Magic- a machine for automatic graphics interface to a computer
Programming and operating procedure for machine for automatic graphics interface to computer - conjunction of display console with automatic data processing system
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Programming and operating procedure for machine for automatic graphics interface to computer - conjunction of display console with automatic data processing system
A Digital Photometric Mapper (DPM) was used in the Fall of 1971 in an airborne survey of New York and Boston area waters to acquire photometric, spectral and polarimetric data. The object of this study is to analyze these data with quantitative computer processing techniques to assess the potential of the DPM in the measurement and regional mapping of water turbidity and depth. These techniques have been developed and an operational potential has been demonstrated. More emphasis is placed at this time on the methodology of data acquisition, analysis and display than on the quantity of data. The results illustrate the type, quantity and format of information that could be generated operationally with the DPM-type sensor characterized by high photometric stability and fast, accurate digital output. The prototype, single-channel DPM is suggested as a unique research tool for a number of new applications. For the operational mapping of water turbidity and depth, the merits of a multichannel DPM coupled with a laser system are stressed.
Color coding techniques used in the processing of remote sensing imagery were adapted and applied to the fluid dynamics problems associated with turbofan mixer nozzles. The computer generated color graphics were found to be useful in reconstructing the measured flow field from low resolution experimental data to give more physical meaning to this information and in scanning and interpreting the large volume of computer generated data from the three dimensional viscous computer code used in the analysis.
(Previously announced in STAR as N81-29782)
A small, portable data-acquisition and display system with a unique coprocessor was designed and built to collect data from infrared array detectors for astronomical and laboratory use. The system consists of a microcomputer board, two 12-bit analog-to-digital data-acquisition boards, a color-graphics driver board, and two coprocessor boards. The coprocessors are arithmetic logic units designed to achieve the high computation rates necessary for real-time infrared background cancellation. The system can collect data from infrared detectors of 1 to 256 pixel elements with a computation rate of 768 kHz. The software for the system was designed to collect data in the system's background, while performing other tasks in the system's foreground. The data for the detector arrays is stored in block fashion to facilitate variable image size.
The creation of artificial ion clouds is a major aspect of the AMPTE program. The IRM Science Data center supports real-time decision for a release with real-time scientific data processing and display including model calculations of ion trajectories. Additionally, survey plots and summary data records are generated in near real time thus allowing to start the data analysis as early as possible. For detailed analyses, interactive programs were developed so that physical parameters of all IRM experiments can be combined to produce common spectra or line plots.
The data visualization techniques used by National Space Science Data Center Graphics System are discussed. The system includes a self-describing data abstraction for the storage and manipulation of multidimensional data for discipline-independent scientific applications. The system's data format and graphics display are described. Consideration is given to techniques developed for the rapid display and manipulation of large, complex data sets and for manipulating algorithms with portable implementations which can operate on any data object or geometry. The processes of operating the system are given and examples are presented from applying the system to various types of data, including TOMS data, data from the International Satellite Cloud Climatology Project, and IRAS data.
The Raman lidar observations of a weak gravity current and an internal bore associated with a thunderstorm gust front are presented. These observations have been complemented by conventional surface meteorologial analyses, special radiosonde data, spectral and bandpass filter analysis of barograph data, and infrared satellite imagery. Results obtained reveal the time-space continuity and dynamic nature of two boundary-layer disturbances seen in the lidar data. A comparison of the lidar display with the rawinsonde data makes it possible to determine the thermal fields associated with these disturbances at high temporal resolution (2 min) and an altitude of 6 km. The airflow associated with the disturbances was inferred by synthesizing the lidar and rawinsonde data. One of the two disturbances represents a dissipating outflow boundary (gust front) and can be characterized as a gravity current. The second disturbance represents an internal bore propagating ahead of the gravity current on a surface-based stable layer, which acted as a waveguide. The lidar revealed a mean bore depth of 1.9 km, observed and calculated speeds were in good agreement (about + or - 20 percent).
Explore the source record for details and available documents.
Low-altitude windshear is the leading weather-related cause of fatal aviation accidents in the U.S. Since 1964, there have been 26 accidents attributed to windshear resulting in over 500 fatalities. Low-altitude windshear can take several forms, including macroscopic forms such as cold-warm gustfronts down to the small, intense downdrafts known as microbursts. Microbursts are particularly dangerous and difficult to detect due to their small size, short duration, and occurrence under both heavy precipitation and virtually dry conditions. For these reasons, the real-time detection of windshear hazards is a very active field of research. Also, the advent of digital ground-to-air datalinks and electronic flight instrumentation opens up many options for implementation of windshear alerts in the terminal area environment. Study is required to determine the best content, format, timing, and cockpit presentation of windshear alerts in the modern ATC environment to best inform the flight crew without significantly increasing crew workload.
The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations-the second or 'Technical Capability Level (TCL 2)' National Campaign of UTM testing-was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; ground-based surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.
The Unmanned Aircraft System (UAS) Traffic Management (UTM) effort at NASA aims to enable access to low-altitude airspace for small UAS. This goal is being pursued partly through partnerships that NASA has developed with the UAS stakeholder community, the FAA, other government agencies, and the designated FAA UAS Test Sites. By partnering with the FAA UAS Test Sites, NASA's UTM project has performed a geographically diverse, simultaneous set of UAS operations at locations in six states. The demonstrations used an architecture that was developed by NASA in partnership with the FAA to safely coordinate such operations. These demonstrations—the second or "Technical Capability Level (TCL 2)" National Campaign of UTM testing—was performed from May 15 through June 9, 2017. Multiple UAS operations occurred during the testing at sites located in Alaska, Nevada, Texas, North Dakota, Virginia, and New York with multiple organizations serving as UAS Service Suppliers and/or UAS Operators per the specifications provided by NASA. By engaging various members of the UAS community in development and operational roles, this campaign provided initial validation of different aspects of the UTM concept including: UAS Service Supplier technologies and procedures; geofencing technologies/conformance monitoring; ground-based surveillance/sense and avoid; airborne sense and avoid; communication, navigation, surveillance; and human factors related to UTM data creation and display. Additionally, measures of performance were defined and calculated from the flight data to establish quantitative bases for comparing flight test activities and to provide potential metrics that might be routinely monitored in future operational UTM systems.
The MSAS Data Monitor is a generic software product that represents the next-generation in real-time data monitoring and analysis tools. It goes beyond conventional text-based displays by representing data in a unique graphical form, conveying system status through the shape, color, motion, and position of graphical objects floating in a three-dimensional cyberspace environment...This paper describes some of the details and benefits of the MSAS monitoring and analysis suite, with primary focus on the Data Monitor.
Constructing a computer image from raw imaging data or encoded imaging data by transforming a first data structure in which the raw imaging data or the encoded imaging data is stored into a second data structure storing reorganized imaging data. The raw imaging data or the encoded imaging data is received, stored in the first data structure. The computer reorganizes the raw imaging data or the encoded imaging data into the reorganized data and stores the reorganized data in the second data structure, which is a multi-dimensional array having subarrays containing local information needed by a convolutional neural network for processing the reorganized data. Other portions of the multi-dimensional array store other portions of the raw imaging data or the encoded imaging data. The computer also processes the reorganized data using the convolutional neural network to construct the image, whereby a constructed image is formed.
Since 1982, the Western Aeronautical Test Range of the Ames-Dryden Flight Research Facility has been separating the data acquisition and processing function required on all telemetry pulse code modulation (PCM) data and the display processing function required in the flight research mission control centers (MCCs). These two functions historically have been done on the same set of superminicomputers remote from the MCCs. Removing the display processing function from the realm of the superminis or telemetry-radar acquisition and processing system (TRAPS) and out into the MCCs will allow the research engineers the flexibility to configure their own display processing system to optimize performance during a flight research mission. Meanwhile, the TRAPS will have more time to acquire data. One of the processors chosen is an IBM PC/AT compatible rack-mounted personal computer. This class and type machine will not only allow the transfer of the display processing function into the MCCs, but also allow the research engineers a personalized set of analytic and display tools for use on their own unique sets of data.
Instructions for using the Pilot Climate Data System (PCDS), an interactive, scientific data management system for locating, obtaining, manipulating, and displaying climate-research data are presented. The PCDS currently provides this supoort for approximately twenty data sets. Figures that illustrate the terminal displays which a user sees when he/she runs the PCDS and some examples of the output from this system are included. The capabilities which are described in detail allow a user to perform the following: (1) obtain comprehensive descriptions of a number of climate parameter data sets and the associated sensor measurements from which they were derived; (2) obtain detailed information about the temporal coverage and data volume of data sets which are readily accessible via the PCDS; (3) extract portions of a data set using criteria such as time range and geographic location, and output the data to tape, user terminal, system printer, or online disk files in a special data-set-independent format; (4) access and manipulate the data in these data-set-independent files, performing such functions as combining the data, subsetting the data, and averaging the data; and (5) create various graphical representations of the data stored in the data-set-independent files.
Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as RAZs. RAZs are displayed as go, no-go signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.
Real time cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed in real time in a useful form on a computer screen or monitor. The monitor displays the high frequency data from the QRS complex in units of microvolts, juxtaposed with a display of conventional ECG data in units of millivolts or microvolts. The high frequency data are analyzed for their root mean square (RMS) voltage values and the discrete RMS values and related parameters are displayed in real time. The high frequency data from the QRS complex are analyzed with imbedded algorithms to determine the presence or absence of reduced amplitude zones, referred to herein as ''RAZs''. RAZs are displayed as ''go, no-go'' signals on the computer monitor. The RMS and related values of the high frequency components are displayed as time varying signals, and the presence or absence of RAZs may be similarly displayed over time.