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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases

We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that are conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. We have collected data on several crewmembers and preliminary findings will be presented. Eventual comparison to results from our parallel bed rest study will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe.

Seidler, R. D.↗
NASA NTRS2017

Effects of One Year of Spaceflight on Neurocognitive Function

It is known that spaceflight adversely affects human sensorimotor function. With interests in longer duration deep space missions it is important to understand microgravity dose-response relationships. NASA's One Year Mission project allows for comparison of the effects of one year in space with those seen in more typical six month missions to the International Space Station. In the Neuromapping project we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre- to post-spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad-ranging battery of sensory, motor, and cognitive assessments that are conducted pre-flight, during flight, and post-flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. With the one year mission we had one crewmember participate in all of the same measures pre-, per- and post-flight as in our ongoing study. During this presentation we will provide an overview of the magnitude of changes observed with our brain and behavioral assessments for the one year crewmember in comparison to participants that have completed our six month study to date.

Seidler, R. D.↗
NASA NTRS1991

Telerobotic rovers for extraterrestrial construction

The topics are presented in viewgraph form and include the following: fundamental concepts; advantages of modularity; modular robot; robot design; motor control system; simple manipulator; 4 degree of freedom manipulator; intermodule communication; network layout; positioning system--IR-TROP; IR TROP system design; testbed layout; modularized control; and centralized control. The objectives are the following: to design small modular robots; to test robotic cooperation and teleoperation; to develop modular control software; to develop intermodule communication network; to develop high accuracy positioning system; and to explore distributed algorithms for coordination.

Grasso, Chris↗
NASA NTRS2001

Nodal endoplasmic reticulum, a specialized form of endoplasmic reticulum found in gravity-sensing root tip columella cells

The endoplasmic reticulum (ER) of columella root cap cells has been postulated to play a role in gravity sensing. We have re-examined the ultrastructure of columella cells in tobacco (Nicotiana tabacum) root tips preserved by high-pressure freezing/freeze-substitution techniques to gain more precise information about the organization of the ER in such cells. The most notable findings are: the identification of a specialized form of ER, termed "nodal ER," which is found exclusively in columella cells; the demonstration that the bulk of the ER is organized in the form of a tubular network that is confined to a peripheral layer under the plasma membrane; and the discovery that this ER-rich peripheral region excludes Golgi stacks, vacuoles, and amyloplasts but not mitochondria. Nodal ER domains consist of an approximately 100-nm-diameter central rod composed of oblong subunits to which usually seven sheets of rough ER are attached along their margins. These domains form patches at the interface between the peripheral ER network and the ER-free central region of the cells, and they occupy defined positions within central and flanking columella cells. Over one-half of the nodal ER domains are located along the outer tangential walls of the flanking cells. Cytochalasin D and latrunculin A cause an increase in size and a decrease in numbers of nodal ER domains. We postulate that the nodal ER membranes locally modulate the gravisensing signals produced by the sedimenting amyloplasts, and that the confinement of all ER membranes to the cell periphery serves to enhance the sedimentability of the amyloplasts in the central region of columella cells.

NASA Discipline Cell Biology↗
NASA NTRS2017

Mesh Network Architecture for Enabling Inter-Spacecraft Communication

To enable communication between spacecraft operating in a formation or small constellation, a mesh network architecture was developed and tested using a time division multiple access (TDMA) communication scheme. The network is designed to allow for the exchange of telemetry and other data between spacecraft to enable collaboration between small spacecraft. The system uses a peer-to-peer topology with no central router, so that it does not have a single point of failure. The mesh network is dynamically configurable to allow for addition and subtraction of new spacecraft into the communication network. Flight testing was performed using an unmanned aerial system (UAS) formation acting as a spacecraft analogue and providing a stressing environment to prove mesh network performance. The mesh network was primarily devised to provide low latency, high frequency communication but is flexible and can also be configured to provide higher bandwidth for applications desiring high data throughput. The network includes a relay functionality that extends the maximum range between spacecraft in the network by relaying data from node to node. The mesh network control is implemented completely in software making it hardware agnostic, thereby allowing it to function with a wide variety of existing radios and computing platforms..

Becker, Christopher↗
NASA NTRS1987

Radio structure in the inner 1 arcsecond of NGC 1068

High-resolution VLA and VLBI observations of NGC 1068 at several different wavelengths are reported. These observations provide new information about the triple radio source in the central arcsec (75 pc) of the galaxy. The 21-cm European VLBI Network observations at 0.05-arcsec resolution detect a core of flux density 89 + or - 10 mJy. This core is dominated by a component less than 20 marcsec (1.5 pc) in diameter that has a brightness temperature of about 10 to the 8th K. Two more components may have been detected by the VLBI observations, one on either side of the compact core. At 0.07-arcsec resolution, the 1.3-cm VLA map of the central arcsec of NGC 1068 reveals a C-shaped radio morphology on scales from 10 to 50 pc. A comparison of the relative locations of the three components in the VLA and VLBI maps shows that the compact core in the VLBI map probably should be identified with the central component in the 1.3- and 2-cm VLA maps. If this identification is correct, the central component in the VLA maps is the actual nucleus of the galaxy. The core of the inner triple radio source may contain a significant flat-spectrum component. The presence of a compact core and the evidence for bending strengthen the argument that the inner triple radio source is produced by a mechanism similar to that operating in powerful radio galaxies. For a minimum-energy configuration, magnetic-field strengths of about 1 mG and energy densities of about 100 nerg/cu cm are found for the components of this source.

Ulvestad, James S.↗
NASA NTRS2011

Collaborative Clustering for Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Wagstaff. Loro :/↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
NASA NTRS2001

Networks for Autonomous Formation Flying Satellite Systems

The performance of three communications networks to support autonomous multi-spacecraft formation flying systems is presented. All systems are comprised of a ten-satellite formation arranged in a star topology, with one of the satellites designated as the central or "mother ship." All data is routed through the mother ship to the terrestrial network. The first system uses a TCP/lP over ATM protocol architecture within the formation the second system uses the IEEE 802.11 protocol architecture within the formation and the last system uses both of the previous architectures with a constellation of geosynchronous satellites serving as an intermediate point-of-contact between the formation and the terrestrial network. The simulations consist of file transfers using either the File Transfer Protocol (FTP) or the Simple Automatic File Exchange (SAFE) Protocol. The results compare the IF queuing delay, and IP processing delay at the mother ship as well as application-level round-trip time for both systems, In all cases, using IEEE 802.11 within the formation yields less delay. Also, the throughput exhibited by SAFE is better than FTP.

Knoblock, Eric J.↗
NASA NTRS2019

Cognitive Communications and Networking Technology Infusion Study Report

As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.

Knoblock, Eric J.↗
NASA NTRS1985

Communicating through deep space

NASA's Deep Space Network (DSN) consists of a worldwide set of communication stations and a central control facility in California, enabling communication with spacecraft thousands of millions of miles from earth. The stations have gone from 26 m diameter antennas operating at 960 MHz to 70 m diameter (by 1988) at 8400 MHz. The DSN provides exceptional performance in high gain steerable antennas, ultra-low noise receivers, high power transmitters, frequency and time standards, and precise radio metric data. Spacecraft missions envisaged in the 1990's for the continuing exploration of the Solar System include an array of increasingly complex visits to the inner planets, asteroids and comets and the outer planets. The Deep Space Network planned for the mid-1980s may not meet all the needs of these missions without substantial change. Deep space stations may require conversion to operation with beam waveguides, higher frequency and relative frequency stability of 10 to the -16th. A deep space relay station in earth orbit could permit operation at higher frequencies, with attendant higher performance. Long range planning to select the appropriate future network configurations and develop the technologies essential to their implementation is underway.

Smith, J. G.↗
NASA NTRS1993

Introduction to the LaRC central scientific computing complex

The computers and associated equipment that make up the Central Scientific Computing Complex of the Langley Research Center are briefly described. The electronic networks that provide access to the various components of the complex and a number of areas that can be used by Langley and contractors staff for special applications (scientific visualization, image processing, software engineering, and grid generation) are also described. Flight simulation facilities that use the central computers are described. Management of the complex, procedures for its use, and available services and resources are discussed. This document is intended for new users of the complex, for current users who wish to keep appraised of changes, and for visitors who need to understand the role of central scientific computers at Langley.

Shoosmith, John N.↗
NASA NTRS2008

Overview of the Smart Network Element Architecture and Recent Innovations

In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM.

Perotti, Jose M.↗
NASA NTRS2016

A Network Scheduling Model for Distributed Control Simulation

Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

distributed control↗
NASA NTRS1978

Submicrosecond comparisons of time standards via the Navigation Technology Satellites (NTS)

An interim demonstration was performed of the time transfer capability of the NAVSTAR GPS system using a single NTS satellite. Measurements of time difference (pseudo-range) are made from the NTS tracking network and at the participating observatories. The NTS network measurements are used to compute the NTS orbit trajectory. The central NTS tracking station has a time link to the Naval Observatory UTC (USNO,MC1) master clock. Measurements are used with the NTS receiver at the remote observatory, the time transfer value UTC (USNO,MC1)-UTC (REMOTE, VIA NTS) is calculated. Intercomparisons were computed using predicted values of satellite clock offset and ephemeus.

Buisson, J. A.↗
NASA NTRS1980

Frequency translating phase conjugation circuit for active retrodirective antenna array

An active retrodirective antenna array which has central phasing from a reference antenna element through a "tree" structured network of transmission lines utilizes a number of phase conjugate circuits (PCCs) at each node and a phase reference regeneration circuit (PRR) at each node except the initial node. Each node virtually coincides with an element of the array. A PCC generates the exact conjugate phase of an incident signal using a phase locked loop which combines the phases in an up converter, divides the sum by 2 and mixes the result with the phase in a down converter for phase detection. The PRR extracts the phase from the conjugate phase. Both the PCC and the PRR are not only exact but also free from mixer degeneracy.

Chernoff, R.↗
NASA NTRS1980

Low-frequency radio navigation for the Army's Mobile Automated Field Instrumentation System /MAFIS/

The Jet Propulsion Laboratory is engaged in the early phases of conceptual, preliminary design, and feasibility demonstration studies leading to determination of a design and implementation of the Army's proposed Mobile Automated Field Instrumentation System (MAFIS). An overview is provided of the current design concept for an experimental radio navigation subsystem to be implemented in the field as a part of the MAFIS study effort. Objectives of MAFIS include mobility, field-ruggedness and longevity of equipment, and low acquisition and life-cycle costs. The navigation subsystem comprises a 4-station radio transmitter network, plus the navigation receivers, network monitoring receivers and communications links, and interface to the command/control 'central' for transmitter status monitoring. Attention is given to accuracy goals, radio propagation effects, transmitter arrangement, radio-frequency allocations, position initialization and lane resolution, and transmitter and receiver functions.

Wallis, D. E.↗
NASA NTRS1984

Wave scheduling - Decentralized scheduling of task forces in multicomputers

Decentralized operating systems that control large multicomputers need techniques to schedule competing parallel programs called task forces. Wave scheduling is a probabilistic technique that uses a hierarchical distributed virtual machine to schedule task forces by recursively subdividing and issuing wavefront-like commands to processing elements capable of executing individual tasks. Wave scheduling is highly resistant to processing element failures because it uses many distributed schedulers that dynamically assign scheduling responsibilities among themselves. The scheduling technique is trivially extensible as more processing elements join the host multicomputer. A simple model of scheduling cost is used by every scheduler node to distribute scheduling activity and minimize wasted processing capacity by using perceived workload to vary decentralized scheduling rules. At low to moderate levels of network activity, wave scheduling is only slightly less efficient than a central scheduler in its ability to direct processing elements to accomplish useful work.

Van Tilborg, A. M.↗