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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

XML-Based Generator of C++ Code for Integration With GUIs

An open source computer program has been developed to satisfy a need for simplified organization of structured input data for scientific simulation programs. Typically, such input data are parsed in from a flat American Standard Code for Information Interchange (ASCII) text file into computational data structures. Also typically, when a graphical user interface (GUI) is used, there is a need to completely duplicate the input information while providing it to a user in a more structured form. Heretofore, the duplication of the input information has entailed duplication of software efforts and increases in susceptibility to software errors because of the concomitant need to maintain two independent input-handling mechanisms. The present program implements a method in which the input data for a simulation program are completely specified in an Extensible Markup Language (XML)-based text file. The key benefit for XML is storing input data in a structured manner. More importantly, XML allows not just storing of data but also describing what each of the data items are. That XML file contains information useful for rendering the data by other applications. It also then generates data structures in the C++ language that are to be used in the simulation program. In this method, all input data are specified in one place only, and it is easy to integrate the data structures into both the simulation program and the GUI. XML-to-C is useful in two ways: 1. As an executable, it generates the corresponding C++ classes and 2. As a library, it automatically fills the objects with the input data values.

Hua, Hook↗
NASA NTRS1991

Consultative Committee for Space Data Systems recommendation for space data system standards: Telecommand. Part 2.1: Command operation procedures

This recommendation contains the detailed specification of the logic required to carry out the Command Operations Procedures of the Transfer Layer. The Recommendation for Telecommand--Part 2, Data Routing Service contains the standard data structures and data communication procedures used by the intermediate telecommand system layers (the Transfer and Segmentation Layers). In particular, it contains a brief description of the Command Operations Procedures (COP) within the Transfer Layer. This recommendation contains the detailed definition of the COP's in the form of state tables, along with definitions of the terms used. It is assumed that the reader of this document is familiar with the data structures and terminology of part 2. In case of conflict between the description of the COP's in part 2 and in this recommendation, the definition in this recommendation will take precedence. In particular, this document supersedes section 4.3.3.1 through 4.3.3.4 of part 2.

Source record↗
NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental building block in numerous image processing applications. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute the coordinates of a set of cluster centers in d-space, such that those centers minimize the mean squared distance from each data point to its nearest center. This clustering algorithm is similar to another well-known clustering method, called k-means. One significant feature of ISOCLUS over k-means is that the actual number of clusters reported might be fewer or more than the number supplied as part of the input. The algorithm uses different heuristics to determine whether to merge lor split clusters. As ISOCLUS can run very slowly, particularly on large data sets, there has been a growing .interest in the remote sensing community in computing it efficiently. We have developed a faster implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm of Kanungo, et al. They showed that, by using a kd-tree data structure for storing the data, it is possible to reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm, and we show that it is possible to achieve essentially the same results as ISOCLUS on large data sets, but with significantly lower running times. This adaptation involves computing a number of cluster statistics that are needed for ISOCLUS but not for k-means. Both the k-means and ISOCLUS algorithms are based on iterative schemes, in which nearest neighbors are calculated until some convergence criterion is satisfied. Each iteration requires that the nearest center for each data point be computed. Naively, this requires O(kn) time, where k denotes the current number of centers. Traditional techniques for accelerating nearest neighbor searching involve storing the k centers in a data structure. However, because of the iterative nature of the algorithm, this data structure would need to be rebuilt with each new iteration. Our approach is to store the data points in a kd-tree data structure. The assignment of points to nearest neighbors is carried out by a filtering process, which successively eliminates centers that can not possibly be the nearest neighbor for a given region of space. This algorithm is significantly faster, because large groups of data points can be assigned to their nearest center in a single operation. Preliminary results on a number of real Landsat datasets show that our revised ISOCLUS-like scheme runs about twice as fast.

Memarsadeghi, Nargess↗
NASA NTRS1993

Dynamic mesh adaption for triangular and tetrahedral grids

The following topics are discussed: requirements for dynamic mesh adaption; linked-list data structure; edge-based data structure; adaptive-grid data structure; three types of element subdivision; mesh refinement; mesh coarsening; additional constraints for coarsening; anisotropic error indicator for edges; unstructured-grid Euler solver; inviscid 3-D wing; and mesh quality for solution-adaptive grids. The discussion is presented in viewgraph form.

Rupak Biswas↗
NASA NTRS2003

Development of an Efficient Binaural Simulation for the Analysis of Structural Acoustic Data

Applying binaural simulation techniques to structural acoustic data can be very computationally intensive as the number of discrete noise sources can be very large. Typically, Head Related Transfer Functions (HRTFs) are used to individually filter the signals from each of the sources in the acoustic field. Therefore, creating a binaural simulation implies the use of potentially hundreds of real time filters. This paper details two methods of reducing the number of real-time computations required by: (i) using the singular value decomposition (SVD) to reduce the complexity of the HRTFs by breaking them into dominant singular values and vectors and (ii) by using equivalent source reduction (ESR) to reduce the number of sources to be analyzed in real-time by replacing sources on the scale of a structural wavelength with sources on the scale of an acoustic wavelength. The ESR and SVD reduction methods can be combined to provide an estimated computation time reduction of 99.4% for the structural acoustic data tested. In addition, preliminary tests have shown that there is a 97% correlation between the results of the combined reduction methods and the results found with the current binaural simulation techniques

Johnson, Marty E.↗
NASA NTRS1989

Visualizing large data sets in the earth sciences

The authors describe the capabilities of McIDAS, an interactive visualization system that is vastly increasing the ability of earth scientists to manage and analyze data from remote sensing instruments and numerical simulation models. McIDAS provides animated three-dimensionsal images and highly interactive displays. The software can manage, analyze, and visualize large data sets that span many physical variables (such as temperature, pressure, humidity, and wind speed), as well as time and three spatial dimensions. The McIDAS system manages data from at least 100 different sources. The data management tools consist of data structures for storing different data types in files, libraries of routines for accessing these data structures, system commands for performing housekeeping functions on the data files, and reformatting programs for converting external data to the system's data structures. The McIDAS tools for three-dimensional visualization of meteorological data run on an IBM mainframe and can load up to 128-frame animation sequences into the workstations. A highly interactive version of the system can provide an interactive window into data sets containing tens of millions of points produced by numerical models and remote sensing instruments. The visualizations are being used for teaching as well as by scientists.

Hibbard, William↗
NASA NTRS1970

XB-70 aerodynamic, geometric, mass, and symmetric structural mode data

XB-70-1 mass, structural, and aerodynamic data were updated to reflect as closely as possible the characteristics of the airplane at three specific flight conditions which were actually flown; a nominal Mach number of 0.90 at an altitude of 25,000 feet (two cases) and a nominal Mach number of 1.6 at an altitude of 40,000 feet (one case). In-flight response characteristics at a number of points on the vehicle were obtained by exciting a pair of shaker vanes on the nose of the airplane. Data were recorded with the basic stability augmentation system (SAS) operating both alone and together with the identical location of accelerometer and force (ILAF) structural mode control system. Detailed total vehicle weight, mass characteristics, structural frequencies, generalized masses, all aerodynamic data used in the present analyses, and a description of the actual mode shapes are tabulated and presented.

Wykes, J. H.↗
NASA NTRS1992

Some practicable applications of quadtree data structures/representation in astronomy

Development of quadtree as hierarchical data structuring technique for representing spatial data (like points, regions, surfaces, lines, curves, volumes, etc.) has been motivated to a large extent by storage requirements of images, maps, and other multidimensional (spatially structured) data. For many spatial algorithms, time-efficiency of quadtrees in terms of execution may be as important as their space-efficiency concerning storage conditions. Briefly, the quadtree is a class of hierarchical data structures which is based on the recursive partition of a square region into quadrants and sub-quadrants until a predefined limit. Beyond the wide applicability of quadtrees in image processing, spatial information analysis, and building digital databases (processes becoming ordinary for the astronomical community), there may be numerous further applications in astronomy. Some of these practicable applications based on quadtree representation of astronomical data are presented and suggested for further considerations. Examples are shown for use of point as well as region quadtrees. Statistics of different leaf and non-leaf nodes (homogeneous and heterogeneous sub-quadrants respectively) at different levels may provide useful information on spatial structure of astronomical data in question. By altering the principle guiding the decomposition process, different types of spatial data may be focused on. Finally, a sampling method based on quadtree representation of an image is proposed which may prove to be efficient in the elaboration of sampling strategy in a region where observations were carried out previously either with different resolution or/and in different bands.

Pasztor, L.↗
NASA NTRS2011

Spatial Query for Planetary Data

Science investigators need to quickly and effectively assess past observations of specific locations on a planetary surface. This innovation involves a location-based search technology that was adapted and applied to planetary science data to support a spatial query capability for mission operations software. High-performance location-based searching requires the use of spatial data structures for database organization. Spatial data structures are designed to organize datasets based on their coordinates in a way that is optimized for location-based retrieval. The particular spatial data structure that was adapted for planetary data search is the R+ tree.

Shams, Khawaja S.↗
NASA NTRS2006

Inspection and Verification of Domain Models with PlanWorks and Aver

When developing a domain model, it seems natural to bring the traditional informal tools of inspection and verification, debuggers and automated test suites, to bear upon the problems that will inevitably arise. Debuggers that allow inspection of registers and memory and stepwise execution have been a staple of software development of all sorts from the very beginning. Automated testing has repeatedly proven its considerable worth, to the extent that an entire design philosophy (Test Driven Development) has been developed around the writing of tests. Unfortunately, while not entirely without their uses, the limitations of these tools and the nature of the complexity of models and the underlying planning systems make the diagnosis of certain classes of problems and the verification of their solutions difficult or impossible. Debuggers provide a good local view of executing code, allowing a fine-grained look at algorithms and data. This view is, however, usually only at the level of the current scope in the implementation language, and the data-inspection capabilities of most debuggers usually consist of on-line print statements. More modem graphical debuggers offer a sort of tree view of data structures, but even this is too low-level and is often inappropriate for the kinds of structures created by planning systems. For instance, god or constraint networks are at best awkward when visualized as trees. Any any non-structural link between data structures, as through a lookup table, isn't captured at all. Further, while debuggers have powerful breakpointing facilities that are suitable for finding specific algorithmic errors, they have little use in the diagnosis of modeling errors.

Bedrax-Weiss, Tania↗
NASA NTRS2014

System for Configuring Modular Telemetry Transponders

A system for configuring telemetry transponder cards uses a database of error checking protocol data structures, each containing data to implement at least one CCSDS protocol algorithm. Using a user interface, a user selects at least one telemetry specific error checking protocol from the database. A compiler configures an FPGA with the data from the data structures to implement the error checking protocol.

Varnavas, Kosta A.↗
NASA NTRS1998

Priority Queues for Computer Simulations

The present invention is embodied in new priority queue data structures for event list management of computer simulations, and includes a new priority queue data structure and an improved event horizon applied to priority queue data structures. ne new priority queue data structure is a Qheap and is made out of linked lists for robust, fast, reliable, and stable event list management and uses a temporary unsorted list to store all items until one of the items is needed. Then the list is sorted, next, the highest priority item is removed, and then the rest of the list is inserted in the Qheap. Also, an event horizon is applied to binary tree and splay tree priority queue data structures to form the improved event horizon for event management.

Steinman, Jeffrey S.↗
NASA NTRS1988

The CSM testbed matrix processors internal logic and dataflow descriptions

This report constitutes the final report for subtask 1 of Task 5 of NASA Contract NAS1-18444, Computational Structural Mechanics (CSM) Research. This report contains a detailed description of the coded workings of selected CSM Testbed matrix processors (i.e., TOPO, K, INV, SSOL) and of the arithmetic utility processor AUS. These processors and the current sparse matrix data structures are studied and documented. Items examined include: details of the data structures, interdependence of data structures, data-blocking logic in the data structures, processor data flow and architecture, and processor algorithmic logic flow.

Regelbrugge, Marc E.↗
NASA NTRS2006

Representation of Serendipitous Scientific Data

A computer program defines and implements an innovative kind of data structure than can be used for representing information derived from serendipitous discoveries made via collection of scientific data on long exploratory spacecraft missions. Data structures capable of collecting any kind of data can easily be implemented in advance, but the task of designing a fixed and efficient data structure suitable for processing raw data into useful information and taking advantage of serendipitous scientific discovery is becoming increasingly difficult as missions go deeper into space. The present software eases the task by enabling definition of arbitrarily complex data structures that can adapt at run time as raw data are transformed into other types of information. This software runs on a variety of computers, and can be distributed in either source code or binary code form. It must be run in conjunction with any one of a number of Lisp compilers that are available commercially or as shareware. It has no specific memory requirements and depends upon the other software with which it is used. This program is implemented as a library that is called by, and becomes folded into, the other software with which it is used.

James, Mark↗
NASA NTRSDate not supplied

Data Sharing in Radiobiology; Towards FAIR

The value of scientific data depends on their findability, accessibility, integrability and reusability according to the FAIR principles. Together with the sustainability of data preservation and access, these principles underpin the long term benefits of scientific research. Within the domain of radiobiology we have a huge array of data types, themes and complexities which make standardisation of metadata, data structure and data integration very challenging. Moreover, it is clear that, for example, in the area of disaster preparedness, the ready discovery and availability of multiple types of data, for example on biological effects of exposure, climatology, ecology, human behavioural and attitudinal studies, is important for an integrated scientific approach. Because these data are spread over many databases, journal supplementary information resources and even the computers of the investigators, their discovery and reuse can be challenging. Despite exhortations from funding agencies and scientific institutions over the past two decades there is still a serious deficit in the willingness and in some cases the ability of investigators to share data, and although much may not be formally „Public domain“, information about the existence of the data, their metadata, and how to obtain them should always be available. We report the progress of work on three databases, the STORE and the NASA GeneLab and LSDA repositories to leverage the Radiation Biology Ontology (RBO), a structured terminology for metadata that can be used by all radiation biology-relevant databases to unite federated and automated data searches across multiple databases, for example using web services, and through semantic web technologies supporting data discovery. The initial primary use-cases for RBO were archiving data in the STORE database (https://www.storedb.org/), the repository used for the RadoNorm and Pianoforte Projects among others, and in the NASA Open Science Data Repository (https://osdr.nasa.gov/bio). The scope of radiobiology research ranges from basic physics to radiation oncology to sociolegal studies; no existing ontology had the necessary breadth or depth to fulfill this need. In addition, a formal ontology has the advantage of being usable for machine learning and, importantly, for tasks like data integration, knowledge extraction from the scientific literature and for query extension and data classification. Standardisation of metadata is one of the primary objectives of the FAIR principles for open data; RBO is an important landmark for FAIR-compliant radiation biology data sharing. The RBO is developed using the open-source tools of GitHub and the OBO Foundry-led Ontology Development Kit, and published through GitHub and the NIH/NCBI BioPortal website. This initial phase of concept modeling has yielded an ontology that has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies with relevance to radiation biology (for example, concepts from the ISO standard Basic Formal Ontology, the Environment Ontology and the Gene Ontology). We welcome input into the development of RBO and encourage its adoption.

ontologies↗