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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Frequency Agile Transceiver for Advanced Vehicle Data Links

Emerging and next-generation test instrumentation increasingly relies on network communication to manage complex and dynamic test scenarios, particularly for uninhabited autonomous systems. Adapting wireless communication infrastructure to accommodate challenging testing needs can benefit from reconfigurable radio technology. Frequency agility is one characteristic of reconfigurable radios that to date has seen only limited progress toward programmability. This paper overviews an ongoing project to validate a promising chipset that performs conversion of RF signals directly into digital data for the wireless receiver and, for the transmitter, converts digital data into RF signals. The Software Configurable Multichannel Transceiver (SCMT) enables four transmitters and four receivers in a single unit, programmable for any frequency band between 1 MHz and 6 GHz.

Freudinger, Lawrence C.↗
NASA NTRS2012

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

photon counting detection↗
NASA NTRS2011

Experimental Evaluation of Optically Polished Aluminum Panels on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground?based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation is to polish the aluminum reflector panels of 34?meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by state of?the?art polished aluminum panels. Theoretical analyses of receiving antenna pointing, temporal synchronization and data detection have been addressed in previous papers. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. Two polished aluminum panels (a standard DSN panel polished to high reflectance, and a custom designed aluminum panel with much better surface quality) have been mounted on the 34 meter research antenna at Deep?Space Station 13 (DSS?13), and a remotely controlled CCD camera with a large CCD sensor in a weather?proof container has been installed next to the subreflector, pointed directly at the custom polished panel. The point?spread function (PSF) generated by the Vertex polished panel has been determined to be smaller than the sensor of the CCD camera, hence a detailed picture of the PSF can be obtained every few seconds, and the sensor array data processed to determine the center of the intensity distribution. In addition to estimating the center coordinates, expected communications performance can also been evaluated with the recorded data. The results of preliminary pointing experiments with the Vertex polished panel receiver using the planet Jupiter to simulate the PSF generated by a deep?space optical transmitter are presented and discussed in this paper.

optical receiver↗
NASA NTRS2019

Streamlining Pre-Launch Support for Missions with Highly Variable Trajectories

As the Goddard Space Flight Center's (GSFC) Flight Dynamics Facility (FDF) continues to ensure mission success in support of Human Space Flight (HSF) and Launch Vehicle (LV) missions, it continues to improve on different areas of mission support. The FDF processes numerous different types of trajectory profiles, and, for each trajectory, the FDF provides multiple acquisition data vectors to the Space Network (SN) White Sands Complex (WSC) for further processing and uplinking to the SN's Tracking and Data Relay Satellites (TDRSs), tracking the vehicle and maintaining communications throughout required support. Some trajectories target a specific orbit plane through Right Ascension of the Ascending Node (RAAN), or yaw, steering, which can yield to high variability across the launch window. Because of this, there can be a large number of trajectory profiles to process and generate acquisition data for that ensure TDRS is accurately pointing to the launch vehicle. Processing many trajectory profiles by FDF and WSC increases the complexity and level of effort associated with support. FDF employs a legacy boundary on the range difference between trajectories, which determines the number of discrete trajectory cases required to maintain communication with TDRS over the full launch window. An analysis was performed to determine a new boundary, taking into consideration current support requirements. The results show the boundary can be expanded beyond the currently employed legacy value without an impact on communication. In doing so, the number of trajectory profiles processed can be reduced, reducing the complexity and level of effort required, with no impact to mission safety.

Flight Operations↗
NASA NTRS1993

Using Manufacturing Message Specification for Monitor and Control at Venus

A new approach to the monitor and control of spacecraft tracking systems has been developedbased on the Open Systems Interconnection (OSI) process control standard Manufacturing MessageSpecification (MMS). Station subsystems are interconnected using commercial MMS software tosupport interprocessor communication across a Local Area Network (LAN). Significant cost savingsare realized through the incorporation of commercial Software Control and Data Acquisition(SCADA) packages to support the operator interface. A pilot system has been installed and is inoperation at the Deep Space Network (DSN) experimental Venus complex. The DSN operates a new34-meter beam waveguide antenna (DSS-13) at the Goldstone Venus complex in California. The complex composed of various pieces of equipment with some equipment under computer automated control...

Urista, J.↗
NASA NTRS2023

Star-Exoplanet Interactions: A Growing Interdisciplinary Field in Heliophysics

Traditionally, heliophysics is characterized as the study of the near-Earth space environment, where plasmas and neutral gases originating from the Earth, the Sun, and other solar system bodies interact in ways that are detectable only through in-situ or close-range (usually within ∼10 AU) remote sensing. As a result, heliophysics has data from the space environment around a handful of solar system objects, in particular the Sun and Earth. Comparatively, astrophysics has data from an extensive array of objects, but is more limited in temporal, spatial, and wavelength information from any individual object. Thus, our understanding of planetary space environments as a complex, multi-dimensional network of specific interacting systems may in the past have seemed to have little to do with the highly diverse space environments detected through astrophysical methods. Recent technological advances have begun to bridge this divide. Exoplanetary studies are opening up avenues to study planetary environments beyond our solar system, with missions like Kepler, TESS, and JWST, along with increasing capabilities of ground-based observations. At the same time, heliophysics studies are pushing beyond the boundaries of our heliosphere with Voyager, IBEX, and the future IMAP mission. The interdisciplinary field of star-exoplanet interactions is a critical, growing area of study that enriches heliophysics. A multidisciplinary approach to heliophysics enables us to better understand universal processes that operate in diverse environments, as well as the evolution of our solar system and extreme space weather. The expertise, data, theory, and modeling tools developed by heliophysicists are crucial in understanding the space environments of exoplanets, their host stars, and their potential habitability. The mutual benefit that heliophysics and exoplanetary studies offer each other depends on strong, continuing solar system-focused and Earth-focused heliophysics studies. The heliophysics discipline requires new targeted funding to support inter-divisional opportunities, including small multi-disciplinary research projects, large collaborative research teams, and observations targeting the heliophysics of planetary and exoplanet systems. Here we discuss areas of heliophysics-relevant exoplanetary research, observational opportunities and challenges, and ways to promote the inclusion of heliophysics within the wider exoplanetary community.

heliophysics↗
NASA NTRS2020

Search Tree Pruning for Progressive Neural Architecture Search

Our neural architecture search algorithm progressively searches a tree of neural network architectures. Child nodes are created by inserting new layers determined by a transition graph into a parent network up to a maximum depth and pruned when performance is worse than its parent. This increases efficiency but makes the algorithm greedy. Simpler networks are successfully found before more complex ones that can achieve benchmark performance similar to other top-performing networks.

Deanna Flynn↗
NASA NTRSDate not supplied

Emulated Spacecraft Communication Testbed for Evaluating Cognitive Networking Technology

The ability to emulate the full space protocol stack is an essential aspect required to evaluate and mature cognitive communication capabilities. The interaction between the physical layer and network layers is key to developing network optimizations for a dynamic and complex environment. We present a laboratory testbed for the evaluation of cognitive radio and networking techniques applied to space communications. The testbed is a high fidelity, flight-like hardware testbed consisting of software-defined radios, channel emulators, modems, and orbital analysis and scheduling software. The testbed uses RF links with signal quality, propagation delay, and Doppler effects driven by orbital mechanics simulations of emulated spacecraft. Our framework enables control of link bidirectionality, data rates, and interference sources. In addition to hardware radio nodes, the testbed can incorporate virtualized emulated nodes for larger and more challenging network scenarios. Our approach to a cognitive communication system uses delay tolerant networking (DTN) to mitigate the challenges of the space environment. While many DTN networks use only preplanned schedules, our system uses User-Initiated Service (UIS) to dynamically schedule service providers. Software-defined radio allows the system to adapt to a variety of service providers. Integration of DTN, UIS, and software-defined radio technologies provides a framework for the implementation of a cognitive communication system. This paper describes the testbed capabilities, network emulation approach, component integration, and initial end-to-end testing results.

cognitive radio↗
NASA NTRS1996

Experiences in Interagency and International Interfaces for Mission Support

The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.

Dell, G. T.↗
NASA NTRS2000

Modeling the Normal and Neoplastic Cell Cycle with 'Realistic Boolean Genetic Networks': Their Application for Understanding Carcinogenesis and Assessing Therapeutic Strategies

In this paper we show how Boolean genetic networks could be used to address complex problems in cancer biology. First, we describe a general strategy to generate Boolean genetic networks that incorporate all relevant biochemical and physiological parameters and cover all of their regulatory interactions in a deterministic manner. Second, we introduce 'realistic Boolean genetic networks' that produce time series measurements very similar to those detected in actual biological systems. Third, we outline a series of essential questions related to cancer biology and cancer therapy that could be addressed by the use of 'realistic Boolean genetic network' modeling.

Szallasi, Zoltan↗
NASA NTRS1981

Networks consolidation program: Maintenance and Operations (M&O) staffing estimates

The Mark IV-A consolidate deep space and high elliptical Earth orbiter (HEEO) missions tracking and implements centralized control and monitoring at the deep space communications complexes (DSCC). One of the objectives of the network design is to reduce maintenance and operations (M&O) costs. To determine if the system design meets this objective an M&O staffing model for Goldstone was developed which was used to estimate the staffing levels required to support the Mark IV-A configuration. The study was performed for the Goldstone complex and the program office translated these estimates for the overseas complexes to derive the network estimates.

Goodwin, J. P.↗
NASA NTRS1991

Radar signal categorization using a neural network

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Anderson, James A.↗
NASA NTRS1992

Using neural networks and Dyna algorithm for integrated planning, reacting and learning in systems

The traditional AI answer to the decision making problem for a robot is planning. However, planning is usually CPU-time consuming, depending on the availability and accuracy of a world model. The Dyna system generally described in earlier work, uses trial and error to learn a world model which is simultaneously used to plan reactions resulting in optimal action sequences. It is an attempt to integrate planning, reactive, and learning systems. The architecture of Dyna is presented. The different blocks are described. There are three main components of the system. The first is the world model used by the robot for internal world representation. The input of the world model is the current state and the action taken in the current state. The output is the corresponding reward and resulting state. The second module in the system is the policy. The policy observes the current state and outputs the action to be executed by the robot. At the beginning of program execution, the policy is stochastic and through learning progressively becomes deterministic. The policy decides upon an action according to the output of an evaluation function, which is the third module of the system. The evaluation function takes the following as input: the current state of the system, the action taken in that state, the resulting state, and a reward generated by the world which is proportional to the current distance from the goal state. Originally, the work proposed was as follows: (1) to implement a simple 2-D world where a 'robot' is navigating around obstacles, to learn the path to a goal, by using lookup tables; (2) to substitute the world model and Q estimate function Q by neural networks; and (3) to apply the algorithm to a more complex world where the use of a neural network would be fully justified. In this paper, the system design and achieved results will be described. First we implement the world model with a neural network and leave Q implemented as a look up table. Next, we use a lookup table for the world model and implement the Q function with a neural net. Time limitations prevented the combination of these two approaches. The final section discusses the results and gives clues for future work.

Lima, Pedro↗
NASA NTRS1979

Modeling of polymer networks for application to solid propellant formulating

Methods for predicting the network structural characteristics formed by the curing of pourable elastomers were presented; as well as the logic which was applied in the development of mathematical models. A universal approach for modeling was developed and verified by comparison with other methods in application to a complex system. Several applications of network models to practical problems are described.

Marsh, H. E.↗
NASA NTRS1965

Numerical operator method

Algebraic solutions of difference equations - stability of sampled-data control systems - numerical method for determining transients in electric systems excited by complex signals

ELECTRIC NETWORK↗
NASA NTRS2020

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process.

Lee, Ritchie↗
NASA NTRS1994

Kinematic structure of the 30 Doradus giant H II region

We have used the echelle CCD spectrograph on the Cerro Tololo Inter-American Observatory (CTIO) 4 m telescope to map the nebular velocity field in the 30 Doradus giant H II region. The kinematics of 30 Dor are very complex. The outer regions are charaterized by a smooth velocity field, but its turbulent velocity, 30-40 km/s Full Width Half Maximum (FWHM), is considerably higher than those in most smaller H II regions. In the central 9 min core, multiple velocity components are observed at most positions. The velocity field is dominated by a large number of expanding structures, ranging in size from 1 to 100 pc and expansion velocities of 20-200 km/s, and often organized into large hierarchical networks. The integral of these complex expanding structures in 30 Dor produces a surprisingly simple profile with a broad Gaussian core and faint extended wings. Several fast-expanding shells, with diameters of 2-20 pc, expansion velocities of 100-300 km/s, and kinetic energies of 0.5-10 x 10(exp 50) ergs have been identified. The large fast-expanding shells and networks are coincident with extended X-ray sources and are probably associated with supernova remnants embedded in supershells produced by the combined effects of stellar winds and supernovae from OB associations. We have used the intensity-calibrated echelle spectra to determine the basic physical and dynamical properties of the kinematic features in 30 Dor. The expanding shells contain roughly half of the kinetic energy in the 30 Dor complex, and this energy is several times higher than the gravitational binding energy of the region. The energetic requirements of the gas are consistent with the observed stellar content of 30 Dor, if the gas is accelerated by a combination of stellar winds and supernovae. Extrapolating the current energy injection rate in the nebula over the lifetime of the OB complex suggest that 30 Dor and its vicinity will evolve into a supergiant shell as seen in the LMC and other nearby galaxies.

Chu, You-Hua↗
NASA NTRS1997

Emulating the Visual Receptive Field Properties of MST Neurons with a Template Model of Heading Estimation

We have previously proposed a computational neural-network model by which the complex patterns of retinal image motion generated during locomotion (optic flow) can be processed by specialized detectors acting as templates for specific instances of self-motion. The detectors in this template model respond to global optic flow by sampling image motion over a large portion of the visual field through networks of local motion sensors with properties similar to neurons found in the middle temporal (MT) area of primate extrastriate visual cortex. The model detectors were designed to extract self-translation (heading), self-rotation, as well as the scene layout (relative distances) ahead of a moving observer, and are arranged in cortical-like heading maps to perform this function. Heading estimation from optic flow has been postulated by some to be implemented within the medial superior temporal (MST) area. Others have questioned whether MST neurons can fulfill this role because some of their receptive-field properties appear inconsistent with a role in heading estimation. To resolve this issue, we systematically compared MST single-unit responses with the outputs of model detectors under matched stimulus conditions. We found that the basic physiological properties of MST neurons can be explained by the template model. We conclude that MST neurons are well suited to support heading estimation and that the template model provides an explicit set of testable hypotheses which can guide future exploration of MST and adjacent areas within the primate superior temporal sulcus.

Perrone, John A.↗