROMPS). It reports on the progress of data processing of calibration samples processed by ROMPS in space and on earth. First data were retrieved using the I/O software and manually processed using MicroSoft Excel. Then the data retrieval and processing process was automated using a program written in C which is able to read the telemetry data and produce plots of time responses of sample temperatures and other desired variables. LabView was also employed to automatically retrieve and process the telemetry data.

Nguyen, Charles C.↗
NASA NTRS2006

Ground Processing of Data From the Mars Exploration Rovers

A computer program implements the Earth side of the protocol that governs the transfer of data files generated by the Mars Exploration Rovers. It also provides tools for viewing data in these files and integrating data-product files into automated and manual processes. It reconstitutes files from telemetry data packets. Even if only one packet is received, metadata provide enough information to enable this program to identify and use partial data products. This software can generate commands to acknowledge received files and retransmit missed parts of files, or it can feed a manual process to make decisions about retransmission. The software uses an Extensible Markup Language (XML) data dictionary to provide a generic capability for displaying files of basic types, and uses external "plug-in" application programs to provide more sophisticated displays. This program makes data products available with very low latency, and can trigger automated actions when complete or partial products are received. The software is easy to install and use. The only system requirement for installing the software is a Java J2SE 1.4 platform. Several instances of the software can be executed simultaneously on the same machine.

Wright, Jesse↗
NASA NTRS2004

Virtual Sensors: Using Data Mining to Efficiently Estimate Spectra

Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.

Srivastava, Ashok↗
NASA NTRS1994

Development of a Prototype Real-Time Automated Filter for Operational Deep Space Navigation

Operational deep space navigation has in the past, and is currently, performed using systems whose architecture was designed to accommodate tape data transfers and computing environments with a tiny fraction of the current capability. Additionally, this architecture requires constant human supervision and intervention. A prototype for a system which allows relatively automated processing of radio metric data received in near real-time from NASA's Deep Space Network (DSN) without any redesign of the existing operational flow has been developed. This system will allow for more rapid response as well as much reduced staffing to support mission navigation operations.

deep↗
NASA NTRS1977

Document restoration by computer techniques

Technique utilizes automated electronic data-processing machine to successfully recover illegible information from faded or age distorted documents. Once recovered, information can be displayed on cathode-ray-tube screen or reproduced in any desired size.

Mogavero, L.↗
NASA NTRS1978

Synoptic analyses, 5-, 2-, 1-, and 0.4-millibar surfaces for July 1976 through June 1977

Meteorological rocketsonde and satellite radiance data are employed for analyses of a continuing series of high-altitude constant-pressure charts. The automated methods of data processing and the objective analysis procedures are described. Broad-scale analyses of temperature and geopotential height for the Northern Hemisphere 5-, 2-, 1-, and 0.4-mb surfaces are presented for each week of the period July 1976 through June 1977. Brief discussions of the variations of the temperature and height fields throughout the period are also given.

Source record↗
NASA NTRS1978

Landsat-D - A systems overview

Landsat D, scheduled for launch in 1981, is designed to be a complete end-to-end highly automated data gathering and processing system providing a major step forward in global remote sensing for earth resources applications. This paper discusses the major components of the flight segment (the Multimission Modular Spacecraft, the Thematic Mapper and the Multispectral Scanner Subsystem) and of the ground segment (the Data Management System and the Landsat-D Assessment System). Descriptions and diagrams illustrating various aspects of flight and ground segments are provided.

Salomonson, V. V.↗
NASA NTRS1979

Landsat-D data acquisition and processing

The Landsat-D system, in which S-band and X-band data links will provide real-time direct transmission of sensor data, is described. System performance objectives include input data quantity of 200 MSS scenes/day and 100 TM scenes/day by the Domestic Communication Satellite, and processing capability, in a pipeline mode, of 2.6 x 10 to the 11th input bits of sensor data per 16-hour day. Data processing techniques, including automated cloudcover assessment, control point library build system, radiometric calibration, and geometric correction are discussed.

Smith, P. L., Jr.↗
NASA NTRS1980

Satellite temperature monitoring and prediction system

The paper describes the Florida Satellite Freeze Forecast System (SFFS) in its current state. All data collection options have been demonstrated, and data collected over a three year period have been stored for future analysis. Presently, specific minimum temperature forecasts are issued routinely from November through March. The procedures for issuing these forecast are discussed. The automated data acquisition and processing system is described, and the physical and statistical models employed are examined.

Barnett, U. R.↗
NASA NTRS1991

Laser interferometric measurement of ion electrode shape and charge exchange erosion

A novel projected fringe profilometry system was applied to surface contour measurements of an accelerator electrode from an ion thruster. The system permitted noncontact, nondestructive evaluation of the fine and gross structure of the electrode. A 3D surface map of a dished electrode was generated without altering the electrode surface. The same system was used to examine charge exchange erosion pits near the periphery of the electrode to determine the depth, location, and volume of material lost. This electro-optical measurement system allowed rapid nondestructive digital data acquisition coupled with automated computer data-processing. In addition, variable sensitivity allowed both coarse and fine measurements of objects having various surface finishes.

Macrae, Gregory S.↗
NASA NTRS2008

Automated Power Assessment for Helicopter Turboshaft Engines

An accurate indication of available power is required for helicopter mission planning purposes. Available power is currently estimated on U.S. Army Blackhawk helicopters by performing a Maximum Power Check (MPC), a manual procedure performed by maintenance pilots on a periodic basis. The MPC establishes Engine Torque Factor (ETF), an indication of available power. It is desirable to replace the current manual MPC procedure with an automated approach that will enable continuous real-time assessment of available power utilizing normal mission data. This report presents an automated power assessment approach which processes data currently collected within helicopter Health and Usage Monitoring System (HUMS) units. The overall approach consists of: 1) a steady-state data filter which identifies and extracts steady-state operating points within HUMS data sets; 2) engine performance curve trend monitoring and updating; and 3) automated ETF calculation. The algorithm is coded in MATLAB (The MathWorks, Inc.) and currently runs on a PC. Results from the application of this technique to HUMS mission data collected from UH-60L aircraft equipped with T700-GE-701C engines are presented and compared to manually calculated ETF values. Potential future enhancements are discussed.

Simon, Donald L.↗
NASA NTRS2012

Using WorldView-2 Imagery to Track Flooding in Thailand in a Multi-Asset Sensorweb

For the flooding seasons of 2011-2012 multiple space assets were used in a "sensorweb" to track major flooding in Thailand. Worldview-2 multispectral data was used in this effort and provided extremely high spatial resolution (2m / pixel) multispectral (8 bands at 0.45-1.05 micrometer spectra) data from which mostly automated workflows derived surface water extent and volumetric water information for use by a range of NGO and national authorities. We first describe how Worldview-2 and its data was integrated into the overall flood tracking sensorweb. We next describe the use of Support Vector Machine learning techniques that were used to derive surface water extent classifiers. Then we describe the fusion of surface water extent and digital elevation map (DEM) data to derive volumetric water calculations. Finally we discuss key future work such as speeding up the workflows and automating the data registration process (the only portion of the workflow requiring human input).

surface water extent↗
NASA NTRS1996

ISS Calibration Analysis Support System

The newest generation of JPL imaging experiments, the Cassini Imaging Science Subsystem, required calibration analysis effort beyond that of its predecessor instruments. This called for streamlining the data reduction process with automation and flexibility while using software inherited from support of the Galileo Solid State Imaging Instrument.

instrument↗