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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Developing processing techniques for Skylab data

The author has identified the following significant results. The effects of misregistration and the scan-line-straightening algorithm on multispectral data were found to be: (1) there is greatly increased misregistration in scan-line-straightening data over conic data; (2) scanner caused misregistration between any pairs of channels may not be corrected for in scan-line-straightened data; and (3) this data will have few pure field center pixels than will conic data. A program SIMSIG was developed implementing the signature simulation model. Data processing stages of the experiment were carried out, and an analysis was made of the effects of spatial misregistration on field center classification accuracy. Fifteen signatures originally used for classifying the data were analyzed, showing the following breakdown: corn (4 signatures), trees (2), brush (1), grasses, weeds, etc. (5), bare soil (1), soybeans (1), and alfalfa (1).

Nalepka, R. F.↗
NASA NTRS2018

Challenges in Development of Online Visualization and Analysis Tools for Satellite Data

Over the years, various online visualization and analysis tools have been developed to facilitate satellite data access and help scientific users around the world to conduct research and develop applications (e.g., data product evaluation, what-if questions, etc.). For those who are new to satellite data products, using them can be a daunting task due to many obstacles in data processing such as data formats, complex data structures, special software packages, unfamiliar terminology, etc., especially when one is not sure whether a dataset is suitable for his/er research project. Even for experienced users, developing software for data processing and analysis can be a costly and time-consuming task. Online visualization tools can overcome many of these difficulties and allow users to focus on scientific questions. For example, Giovanni (the Geospatial Interactive Online Visualization and Analysis Infrastructure, https://giovanni.gsfc.nasa.gov), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), allows access over 1900 satellite and model variables in 82 measurement groups of 8 disciplines without downloading data and software. Main features include basic functions for data analysis and visualization, data provenance, output data in different formats (ASCII, NetCDF, GeoTIFF), and more. Over the years, ~1700 peer-reviewed publications in different disciplines have been benefited from Giovanni in research activities (e.g. initial investigation, what-if questions, product evaluation). Despite the success of online visualization and analysis tools, challenges and new opportunities still exist and more can be done with new requirements and technology. Examples are: a) how to increase the efficiency of dataset search by enhancing intuitive aspects; b) how to facilitate interdisciplinary research; c) how to provide data quality information; d) how to engage users to participate in data quality assessment; and more. NASA Earth Observing System Data and Information System (EOSDIS) satellite-based data products are processed at various levels ranging from Level 0 to Level 4. While most users use data products at higher levels (Level-3 and 4), products at lower levels are still important for case studies, algorithm development, ground validation, etc. In this presentation, we will use Giovanni as an example to present and discuss challenges and near-future opportunities for satellite data online visualization and analysis tools.

Liu, Zhong↗
NASA NTRS1980

Geologic mapping of the Ladakh Himalaya by computer processing of Landsat data

Computer processed Landsat digital data and field studies have been integrated to make a geologic map of the Indus Suture in the Ladakh Himalaya. This coordinated approach has been successful at locating and identifying the areal extent of the major rock bodies in a 2500 square kilometer area, much of which is inaccessable for conventional field geologic studies.

Francica, J. R.↗
DOE OSTI2022

Data reduction through optimized scalar quantization for more compact neural networks

Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information. Meanwhile, Machine Learning algorithms are now becoming an essential part of data processing and data analysis. Those algorithms can be used offline for post processing and post data analysis, or they can be used online for real time processing providing ultra low latency experiment monitoring. Both use cases would benefit from data throughput reduction while preserving relevant information: one by reducing the offline storage requirements by several orders of magnitude and the other by allowing ultra fast online inferencing with low complexity Machine Learning models. Moreover, reducing the data source throughput also reduces material cost, power and data management requirements. In this work we demonstrate optimized nonuniform scalar quantization for data source reduction. This data reduction allows lower dimensional representations while preserving the relevant information of the data, thus enabling high accuracy Tiny Machine Learning classifier models for online fast inferences. We demonstrate this approach with an initial proof of concept targeting the CookieBox, an array of electron spectrometers used for angular streaking, that was developed for LCLS-II as an online beam diagnostic tool. We used the Lloyd-Max algorithm with the CookieBox dataset to design an optimized nonuniform scalar quantizer. Optimized quantization lets us reduce input data volume by 69% with no significant impact on inference accuracy. When we tolerate a 2% loss on inference accuracy, we achieved 81% of input data reduction. Finally, the change from a 7-bit to a 3-bit input data quantization reduces our neural network size by 38%.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2000

Parallel integrated frame synchronizer chip

A parallel integrated frame synchronizer which implements a sequential pipeline process wherein serial data in the form of telemetry data or weather satellite data enters the synchronizer by means of a front-end subsystem and passes to a parallel correlator subsystem or a weather satellite data processing subsystem. When in a CCSDS mode, data from the parallel correlator subsystem passes through a window subsystem, then to a data alignment subsystem and then to a bit transition density (BTD)/cyclical redundancy check (CRC) decoding subsystem. Data from the BTD/CRC decoding subsystem or data from the weather satellite data processing subsystem is then fed to an output subsystem where it is output from a data output port.

Ghuman, Parminder Singh↗
NASA NTRS1984

A fast, programmable hardware architecture for the processing of spaceborne SAR data

The development of high-throughput SAR processors (HTSPs) for the spaceborne SARs being planned by NASA, ESA, DFVLR, NASDA, and the Canadian Radarsat Project is discussed. The basic parameters and data-processing requirements of the SARs are listed in tables, and the principal problems are identified as real-operations rates in excess of 2 x 10 to the 9th/sec, I/O rates in excess of 8 x 10 to the 6th samples/sec, and control computation loads (as for range cell migration correction) as high as 1.4 x 10 to the 6th instructions/sec. A number of possible HTSP architectures are reviewed; host/array-processor (H/AP) and distributed-control/data-path (DCDP) architectures are examined in detail and illustrated with block diagrams; and a cost/speed comparison of these two architectures is presented. The H/AP approach is found to be adequate and economical for speeds below 1/200 of real time, while DCDP is more cost-effective above 1/50 of real time.

Bennett, J. R.↗
NASA NTRS1992

Experiment specific processing of residual acceleration data

To date, most Spacelab residual acceleration data collection projects have resulted in data bases that are overwhelming to the investigator of low-gravity experiments. This paper introduces a simple passive accelerometer system to measure low-frequency accelerations. Model responses for experiments using actual acceleration data are produced and correlations are made between experiment response and the accelerometer time history in order to test the idea that recorded acceleration data and experimental responses can be usefully correlated. Spacelab 3 accelerometer data are used as input to a variety of experiment models, and sensitivity limits are obtained for particular experiment classes. The modeling results are being used to create experiment-specific residual acceleration data processing schemes for interested investigators.

Rogers, Melissa J. B.↗
NASA NTRSDate not supplied

Signal and Power Integrity Design Methodology for High-Performance Flight Computing Systems

Computing capabilities of space systems have in-creased onboard performance by orders of magnitude with the use of radiation-tolerant field-programmable gate arrays (FPGA)and processors. The incorporation of signal and power integrity analysis with printed circuit board (PCB) design in reliable computing architectures for space systems has become critical to enable future mission capabilities. Developers launch high-performance processors into a breadth of orbits and missions, running varying applications that create challenges for designing reliable computing hardware. Specifically, for these designs, academic and industry research has focused on component radiation performance, fault mitigation, and reliable architectures. How-ever, other design parameters including electromagnetic interference (EMI), PCB stackup, signal integrity (SI), voltage regulator module (VRM) design, and power distribution network (PDN)are often deprioritized or disregarded as the design matures. Since these characteristics are becoming more significant in high-performance processor designs, this research presents a hardware design and analysis methodology for high-performance, space-computing systems that focuses on a holistic design approach and PDN reliability. While these challenges exist across all space hardware, the reduced PCB dimensions imposed by SmallSats and CubeSats introduce additional hurdles, specifically to VRM and decoupling design. By examining the relationship between the PDN and radiation performance, an analytical relationship is developed that incorporates Total Ionizing Dose and Single-Event Transients to ensure reliability throughout the mission duration. The presented design methodology is applied to the SpaceCube v3.0 Mini, an FPGA-based on-board science data processing system developed at NASA Goddard Space Flight Center.

Advanced avionics↗
NASA NTRS2009

Synthetic Foveal Imaging Technology

Synthetic Foveal imaging Technology (SyFT) is an emerging discipline of image capture and image-data processing that offers the prospect of greatly increased capabilities for real-time processing of large, high-resolution images (including mosaic images) for such purposes as automated recognition and tracking of moving objects of interest. SyFT offers a solution to the image-data processing problem arising from the proposed development of gigapixel mosaic focal-plane image-detector assemblies for very wide field-of-view imaging with high resolution for detecting and tracking sparse objects or events within narrow subfields of view. In order to identify and track the objects or events without the means of dynamic adaptation to be afforded by SyFT, it would be necessary to post-process data from an image-data space consisting of terabytes of data. Such post-processing would be time-consuming and, as a consequence, could result in missing significant events that could not be observed at all due to the time evolution of such events or could not be observed at required levels of fidelity without such real-time adaptations as adjusting focal-plane operating conditions or aiming of the focal plane in different directions to track such events. The basic concept of foveal imaging is straightforward: In imitation of a natural eye, a foveal-vision image sensor is designed to offer higher resolution in a small region of interest (ROI) within its field of view. Foveal vision reduces the amount of unwanted information that must be transferred from the image sensor to external image-data-processing circuitry. The aforementioned basic concept is not new in itself: indeed, image sensors based on these concepts have been described in several previous NASA Tech Briefs articles. Active-pixel integrated-circuit image sensors that can be programmed in real time to effect foveal artificial vision on demand are one such example. What is new in SyFT is a synergistic combination of recent advances in foveal imaging, computing, and related fields, along with a generalization of the basic foveal-vision concept to admit a synthetic fovea that is not restricted to one contiguous region of an image.

Hoenk, Michael↗
NASA NTRS1981

LANDSAT-D data format control book. Volume 6: (Products)

Four basic product types are generated from the raw thematic mapper (TM) and multispectral scanner (MSS) payload data by the NASA GSFC LANDSAT 4 data management system: (1) unprocessed data (raw sensor data); (2) partially processed data, which consists of radiometrically corrected sensor data with geometric correction information appended; (3) fully processed data, which consists of radiometrically and geometrically corrected sensor data; and (4) inventory data which consists of summary information about product types 2 and 3. High density digital recorder formatting and the radiometric correction process are described. Geometric correction information is included.

Kabat, F.↗
NASA NTRS1976

Detailed design specification for the ALT Shuttle Information Extraction Subsystem (SIES)

The approach and landing test (ALT) shuttle information extraction system (SIES) is described in terms of general requirements and system characteristics output products and processing options, output products and data sources, and system data flow. The ALT SIES is a data reduction system designed to satisfy certain data processing requirements for the ALT phase of the space shuttle program. The specific ALT SIES data processing requirements are stated in the data reduction complex approach and landing test data processing requirements. In general, ALT SIES must produce time correlated data products as a result of standardized data reduction or special purpose analytical processes. The main characteristics of ALT SIES are: (1) the system operates in a batch (non-interactive) mode; (2) the processing is table driven; (3) it is data base oriented; (4) it has simple operating procedures; and (5) it requires a minimum of run time information.

Clouette, G. L.↗
NASA NTRS2018

Improving NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA: The Research Access initiative is part of the agency's framework for increasing public access to scientific publications and digital scientific data. The initiative follows the release of White House Office of Science and Technology Policy's (OSTP) memorandum "Increasing Access to the Results of Federally Funded Research," to ensure federally funded research is available to the public within one year of publication. NASA answered the mandate by creating an agency plan entitled "NASA Plan for Increasing Access to the Results of Scientific Research" and associated policy, NPD 2230.1, Research Data and Publication Access. Principles in NASA SMD Strategic Plan for Scientific Data and Computing: Continued free and open access to scientific data for any use. Improved ease of use and discoverability. Enhanced science applications and new use cases. Incorporates best practices and "state of the art" through partnerships. Earth Data and Systems are Evolving: Increasing archive and file sizes. More complicated data structures. More user-friendly and data services. What is the future direction?

Liu, Zhong↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
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 NTRS1978

Fast processing of data from Sneg-2MP experiment

The following subjects are covered: Basic stages during computer processing of data from Sneg-2MP instrument, basic modes during separation and fast processing (separation of data during satellite flight, separation of burst data segments, sampling and analysis of initial burst data segment). Experimental results obtained on the basis of fast processed data are reported.

Dumer, I. I.↗
NASA NTRS1974

Skylab Medical Data Center and Archives

The founding of the Skylab medical data center and archives as a central area to house medical data from space flights is described. Skylab program strip charts, various daily reports and summaries, experiment reports and logs, status report on Skylab data quality, raw data digital tapes, processed data microfilm, and other Skylab documents are housed in the data center. In addition, this memorandum describes how the data center acted as a central point for the coordination of preflight and postflight baseline data and how it served as coordinator for all data processing through computation and analysis. Also described is a catalog identifying Skylab medical experiments and all related data currently archived in the data center.

Spross, F. R.↗