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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Expandable and reconfigurable instrument node arrays

An expandable and reconfigurable instrument node includes a feature detection means and a data processing portion in communication with the feature detection means, the data processing portion configured and disposed to process feature information. The instrument node further includes a phase locked loop (PLL) oscillator in communication with the data processing portion, the PLL oscillator configured and disposed to provide PLL information to the processing portion. The instrument node further includes a single tone transceiver and a pulse transceiver in communication with the PLL oscillator, the single tone transceiver configured and disposed to transmit or receive a single tone for phase correction of the PLL oscillator and the pulse transceiver configured and disposed to transmit and receive signals for phase correction of the PLL oscillator. The instrument node further includes a global positioning (GPA) receiver in communication with the processing portion, the GPS receiver configured and disposed to establish a global position of the instrument node.

Hilliard, Lawrence M.↗
NASA NTRS1974

Evaluation of satellite remote sensing and automatic data techniques for characterization of wetlands and coastal marshlands

The author has identified the following significant results. The evaluation was conducted in a humid swamp and marsh area of southern Louisiana. ERTS digital multispectral scanner data was compared with similar data gathered by intermediate altitude aircraft. Automatic data processing was applied to several data sets to produce simulated color infrared images, analysis of single bands, thematic maps, and surface classifications. These products were used to determine the effectiveness of satellites to monitor accretion of land, locate aquatic plants, determine water characteristics, and identify marsh and forest species. The results show that to some extent all of these can be done with satellite data. It is most effective for monitoring accretion and least effective in locating aquatic plants. The data sets used show that the ERTS data is superior in mapping quality and accuracy to the aircraft data. However, in some applications requiring high resolution or maximum use of intermittent clear weather conditions, data gathering by aircraft is preferable. Data processing costs for equivalent areas are about three times greater for aircraft data than ERTS data. This is primarily because of the larger volume of data generated by the high resolution aircraft system.

Cartmill, R. H.↗
NASA NTRS2003

Efficient Processing of Data for Locating Lightning Strikes

Two algorithms have been devised to increase the efficiency of processing of data in lightning detection and ranging (LDAR) systems so as to enable the accurate location of lightning strikes in real time. In LDAR, the location of a lightning strike is calculated by solving equations for the differences among the times of arrival (DTOAs) of the lightning signals at multiple antennas as functions of the locations of the antennas and the speed of light. The most difficult part of the problem is computing the DTOAs from digitized versions of the signals received by the various antennas. One way (a time-domain approach) to determine the DTOAs is to compute cross-correlations among variously differentially delayed replicas of the digitized signals and to select, as the DTOAs, those differential delays that yield the maximum correlations. Another way (a frequency-domain approach) to determine the DTOAs involves the computation of cross-correlations among Fourier transforms of variously differentially phased replicas of the digitized signals, along with utilization of the relationship among phase difference, time delay, and frequency.

Medelius, Pedro J.↗
NASA NTRS1980

Attitude ground support system for the solar maximum mission spacecraft

The SMM attitude ground support system (AGSS) supports the acquisition of spacecraft roll attitude reference, performs the in-flight calibration of the attitude sensor complement, supports onboard control autonomy via onboard computer data base updates, and monitors onboard computer (OBC) performance. Initial roll attitude acquisition is accomplished by obtaining a coarse 3 axis attitude estimate from magnetometer and Sun sensor data and subsequently refining it by processing data from the fixed head star trackers. In-flight calibration of the attitude sensor complement is achieved by processing data from a series of slew maneuvers designed to maximize the observability and accuracy of the appropriate alignments and biases. To ensure autonomy of spacecraft operation, the AGSS selects guide stars and computes sensor occultation information for uplink to the OBC. The onboard attitude control performance is monitored on the ground through periodic attitude determination and processing of OBC data in downlink telemetry. In general, the control performance has met mission requirements. However, software and hardware problems have resulted in sporadic attitude reference losses.

Nair, G.↗
NASA NTRS1966

The processing of photoclinometric data

Improved rapid processing methods for handling large amounts of photoclinometric data, and maximizing ratio of work done by computer relative to manual reduction

DATA PROCESSING↗
NASA NTRS1977

DKDP /potassium dideuterium phosphate/ light valves

The use of potassium dideuterium phosphate (DKDP) light valves for optical data processing is discussed. The operating principles and structure of optically and electron beam addressed DKDPs are compared, and specifications for both devices given. Optically addressed DKDPs are capable of higher resolution and contrast, but both systems represent viable real time spatial light modulators adaptable to optical processing. Examples of real time data processing performed by the DKDPs include: image addition and subtraction; reduction of noise introduced by scattered light in recording media; reconstruction of computer generated and acoustic holograms; reconstruction of synthetic aperture radar data; retrieval of three-dimensional information in X-ray diagnoses; radar signal processing and display; and optical pattern recognition and correlation of images, which has applications for missile guidance systems.

Casasent, D.↗
NASA NTRS2018

Systems and Methods for Advanced Rapid Imaging and Analysis for Earthquakes

Many embodiments provide a hybrid data processing system (HySDS) of an end-to-end geodetic imaging data system enabling near-real-time science, assessment, response, and rapid recovery. The HySDS may be an operation data processing system that integrates data from many different geodetic data sources and/or sensors, including interferometric synthetic aperture radar (InSAR), GPS, pixel tracking, seismology, and/or modeling, and processes the data to generate actionable high quality science data products. The HySDS may provide for an automated imaging and analysis capabilities that is able to handle the imminent increases in raw data from new and existing geodetic monitoring sensor systems.

Owen, Susan Ethel↗
NASA NTRS2008

Spaceborne Hybrid-FPGA System for Processing FTIR Data

Progress has been made in a continuing effort to develop a spaceborne computer system for processing readout data from a Fourier-transform infrared (FTIR) spectrometer to reduce the volume of data transmitted to Earth. The approach followed in this effort, oriented toward reducing design time and reducing the size and weight of the spectrometer electronics, has been to exploit the versatility of recently developed hybrid field-programmable gate arrays (FPGAs) to run diverse software on embedded processors while also taking advantage of the reconfigurable hardware resources of the FPGAs.

Bekker, Dmitriy↗
NASA NTRS2010

Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

Spiering, Bruce↗
NASA NTRS1996

High Performance Input/Output for Parallel Computer Systems

The goal of our project is to study the I/O characteristics of parallel applications used in Earth Science data processing systems such as Regional Data Centers (RDCs) or EOSDIS. Our approach is to study the runtime behavior of typical programs and the effect of key parameters of the I/O subsystem both under simulation and with direct experimentation on parallel systems. Our three year activity has focused on two items: developing a test bed that facilitates experimentation with parallel I/O, and studying representative programs from the Earth science data processing application domain. The Parallel Virtual File System (PVFS) has been developed for use on a number of platforms including the Tiger Parallel Architecture Workbench (TPAW) simulator, The Intel Paragon, a cluster of DEC Alpha workstations, and the Beowulf system (at CESDIS). PVFS provides considerable flexibility in configuring I/O in a UNIX- like environment. Access to key performance parameters facilitates experimentation. We have studied several key applications fiom levels 1,2 and 3 of the typical RDC processing scenario including instrument calibration and navigation, image classification, and numerical modeling codes. We have also considered large-scale scientific database codes used to organize image data.

Ligon, W. B.↗
NASA NTRS1988

Ocean topography experiment (TOPEX) radar altimeter

A spaceflight qualified Radar Altimeter capable of achieving the TOPEX Mission measurement precision requirement of 2-centimeters, is provided and its performance (Engineering Assessment) will be evaluated after launch and continuously during its 3-year mission operational period. Information will be provided to JPL about the calibration of the TOPEX Radar Altimeter. The specifications for the required data processing algorithms which will be necessary to convert the Radar Altimeter mission telemetry data into the geophysical data will also be provided. The stringent 2 cm precision requirement for ocean topography determination from space necessitated examining existing Radar Altimeter designs for their applicability towards TOPEX. As a result, a system configuration evolved using some flight proven designs in conjunction with needed improvements which include: (1) a second frequency or channel to remove the range delay or apparent height bias caused by the electron content of the ionosphere; (2) higher transmit pulse repetition frequencies for correlation benefits at higher sea states to maintain precision; and (3) a faster microprocessor to accommodate two channels of altimetry data. Additionally, examination of past altimeter programs associated data processing algorithms was accomplished to establish the TOPEX-class Radar Altimeter data processing algorithms, and the necessary direction was outlined to begin to generate these for the TOPEX Mission.

Rossi, L. C.↗
NASA NTRS1976

Low-cost data analysis systems for processing multispectral scanner data

The basic hardware and software requirements are described for four low cost analysis systems for computer generated land use maps. The data analysis systems consist of an image display system, a small digital computer, and an output recording device. Software is described together with some of the display and recording devices, and typical costs are cited. Computer requirements are given, and two approaches are described for converting black-white film and electrostatic printer output to inexpensive color output products. Examples of output products are shown.

Whitely, S. L.↗
NASA NTRS1979

Cost and performance characteristics of data system configurations for processing remotely sensed data

Some alternative approaches to constructing a large remote-sensing data system are explored. The cost and performance implications of using a collection of 'small' computers versus using one large computer are examined. Several architectures and associated costs for the large data system supporting a single data center are presented. Recurring cost factors (maintenance and operations) currently slightly favor the single large-machine architecture, but other factors may dictate the choice of one of the two multimachine architectures discussed.

Gregor, P. J.↗
NASA NTRSDate not supplied

Onboard Hyperspectral Image Classification via Transfer Learning for Communication-Limited Spacecraft

Employing deep-learning and artificial-intelligence (AI) techniques onboard spacecraft can dramatically improve priority data selection to ensure more effective use of the available downlink. However, deployment of effective deep-learning models requires significant training on the ground, which may not be feasible, due to limited data available in an unexplored environment. Therefore, this research explores building robust classification models for onboard data processing where training data is highly limited using transfer-learning techniques. In this paper, we focus on the use case of hyperspectral imaging for remote sensing, a domain where the high dimensionality of the data from the sensor can rapidly saturate the downlink bandwidth. With this bottleneck, there is an impending need to autonomously and robustly classify data onboard to optimize downlink of high-impact measurements, thus maximizing the scientific utility per bit transmitted to the ground. This paper examines the use of deep neural networks onboard for hyperspectral image classification in a communication-limited scenario to analyze how the models perform with limited training data. The use of transfer learning can ameliorate the issue of poor generalization by transferring features learned from training on a large source dataset for one classification task to the target classification task with limited training data. For two deep-learning models from literature, we compare the accuracy of the models trained using transfer learning to models trained from scratch using a random weight initialization with varying amounts of training data. We demonstrate the feasibility and performance of running inference of the deep-learning models on representative flight-like hardware.

Advanced Avionics, Machine Learning, Data Processi↗
NASA NTRS2014

Architectures Toward Reusable Science Data Systems

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAA's Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today.

Data Processing↗