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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Ellerbrock, Philip J.↗
NASA NTRS2004

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface uses a specialized protocol for communicating across the network bus that uses a low-level instruction set and has low overhead for data communication.

Ellerbrock, Philip J.↗
NASA NTRS2006

Network device interface for digitally interfacing data channels to a controller via a network

The present invention provides a network device interface and method for digitally connecting a plurality of data channels, such as sensors, actuators, and subsystems, to a controller using a network bus. The network device interface interprets commands and data received from the controller and polls the data channels in accordance with these commands. Specifically, the network device interface receives digital commands and data from the controller, and based on these commands and data, communicates with the data channels to either retrieve data in the case of a sensor or send data to activate an actuator. Data retrieved from the sensor is then converted by the network device interface into digital signals and transmitted back to the controller. In one advantageous embodiment, the network device interface is a state machine, such as an ASIC, that operates independent of a processor in communicating with the bus controller and data channels.

Ellerbrock, Philip J.↗
NASA NTRS2011

Data Mining of Network Logs

The statement of purpose is to analyze network monitoring logs to support the computer incident response team. Specifically, gain a clear understanding of the Uniform Resource Locator (URL) and its structure, and provide a way to breakdown a URL based on protocol, host name domain name, path, and other attributes. Finally, provide a method to perform data reduction by identifying the different types of advertisements shown on a webpage for incident data analysis. The procedures used for analysis and data reduction will be a computer program which would analyze the URL and identify and advertisement links from the actual content links.

Collazo, Carlimar↗
NASA NTRS1973

The role of computer networks in remote sensing data analysis

It has been hypothesized that computer networks can be used to make data processing facilities available to the remote sensing community both quickly and effectively. An experiment to test this hypothesis is being conducted by the Laboratory for Applications of Remote Sensing at Purdue University, with the participation of potential users at several remote sites. Initial indications have been highly favorable, although final evaluation awaits further experience and the accumulation of usage data.

Swain, P. H.↗
NASA NTRS1989

Neural network implementations of data association algorithms for sensor fusion

The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.

Brown, Donald E.↗
NASA NTRS1992

AVIRIS data and neural networks applied to an urban ecosystem

Urbanization is expanding on every continent. Although urban/industrial areas occupy a small percentage of the total landscape of the earth, their influence extends far beyond their borders, affecting terrestrial, aquatic, and atmospheric systems globally. Yet little has been done to characterize urban ecosystems of their linkages to other systems horizontally or vertically. With remote sensing we now have the tools to characterize, monitor, and model urban landscapes world-wide. However, the remote sensing performed on cities so far has concentrated on land-use patterns as distinct from land-cover or composition. The popular Anderson system is entirely land-use oriented in urban areas. This paper begins with the premise that characterizing the biophysical composition of urban environments is fundamental to understanding urban/industrial ecosystems, and, in turn, supports the modeling of other systems interfacing with urban systems. Further, it is contended that remote sensing is a tool poised to provide the biophysical composition data to characterize urban landscapes.

Ridd, Merrill K.↗
NASA NTRS1976

Theoretical and experimental studies of atmospheric structure and dynamics, using high altitude chemical release, Radio meteor, and meteorological rocket network and other data

Data collected by the Georgia Tech Radio Meteor Wind Facility during the fall and winter of 1975 are analyzed indicating a relationship between lower thermospheric circulation at mid latitudes and polar stratospheric dynamics. Techniques of measurement of mixing processes in the upper atmosphere and the interpretation of those measurements are described along with a diffusion simulation program based on the Global Reference Atmosphere program.

Edwards, H. D.↗
NASA NTRS1990

Extremely high data-rate, reliable network systems research

Significant progress was made over the year in the four focus areas of this research group: gigabit protocols, extensions of metropolitan protocols, parallel protocols, and distributed simulations. Two activities, a network management tool and the Carrier Sensed Multiple Access Collision Detection (CSMA/CD) protocol, have developed to the point that a patent is being applied for in the next year; a tool set for distributed simulation using the language SIMSCRIPT also has commercial potential and is to be further refined. The year's results for each of these areas are summarized and next year's activities are described.

Foudriat, E. C.↗
NASA NTRS1992

OCEAN-PC and a distributed network for ocean data

The Intergovernmental Oceanographic Commission (IOC) wishes to develop an integrated software package for oceanographic data entry and access in developing countries. The software, called 'OCEAN-PC', would run on low cost PC microcomputers and would encourage and standardize: (1) entry of local ocean observations; (2) quality control of the local data; (3) merging local data with historical data; (4) improved display and analysis of the merged data; and (5) international data exchange. OCEAN-PC will link existing MS-DOS oceanographic programs and data sets with table-driven format conversions. Since many ocean data sets are now being distributed on optical discs (Compact Discs - Read Only Memory, CD-ROM, Mass et al. 1987), OCEAN-PC will emphasize access to CD-ROMs.

Mclain, Douglas R.↗
NASA NTRS1995

Analyzing rocket plume spectral data with neural networks

The Optical Plume Anomaly Detection (OPAD) system is under development to provide early-warning failure detection in support of ground-level testing of the Space Shuttle Main Engine (SSME). Failure detection is to be achieved through the acquisition of spectrally resolved plume emissions and subsequent identification of abnormal levels indicative of engine corrosion or component failure. Two computer codes (one linear and the other non-linear) are used by the OPAD system to iteratively determine specific element concentrations in the SSME plume, given emission intensity and wavelength information. Since this analysis is extremely labor intensive, a study was initiated to develop neural networks that would model the 'inverse' of these computer codes. Optimally connected feed-forward networks with imperceptible prediction error have been developed for each element modeled by the linear code, SPECTRA4. Radial basis function networks were developed for the non-linear code, SPECTRA5, and predict combustion temperature in addition to element concentrations.

Whitaker, Kevin W.↗
NASA NTRS2007

Sensor Network Provides Environmental Data

The National Biocomputation Center, a joint partnership between the Stanford University School of Medicine's Department of Surgery and NASA's Ames Research Center, is the test bed for much of NASA's research in telemedicine, the remote delivery of medical care. In early 2005, researchers at the National Biocomputation Center formed a spinoff company, Intelesense Technologies, to use the telemedicine sensors to provide integrated global monitoring systems. Intelesense uses the systems to better understand how environments and people are linked, monitor and protect natural resources, predict and adapt to environmental changes, provide for sustainable development, reduce the costs and impacts of natural disasters, and provide an effective and intelligent response to such disasters. Current projects range from protecting the environment to tracking emerging infectious diseases like avian influenza (bird flu) and helping people from around the world connect and interact with each other to better understand their environment and themselves.

Source record↗
NASA NTRS2014

Neural Network Machine Learning and Dimension Reduction for Data Visualization

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Liles, Charles A.↗
NASA NTRSDate not supplied

ASDC’s Python-Based Metadata Extraction Pipeline for Suborbital Campaigns

The FAIRness of data products, especially findability and accessibility depend on rich metadata which, when extracted, can allow for proper curation. Over the past few years, the Atmospheric Science Data Center (ASDC) suborbital science support team has developed a metadata extraction pipeline to ensure the required metadata can be retrieved systematically, effectively, and efficiently to ensure the data can be used by a broad community. The development of a pipeline has presented many, but necessary, challenges to support archival and distribution of ASDC’s 30+ suborbital missions. Though sufficient metadata is provided by instrument scientists, the metadata may not be readily machine actionable due to different formats and templates. Further complicating metadata extraction, our team has found that the nature of metadata can be quite diverse given the difference in measurement types, instruments, and measurement platforms. A metadata extraction pipeline has been developed to provide an efficient, plugin-in based, method for adding new parsers, a configuration system that lets non-developers customize how files are processed, and a system for identifying and logging metadata quality issues to ensure they are readily found and addressed. The metadata extraction pipeline identifies critical pieces of metadata that are needed to promote data FAIRness, including location, file revision, measurement start/end datetime and can be easily modified to extract further information (such as variables). Given the wide-ranging datasets, the pipeline has been modified to accommodate multiple file formats, including multiple versions of ICARTT (International Consortium for Atmospheric Research on Transport and Transformation), HDF (Hierarchical Data Format), netCDF (network Common Data Form), and multiple versions of the Ames File Format. The pipeline also supports building metadata for file formats that cannot have metadata easily extracted from them, such as PDF (Portable Document Format) and GIF (Graphics Interchange Format). The pipeline has allowed our team to maintain a consistent flow of data and metadata to archival and distribution services, ensuring the ASDC meets the needs of the suborbital science community. This presentation will highlight the ASDC’s suborbital metadata extraction pipeline, its development, how it’s been modified to support data FAIRness, and plans for maintaining the pipeline and adding new features.

Abraham Porter↗