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NASA NTRS1992

Compiler analysis for irregular problems in FORTRAN D

We developed a dataflow framework which provides a basis for rigorously defining strategies to make use of runtime preprocessing methods for distributed memory multiprocessors. In many programs, several loops access the same off-processor memory locations. Our runtime support gives us a mechanism for tracking and reusing copies of off-processor data. A key aspect of our compiler analysis strategy is to determine when it is safe to reuse copies of off-processor data. Another crucial function of the compiler analysis is to identify situations which allow runtime preprocessing overheads to be amortized. This dataflow analysis will make it possible to effectively use the results of interprocedural analysis in our efforts to reduce interprocessor communication and the need for runtime preprocessing.

Vonhanxleden, Reinhard↗

NASA NTRS2007

Automated Recognition of 3D Features in GPIR Images

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Park, Han↗

NASA NTRS2007

Simple, Script-Based Science Processing Archive

The Simple, Scalable, Script-based Science Processing (S4P) Archive (S4PA) is a disk-based archival system for remote sensing data. It is based on the data-driven framework of S4P and is used for data transfer, data preprocessing, metadata generation, data archive, and data distribution. New data are automatically detected by the system. S4P provides services such as data access control, data subscription, metadata publication, data replication, and data recovery. It comprises scripts that control the data flow. The system detects the availability of data on an FTP (file transfer protocol) server, initiates data transfer, preprocesses data if necessary, and archives it on readily available disk drives with FTP and HTTP (Hypertext Transfer Protocol) access, allowing instantaneous data access. There are options for plug-ins for data preprocessing before storage. Publication of metadata to external applications such as the Earth Observing System Clearinghouse (ECHO) is also supported. S4PA includes a graphical user interface for monitoring the system operation and a tool for deploying the system. To ensure reliability, S4P continuously checks stored data for integrity, Further reliability is provided by tape backups of disks made once a disk partition is full and closed. The system is designed for low maintenance, requiring minimal operator oversight.

Lynnes, Christopher↗

NASA NTRS2021

Improving Automated Strategies for Univariate Quantifier Elimination

This report discusses improved support for univariate quantifier elimination in the Prototype Verification System (PVS). Previously, PVS had three strategies for quantifier elimination—hutch, tarski, and sturm. Of these, only hutch is able to decide queries in any input format—sturm only works on queries regarding a single polynomial on an interval and tarski resolves queries in the universal existential fragment. This paper describes an extended version of tarski. The extension is accomplished by formally verifying a disjunctive normal form transformation in PVS and using tarski on each conjunctive clause. Additionally, a preprocessing step is added to the decision procedure underlying tarski. This preprocessing is designed to exploit properties of polynomial structure to quickly resolve queries that have certain formats. The preprocessing produces dramatic speedup when it succeeds in resolving a query, and seems to introduce negligible overhead when it does not resolve a query. Finally, testing reveals some ways to improve the hutch and tarski strategies.

Polynomial Constraints↗

NASA NTRS1971

Layered 'recognition cone' networks that pre-process, classify, and describe.

A sequence of six types of pattern recognition system is examined. A program is described to illustrate some of the features developed. The first type (similar to many of the programs currently used) preprocesses by applying layers of local averaging and differencing transforms to smooth, fill in gaps and heighten contours, curves, and angles. It then applies a set of characterizers, each of which implies a set of names. The program chooses the single most high implied name. The second type combines the preprocessing transforms and the characterizers into a single operation of general type. Transforms build up a next representation of the input, while the characterizers imply the output name. The third type erases the distinction between a transform and an implication. Now all outputs are stored in the next transform layer. As the program averages information, its layers shrink, so that the system builds a cone of layers. When the program reaches the apex (a layer of only one cell that contains all the information), it chooses the single name with which it classifies the input. The fourth type is capable of choosing more than one name and, therefore, can both describe and classify the scene. The fifth type examines the interrelations among the set of names chosen. The sixth step can be taken to converse about the scene, developing an appropriate description in response to suggestions and queries. This allows the program to perform more computations and to look again on demand.

Uhr, L.↗

NASA NTRS1972

Layered 'recognition cone' networks that pre-process, classify, and describe.

Discussion of pattern recognition programs for input data preprocessing with simultaneous or subsequent characterization, or characterization into a 'recognition cone,' or description and naming, interrelated descriptions, and conversion. A computer program is described that transforms and characterizes the input through the successive layers of a recognition cone. The program can choose and put forth names of parts of the input scene. It combines pieces of a description into interrelated wholes by using n-tuple characterizers and conducts a simple and stylized conversation about what it has seen. The technique of combining recognition cones with preprocessing transformations and characterizations is expected to contribute to technology in this field.

Uhr, L.↗

NASA NTRS1971

Automatic Computer Mapping of Terrain

Computer processing of 17 wavelength bands of visible, reflective infrared, and thermal infrared scanner spectrometer data, and of three wavelength bands derived from color aerial film has resulted in successful automatic computer mapping of eight or more terrain classes in a Yellowstone National Park test site. The tests involved: (1) supervised and non-supervised computer programs; (2) special preprocessing of the scanner data to reduce computer processing time and cost, and improve the accuracy; and (3) studies of the effectiveness of the proposed Earth Resources Technology Satellite (ERTS) data channels in the automatic mapping of the same terrain, based on simulations, using the same set of scanner data. The following terrain classes have been mapped with greater than 80 percent accuracy in a 12-square-mile area with 1,800 feet of relief; (1) bedrock exposures, (2) vegetated rock rubble, (3) talus, (4) glacial kame meadow, (5) glacial till meadow, (6) forest, (7) bog, and (8) water. In addition, shadows of clouds and cliffs are depicted, but were greatly reduced by using preprocessing techniques.

Smedes, H. W.↗

NASA NTRS1973

Signature extension techniques applied to multispectral scanner data.

Review of a number of spectral radiance signature extension techniques based on the concept of preprocessing the data to reduce the effects due to atmospheric effects, scanner look angle, etc. One of the promising methods studied to date involves using a ratio preprocessing transformation wherein the signals generated in adjacent spectral bands are ratioed on a point-by-point basis prior to classification. This method is easily and efficiently implemented and tests to date have yielded excellent results. Signatures have been successfully extended over 100+ miles, four days, different times of day, and very different atmospheric conditions.

Nalepka, R. F.↗

NASA NTRS1973

Constrained optimization of image restoration filters.

A preprocessing method to correct for image degradation is proposed which can be thought of as a generalization and extension of previous work by Smith (1966) and Stuller (1972). This method accomodates the problem of noncircularly symmetric imaging system point-spread functions, provides for controlled extent of the preprocessing filter to minimize distortion due to transients resulting from truncation errors and edge effects, can be used with various kinds of system noise, and can be readily extended to provide constraint of other system parameters. The analysis relates to a line-scanner system, although it is applicable in principle to many other system configurations.

Riemer, T. E.↗

NASA NTRS1973

Machine processing methods for earth observational data

A brief review of the development over the last decade of earth resource information systems is presented. Machine data preprocessing and analysis methods are surveyed and illustrated. These include preprocessing steps intended to modify geometric and radiometric aspects of earth observational image data to enhance the ability of either human interpreters or machine algorithms to extract information from the data. Illustrations of processed and analyzed images from spaceborne sensors including the Earth Resources Technology Satellite are discussed.

Landgrebe, D. A.↗

NASA NTRS1976

User data dissemination concepts for earth resources: Executive summary

The impact of the future capabilities of earth-resources data sensors (both satellite and airborne) and their requirements on the data dissemination network were investigated and optimum ways of configuring this network were determined. The scope of this study was limited to the continental U.S.A. (including Alaska) and to the 1985-1995 time period. Some of the conclusions and recommendations reached were: (1) Data from satellites in sun-synchronous polar orbits (700-920 km) will generate most of the earth-resources data in the specified time period. (2) Data from aircraft and shuttle sorties cannot be readily integrated in a data-dissemination network unless already preprocessed in a digitized form to a standard geometric coordinate system. (3) Data transmission between readout stations and central preprocessing facilities, and between processing facilities and user facilities are most economically performed by domestic communication satellites. (4) The effect of the following factors should be studied: cloud cover, expanded coverage, pricing strategies, multidiscipline missions.

Davies, R.↗

NASA NTRS1976

User data dissemination concepts for earth resources

Domestic data dissemination networks for earth-resources data in the 1985-1995 time frame were evaluated. The following topics were addressed: (1) earth-resources data sources and expected data volumes, (2) future user demand in terms of data volume and timeliness, (3) space-to-space and earth point-to-point transmission link requirements and implementation, (4) preprocessing requirements and implementation, (5) network costs, and (6) technological development to support this implementation. This study was parametric in that the data input (supply) was varied by a factor of about fifteen while the user request (demand) was varied by a factor of about nineteen. Correspondingly, the time from observation to delivery to the user was varied. This parametric evaluation was performed by a computer simulation that was based on network alternatives and resulted in preliminary transmission and preprocessing requirements. The earth-resource data sources considered were: shuttle sorties, synchronous satellites (e.g., SEOS), aircraft, and satellites in polar orbits.

Davies, R.↗

NASA NTRS1977

Altimeter waveform software design

Techniques are described for preprocessing raw return waveform data from the GEOS-3 radar altimeter. Topics discussed include: (1) general altimeter data preprocessing to be done at the GEOS-3 Data Processing Center to correct altimeter waveform data for temperature calibrations, to convert between engineering and final data units and to convert telemetered parameter quantities to more appropriate final data distribution values: (2) time "tagging" of altimeter return waveform data quantities to compensate for various delays, misalignments and calculational intervals; (3) data processing procedures for use in estimating spacecraft attitude from altimeter waveform sampling gates; and (4) feasibility of use of a ground-based reflector or transponder to obtain in-flight calibration information on GEOS-3 altimeter performance.

Hayne, G. S.↗

NASA NTRS1977

Cluster compression algorithm: A joint clustering/data compression concept

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Hilbert, E. E.↗

NASA NTRS1978

New languages for numerical software

Existing languages for numerical software are not altogether satisfactory. FORTRAN, even preprocessed, has troublesome limitations. Unfortunately, proposed replacements are discouragingly large, or omit essential features like variably dimensioned arrays and FORTRAN capability. A new language was designed to include such crucial features, but otherwise be as small as possible. This language, called T, includes: indention to indicate block structure, novel loop syntax, and engineering format for real constants. By preprocessing into PL/1, implementation cost was kept low.

Grosse, E.↗

NASA NTRS1979

Nonterrestrial material processing and manufacturing of large space systems

Nonterrestrial processing of materials and manufacturing of large space system components from preprocessed lunar materials at a manufacturing site in space is described. Lunar materials mined and preprocessed at the lunar resource complex will be flown to the space manufacturing facility (SMF), where together with supplementary terrestrial materials, they will be final processed and fabricated into space communication systems, solar cell blankets, radio frequency generators, and electrical equipment. Satellite Power System (SPS) material requirements and lunar material availability and utilization are detailed, and the SMF processing, refining, fabricating facilities, material flow and manpower requirements are described.

Von Tiesenhausen, G.↗

NASA NTRS1980

NEEDS - Information Adaptive System

The Information Adaptive System (IAS) is an element of the NASA End-to-End Data System (NEEDS) Phase II and is focused toward onboard image processing. The IAS is a data preprocessing system which is closely coupled to the sensor system. Some of the functions planned for the IAS include sensor response nonuniformity correction, geometric correction, data set selection, data formatting, packetization, and adaptive system control. The inclusion of these sensor data preprocessing functions onboard the spacecraft will significantly improve the extraction of information from the sensor data in a timely and cost effective manner, and provide the opportunity to design sensor systems which can be reconfigured in near real-time for optimum performance. The purpose of this paper is to present the preliminary design of the IAS and the plans for its development.

Kelly, W. L.↗

NASA NTRS1980

NASA End-to-End Data System /NEEDS/ information adaptive system - Performing image processing onboard the spacecraft

The Information Adaptive System (IAS) is an element of the NASA End-to-End Data System (NEEDS) Phase II and is focused toward onbaord image processing. Since the IAS is a data preprocessing system which is closely coupled to the sensor system, it serves as a first step in providing a 'Smart' imaging sensor. Some of the functions planned for the IAS include sensor response nonuniformity correction, geometric correction, data set selection, data formatting, packetization, and adaptive system control. The inclusion of these sensor data preprocessing functions onboard the spacecraft will significantly improve the extraction of information from the sensor data in a timely and cost effective manner and provide the opportunity to design sensor systems which can be reconfigured in near real time for optimum performance. The purpose of this paper is to present the preliminary design of the IAS and the plans for its development.

Kelly, W. L.↗