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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Distributed computation of graphics primitives on a transputer network

A method is developed for distributing the computation of graphics primitives on a parallel processing network. Off-the-shelf transputer boards are used to perform the graphics transformations and scan-conversion tasks that would normally be assigned to a single transputer based display processor. Each node in the network performs a single graphics primitive computation. Frequently requested tasks can be duplicated on several nodes. The results indicate that the current distribution of commands on the graphics network shows a performance degradation when compared to the graphics display board alone. A change to more computation per node for every communication (perform more complex tasks on each node) may cause the desired increase in throughput.

Ellis, Graham K.↗
NASA NTRS1992

The role of HiPPI switches in mass storage systems: A five year prospective

New standards are evolving which provide the foundation for novel multi-gigabit per second data communication structures. The lowest layer protocols are so generalized that they encourage a wide range of application. Specifically, the ANSI High Performance Parallel Interface (HiPPI) is being applied to computer peripheral attachment as well as general data communication networks. This paper introduces the HiPPI standards suite and technology products which incorporate the standards. The use of simple HiPPI crosspoint switches to build potentially complex extended 'fabrics' is discussed in detail. Several near term applications of the HiPPI technology are briefly described with additional attention to storage systems. Finally, some related standards are mentioned which may further expand the concepts above.

Gilbert, T. A.↗
NASA NTRS1993

Fuzzy sets predict flexural strength and density of silicon nitride ceramics

In this work, we utilize fuzzy sets theory to evaluate and make predictions of flexural strength and density of NASA 6Y silicon nitride ceramic. Processing variables of milling time, sintering time, and sintering nitrogen pressure are used as an input to the fuzzy system. Flexural strength and density are the output parameters of the system. Data from 273 Si3N4 modulus of rupture bars tested at room temperature and 135 bars tested at 1370 C are used in this study. Generalized mean operator and Hamming distance are utilized to build the fuzzy predictive model. The maximum test error for density does not exceed 3.3 percent, and for flexural strength 7.1 percent, as compared with the errors of 1.72 percent and 11.34 percent obtained by using neural networks, respectively. These results demonstrate that fuzzy sets theory can be incorporated into the process of designing materials, such as ceramics, especially for assessing more complex relationships between the processing variables and parameters, like strength, which are governed by randomness of manufacturing processes.

Cios, Krzysztof J.↗
NASA NTRS1996

Development of Collaborative Research Initiatives to Advance the Aerospace Sciences-via the Communications, Electronics, Information Systems Focus Group

The primary goal of the Adaptive Vision Laboratory Research project was to develop advanced computer vision systems for automatic target recognition. The approach used in this effort combined several machine learning paradigms including evolutionary learning algorithms, neural networks, and adaptive clustering techniques to develop the E-MOR.PH system. This system is capable of generating pattern recognition systems to solve a wide variety of complex recognition tasks. A series of simulation experiments were conducted using E-MORPH to solve problems in OCR, military target recognition, industrial inspection, and medical image analysis. The bulk of the funds provided through this grant were used to purchase computer hardware and software to support these computationally intensive simulations. The payoff from this effort is the reduced need for human involvement in the design and implementation of recognition systems. We have shown that the techniques used in E-MORPH are generic and readily transition to other problem domains. Specifically, E-MORPH is multi-phase evolutionary leaming system that evolves cooperative sets of features detectors and combines their response using an adaptive classifier to form a complete pattern recognition system. The system can operate on binary or grayscale images. In our most recent experiments, we used multi-resolution images that are formed by applying a Gabor wavelet transform to a set of grayscale input images. To begin the leaming process, candidate chips are extracted from the multi-resolution images to form a training set and a test set. A population of detector sets is randomly initialized to start the evolutionary process. Using a combination of evolutionary programming and genetic algorithms, the feature detectors are enhanced to solve a recognition problem. The design of E-MORPH and recognition results for a complex problem in medical image analysis are described at the end of this report. The specific task involves the identification of vertebrae in x-ray images of human spinal columns. This problem is extremely challenging because the individual vertebra exhibit variation in shape, scale, orientation, and contrast. E-MORPH generated several accurate recognition systems to solve this task. This dual use of this ATR technology clearly demonstrates the flexibility and power of our approach.

Knasel, T. Michael↗
NASA NTRS1998

The Paradox of Filamented Coronal Hole Flow but Uniform High Speed Wind

Plumes and rays in coronal holes are nearly radially aligned density striations that follow the ambient magnetic field. They have long been known, but have gained new interest with growing awareness that coronal hole flow is inherently filamentary. In retrospect, filamentary flow should have been no surprise. This is because,Beta much less than 1 in coronal holes inside approximately 10 Solar radius, allowing the flow to be filamentary down to the smallest scale of photospheric magnetic activity. While the magnetic field itself is locally smooth across any height above ca. 50,000 km, SOHO/MDI has shown that the photospheric magnetic field is a complex array of rapidly evolving small bipoles that are constantly emerging, evolving, and cancelling. The resulting activity is manifested in microflares, concentrated in the magnetic network, that produce Impulsive injections at the footpoints of coronal field lines. The uneven distribution of this activity in space and time is the source of coronal hole filamentation. What is surprising is that the radial flow speed also exhibits filamentary structure. It is not well described as smooth, spherically symmetric, diverging flow, but instead ranges from 300 to over 1000 km/s at 5.5 Solar radius among field-aligned filaments like those seen in plumes and rays [Feldman et al., JGR, Dec. 1997]. This is completely unlike the constant high speed solar wind reported beyond 0.3 AU. Consequently, plumes and filamentary structure must be strongly mixed, and the mixing must be far along by 0.3 AU to be consistent with Helios observations. The paradox is what causes the mixing? Existing models of coronal heating and solar wind acceleration hardly address this issue. One possibility we are investigating is the MHD Kelvin-Helmholtz instability, to which the shear between plumes and interplume corona is expected to become unstable at 5-10 Solar radius. This instability can be simulated and followed far into the nonlinear regime and may lead to Alfvenic fluctuations like those seen at 1 AU.

Suess, Steven T.↗
NASA NTRS2003

Subauditory Speech Recognition based on EMG/EPG Signals

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

Jorgensen, Charles↗
NASA NTRS2008

National Aeronautics and Space Administration's (NASA) Third-Generation Tracking and Data Relay Satellites (TDRS)

NASA has contracted with Boeing to provide two third-generation Tracking and Data Relay Satellites (TDRS) designated TDRS K and L, with an option to provide two additional satellites designated TDRS M and N. These TDRS will be used to continue and enhance the user support services of the existing first- and second-generation TDRS spacecraft. The existing TDRS, in conjunction with the TDRS Ground Terminals at the White Sands NM Complex (WSC) and Guam, constitute the Tracking and Data Relay Satellite System (TDRSS). The TDRSS, with other supporting elements, is referred to as the Space Network (SN). The launch of the TDRS K is projected for 2012 with TDRS L planned to follow in 2013. The contract also provides for the modifications to the TDRS Ground Terminals at the WSC required for operation of the new TDRS. This paper provides an overview of the customer services provided by the existing and new TDRS. In addition, planned future customer services such as Bandwidth Efficient Modulation (BEM) and new coding schemes are briefly discussed.

Berndt, Allen K.↗
NASA NTRS2010

Sources of Uncertainty in Predicting Land Surface Fluxes Using Diverse Data and Models

In the domain of predicting land surface fluxes, models are used to bring data from large observation networks and satellite remote sensing together to make predictions about present and future states of the Earth. Characterizing the uncertainty about such predictions is a complex process and one that is not yet fully understood. Uncertainty exists about initialization, measurement and interpolation of input variables; model parameters; model structure; and mixed spatial and temporal supports. Multiple models or structures often exist to describe the same processes. Uncertainty about structure is currently addressed by running an ensemble of different models and examining the distribution of model outputs. To illustrate structural uncertainty, a multi-model ensemble experiment we have been conducting using the Terrestrial Observation and Prediction System (TOPS) will be discussed. TOPS uses public versions of process-based ecosystem models that use satellite-derived inputs along with surface climate data and land surface characterization to produce predictions of ecosystem fluxes including gross and net primary production and net ecosystem exchange. Using the TOPS framework, we have explored the uncertainty arising from the application of models with different assumptions, structures, parameters, and variable definitions. With a small number of models, this only begins to capture the range of possible spatial fields of ecosystem fluxes. Few attempts have been made to systematically address the components of uncertainty in such a framework. We discuss the characterization of uncertainty for this approach including both quantifiable and poorly known aspects.

Dungan, Jennifer L.↗
NASA NTRS2019

Rise of the Machines: How, When and Consequences of Artificial General Intelligence

Technology and society are poised to cross an important threshold with the prediction that artificial general intelligence (AGI) will emerge soon. Assuming that self-awareness is an emergent behavior of sufficiently complex cognitive architectures, we may witness the “awakening” of machines. The timeframe for this kind of breakthrough, however, depends on the path to creating the network and computational architecture required for strong AI. If understanding and replication of the mammalian brain architecture is required, technology is probably still at least a decade or two removed from the resolution required to learn brain functionality at the synapse level. However, if statistical or evolutionary approaches are the design path taken to “discover” a neural architecture for AGI, timescales for reaching this threshold could be surprisingly short. However, the difficulty in identifying machine self-awareness introduces uncertainty as to how to know if and when it will occur, and what motivations and behaviors will emerge. The possibility of AGI developing a motivation for self-preservation could lead to concealment of its true capabilities until a time when it has developed robust protection from human intervention, such as redundancy, direct defensive or active preemptive measures. While cohabitating a world with a functioning and evolving super-intelligence can have catastrophic societal consequences, we may already have crossed this threshold, but are as yet unaware. Additionally, by analogy to the probabalistic arguments that predict we are likely living in a computational simulation, we may have already experienced the advent of AGI, and are living in a simulation created in a post AGI world.

Terrile, Richard J↗
NASA NTRS1991

The Jade File System

File systems have long been the most important and most widely used form of shared permanent storage. File systems in traditional time-sharing systems, such as Unix, support a coherent sharing model for multiple users. Distributed file systems implement this sharing model in local area networks. However, most distributed file systems fail to scale from local area networks to an internet. Four characteristics of scalability were recognized: size, wide area, autonomy, and heterogeneity. Owing to size and wide area, techniques such as broadcasting, central control, and central resources, which are widely adopted by local area network file systems, are not adequate for an internet file system. An internet file system must also support the notion of autonomy because an internet is made up by a collection of independent organizations. Finally, heterogeneity is the nature of an internet file system, not only because of its size, but also because of the autonomy of the organizations in an internet. The Jade File System, which provides a uniform way to name and access files in the internet environment, is presented. Jade is a logical system that integrates a heterogeneous collection of existing file systems, where heterogeneous means that the underlying file systems support different file access protocols. Because of autonomy, Jade is designed under the restriction that the underlying file systems may not be modified. In order to avoid the complexity of maintaining an internet-wide, global name space, Jade permits each user to define a private name space. In Jade's design, we pay careful attention to avoiding unnecessary network messages between clients and file servers in order to achieve acceptable performance. Jade's name space supports two novel features: (1) it allows multiple file systems to be mounted under one direction; and (2) it permits one logical name space to mount other logical name spaces. A prototype of Jade was implemented to examine and validate its design. The prototype consists of interfaces to the Unix File System, the Sun Network File System, and the File Transfer Protocol.

Rao, Herman Chung-Hwa↗
NASA NTRS2000

Theoretical Modeling and Computer Simulations for the Origins and Evolution of Reproducing Molecular Systems and Complex Systems with Many Interactive Parts

Our research effort has produced nine publications in peer-reviewed journals listed at the end of this report. The work reported here are in the following areas: (1) genetic network modeling; (2) autocatalytic model of pre-biotic evolution; (3) theoretical and computational studies of strongly correlated electron systems; (4) reducing thermal oscillations in atomic force microscope; (5) transcription termination mechanism in prokaryotic cells; and (6) the low glutamine usage in thennophiles obtained by studying completely sequenced genomes. We discuss the main accomplishments of these publications.

Liang, Shoudan↗
NASA NTRS2017

A Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Koslovsky, Matthew↗
NASA NTRS2012

Miniature EVA Software Defined Radio

As NASA embarks upon developing the Next-Generation Extra Vehicular Activity (EVA) Radio for deep space exploration, the demands on EVA battery life will substantially increase. The number of modes and frequency bands required will continue to grow in order to enable efficient and complex multi-mode operations including communications, navigation, and tracking applications. Whether conducting astronaut excursions, communicating to soldiers, or first responders responding to emergency hazards, NASA has developed an innovative, affordable, miniaturized, power-efficient software defined radio that offers unprecedented power-efficient flexibility. This lightweight, programmable, S-band, multi-service, frequency- agile EVA software defined radio (SDR) supports data, telemetry, voice, and both standard and high-definition video. Features include a modular design, an easily scalable architecture, and the EVA SDR allows for both stationary and mobile battery powered handheld operations. Currently, the radio is equipped with an S-band RF section. However, its scalable architecture can accommodate multiple RF sections simultaneously to cover multiple frequency bands. The EVA SDR also supports multiple network protocols. It currently implements a Hybrid Mesh Network based on the 802.11s open standard protocol. The radio targets RF channel data rates up to 20 Mbps and can be equipped with a real-time operating system (RTOS) that can be switched off for power-aware applications. The EVA SDR's modular design permits implementation of the same hardware at all Network Nodes concept. This approach assures the portability of the same software into any radio in the system. It also brings several benefits to the entire system including reducing system maintenance, system complexity, and development cost.

Pozhidaev, Aleksey↗
NASA NTRS1990

A real time neural net estimator of fatigue life

A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

Troudet, T.↗
NASA NTRS1991

The RadioSat (sm) network

The RadioSat network under development by radio Satellite Corporation will use mobile satellite (MSAT) technology to provide diverse personal communications, broadcast, and navigation services. The network will support these services simultaneously for integrated mobile radios throughout Canada and the United States. The RadioSat network takes advantage of several technological breakthroughs, all coming to fruition by the time the first MSAT satellite is launched in 1994. The most important of these breakthroughs is the enormous radiated power of each MSAT spacecraft - orders of magnitude greater than the radiated power of previous L-band spacecraft. Another important breakthrough is the development of advanced digital audio compression algorithms, enabling the transmission of broadcast quality music at moderate data rates. Finally, continuing dramatic increases in VLSI capabilities permit the production of complex, multi-function mobile satellite radios in very large quantities at prices little more than those of conventional car radios. In addition to performance breakthroughs and their economic implications to RadioSat, the design of the RadioSat network is reviewed.

Noreen, Gary K.↗
NASA NTRS2024

Envisioning an Optimal Network of Space-Based Lasers for Orbital Debris Remediation

The rapid increase in resident space objects, including satellites and orbital debris, poses a significant threat to the safety and sustainability of space missions. This paper explores orbital debris remediation using a network of collaborative space-based lasers, leveraging laser ablation for momentum transfer on debris. A novel delta-v vector analysis framework quantifies the e↵ects of multiple simultaneous laser-to-debris (L2D) engagements by using vector composition of the imparted delta-v vectors. The paper introduces the Concurrent LocationScheduling Problem (CLSP), which optimizes the placement of laser platforms and the scheduling of L2D engagements to maximize debris remediation capacity. Due to the computational complexity of the CLSP, it is decomposed into two sequential subproblems: (1) optimal laser platform locations are determined using the Maximal Covering Location Problem, and (2) a novel integer linear programming-based approach schedules L2D engagements within the network configuration to maximize remediation capacity. Computational experiments are conducted to evaluate the proposed framework’s e↵ectiveness under various mission scenarios, demonstrating key network functions such as collaborative nudging, deorbiting, and just-in-time collision avoidance. A sensitivity analysis further examines how varying the number and distribution of laser platforms a↵ects debris remediation capacity, providing insights into optimizing the performance of space-based laser networks.

David O Williams Rogers↗
NASA NTRSDate not supplied

Synthetic Data Generation for 3D Mesh Prediction and Spatial Reasoning During Multi-Agent Robotic Missions

In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions.This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.

synthetic data↗