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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Remote sensing data processing - Two years ago, today, and two years from today

Certain technical problems arising in the recent past (1975) in the field of the processing of remote sensing data are reviewed including approaches to the analysis of Landsat MSS data and technical difficulties which must be overcome to achieve operational data processing. The current status of remote sensing data processing is then examined with emphasis on such current technical issues as training selection and labeling, sampling schemes and classification and mensuration. Hardware projections are made for the near future (1979) relative to the development of remote sensing data processing.

Holmes, Q. A.↗
NASA NTRS1978

On-board data processing for the IRAS telescope system

Onboard data processing has been developed for the Infrared Astronomical Satellite (IRAS) telescope system to effectively utilize the RF bandwidth and minimize the ground-data processing costs. Use of filtering techniques, data deglitching circuits, and data encoding algorithms are combined with a microprocessor-controlled telescope system to achieve a system dynamic range of 100,000 with a noise equivalent flux density of no less than 10 to the minus 19th W/sq cm. Extraneous data, e.g., noise due to radiation hits, will be removed prior to transmission, thereby optimizing ground-data processing for the highest sensitivity and reliability. Simulation results are presented which demonstrate the advantages gained through the use of onboard data processing and illustrate the possible merging of these techniques with an array for the future evolution of 'smart' sensors.

Lum, H.↗
NASA NTRS1973

Extension of ERIM multispectral data processing capabilities through improved data handling techniques

The improvement and extension of the capabilities of the Environmental Research Institute of Michigan processing facility in handling multispectral data are discussed. Improvements consisted of implementing hardware modifications which permitted more rapid access to the recorded data through improved numbering and indexing of such data. In addition, techniques are discussed for handling data from sources other than the ERIM M-5 and M-7 scanner systems.

Kriegler, F. J.↗
NASA NTRS2014

High-Speed On-Board Data Processing for Science Instruments

A new development of on-board data processing platform has been in progress at NASA Langley Research Center since April, 2012, and the overall review of such work is presented in this paper. The project is called High-Speed On-Board Data Processing for Science Instruments (HOPS) and focuses on a high-speed scalable data processing platform for three particular National Research Council's Decadal Survey missions such as Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS), Aerosol-Cloud-Ecosystems (ACE), and Doppler Aerosol Wind Lidar (DAWN) 3-D Winds. HOPS utilizes advanced general purpose computing with Field Programmable Gate Array (FPGA) based algorithm implementation techniques. The significance of HOPS is to enable high speed on-board data processing for current and future science missions with its reconfigurable and scalable data processing platform. A single HOPS processing board is expected to provide approximately 66 times faster data processing speed for ASCENDS, more than 70% reduction in both power and weight, and about two orders of cost reduction compared to the state-of-the-art (SOA) on-board data processing system. Such benchmark predictions are based on the data when HOPS was originally proposed in August, 2011. The details of these improvement measures are also presented. The two facets of HOPS development are identifying the most computationally intensive algorithm segments of each mission and implementing them in a FPGA-based data processing board. A general introduction of such facets is also the purpose of this paper.

Beyon, Jeffrey Y.↗
NASA NTRS1998

Enabling High Performance Reconfigurable Ground Data Processing Systems

The deployment of high data rate instruments onboard satellites with direct broadcast capability demands the implementation of powerful ground data processing systems. Conversely, to enable the widespread usage of direct broadcast data and the generation of subsequent data products, low cost data processing systems must be available. Reconfigurable computers based on Field Programmable Gate Arrays (FPGA) enable the implementation of ground data processing systems that lead to higher performance and lower cost while keeping a high degree of programmability and maintainability. This paper presents the conceptualization of the MODIS Adaptive Level One Accelerator (ALOA), a prototype under construction at NASA's Goddard Space Flight Center. The ALOA is a computer workstation augmented by a reconfigurable computer. The system will calibrate and geographically locate data from the EOS-AM1 MODerate resolution Imaging Spectroradiometer (MODIS) instrument at near real-time data rates.

Hines, Tonjua M.↗
NASA NTRS1972

Data processing 1: Advancements in machine analysis of multispectral data

Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.

Swain, P. H.↗
NASA NTRS1976

Preliminary design review package for the solar heating and cooling central data processing system

The Central Data Processing System (CDPS) is designed to transform the raw data collected at remote sites into performance evaluation information for assessing the performance of solar heating and cooling systems. Software requirements for the CDPS are described. The programming standards to be used in development, documentation, and maintenance of the software are discussed along with the CDPS operations approach in support of daily data collection and processing.

Source record↗
NASA NTRS1983

Spacelab Data Processing Facility

The Spacelab Data Processing Facility (SDPF) processes, monitors, and accounts for the payload data from Spacelab and other Shuttle missions and forwards relevant data to various user facilities worldwide. The SLDPF is divided into the Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS). The SIPS division demultiplexes, synchronizes, time tags, quality checks, accounts for the data, and formats the data onto tapes. The SOPS division further edits, blocks, formats, and records the data on tape for shipment to users. User experiments must conform to the Spacelab's onboard High Rate Multiplexer (HRM) format for maximum process ability. Audio, analog, instrumentation, high density, experiment data, input/output data, quality control and accounting, and experimental channel tapes along with a variety of spacelab ancillary tapes are provided to the user by SLDPF.

Source record↗
DOE OSTI2021

EGS Collab Experiment 1: Circulation Testing Processed data

This submission includes processed and reduced data for circulation testing that was conducted at the 164' fracture on the 4850 ft level of the Sanford Underground Research Facility. The circulation tests were done to test the flow through the 164' fracture in the EGS Collab Experiment 1 testbed on the 4850 ft level of the Sanford Underground Research Facility in order to validate computer models concerned with flow processes and heat exchange processes in EGS. This data was processed from raw data that is available in the GDR submission linked below: "EGS Collab Circulation Testing Raw Data and Documentation." Python scripts used to process and reduce the data are included. The scripts also serve as "documentation" as well as examples for data processing. More information about the test itself as well as the rationale and process of data processing is available at wiki page linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

LiDAR-Based Point Cloud Data Processing

The presentation includes the procedures of utilizing LiDAR-based point cloud data in WebGL applications, such as capturing data, processing data, importing point cloud into Unity software, and improving application performance. It uses IRC BLD 603 Lab B-12 as an example. It'll be presented at Viz n Learn workshop which is a collaboration series between INL's Applied Visualization Laboratory (AVL) and University of Wyoming (UW)'s Shell 3D Visualization center. Each session will highlight 2 projects from each institution monthly, followed by discussion on areas of collaborative interest and action.

3D↗
DOE OSTI2022

Development of nuclear data processing code FRENDY version 2

Nuclear data processing is an important interface between an evaluated nuclear data library and neutronics calculation codes. JAEA has been developed the new nuclear data processing code FRENDY from 2013. FRENDY version 1 generates the ACE files used for the continuous-energy Monte Carlo codes including PHITS, Solomon, Serpent,and MCNP and it was released as the open-source software under the 2-clause BSD license in 2019.After we released FRENDY version 1, many functions, e.g., the multi-group neutron cross-section library generation, the statistical uncertainty quantification of the probability tables for unresolved resonance cross-section, the 'perturbation' of the ACE file for uncertainty quantification, and the modification of the ENDF-6 formatted nuclear data file, have been developed. We released FRENDY version 2 including these functions. The present paper gives an overview of FRENDY and presents features of new functions implemented in FRENDY version 2. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2003

A Domain Description Language for Data Processing

We discuss an application of planning to data processing, a planning problem which poses unique challenges for domain description languages. We discuss these challenges and why the current PDDL standard does not meet them. We discuss DPADL (Data Processing Action Description Language), a language for describing planning domains that involve data processing. DPADL is a declarative, object-oriented language that supports constraints and embedded Java code, object creation and copying, explicit inputs and outputs for actions, and metadata descriptions of existing and desired data. DPADL is supported by the IMAGEbot system, which we are using to provide automation for an ecological forecasting application. We compare DPADL to PDDL and discuss changes that could be made to PDDL to make it more suitable for representing planning domains that involve data processing actions.

Golden, Keith↗
DOE OSTI2011

Data processing and analysis with the autoPROC toolbox

Typical topics and problems encountered during data processing of diffraction experiments are discussed and the tools provided in the autoPROC software are described. A typical diffraction experiment will generate many images and data sets from different crystals in a very short time. This creates a challenge for the high-throughput operation of modern synchrotron beamlines as well as for the subsequent data processing. Novice users in particular may feel overwhelmed by the tables, plots and numbers that the different data-processing programs and software packages present to them. Here, some of the more common problems that a user has to deal with when processing a set of images that will finally make up a processed data set are shown, concentrating on difficulties that may often show up during the first steps along the path of turning the experiment (i.e. data collection) into a model (i.e. interpreted electron density). Difficulties such as unexpected crystal forms, issues in crystal handling and suboptimal choices of data-collection strategies can often be dealt with, or at least diagnosed, by analysing specific data characteristics during processing. In the end, one wants to distinguish problems over which one has no immediate control once the experiment is finished from problems that can be remedied a posteriori. A new software package, autoPROC, is also presented that combines third-party processing programs with new tools and an automated workflow script that is intended to provide users with both guidance and insight into the offline processing of data affected by the difficulties mentioned above, with particular emphasis on the automated treatment of multi-sweep data sets collected on multi-axis goniostats.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1983

Optical data processing for aerospace applications

Optical data processing has a significant potential in future aerospace systems. In this paper, potential system applications are identified. One of the more important applications is the determination of errors of large antennas or reflector surfaces and the active control or compensation of the surface. Technological challenges to the application of optical data processing technology to aerospace systems are identified, and current NASA research efforts are discussed.

Stermer, R. L.↗
DOE OSTI2026

A Discrete Hankel Transform Approach to Nuclear Data Processing for Fusion Applications

This study introduces advancements to the numerical solutions employed in the processing of nuclear data for fusion applications. It leverages the convolution theorem and Fourier transform techniques to enhance computational efficiency and broaden applicability. Building upon a previously reported discrete Hankel transform approach for Doppler broadening, this work refines the solution of convolution integrals central to these applications. The methodology provides a general and unified framework for evaluating any convolution operation, regardless of whether the underlying problem involves temperature effects in nuclear reactions. The applicability to the nuclear data processing for fusion is demonstrated by deriving the convolution integrals for some of the fusion-related quantities. As before, the convolution operation utilizes a Gaussian-based kernel; however, the discrete Hankel transform of order $𝛼$ = $\frac{1}{2}$ is now applied to the forward Fourier transform of the nonkernel argument, rather than the inverse Fourier transform. This modification eliminates the need for the integration of the nonkernel, cross section–based function, which is a step that posed challenges for certain pointwise cross-section representations. It also removes the requirement for cross-section linearization. Optimized for graphics processing unit architectures, the approach significantly improves computational performance. These advancements are currently under evaluation as the foundation for the next-generation thermonuclear data file processing codes being developed at Lawrence Livermore National Laboratory.

Nuclear science and engineering↗
DOE OSTI2023

Isolated data processing modules

This disclosure relates to computer systems that isolate data processing modules and methods of operating the same. In one embodiment of a method, a computer system receives a first data processing request for a first data file. The computer system may determine that a first data type of the first data file is supported by an encapsulated data processing module and determine a first communication protocol from a set of communication protocols that can be used to receive the first data file. The computer system can then receive the first data file from the first storage location in accordance with the first communication protocol and the encapsulated data processing module may be executed to convert the first data file in the first data format into second data in a second data format.

Penton, Joshua Michael↗