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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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NASA NTRS1983

Development of land based radar polarimeter processor system

The processing subsystem of a land based radar polarimeter was designed and constructed. This subsystem is labeled the remote data acquisition and distribution system (RDADS). The radar polarimeter, an experimental remote sensor, incorporates the RDADS to control all operations of the sensor. The RDADS uses industrial standard components including an 8-bit microprocessor based single board computer, analog input/output boards, a dynamic random access memory board, and power supplis. A high-speed digital electronics board was specially designed and constructed to control range-gating for the radar. A complete system of software programs was developed to operate the RDADS. The software uses a powerful real time, multi-tasking, executive package as an operating system. The hardware and software used in the RDADS are detailed. Future system improvements are recommended.

Kronke, C. W.↗

NASA NTRS1990

Automated extraction of knowledge for model-based diagnostics

The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

Gonzalez, Avelino J.↗

NASA NTRS2012

Trick Simulation Environment 07

The Trick Simulation Environment is a generic simulation toolkit used for constructing and running simulations. This release includes a Monte Carlo analysis simulation framework and a data analysis package. It produces all auto documentation in XML. Also, the software is capable of inserting a malfunction at any point during the simulation. Trick 07 adds variable server output options and error messaging and is capable of using and manipulating wide characters for international support. Wide character strings are available as a fundamental type for variables processed by Trick. A Trick Monte Carlo simulation uses a statistically generated, or predetermined, set of inputs to iteratively drive the simulation. Also, there is a framework in place for optimization and solution finding where developers may iteratively modify the inputs per run based on some analysis of the outputs. The data analysis package is capable of reading data from external simulation packages such as MATLAB and Octave, as well as the common comma-separated values (CSV) format used by Excel, without the use of external converters. The file formats for MATLAB and Octave were obtained from their documentation sets, and Trick maintains generic file readers for each format. XML tags store the fields in the Trick header comments. For header files, XML tags for structures and enumerations, and the members within are stored in the auto documentation. For source code files, XML tags for each function and the calling arguments are stored in the auto documentation. When a simulation is built, a top level XML file, which includes all of the header and source code XML auto documentation files, is created in the simulation directory. Trick 07 provides an XML to TeX converter. The converter reads in header and source code XML documentation files and converts the data to TeX labels and tables suitable for inclusion in TeX documents. A malfunction insertion capability allows users to override the value of any simulation variable, or call a malfunction job, at any time during the simulation. Users may specify conditions, use the return value of a malfunction trigger job, or manually activate a malfunction. The malfunction action may consist of executing a block of input file statements in an action block, setting simulation variable values, call a malfunction job, or turn on/off simulation jobs.

Lin, Alexander S.↗

NASA NTRS1981

Prime agricultural land monitoring and assessment component of the California Integrated Remote Sensing System

The use of digital LANDSAT techniques for monitoring agricultural land use conversions was studied. Two study areas were investigated: one in Ventura County and the other in Fresno County (California). Ventura test site investigations included the use of three dates of LANDSAT data to improve classification performance beyond that previously obtained using single data techniques. The 9% improvement is considered highly significant. Also developed and demonstrated using Ventura County data is an automated cluster labeling procedure, considered a useful example of vertical data integration. Fresno County results for a single data LANDSAT classification paralleled those found in Ventura, demonstrating that the urban/rural fringe zone of most interest is a difficult environment to classify using LANDSAT data. A general raster to vector conversion program was developed to allow LANDSAT classification products to be transferred to an operational county level geographic information system in Fresno.

Estes, J. E.↗

NASA NTRS2006

Enhancement of the MODIS Snow and Ice Product Suite Utilizing Image Segmentation

A problem has been noticed with the current NODIS Snow and Ice Product in that fringes of certain snow fields are labeled as "cloud" whereas close inspection of the data indicates that the correct labeling is a non-cloud category such as snow or land. This occurs because the current MODIS Snow and Ice Product generation algorithm relies solely on the MODIS Cloud Mask Product for the labeling of image pixels as cloud. It is proposed here that information obtained from image segmentation can be used to determine when it is appropriate to override the cloud indication from the cloud mask product. Initial tests show that this approach can significantly reduce the cloud "fringing" in modified snow cover labeling. More comprehensive testing is required to determine whether or not this approach consistently improves the accuracy of the snow and ice product.

Tilton, James C.↗

NASA NTRS1981

Analysis of scanner data for crop inventories

Accomplishments for a machine-oriented small grains labeler T&E, and for Argentina ground data collection are reported. Features of the small grains labeler include temporal-spectral profiles, which characterize continuous patterns of crop spectral development, and crop calendar shift estimation, which adjusts for planting date differences of fields within a crop type. Corn and soybean classification technology development for area estimation for foreign commodity production forecasting is reported. Presentations supporting quarterly project management reviews and a quarterly technical interchange meeting are also included.

Horvath, R.↗

NASA NTRS1981

On the accuracy of pixel relaxation labeling

An analysis of pixel labeling by probabilistic relaxation techniques is presented to demonstrate that these labeling procedures degenerate to weighted averages in the vicinity of fixed points. A consequence of this is that undesired label conversions can occur, leading to a deterioration of labeling accuracy at a stage after an improvement has already been achieved. Means for overcoming the accuracy deterioration are suggested and used as the basis for a possible design strategy for using probabilistic relaxation procedures. The results obtained are illustrated using simple data sets in which labeling on individual pixels can be examined and also using Landsat imagery to show application to data typical of that encountered in remote sensing applications.

Richards, J. A.↗

NASA NTRS1987

Digital autonomous terminal access communications

A significant problem for the Bus Monitor Unit is to identify the source of a given transmission. This problem arises from the fact that the label which identifies the source of the transmission as it is put into the bus is intercepted by the Digital Autonomous Terminal Access Communications (DATAC) terminal and removed from the transmission. Thus, a given subsystem will see only data associated with a label and never the identifying label itself. The Bus Monitor must identify the source of the transmission so as to be able to provide some type of error identification/location in the event that some problem with the data transmission occurs. Steps taken to alleviate this problem by modifications to the DATAC terminal are discussed.

Novacki, S.↗

NASA NTRS1976

Viking labeled release biology experiment - Interim results

All results of the labeled-release life-detection experiment conducted on Mars prior to conjunction are summarized. Tests at both landing sites provide remarkably similar evolution of radioactive gas upon addition of a radioactive nutrient to the Mars sample. The 'active' agent in the sample is stable to 18 C, but is substantially inactivated by heat treatment for 3 hours at 50 C and completely inactivated at 160 C, as would be anticipated if the active response were caused by microorganisms. Results from test and heat-sterilized control samples are compared with those obtained from terrestrial soils and a lunar sample. Possible nonbiological explanations of the Mars data are reviewed. Although such explanations of the labeled-release data depend on UV irradiation, the labeled-release response does not appear to depend on recent direct UV activation of surface material. Available facts do not yet permit a conclusion regarding the existence of life on Mars.

Levin, G. V.↗

NASA NTRSDate not supplied

Automated classification of scientific publications linked to GES DISC datasets

The data collections archived and distributedby the GES DISC NASA data center arewidely utilized for various Earth Science studies.As these collections are created, many researchworks are published regarding the collections, algorithms,validations and applications. SinceGES DISC collects these publications and providestheir citations for the users, it is helpful tocategorize them based on how they relate to the datasetsthey are associated with. Specifically,whether the publication that is linked to GES DISCdataset is using it for applicational research,or if it describes the algorithm for dataset creation,or the validation of the dataset, or providesthe general overview of the data collection. Currently,this process requires simple manuallabelling, and as such, may be possible to solve viaautomation. To approach this problem, wedeveloped machine learning classifiers to predictthe category a publication belongs to. We usedmanually labeled publications as training data forsupervised machine learning algorithms:Random Forest and Naive Bayes. We achieved classificationaccuracy that is substantially betterthan the baseline accuracy, thus greatly improvingthe efficiency of the publication internalanalysis.

Rohan Dayal↗

NASA NTRSDate not supplied

Supervised Machine Learning Approach for Classifying Earth Science Publications

The data collections archived and distributed by the GES DISC NASA data center are widely utilized for various Earth Science studies. As these collections are created, many research works are published regarding these collections' algorithms, their validation, and their applications. As NASA data centers collect these publications for public use, it is helpful to categorize them based on how they relate to their associated datasets. Specifically, whether the publication linked to the GES DISC dataset is using it for applicational research, describing the algorithm used for the dataset creation, validating the dataset, or providing a general overview of the data collection. Currently, this process requires simple manual labeling, and as such, it may be possible to solve via automation. To approach this problem, machine learning classifiers were developed to predict a publication's category. Manually labeled publications were used as the training data for the supervised machine learning algorithms, specifically Random Forest and Multinomial Naïve Bayes. After balancing the dataset and implementing the Multinomial Naïve Bayes algorithm, the classification accuracy achieved was substantially higher than the baseline accuracy, thus significantly improving the efficiency of publication labeling.

Rohan Dayal↗

NASA NTRS1989

Facilitating information transfer in the EOS era

A simple interactive demonstration program has been written in C to allow a user to input data field descriptions as label format. This program generates a full RECFMT (record format) description and the complete transfer syntax description notation (TSDN) file. It is intended that this program be upgraded to operational quality and be made available to users to simplify the description and TSDN file construction task. The total set of capabilities, from the standard formatted data unit packaging of related files and consistent segment structures, through the type definition techniques and the call server, will constitute a unique tool for the systematic transfer of data. This software on each end may be independent, one end from the other. With it available, local software that will be needed to convert user files to and from the canonical interface will be appreciably simplified.

Billingsley, Frederic C.↗

NASA NTRS1975

Computer graphics for management: An abstract of capabilities and applications of the EIS system

The Executive Information Services (EIS) system, developed as a computer-based, time-sharing tool for making and implementing management decisions, and including computer graphics capabilities, was described. The following resources are available through the EIS languages: centralized corporate/gov't data base, customized and working data bases, report writing, general computational capability, specialized routines, modeling/programming capability, and graphics. Nearly all EIS graphs can be created by a single, on-line instruction. A large number of options are available, such as selection of graphic form, line control, shading, placement on the page, multiple images on a page, control of scaling and labeling, plotting of cum data sets, optical grid lines, and stack charts. The following are examples of areas in which the EIS system may be used: research, estimating services, planning, budgeting, and performance measurement, national computer hook-up negotiations.

Solem, B. J.↗

NASA NTRS1983

Geometric and radiometric characterization of LANDSAT-D thematic mapper and multispectral scanner data

A geometrically raw image of Washington, D.C. was acquired and radiometrically corrected. The data show little of the detector stripping common in earlier MSS images. The radiometrically corrected data have uniform means and standard deviations for the detectors in each band; however, the data for different detectors utilize a different pattern of DN levels, resulting in ubiquitous stripping of 1 DN amplitude. Band-to-band registration was assessed using color composites and small area correlation techniques. The spectral equivalency of the first four bands of the thematic mapper with the four bands of the MSS is being examined. Geometric analysis of the Washington, D.C. scene have started and a generalized routine for examining the contents of the label files and nonvideo data files was implemented. Several discrepancies from the documentation are described. Night scenes and daytime ocean scenes required for radiometric purposes were identified and the data ordered.

Kieffer, H. H.↗

NASA NTRS1992

Identification of sea ice types in spaceborne synthetic aperture radar data

This study presents an approach for identification of sea ice types in spaceborne SAR image data. The unsupervised classification approach involves cluster analysis for segmentation of the image data followed by cluster labeling based on previously defined look-up tables containing the expected backscatter signatures of different ice types measured by a land-based scatterometer. Extensive scatterometer observations and experience accumulated in field campaigns during the last 10 yr were used to construct these look-up tables. The classification approach, its expected performance, the dependence of this performance on radar system performance, and expected ice scattering characteristics are discussed. Results using both aircraft and simulated ERS-1 SAR data are presented and compared to limited field ice property measurements and coincident passive microwave imagery. The importance of an integrated postlaunch program for the validation and improvement of this approach is discussed.

Kwok, Ronald↗

NASA NTRS2001

Analysis of the Westland Data Set

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Wen, Fang↗

NASA NTRS1981

Development and evaluation of an automatic labeling technique for spring small grains

A labeling technique is described which seeks to associate a sampling entity with a particular crop or crop group based on similarity of growing season and temporal-spectral patterns of development. Human analyst provide contextual information, after which labeling decisions are made automatically. Results of a test of the technique on a large, multi-year data set are reported. Grain labeling accuracies are similar to those achieved by human analysis techniques, while non-grain accuracies are lower. Recommendations for improvments and implications of the test results are discussed.

Crist, E. P.↗

NASA NTRSDate not supplied

Analysis Ready Satellite Data

Analyais-Ready Data (ARD) specifications have gained rcent prominence in the field of land-related Earth Observations. The ARD label enables users to recognize data that need a minimum of preprocessing before analysis. However, the regular geolocation requirements make Level 2 data in other disciplines problematic. While Level 3 gridded data can satisfy the geolocation requirement, they often sacrifice spatial resolution and other information, such as extreme values. This talk outlines this dilemma with some potential approaches to it.

Analysis-Ready Data↗