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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Neural network post-processing of grayscale optical correlator

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

grayscale optical correlator↗
NASA NTRS2017

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent Surface Lambertian-Equivalent Reflectivity Calculations

Surface Lambertian-equivalent reflectivity (LER) is important for trace gas retrievals in the direct calculation of cloud fractions and indirect calculation of the air mass factor. Current trace gas retrievals use climatological surface LER's. Surface properties that impact the bidirectional reflectance distribution function (BRDF) as well as varying satellite viewing geometry can be important for retrieval of trace gases. Geometry Dependent LER (GLER) captures these effects with its calculation of sun normalized radiances (I/F) and can be used in current LER algorithms (Vasilkov et al. 2016). Pixel by pixel radiative transfer calculations are computationally expensive for large datasets. Modern satellite missions such as the Tropospheric Monitoring Instrument (TROPOMI) produce very large datasets as they take measurements at much higher spatial and spectral resolutions. Look up table (LUT) interpolation improves the speed of radiative transfer calculations but complexity increases for non-linear functions. Neural networks perform fast calculations and can accurately predict both non-linear and linear functions with little effort.

Geometry Dependent LER (GLER) capture↗
NASA NTRS1992

Fault detection and diagnosis using neural network approaches

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Kramer, Mark A.↗
NASA NTRS2005

Medial vestibular connections with the hypocretin (orexin) system

The mammalian medial vestibular nucleus (MVe) receives input from all vestibular endorgans and provides extensive projections to the central nervous system. Recent studies have demonstrated projections from the MVe to the circadian rhythm system. In addition, there are known projections from the MVe to regions considered to be involved in sleep and arousal. In this study, afferent and efferent subcortical connectivity of the medial vestibular nucleus of the golden hamster (Mesocricetus auratus) was evaluated using cholera toxin subunit-B (retrograde), Phaseolus vulgaris leucoagglutinin (anterograde), and pseudorabies virus (transneuronal retrograde) tract-tracing techniques. The results demonstrate MVe connections with regions mediating visuomotor and postural control, as previously observed in other mammals. The data also identify extensive projections from the MVe to regions mediating arousal and sleep-related functions, most of which receive immunohistochemically identified projections from the lateral hypothalamic hypocretin (orexin) neurons. These include the locus coeruleus, dorsal and pedunculopontine tegmental nuclei, dorsal raphe, and lateral preoptic area. The MVe itself receives a projection from hypocretin cells. CTB tracing demonstrated reciprocal connections between the MVe and most brain areas receiving MVe efferents. Virus tracing confirmed and extended the MVe afferent connections identified with CTB and additionally demonstrated transneuronal connectivity with the suprachiasmatic nucleus and the medial habenular nucleus. These anatomical data indicate that the vestibular system has access to a broad array of neural functions not typically associated with visuomotor, balance, or equilibrium, and that the MVe is likely to receive information from many of the same regions to which it projects.

Non-NASA Center↗
NASA NTRS2001

Effects of exposure to 56Fe particles on the acquisition of a conditioned place preference in rats

Exposure to low doses of 56Fe particles produces changes in neural function and behavior. The present experiments were designed to examine the effects of irradiation on the acquisition of a dopamine-mediated conditioned place preference (CPP). In the CPP procedure, rats are given an injection of the dopamine agonist amphetamine in one distinctive compartment and a saline injection in a different compartment of a three-compartment apparatus. Control rats develop a preference for the amphetamine-paired compartment. In contrast, rats exposed to 1 Gy of 56Fe particles fail to develop a similar preference. The results of the experiment indicate that exposure to low doses of heavy particles can disrupt the neural mechanisms that mediate the reinforcement of behavior.

Non-NASA Center↗
NASA NTRS1990

Neuronal plasticity in relation to long-duration spaceflight

Exposure to microgravity leads to a marked reduction in sensory-motor stimuli to the vestibular, proprioceptive and somatosensory systems. Long-duration missions, such as those proposed for a trip to Mars, may lead to significant changes in neural function. This paper presents results based on studies of sensory deafferentation of specific brain regions and detailed changes which occur in neuronal architecture. Data from these studies emphasize the need for further research related to sensory system deprivation and the development of new unique countermeasures for long-duration space flight.

Hillman, Dean E.↗
NASA NTRS1999

Some Behavioral Effects of Exposure to Low Doses of Fe-56 Particles

Future missions in space (such as a mission to Mars) will involve long-term travel beyond the magnetic field of the Earth. As a result, astronauts will be exposed to radiation qualities and doses that differ from those experienced in low earth orbit, including exposure to heavy particles, such as Fe-56, which are a component of cosmic rays. Although the hazards of exposure to heavy particles are often minimized, they can affect neural functioning, and as a consequence, behavior. Unless the effects of exposure to cosmic rays can somehow be reduced, their effects on the brain throughout long duration flights could be disastrous. In the extreme case, it is possible that the effects of cosmic rays on space travelers could result in symptomatology resembling that of Alzheimer's or Parkinson's diseases or of advancing age, including significant cognitive and/or motor impairments. Because successful operations in space depend in part on the performance capabilities of astronauts, such impairments could jeopardize their ability to satisfy mission requirements, as well as have long-term consequences on the health of astronauts. As such, understanding the nature and extent of this risk may be vital to the effective performance and possibly the survival of astronauts during future missions in space.

Rabin, Bernard M.↗
NASA NTRS2004

The effects of proton exposure on neurochemistry and behavior

Future space missions will involve long-term travel beyond the magnetic field of the Earth, where astronauts will be exposed to radiation hazards such as those that arise from galactic cosmic rays. Galactic cosmic rays are composed of protons, alpha particles, and particles of high energy and charge (HZE particles). Research by our group has shown that exposure to HZE particles, primarily 600 MeV/n and 1 GeV/n 56Fe, can produce significant alterations in brain neurochemistry and behavior. However, given that protons can make up a significant portion of the radiation spectrum, it is important to study their effects on neural functioning and on related performance. Therefore, these studies examined the effects of exposure to proton irradiation on neurochemical and behavioral endpoints, including dopaminergic functioning, amphetamine-induced conditioned taste aversion learning, and spatial learning and memory as measured by the Morris water maze. Male Sprague-Dawley rats received a dose of 0, 1.5, 3.0 or 4.0 Gy of 250 MeV protons at Loma Linda University and were tested in the different behavioral tests at various times following exposure. Results showed that there was no effect of proton irradiation at any dose on any of the endpoints measured. Therefore, there is a contrast between the insignificant effects of high dose proton exposure and the dramatic effectiveness of low dose (<0.1 Gy) exposures to 56Fe particles on both neurochemical and behavioral endpoints. Published by Elsevier Ltd on behalf of COSPAR.

Non-NASA Center↗
NASA NTRS2005

Coping with Trial-to-Trial Variability of Event Related Signals: A Bayesian Inference Approach

In electro-neurophysiology, single-trial brain responses to a sensory stimulus or a motor act are commonly assumed to result from the linear superposition of a stereotypic event-related signal (e.g. the event-related potential or ERP) that is invariant across trials and some ongoing brain activity often referred to as noise. To extract the signal, one performs an ensemble average of the brain responses over many identical trials to attenuate the noise. To date, h s simple signal-plus-noise (SPN) model has been the dominant approach in cognitive neuroscience. Mounting empirical evidence has shown that the assumptions underlying this model may be overly simplistic. More realistic models have been proposed that account for the trial-to-trial variability of the event-related signal as well as the possibility of multiple differentially varying components within a given ERP waveform. The variable-signal-plus-noise (VSPN) model, which has been demonstrated to provide the foundation for separation and characterization of multiple differentially varying components, has the potential to provide a rich source of information for questions related to neural functions that complement the SPN model. Thus, being able to estimate the amplitude and latency of each ERP component on a trial-by-trial basis provides a critical link between the perceived benefits of the VSPN model and its many concrete applications. In this paper we describe a Bayesian approach to deal with this issue and the resulting strategy is referred to as the differentially Variable Component Analysis (dVCA). We compare the performance of dVCA on simulated data with Independent Component Analysis (ICA) and analyze neurobiological recordings from monkeys performing cognitive tasks.

Ding, Mingzhou↗
NASA NTRS1990

Neural computation of arithmetic functions

An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.

Siu, Kai-Yeung↗
NASA NTRS1999

Classification of ion mobility spectra by functional groups using neural networks

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Non-NASA Center↗
NASA NTRS2007

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

Burken, John J.↗
NASA NTRS1991

Learning control of inverted pendulum system by neural network driven fuzzy reasoning: The learning function of NN-driven fuzzy reasoning under changes of reasoning environment

Whereas conventional fuzzy reasonings are associated with tuning problems, which are lack of membership functions and inference rule designs, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions by neural network is formulated. In the antecedent parts of the neural network driven fuzzy reasoning, the optimum membership function is determined by a neural network, while in the consequent parts, an amount of control for each rule is determined by other plural neural networks. By introducing an algorithm of neural network driven fuzzy reasoning, inference rules for making a pendulum stand up from its lowest suspended point are determined for verifying the usefulness of the algorithm.

Hayashi, Isao↗
NASA NTRS1993

Classifying multispectral data by neural networks

Several energy functions for synthesizing neural networks are tested on 2-D synthetic data and on Landsat-4 Thematic Mapper data. These new energy functions, designed specifically for minimizing misclassification error, in some cases yield significant improvements in classification accuracy over the standard least mean squares energy function. In addition to operating on networks with one output unit per class, a new energy function is tested for binary encoded outputs, which result in smaller network sizes. The Thematic Mapper data (four bands were used) is classified on a single pixel basis, to provide a starting benchmark against which further improvements will be measured. Improvements are underway to make use of both subpixel and superpixel (i.e. contextual or neighborhood) information in tile processing. For single pixel classification, the best neural network result is 78.7 percent, compared with 71.7 percent for a classical nearest neighbor classifier. The 78.7 percent result also improves on several earlier neural network results on this data.

Telfer, Brian A.↗
NASA NTRS1993

Functional approximation using artificial neural networks in structural mechanics

The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

Alam, Javed↗
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 NTRS2014

The Effects of Spaceflight and a Spaceflight Analog on Neurocognitive Perfonnance: 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 and following 70 days exposure to a spaceflight analog, head down tilt bedrest. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post intervention (spaceflight, bedrest). 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 will be 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. With the bedrest study, we will be able to determine the neural and neurocognitive effects of extended duration unloading, reduced sensory inputs, and increased cephalic fluid distribution. This will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe in the flight study. In this presentation I will discuss preliminary results from six participants who have undergone the bed rest protocol. These individuals show decrements in balance and functional mobility, and alterations in brain structure and function, in association with extended bed rest.

Seidler, R. D.↗
NASA NTRS2014

Protecting Neural Structures and Cognitive Function During Prolonged Space Flight by Targeting the Brain Derived Neurotrophic Factor Molecular Network

Brain derived neurotrophic factor (BDNF) is the main activity-dependent neurotrophin in the human nervous system. BDNF is implicated in production of new neurons from dentate gyrus stem cells (hippocampal neurogenesis), synapse formation, sprouting of new axons, growth of new axons, sprouting of new dendrites, and neuron survival. Alterations in the amount or activity of BDNF can produce significant detrimental changes to cortical function and synaptic transmission in the human brain. This can result in glial and neuronal dysfunction, which may contribute to a range of clinical conditions, spanning a number of learning, behavioral, and neurological disorders. There is an extensive body of work surrounding the BDNF molecular network, including BDNF gene polymorphisms, methylated BDNF gene promoters, multiple gene transcripts, varied BDNF functional proteins, and different BDNF receptors (whose activation differentially drive the neuron to neurogenesis or apoptosis). BDNF is also closely linked to mitochondrial biogenesis through PGC-1alpha, which can influence brain and muscle metabolic efficiency. BDNF AS A HUMAN SPACE FLIGHT COUNTERMEASURE TARGET Earth-based studies reveal that BDNF is negatively impacted by many of the conditions encountered in the space environment, including oxidative stress, radiation, psychological stressors, sleep deprivation, and many others. A growing body of work suggests that the BDNF network is responsive to a range of diet, nutrition, exercise, drug, and other types of influences. This section explores the BDNF network in the context of 1) protecting the brain and nervous system in the space environment, 2) optimizing neurobehavioral performance in space, and 3) reducing the residual effects of space flight on the nervous system on return to Earth

Schmidt, M. A.↗