Development of advanced digital techniques for data acquisition processing and communication Interim scientific report
Design, video data characteristics, error control, and compression algorithms for Mars television mapping mission
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Design, video data characteristics, error control, and compression algorithms for Mars television mapping mission
Digital techniques for automatic evaluation of images and data compression algorithms
A comparative evaluation was made of eight higher order languages of general interest in the aerospace field: PL/1; HAL; JOVIAL/J3; SPL/J6; CLASP; ALGOL 60; FORTRAN 4; and MAC360. A summary of the functional requirements for a language for general use in manned aerodynamic applications is presented. The evaluation supplies background material to be used in assessing the worth of each language for some particular application.
An overview of the executive system design task is presented. The flight software executive system, software verification, phase B baseline avionics system review, higher order languages and compilers, and computer hardware features are also discussed.
A functional design of software executive system for the space shuttle avionics computer is presented. Three primary functions of the executive are emphasized in the design: task management, I/O management, and configuration management. The executive system organization is based on the applications software and configuration requirements established during the Phase B definition of the Space Shuttle program. Although the primary features of the executive system architecture were derived from Phase B requirements, it was specified for implementation with the IBM 4 Pi EP aerospace computer and is expected to be incorporated into a breadboard data management computer system at NASA Manned Spacecraft Center's Information system division. The executive system was structured for internal operation on the IBM 4 Pi EP system with its external configuration and applications software assumed to the characteristic of the centralized quad-redundant avionics systems defined in Phase B.
The computation and logical functions which are performed by the data processing assembly of the modular space station are defined. The subjects discussed are: (1) requirements analysis, (2) baseline data processing assembly configuration, (3) information flow study, (4) throughput simulation, (5) redundancy study, (6) memory studies, and (7) design requirements specification.
There are no author-identified significant results in this report.
Technological developments in satellite data collection are aimed at relieving constraints of existing systems to permit expanded capability at lower costs in future operations. Constraints imposed by the limited electromagnetic spectrum available in the UHF band and the cost of user equipment are principal targets for improvement through technology. This paper describes ongoing developmental activities in system and component areas which will become available for the next generation of operations.
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The goal of NASA's Terminal Area Productivity (TAP) Low-Visibility Landing and Surface Operations (LVLASO) subelement is to improve the efficiency of airport surface operations for commercial aircraft operating in weather conditions to Category IIIB while maintaining a high degree of safety. Currently, surface operations are one of the least technologically sophisticated components of the air transport system, being conducted in the 1990's with the same basic technology as in the 1930's. Pilots are given little or no explicit information about their current position, and routing information is limited to ATC communications and airport charts. In TAP/LVLASO, advanced technologies such as satellite navigation systems, digital data communications, advanced information presentation technology, and ground surveillance systems will be integrated into flight deck displays to enable expeditious and safe traffic movement on the airport surface. The cockpit display suite is called the T-NASA (Taxiway Navigation and Situation Awareness) System. This system has three integrated components: 1) Moving Map track-up airport surface display with own-ship, traffic and graphical route guidance 2) Scene-Linked Symbology - route/taxi information virtually projected via a Head-up Display (HUD) onto the forward scene; and, 3) 3-D Audio Ground Collision Avoidance and Navigation system - spatially-localized auditory traffic and navigation alerts. In the current paper, the design philosophy of the T-NASA system will be presented, and the T-NASA system display components described.
LiveView3D is a software application that imports and displays a variety of wind tunnel derived data in an interactive virtual environment in real time. LiveView3D combines the use of streaming video fed into a three-dimensional virtual representation of the test configuration with networked communications to the test facility Data Acquisition System (DAS). This unified approach to real time data visualization provides a unique opportunity to comprehend very large sets of diverse forms of data in a real time situation, as well as in post-test analysis. This paper describes how LiveView3D has been implemented to visualize diverse forms of aerodynamic data gathered during wind tunnel experiments, most notably at the NASA Langley Research Center Unitary Plan Wind Tunnel (UPWT). Planned future developments of the LiveView3D system are also addressed.
A large-scale, distributed, high-speed data acquisition system (HSDAS) is currently being installed at the Space Power Facility (SPF) at NASA Glenn Research Center s Plum Brook Station in Sandusky, OH. This installation is being done as part of a facility construction project to add Vibro-acoustic Test Capabilities (VTC) to the current thermal-vacuum testing capability of SPF in support of the Orion Project s requirement for Space Environments Testing (SET). The HSDAS architecture is a modular design, which utilizes fully-remotely managed components, enables the system to support multiple test locations with a wide-range of measurement types and a very large system channel count. The architecture of the system is presented along with details on system scalability and measurement verification. In addition, the ability of the system to automate many of its processes such as measurement verification and measurement system analysis is also discussed.
Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.
Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.
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Operational satellite imager radiances are valuable for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. The use of cloud computing has enabled recent enhancements to process a constellation of geostationary satellites at higher spatiotemporal resolutions than previously possible that meet low latency and near real-time needs. Data taken from Meteosat-8 and -11, Himawari, GOES-16, and -17 are processed and combined with operational polar orbiting satellite data and composited on a 3-km grid to provide global coverage. To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. An update on recent SatCORPS enhancements is presented, highlighting the community benefits achieved with the use of cloud computing and machine learning.