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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
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 NTRS2010

Thermal Insulation System Analysis Tool (TISTool) User's Manual. Version 1.0.0

The Thermal Insulation System Analysis Tool (TISTool) was developed starting in 2004 by Jonathan Demko and James Fesmire. The first edition was written in Excel and Visual BasIc as macros. It included the basic shapes such as a flat plate, cylinder, dished head, and sphere. The data was from several KSC tests that were already in the public literature realm as well as data from NIST and other highly respectable sources. More recently, the tool has been updated with more test data from the Cryogenics Test Laboratory and the tank shape was added. Additionally, the tool was converted to FORTRAN 95 to allow for easier distribution of the material and tool. This document reviews the user instructions for the operation of this system.

Johnson, Wesley↗
NASA NTRS2016

The Matsu Wheel: A Cloud-Based Framework for Efficient Analysis and Reanalysis of Earth Satellite Imagery

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

0↗
NASA NTRS2007

SERVIR: From Space to Village. A Regional Monitoring and Visualization System For Environmental Management Using Satellite Applications For Sustainable Development

NASA is committed to providing technological support and expertise to regional and national organizations for earth science monitoring and analysis. This commitment is exemplified by NASA's long-term relationship with Central America. The focus of these efforts has primarily been to measure the impact of human development on the environment and to provide data for the management of human settlement and expansion in the region. Now, NASA is planning to extend and expand this capability to other regions of the world including Africa and the Caribbean. NASA began using satellite imagery over twenty-five years ago to locate important Maya archeological sites in Mesoamerica and to quantify the affect of deforestation on those sites. Continuing that mission, NASA has partnered with the U.S. Agency for International Development (USAID), the World Bank, the Water Center for the Humid Tropics of Latin America and the Caribbean (CATHALAC) and the Central American Commission for Environment and Development (CCAD) to develop SERVIR (Sistema Regional de Visualizacion y Monitoreo), for the Mesoamerican Biological Corridor. SERVIR has become one of the most important aspects of NASA's geospatial efforts in Central America by establishing a common access portal for information that affects the lives, livelihood and future of everyone in the region. SERVIR, most commonly referred to as a regional visualization and monitoring system, is a scientific and technological platform that integrates satellite and other geospatial data sets to generate tools for improved decision-making capabilities. It has a collection of data and models that are easily accessible to earth science managers, first responders, NGO's (Non-Government Organizations) and a host of others. SERVIR is currently used to monitor and forecast ecological changes as well as provide information for decision support during severe events such as forest fires, red tides,and tropical storms. Additionally, SERVIR addresses the nine societal benefit areas of the Global Earth Observation System (GEOSS): disasters, ecosystems, biodiversity, weather, water, climate, health, agriculture and energy.

Sever, Tom↗
NASA NTRSDate not supplied

NASA GeneLab: The NASA Systems Biology Platform for Spaceomics Repository, Analysis and Visualization

At NASA Ames Research Center, the GeneLab Open Science Project is on a mission to gather all large -omics datasets relevant to space biology research. These datasets come from various organisms flown in multiple space habitats such as the International Space Station or the Space Shuttle, in addition to mimicking space-like conditions on ground. Researchers and citizen scientists all around the world have used the data and the analytical tools put together by the GeneLab team to start deciphering new biological impact of microgravity, space ionizing radiation and other space stressors.

GeneLab↗
NASA NTRS1974

Skylab and ERTS-1 investigations of coastal land-use and water properties in Delaware Bay

Study of coastal land use and water properties of Delaware Bay using digital and visual analysis of enhanced imagery from NASA's Earth Resources Technology Satellite (ERTS) and from the Skylab Earth Resources Experimental Package (EREP). ERTS is shown to have the advantage of repetitive coverage of the test site which facilitates change detection experiments by gathering data under a variety of tidal, seasonal, and atmospheric conditions. Skylab-EREP data, on the other hand, are superior in both spatial and spectral resolution.

Klemas, V.↗
NASA NTRS1992

Visualization of second order tensor fields and matrix data

We present a study of the visualization of 3-D second order tensor fields and matrix data. The general problem of visualizing unsymmetric real or complex Hermitian second order tensor fields can be reduced to the simultaneous visualization of a real and symmetric second order tensor field and a real vector field. As opposed to the discrete iconic techniques commonly used in multivariate data visualization, the emphasis is on exploiting the mathematical properties of tensor fields in order to facilitate their visualization and to produce a continuous representation of the data. We focus on interactively sensing and exploring real and symmetric second order tensor data by generalizing the vector notion of streamline to the tensor concept of hyperstreamline. We stress the importance of a structural analysis of the data field analogous to the techniques of vector field topology extraction in order to obtain a unique and objective representation of second order tensor fields.

Delmarcelle, Thierry↗
NASA NTRS2019

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.

Risk modeling↗
NASA NTRSDate not supplied

Suited User Incident Tracking System

The Suited User Incident Tracking System (SUITS) tracks all suited exposures during terrestrial analog simulations and trainings with the objective of recording any related issues, pains, or discomforts that arise from the exposure. This information is vital for informing where, how, and why issues are occurring during suited extravehicular activity (EVA) training and operations. The outputs of SUITS will be the foundation for the newly formed Suited Anomaly Assessment Team (SAAT) which consists of a broad range of EVA stakeholders including suit engineers, biomechanists, ergonomists, flight surgeons, athletic trainers, and physiologists. This team has been formed to provide robust, wholistic solutions to suit anomalies and issues identified by SUITS. SUITS data collected from September 2017 to June 2022 was analyzed for issue frequency and severity for each body location and plotted to visualize where issues were occurring. Clustering techniques using a k-modes approach were also applied to the data set to generate profiles of populations that experienced issues in the suit and populations that did not. A key finding identified by the SUITs analysis was that suited issues translate from predominantly upper body areas in microgravity space suits and associated analog simulations to lower body areas in planetary space suits and analog simulations. In addition, specific injury frequency and severity metrics were identified that will help drive requirements for commercially provided space suits. The SAAT will continue to meet on a biannual basis to identify trends in issues and injuries from the SUITS database to evaluate and develop solution strategies where needed for the purposes of suit design, EVA operations optimization, injury characterization and mitigation, and personalized improvements for human-suit interactions.

K. Dolick↗
NASA NTRS1998

Analysis of a Memory Device Failure

The recent failure of a vintage memory device presented a unique challenge to failure analysts. Normally device layouts, fabrication parameters and other technical information were available to assist the analyst in the analysis. However, this device was out of production for many years and the manufacturer was no longer in business, so the information was not available. To further complicate this analysis, the package leads were all but removed making additional electrical testing difficult. Under these conditions, new and innovative methods were used to analyze the failure. The external visual exam, radiography, PIND, and leak testing were performed with nominal results. Since electrical testing was precluded by the short lead lengths, the device was delidded to expose the internal structures for microscopic examination. No failure mechanism was identified. The available electrical data suggested an ESD or low level EOS type mechanism which left no visible surface damage. Due to parallel electrical paths, electrical probing on the chip failed to locate the failure site. Two non-destructive Scanning Electron Microscopy techniques, CIVA (Charge Induced Voltage Alteration) and EBIC (Electron Beam Induced Current), and a liquid crystal decoration technique which detects localized heating were employed to aid in the analysis. CIVA and EBIC isolated two faults in the input circuitry, and the liquid crystal technique further localized two hot spots in regions on two input gates. Removal of the glassivation and metallization revealed multiple failure sites located in the gate oxide of two input transistors suggesting machine (testing) induced damage.

Nicolas, David P.↗
NASA NTRS1975

Study of the utilization of EREP data from the Wabash River Basin

The author has identified the following significant results. Analysis of the digitized SL/4 S190A color IR photography proved very difficult. An area within Allen County, including Ft. Wayne, was studied. Eight segments of the study area were clustered separately and the cluster maps were then compared with the photography and maps available. The training areas for the land use classes were selected from the cluster maps. The separability measures (transformed divergence) of the classes indicated that many of the land use classes were not spectrally separable. The classification results bore this out. Visually the resulting classification map was poor, with 67 percent correct data. These results were of significantly lower quality than those obtained for the summertime SL/2 data near Lake Monroe, Indiana. Low contrast between land use classes during the wintertime and the limited spectral range and resolution are the major causes for the poor performance.

Silva, L. F.↗
NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗
NASA NTRS1974

OAO-2 observations of the helium spectrum variable a Centauri

Ultraviolet photometry and spectrometer data are presented for a Cen, which exhibits large variations in the strength of its He I lines. The photometry indicates that the star becomes brighter at the helium-weak phase by 0.05 mag throughout the ultraviolet and visual region. A model atmosphere analysis indicates that the photometric variations are not due to flux redistribution from back-warming effects, but can be interpreted as due to the rotation of a distorted stellar disk in the shape of a prolate spheroid.

Molnar, M. R.↗
NASA NTRS1978

Definition and detailed characterization of lunar surface units using remote observations

Remote sensing techniques are reviewed in terms of their application to the study of the lunar surface. These techniques include: reflectance spectroscopy (UV and IR), X-ray and gamma-ray analysis, alpha-particle spectroscopy, thermal emission, polarimetry, photography, and visual observation. Each technique is indexed with respect to its usefulness in either describing the appearance and location of a feature, classifying the feature in terms of a broader category, or supplying additional quantitative data. Special attention is given to the mare deposits of the Imbrium basin.

Head, J. W.↗
NASA NTRS1990

Intraocular pressure, retinal vascular, and visual acuity changes during 48 hours of 10-deg head-down tilt

Intraocular pressures, retinal vascular diameters, and visual acuities of nine men, were repeatedly measured while the subjects were tilted 10 deg head-down for 48 h and while they were seated before (baseline), and after the tilt. An immediate increase in intraocular pressure, measured by pneumatonometer was recorded when subjects assumed the head-down position, and diurnal variations in intraocular pressures were observed for the 48 h. The initial and final head-down intraocular pressures were not significantly different. However, when subjects resumed the sitting position, intraocular pressures fell below the initial sitting values. Computer image analysis of the retinal vasculature detected a 6 percent and 2 percent reduction in the caliber of arteries and veins, respectively, as compared with sitting baseline values. No changes in visual acuity were documented during the 48 h of head-down tilt. The data suggest that the choroidal blood reservoir increases in volume over 48 h at continuous head-down position with a compensatory decrease in aqueous volume. These findings may explain intraocular pressure changes noted in astronauts during previous space missions and in studies associated with change in body position.

Mader, Thomas H.↗
NASA NTRS1991

Scalable Performance Environments for Parallel Systems

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already difficult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer afford to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-specified ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and configure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal definitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques.

Reed, Daniel A.↗
NASA NTRS1999

3D Multistage Simulation of Each Component of the GE90 Turbofan Engine

A 3D multistage simulation of each component of the GE90 Turbofan engine has been made. This includes 49 blade rows. A coupled simulation of all blade rows will be made very soon. The simulation is running using two levels of parallelism. The first level is on a blade row basis with information shared using files. The second level is using a grid domain decomposition with information shared using MPI. Timings will be shown for running on the SP2, an SGI Origin and a distributed system of HP workstations. On the HP workstations, the CHIMP version of MPI is used, with queuing supplied by LSF (Load Sharing Facility). A script-based control system is used to ensure reliability. An MPEG movie illustrating the flow simulation of the engine has been created using PV3, a parallel visualization library created by Bob Haimes of MIT. PVM is used to create a virtual machine from 10 HP workstations and display on an SGI workstation. A representative component simulation will be compared to rig data to demonstrate its usefulness in turbomachinery design and analysis.

Turner, Mark↗
NASA NTRS1982

Star-Catalog Data Base

SKYMAP is collection of computer programs and utility software for creating and maintaining master star catalog and hierarchical set of derivative star catalogs. Developed to provide accurate stellar position and magnitude information for attitude-determination and analysis systems utilizing star sensor observations. Current master star catalog includes all documented stars with blue or visual magnitudes brighter than magnitude 9.0

Gotlieb, D.↗