Results for “Streaming data” — Page 15

NASA NTRS2005

CDEP Consortium on Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI)

The ODASI consortium is focused activity of the NOAA/OGP/Climate Diagnostics and Experimental Prediction Program with the goal of improving ocean data assimilation methods and their implementations in support of seasonal forecasts with coupled general circulation models. The consortium is undertaking coordinated assimilation experiments, with common forcing data sets and common input data streams. With different assimilation systems and different models, we aim to understand what approach works best in improving forecast skill in the equatorial Pacific. The presentation will provide an overview of the consortium goals and plans and recent results focused towards evaluating data impacts.

Rienecker, Michele↗

NASA NTRS2020

Global Data Assembly Center (gdac) Report to the GHRSST Science Team

In 2019-2020 the Global Data Assembly Center (GDAC) at NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC) provided ingest, archive, distribution and user services for GHRSST operational data streams with improved and evolved tools, services, and tutorials, and interfaced with the user community to address technical inquiries. Several new GHRSST datasets including reprocessed MODIS Aqua/Terra L2P with improved cloud screening were made available. The PO.DAAC participated in the evolving GHRSST data management re architecture and development activities. GHRSST Science Team member Edward Armstrong assumed the role of co-leader of the CEOS SST Virtual Constellation. The following sections summarize and document the specific achievements of the GDAC to the GHRSST community.

Finch, Chris↗

NASA NTRS2006

Wireless augmented reality communication system

The system of the present invention is a highly integrated radio communication system with a multimedia co-processor which allows true two-way multimedia (video, audio, data) access as well as real-time biomedical monitoring in a pager-sized portable access unit. The system is integrated in a network structure including one or more general purpose nodes for providing a wireless-to-wired interface. The network architecture allows video, audio and data (including biomedical data) streams to be connected directly to external users and devices. The portable access units may also be mated to various non-personal devices such as cameras or environmental sensors for providing a method for setting up wireless sensor nets from which reported data may be accessed through the portable access unit. The reported data may alternatively be automatically logged at a remote computer for access and viewing through a portable access unit, including the user's own.

Devereaux, Ann↗

NASA NTRS2012

NASA/SPoRT's GOES-R Activities in Support of Product Development, Management, and Training

The NASA Short-term Prediction Research and Transition (SPoRT) Center supports many activities within the GOES-R Proving Grounds (PG). These include the development of imagery from existing instrumentation as a proxy to future Advanced Baseline Imager (ABI) capabilities on GOES-R. The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible/Infrared Imager/Radiometer Suite (VIIRS) instruments are used to provide a glimpse of the multi-spectral capabilities that will become the norm as the number of channels and data rate dramatically increase with GOES-R. The NOAA/NWS has plans to provide operational users with all ABI channels at the highest resolution. Data fusion of individual channels into composite red, green, and blue imagery products will assist the end user with this future wave of information. While increasing the efficiency in the operational use of ABI channels, these composites provide only qualitative information. Within the GOES-R PG, SPoRT and other partners are exploring ways to include quantitative information as part of the composite imagery. However, limitations in local hardware processing and/or data bandwidth for users of the GOES-R data stream are challenges to overcome. This presentation will discuss the creation of these composite images as well as possible solutions to address these processing challenges. In a similar manner the Geostationary Lightning Mapper (GLM) to be launched on GOES-R presents several data management challenges. The GLM is a pioneering instrument to quantify total lightning from a geostationary platform. The expected data frequency from the GLM is to be at a sub-minute interval. Users of such a data set may have little experience in handling such a rapid update of information. To assist users, SPoRT is working with the NWS to develop tools within the user fs decision support system to allow tracking and analysis of total lightning from a storm-based perspective. This presentation will discuss the challenges and progress of this tool development work. With new data and products comes the need for user Training. Within the GOES-R PG SPoRT is supporting the demonstration of these future products by providing various training materials to end users. A summary of training provided to operational users will be discussed.

Fuell, Kevin K.↗

NASA NTRS2014

Wireless Augmented Reality Communication System

The system of the present invention is a highly integrated radio communication system with a multimedia co-processor which allows true two-way multimedia (video, audio, data) access as well as real-time biomedical monitoring in a pager-sized portable access unit. The system is integrated in a network structure including one or more general purpose nodes for providing a wireless-to-wired interface. The network architecture allows video, audio and data (including biomedical data) streams to be connected directly to external users and devices. The portable access units may also be mated to various non-personal devices such as cameras or environmental sensors for providing a method for setting up wireless sensor nets from which reported data may be accessed through the portable access unit. The reported data may alternatively be automatically logged at a remote computer for access and viewing through a portable access unit, including the user's own.

Devereaux, Ann↗

NASA NTRS2014

Wireless Augmented Reality Communication System

The system of the present invention is a highly integrated radio communication system with a multimedia co-processor which allows true two-way multimedia (video, audio, data) access as well as real-time biomedical monitoring in a pager-sized portable access unit. The system is integrated in a network structure including one or more general purpose nodes for providing a wireless-to-wired interface. The network architecture allows video, audio and data (including biomedical data) streams to be connected directly to external users and devices. The portable access units may also be mated to various non-personal devices such as cameras or environmental sensors for providing a method for setting up wireless sensor nets from which reported data may be accessed through the portable access unit. The reported data may alternatively be automatically logged at a remote computer for access and viewing through a portable access unit, including the user's own.

Devereaux, Ann↗

NASA NTRS2016

Wireless Augmented Reality Communication System

The system of the present invention is a highly integrated radio communication system with a multimedia co-processor which allows true two-way multimedia (video, audio, data) access as well as real-time biomedical monitoring in a pager-sized portable access unit. The system is integrated in a network structure including one or more general purpose nodes for providing a wireless-to-wired interface. The network architecture allows video, audio and data (including biomedical data) streams to be connected directly to external users and devices. The portable access units may also be mated to various non-personal devices such as cameras or environmental sensors for providing a method for setting up wireless sensor nets from which reported data may be accessed through the portable access unit. The reported data may alternatively be automatically logged at a remote computer for access and viewing through a portable access unit, including the user's own.

Devereaux, Ann↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

NASA NTRS1989

Fractal image compression

Fractals are geometric or data structures which do not simplify under magnification. Fractal Image Compression is a technique which associates a fractal to an image. On the one hand, the fractal can be described in terms of a few succinct rules, while on the other, the fractal contains much or all of the image information. Since the rules are described with less bits of data than the image, compression results. Data compression with fractals is an approach to reach high compression ratios for large data streams related to images. The high compression ratios are attained at a cost of large amounts of computation. Both lossless and lossy modes are supported by the technique. The technique is stable in that small errors in codes lead to small errors in image data. Applications to the NASA mission are discussed.

Barnsley, Michael F.↗

NASA NTRS2005

Support for the Harvard University Water Vapor and Total Water Instruments for the 2004 NASA WB57 Middle Latitude Cirrus Experiment

In order to improve our understanding of the role clouds play in the climate system, NASA is investing considerable effort in characterizing clouds with instruments ranging from passive remote sensors on board the EOS platforms, to the forthcoming active remote sensors on Cloudsat and Calipso. These missions, when taken together, have the capacity to advance our understanding of the coupling between various components of the hydrologic cycle and the atmospheric circulation, and hold the additional potential of leading to significant improvements in the characterization of cloud feedbacks in global models. This is especially true considering that several of these platforms will be flown in an identical orbit within several minutes of one another-a constellation of satellites known as the A-Train. The algorithms that are being implemented and developed to convert these new data streams from radiance and reflectivity measurements into geophysical parameters invariably rely on some set of simplifymg assumptions and empirical constants. Uncertainties in these relationships lead to poorly understood random and systematic errors in the retrieved properties. This lack of understanding introduces ambiguity in interpreting the data and in using the global data sets for their intended purposes. In light of this, a series of flights with the W57F was proposed to address certain specific issues related to the basic properties of mid latitude cirrus clouds: the NASA WE357 Middle Latitude Cirrus Experiment ("MidCiX"). The science questions addressed are: 1) Can cloud property retrieval algorithms developed for A-Train active and passive remote sensing measurements accurately characterize the microphysical properties of synoptic and convectively generated cirrus cloud systems? 2) What are the relationships between the cirrus particle mass, projected area, and particle size spectrum in various genre of cirrus clouds? 3) Does the present compliment of state of the art in situ cloud probes provide the level of precision and accuracy needed to develop and validate algorithms and to contribute to our understanding of the characteristics and microphysical processes operating in cirrus clouds?

Anderson, James G.↗

NASA NTRS2023

Statistical Decomposition and Machine Learning to Clean In Situ Spaceflight Magnetic Field Measurements

Robust in situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time-varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high-fidelity magnetic field data even in cases where dual-sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.

magnetic field data↗

NASA NTRS1979

Carrier phase recovery performance for PN-spread TDRSS link with radio-frequency interference

The carrier-phase recovery performance of a Costas loop for a nonlinear satellite channel with uplink and downlink noise is analyzed, and the extension of these results to the case where uplink radio-frequency interference (RFI) is present is considered. The signal format here is binary phase-shift-keyed (BPSK) or unbalanced quaternary phase-shift-keyed (UQPSK), the signals being either the non-return-to-zero (NRZ) or biphase (Manchester) type. With UQPSK, at least one data stream must be PN-spread. The channel comprises additive white Gaussian noise (WGN), a wideband filter, a bandpass nonlinearity and more additive WGN. The RFI being considered is pulsed continuous-wave, pulsed WGN, or a combination of the two. It is noted that the Tracking and Data Relay Satellite System (TDRSS) will be subjected to RFI from certain locations, especially in the S-band of frequencies. The characteristics of this link are generalized to form the assumptions underlying this analysis. The analytical results given include the S-curve of the equivalent loop and the phase-error variance of the linearized loop.

Mckenzie, T. M.↗

NASA NTRS2014

A User Guide for Smoothing Air Traffic Radar Data

Matlab software was written to provide smoothing of radar tracking data to simulate ADS-B (Automatic Dependent Surveillance-Broadcast) data in order to test a tactical conflict probe. The probe, called TSAFE (Tactical Separation-Assured Flight Environment), is designed to handle air-traffic conflicts left undetected or unresolved when loss-of-separation is predicted to occur within approximately two minutes. The data stream that is down-linked from an aircraft equipped with an ADS-B system would include accurate GPS-derived position and velocity information at sample rates of 1 Hz. Nation-wide ADS-B equipage (mandated by 2020) should improve surveillance accuracy and TSAFE performance. Currently, position data are provided by Center radar (nominal 12-sec samples) and Terminal radar (nominal 4.8-sec samples). Aircraft ground speed and ground track are estimated using real-time filtering, causing lags up to 60 sec, compromising performance of a tactical resolution tool. Offline smoothing of radar data reduces wild-point errors, provides a sample rate as high as 1 Hz, and yields more accurate and lag-free estimates of ground speed, ground track, and climb rate. Until full ADS-B implementation is available, smoothed radar data should provide reasonable track estimates for testing TSAFE in an ADS-B-like environment. An example illustrates the smoothing of radar data and shows a comparison of smoothed-radar and ADS-B tracking. This document is intended to serve as a guide for using the smoothing software.

Center/Tracon Radar↗

NASA NTRS1975

Aerodynamic results of a separation effects test conducted in the AEDC 40 by 40 inch tunnel A facility on the Rockwell International launch configuration 3 (model-OTS) integrated vehicle (IA13), volume 1

Experimental aerodynamic investigations were conducted from July 5 through July 17, 1973, on a 0.01 scale model. The AEDC captive trajectory system was utilized in conjunction with the tunnel primary sector to obtain grid-type data for external tank abort from the orbiter, and for nominal separation of one solid rocket booster from the orbiter-tank combination. Booster separation was investigated with and without separation motors plume simulation. The plumes were generated by eight M sub j = 2.15 nozzles using a 1500 psia cold air supply. Free stream data were obtained for all models (orbiter, tank, orbiter-tank, and right-hand booster) to provide baselines for evaluation of proximity effects.

Campbell, J. H., II↗

NASA NTRS2005

Launch Commit Criteria Monitoring Agent

The Spaceport Processing Systems Branch at NASA Kennedy Space Center has developed and deployed a software agent to monitor the Space Shuttle's ground processing telemetry stream. The application, the Launch Commit Criteria Monitoring Agent, increases situational awareness for system and hardware engineers during Shuttle launch countdown. The agent provides autonomous monitoring of the telemetry stream, automatically alerts system engineers when predefined criteria have been met, identifies limit warnings and violations of launch commit criteria, aids Shuttle engineers through troubleshooting procedures, and provides additional insight to verify appropriate troubleshooting of problems by contractors. The agent has successfully detected launch commit criteria warnings and violations on a simulated playback data stream. Efficiency and safety are improved through increased automation.

Semmel, Glenn S.↗

NASA NTRSDate not supplied

AN INTRODUCTION TO THE GEONEX LEVEL-1G PRODUCTS: TOP-OF-ATMOSPHERE REFLECTANCE AND BRIGHTNESS TEMPERATURE

This paper introduces the GeoNEX (Geostationary-NASA Earth eXchange) Level-1G products of top-of-atmosphere (TOA) reflectance and brightness temperature. The products use data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. The GeoNEX processing pipeline starts by converting digital numbers to physical quantities with the latest radiometric calibration information. It integrates algorithms to automatically detect and remove residual geolocation errors, to estimate the pixel-wise data-acquisition time, and to accurately calculate the solar illumination angles for each pixel in the domain at every time step. The outputs are reprojected to a globally tiled common grid in geographic coordinates designed to facilitate inter-comparisons and/or synergies between the GeoNEX products and existing Earth observation datasets from polar-orbiting satellites. Therefore, the GeoNEX L1G products provide accurate and consistent TOA reflectance and brightness temperature datasets for scientific analyses and downstream product development.

Geostationary satellite, GOES-16, Himawari-8, NASA↗

NASA NTRS1983

Spacelab high density digital recorders

The design and performance of the high-density digital recorder (HDDR) developed for use at the NASA centers (KSC, JSC, and GSFC) and at the JPL to store and retrieve 50-Mb/s PCM data streams from the Spacelab experiments are reported. The recording reproduction, and transport requirements are reviewed; and the design solutions adopted in the final version of the HDDR are described, incuding three-position-modulation and Y-phase encoding, microprocessor-controlled automatic bit synchronization and equalization, cyclic-redundancy-check error detection and correction, clock regeneration, data and clock variations, tape-speed control, and EEE-488 remote control. Reliable performance, with bit error rates 1 in 10 to the 10th forward and 1 in 10 to the 9th reverse or better and packing density up to 50 percent greater than that obtainable using conventional codes, is reported after 1.5 years of service.

Blais, R. A.↗

NASA NTRSDate not supplied

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗