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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Proposal Drafted for Allocating Space-to-Space Frequencies in the GPS Spectrum Bands

Radionavigation Satellite Service (RNSS) systems such as the U.S. Global Positioning System (GPS) and the Russian Global Navigation Satellite System (GLONASS) are primarily being used today in the space-to-Earth direction (i.e., from GPS satellite to Earth user) for a broad range of applications such as geological surveying; aircraft, automobile, and maritime navigation; hiking and mountain climbing; and precision farming and mining. However, these navigation systems are being used increasingly in space. Beginning with the launch of the TOPEX/Poseidon remote-sensing mission in 1992, over 90 GPS receivers have flown onboard spacecraft for such applications as real-time spacecraft navigation, three-axis attitude control, precise time synchronization, precision orbit determination, and atmospheric profiling. In addition to use onboard many science spacecraft, GPS has been used or is planned to be used onboard the shuttles, the International Space Station, the International Space Station Emergency Crew Return Vehicle, and many commercial satellite systems such as Orbcomm, Globalstar, and Teledesic. From a frequency spectrum standpoint, however, one important difference between the space and terrestrial uses of GPS is that it is being used in space with no interference protection. This is because there is no frequency allocation for the space-to-space use of GPS (i.e., from GPS satellite to user spacecraft) in the International Telecommunications Union (ITU) regulatory table of frequency allocations. If another space-based or groundbased radio system interferes with a spaceborne GPS user, the spaceborne user presently has no recourse other than to accept the interference. Consequently, for the past year and a half, the NASA Glenn Research Center at Lewis Field and other Government agencies have been working within ITU toward obtaining a GPS space-to-space allocation at the next World Radio Conference in the year 2000 (WRC 2000). Numerous interference studies have been conducted in support of a primary space-tospace allocation in the 1215- to 1260-MHz and 1559- to 1610-MHz RNSS bands. Most of these studies and analyses were performed by Glenn and submitted as U.S. input documents to the international Working Party 8D meetings in Geneva, Switzerland. In the structure of the ITU, Working Party 8D is responsible for frequency spectrum issues in the RNSS and the mobile satellite service (MSS). The full texts of the studies are available from the ITU web site under Working Party 8D contributions. Note that because spaceborne RNSS receivers operate in a receive-only mode with navigation signals already being broadcast toward the Earth, the addition of a space-tospace allocation will not result in interference with other systems. A space-based RNSS receiver, however, could experience interference from systems of other services, including intraservice interference from other RNSS systems. The interference scenarios examined in the studies can be inferred from the following frequency allocation charts. In these charts, services labeled in all capital letters (e.g., "ARNS") have primary status, whereas those labeled with sentence-style capitalization (e.g., "Amateur radio") have secondary status (i.e., a service with secondary status cannot claim interference protection from or cause harmful interference to a service with primary status). Charts showing the ITU frequency allocations in the 960 to 1350 MHZ range and the 1525-1660 MHZ range are discussed and presented.

Spence, Rodney L.↗
NASA NTRS2007

Risk Reduction for Use of Complex Devices in Space Projects

We present guidel!nes to reduce risk to an acceptable level when using complex devices in space applications. Application to Virtex 4 Field Programmable Gate Array (FPGA) on Express Logistic Carrier (ELC) project is presented.

Berg, Melanie↗
NASA NTRS1989

The diageotropica mutant of tomato lacks high specific activity auxin binding sites

Tomato plants homozygous for the diageotropica (dgt) mutation exhibit morphological and physiological abnormalities which suggest that they are unable to respond to the plant growth hormone auxin (indole-3-acetic acid). The photoaffinity auxin analog [3H]5N3-IAA specifically labels a polypeptide doublet of 40 and 42 kilodaltons in membrane preparations from stems of the parental variety, VFN8, but not from stems of plants containing the dgt mutation. In roots of the mutant plants, however, labeling is indistinguishable from that in VFN8. These data suggest that the two polypeptides are part of a physiologically important auxin receptor system, which is altered in a tissue-specific manner in the mutant.

NASA Discipline Number 29-20↗
NASA NTRS2024

The NASA Radiation Hardness Assurance (RHA) Process Standard

This paper presents status on the upcoming NASA Agency-level RHA Process Standard. Based on recommendations provided by the NASA Engineering and Safety Center (NESC), the NASA Electronic Parts and Packaging (NEPP) Program has led development of an agency-level standard for space flight avionics and electronics RHA. The standard introduces a novel RHA taxonomy and prescribes the process required by NASA programs and projects to baseline their RHA programs consistent with the MEAL (Mission, Environment, Application, and Lifetime) criteria including the risk tolerance posture. The standard also provides requirements for the RHA schedule integration in the program or project lifecycle and requirements for data deliverables content. Additional non-prescriptive technical content is provided in the standard appendices to guide development of RHA programs for NASA missions.

Avionics↗
NASA NTRS2016

Radiation Failures in Intel 14nm Microprocessors

In this study the 14 nm Intel Broadwell 5th generation core series 5005U-i3 and 5200U-i5 was mounted on Dell Inspiron laptops, MSI Cubi and Gigabyte Brix barebones and tested with Windows 8 and CentOS7 at idle. Heavy-ion-induced hard- and catastrophic failures do not appear to be related to the Intel 14nm Tri-Gate FinFET process. They originate from a small (9 m 140 m) area on the 32nm planar PCH die (not the CPU) as initially speculated. The hard failures seem to be due to a SEE but the exact physical mechanism has yet to be identified. Some possibilities include latch-ups, charge ion trapping or implantation, ion channels, or a combination of those (in biased conditions). The mechanism of the catastrophic failures seems related to the presence of electric power (1.05V core voltage). The 1064 nm laser mimics ionization radiation and induces soft- and hard failures as a direct result of electron-hole pair production, not heat. The 14nm FinFET processes continue to look promising for space radiation environments.

Microprocessors↗
NASA NTRSDate not supplied

Revolutionizing Earth Science with Generalized AI Models

Foundation Models (FM) are generalized Artificial Intelligence (AI) models that are designed to replace a task or an application-specific model and can be used for many downstream applications. These FM can be built on any sequence data and are trained utilizing self-supervised approaches. The obstacle of creating a sizable labeled dataset for training is removed by using self-supervised learning. Most FM employ transformer design that takes advantage of the idea of self-attention, allowing the network to represent the impact of distant data points on one another in space and time. The FM models show emergent qualities that are induced from the data. FM can become a valuable tool for Earth science researchers. Due to the size of these models, downstream applications built fine-tuning these FM perform better and exhibit greater accuracy than models created from scratch. FM significantly lowers the entry barrier in terms of both the time and effort required to develop various downstream applications. For some scientific datasets, such as optical remote sensing data, FM can speed up processes like classification, object detection and prediction. By eliminating the training data bottleneck and maximizing the usage of science data, FM can make it simpler to integrate AI into scientific research. Initial results for three different FMs will be presented.

Rahul Ramachandran↗
NASA NTRS2019

Carbon Nanostructures in Biological Sensors for Space and Terrestrial Applications

Biosensing devices comprised of carbon nanotubes and nanofibers have been developed for astronaut crew point-of-care. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using carbon nanotube and carbon nanofiber electrodes for biomedical applications. First, a 3x3 electrode device, with each electrode containing 40,000 carbon nanofiber nanoelectrodes was fabricated on silicon using traditional microfabrication processing. The device was demonstrated as a multiplexed immunosensor for simultaneous, label-free detection of cardiac troponin-I, C-reactive protein and myoglobin. Antibodies specific to cardiac troponin-I, C-reactive protein and myoglobin were covalently bound to the CNF surface and were characterized using electrochemical impedance spectroscopy and differential pulse voltammetry. Each step of the modification process resulted in changes in resistance to charge transfer due to the changes at the electrode surface upon antibody immobilization and binding to the specific cardiac protein. The real-time label free detection of the three cardiac markers from pure components and mixtures was demonstrated with high sensitivity, down to 0.2 ng/mL, and good selectivity. Detection in human blood serum did not present false positives from non-specific protein adsorption. More recently, this detection scheme has been applied to inkjet printed carbon nanotube electrodes on Kapton and paper. Printed devices have several unique advantages including simple and inexpensive fabrication. The results demonstrate that these sensors can serve a miniaturized, low cost device for detection of proteins in complex mixtures making this platform a good candidate for early stage diagnosis of myocardial infarction. Future inkjet printed devices can be fabricated have the added advantage in their suitability to be manufactured in an in-space, microgravity environment.

carbon nanotubes↗
NASA NTRS2017

Interactive Visualization of High-Dimensional Petascale Ocean Data

We describe an application for interactive visualization of 5 petabytes of time-varying multivariate data from a high-resolution global ocean circulation model. The input data are 10311 hourly (ocean time) time steps of various 2D and 3D fields from a 22-billion point 1/48- degree “lat-lon cap” configuration of the MIT General Circulation Model (MITgcm). We map the global horizontal model domain onto our 128-screen (8x16) tiled display wall to produce a canonical tiling with approximately one MITgcm grid point per display pixel, and using this tiling we encode the entire time series for multiple native and computed scalar quantities at a collection of ocean depths. We reduce disk bandwidth requirements by converting the model’s floating point data to 16-bit fixed point values, and compressing those values with a lossless video encoder, which together allow synchronized playback at 24 time steps per second across all 128 displays. The application allows dynamic assignment of any two encoded tiles to any display, and has multiple interfaces for quickly specifying various orderly arrangements of tiles. All subsequent rendering is done on the fly, with run time control of colormaps, transfer functions, histogram equalization, and labeling. The two data streams on each screen can be rendered independently and combined in various ways, including blending, differencing, horizontal/ vertical wipes, and checkerboarding. The two data streams on any screen can optionally be displayed as a scatterplot in their joint attribute space. All scatterplots and map-view plots from the same x/y location and depth are linked so they all show the current brushable selection. Ocean scientists have used the system, and have found previously unidentified features in the data.

Interactive↗
NASA NTRS2019

State Predictor of Classification Cognitive Engine Applied to Channel Fading

This study presents the application of machine learning (ML) to a space-to-ground communication link, showing how ML can be used to detect the presence of detrimental channel fading. Using this channel state information, the communication link can be used more efficiently by reducing the amount of lost data during fading. The motivation for this work is based on channel fading observed during on-orbit operations with NASA's Space Communication and Navigation (SCaN) testbed on the International Space Station (ISS). This paper presents the process to extract a target concept (fading and not-fading) from the raw data. The pre-processing and data exploration effort is explained in detail, with a list of assumptions made for parsing and labelling the dataset. The model selection process is explained, specifically emphasizing the benefits of using an ensemble of algorithms with majority voting for binary classification of the channel state. Experimental results are shown, highlighting how an end-to-end communication system can utilize knowledge of the channel fading status to identity fading and take appropriate action. With a laboratory testbed to emulate channel fading, the overall performance is compared to standard adaptive methods without fading knowledge, such as adaptive coding and modulation.

Fading↗
NASA NTRS2004

Fast Query-Optimized Kernel-Machine Classification

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Mazzoni, Dominic↗
NASA NTRSDate not supplied

Development of Machine Learning Algorithms to Segment and Study Images of Astromaterial Samples

Introduction: Micrometer-scale chemical analyses of chondritic meteorites and mission-returned asteroid samples can reveal details of the physical and chemical processes operating in the early solar system, including processes that gave rise to planets, moons, and minor bodies. These primitive astromaterials are comprised of chondrules, calcium- and aluminum-rich inclusions (CAI), and many other silicates, oxides, metals, sulfides, and fine-grained materials. The chemical and mineralogical complexity of these samples, vast populations of different components, and heterogeneity across mm to km scales, all limit our understanding of the origin and evolution of these materials. Here, we describe recent efforts to use machine learning techniques to automate the segmentation of chemical maps of chondritic meteorites, designed to aid studies of asteroid samples returned by spacecraft. By automating the task of segmentation it will become possible to rapidly analyze and interpret the sizes, shapes, mineralogy, chemistry, and other properties of every chondrule, calcium- and aluminum-rich inclusion (CAI) and other clast within and between asteroid samples. Sample return missions significantly accelerate and heighten the need to develop such new data analysis techniques, and associated data repositories. Techniques: Neural networks require abundant training data, i.e. images which have been segmented by a human user. We have manually segmented data available from previous petrologic and chemical work at NASA Johnson Space Center and the American Museum of Natural History [1-4]. These data were derived from energy- and wavelength-dispersive X-ray spectroscopy (EDS, WDS) mapping of samples from many chondrite groups. The Deeplabv3+ [5] neural network architecture was trained on human-labeled masks and used to create machine-labeled masks. Several different algorithms were investigated, with inputs ranging from common RGB image formats through to hyperspectral datasets, with raw data comprising greyscale maps of Mg, Ca, and Al, with or without Si, Fe, Ti for both EDS and WDS data, and extending to other elements in EDS only. Each greyscale image was paired with a binary mask for each labelled particle type. Results: The trained algorithms can segment (Fig 1), classify, and measure the dimensions of thousands of particles in chemical maps of a standard 1-inch round petrographic section in seconds to minutes, rather than many hours needed by a human. Accuracy of the algorithms varied from chondrite to chondrite and across particle types. Further results and details of the algorithms will be presented at the workshop. Future directions: Machine learning has the potential to revolutionize our understanding of complex particle populations contained within primitive astromaterial, with segmentation being a critical first step. Example applications include better understanding of particle transport, nebular reservoirs, parent body accretion, and a deeper understanding of the relationships between particle populations and bulk rock elemental and isotopic compositions. In addition to benefits that machine learning can bring to individual researchers, building a community data repository of thousands to millions of particles across hundreds of samples will open up many other possibilities. For example, with a large enough dataset it will be possible to search for exceptionally closely matching particles across disparate samples. Such a capability would enable a single CAI from OSIRISREx or Hayabusa/II samples to be matched to chondritic CAIs that exhibit near-identical size, texture, and mineralogy, down to the level of similar core phenocrysts, zonation, and rim sequences. Such comparative analyses will help to disentangle precursor chemistry, chronology, gas/dust reservoirs during heating, and accretion. Such an endeavor would be impossible without machine learning and a large community data repository of astromaterial chemical/mineralogic maps.

Machine Learning↗
NASA NTRS2021

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.

space radiation↗
NASA NTRS1992

Effect of spaceflight on natural killer cell activity

The effects of spaceflight on immune cell function were determined in rats flown on Cosmos 2044. Control groups included vivarium, synchronous, and antiorthostatically suspended rats. The ability of natural killer cells to lyse two different target cell lines was determined. Spleen and bone marrow cells obtained from flight rats showed significantly inhibited cytotoxicity for YAC-1 target cells compared with cells from synchronous control rats. This could have been due to exposure of the rats to microgravity. Antiorthostatic suspension did not affect the level of cytotoxicity from spleen cells of suspended rats for YAC-1 cells. On the other hand, cells from rats flown in space showed no significant differences from vivarium and synchronous control rats in cytotoxicity for K-562 target cells. Binding of natural killer cells to K-562 target cells was unaffected by spaceflight. Antiorthostatic suspension resulted in higher levels of cytotoxicity from spleen cells for Cr-51-labeled K-562 cells. The results indicate differential effects of spaceflight on function of natural killer cells. This shows that spaceflight has selective effects on the immune response.

Rykova, Marina P.↗
NASA NTRS2010

Planetary Image Geometry Library

The Planetary Image Geometry (PIG) library is a multi-mission library used for projecting images (EDRs, or Experiment Data Records) and managing their geometry for in-situ missions. A collection of models describes cameras and their articulation, allowing application programs such as mosaickers, terrain generators, and pointing correction tools to be written in a multi-mission manner, without any knowledge of parameters specific to the supported missions. Camera model objects allow transformation of image coordinates to and from view vectors in XYZ space. Pointing models, specific to each mission, describe how to orient the camera models based on telemetry or other information. Surface models describe the surface in general terms. Coordinate system objects manage the various coordinate systems involved in most missions. File objects manage access to metadata (labels, including telemetry information) in the input EDRs and RDRs (Reduced Data Records). Label models manage metadata information in output files. Site objects keep track of different locations where the spacecraft might be at a given time. Radiometry models allow correction of radiometry for an image. Mission objects contain basic mission parameters. Pointing adjustment ("nav") files allow pointing to be corrected. The object-oriented structure (C++) makes it easy to subclass just the pieces of the library that are truly mission-specific. Typically, this involves just the pointing model and coordinate systems, and parts of the file model. Once the library was developed (initially for Mars Polar Lander, MPL), adding new missions ranged from two days to a few months, resulting in significant cost savings as compared to rewriting all the application programs for each mission. Currently supported missions include Mars Pathfinder (MPF), MPL, Mars Exploration Rover (MER), Phoenix, and Mars Science Lab (MSL). Applications based on this library create the majority of operational image RDRs for those missions. A Java wrapper around the library allows parts of it to be used from Java code (via a native JNI interface). Future conversions of all or part of the library to Java are contemplated.

Deen, Robert C.↗