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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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13 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Method of Improving a Digital Image as a Function of its Dynamic Range

The present invention is a method of processing a digital image that is initially represented by digital data indexed to represent position on a display. The digital data is indicative of an intensity value I(sub i)(x,y) for each position (x,y) in each i-th spectral band. A classification of the image based on its dynamic range is then defined in each of the image's S spectral bands. The intensity value for each position in each i-th spectral band is adjusted to generate an adjusted intensity value for each position in each i-th spectral band in accordance with SIGMA (sup n)(sub n=1) W(sub n)(log I (sub i)(x,y) - log[I(sub i)(x,y)*F(sub n)(x,y)]), i=1,...,S where W(sub n) is a weighting factor, "*" is the convolution operator and S is the total number of unique spectral bands. For each n, the function F(sub n)(x,y) is a unique surround function applied to each position (x,y) and N is the total number of unique surround functions. Each unique surround function is scaled to improve some aspect of the digital image, e.g., dynamic range compression, color constancy, and lightness rendition. The adjusted intensity value to each position in each i-th spectral band of the image is then filtered with a filter function that is based on the dynamic range classification of the image.

Glenn A Woodell↗
NASA NTRS2005

Methods of Improving a Digital Image Having White Zones

The present invention is a method of processing a digital image that is initially represented by digital data indexed to represent positions on a display. The digital data is indicative of an intensity value I,(x,y) for each position (x,y) in each i-th spectral band. The intensity value for each position in each i-th spectral band is adjusted to generate an adjusted intensity value for each position in each i-th spectral band in accordance with SIGMA (sup N)(sub n=1)W(sub n)(log I(sub i)(x,y)-log[I(sub i)(x,y)*F(sub n)(x,y)]), i = 1,...,S where W(sub n) is a weighting factor, "*" is the convolution operator and S is the total number of unique spectral bands. For each n, the function F(sub n)(x,y) is a unique surround function applied to each position (x,y) and N is the total number of unique surround functions. Each unique surround function is scaled to improve some aspect of the digital image, e.g., dynamic range compression, color constancy, and lightness rendition. The adjusted intensity value for each position in each i-th spectral band of the image is then filtered with a filter function to generate a filtered intensity value R(sub i)(x,y). To Prevent graying of white zones in the image, the maximum of the original intensity value I(sub i)(x,y) and filtered intensity value R(sub i)(x,y) is selected for display.

Glenn A Woodell↗
NASA NTRS1999

Method of improving a digital image

A method of improving a digital image is provided. The image is initially represented by digital data indexed to represent positions on a display. The digital data is indicative of an intensity value I.sub.i (x,y) for each position (x,y) in each i-th spectral band. The intensity value for each position in each i-th spectral band is adjusted to generate an adjusted intensity value for each position in each i-th spectral band in accordance with ##EQU1## where S is the number of unique spectral bands included in said digital data, W.sub.n is a weighting factor and * denotes the convolution operator. Each surround function F.sub.n (x,y) is uniquely scaled to improve an aspect of the digital image, e.g., dynamic range compression, color constancy, and lightness rendition. The adjusted intensity value for each position in each i-th spectral band is filtered with a common function and then presented to a display device. For color images, a novel color restoration step is added to give the image true-to-life color that closely matches human observation.

Rahman, Zia-ur↗
NASA NTRS1973

Experimental estimation of the statistical parameters of vibrations of a shell (using a digital computer), 2

The statistical characteristics of complex variation amplitudes of shells excited by harmonic force sources in the sound frequency range are examined. Vibrations were measured with a multichannel unit, permitting the component values of a complex vibration amplitude for the i-th measurement point to be obtained at each measured point. An estimation was also made of the nature of the vibration field structure.

Kanayev, B. A.↗
NASA NTRS1976

Technique for atmospheric rate chemistry calculations

The possibility that predictions of atmospheric photochemistry/transport models are sensitive to uncertainties in reaction rates and other inputs stresses the need for rapid numerical integration schemes in rate photochemistry problems. Reducing the computational burden has a major merit in facilitating sensitivity studies to assess the effect of uncertainties on predicted ozone diminutions from NOx (NO + NO2) in the exhaust plume of SST engines. The paper discusses the validity of an algorithmic approach to integration of rate chemistry problems in combustion, developed by Rubel and Baronti for an approximate calculation of the production rate of the i-th chemical species involved. An analysis of two projected SST engines confirms the validity of the proposed algorithm. Because of the relative arithmetical simplicity, it may be easier to treat diffusion rate chemistry calculations using the Rubel and Baronti approximation than would be possible by other approaches.

Matloff, G. L.↗
NASA NTRS1990

Characterization of the tip field of a discrete dislocation pileup for the development of physically based micromechanics

It is shown, on the basis of calculations by Eshelby et al. (1951), Armstrong et al. (1966), and Chou and Li (1969), that a single parameter, such as the force on the leading dislocation (F), the crack extension force, or the stress intensity factor, is capable of characterizing uniquely the entire tip field of a discrete dislocation pileup, including the positions of mobile dislocations behind the locked leading dislocation at the tip. Conversely, the position of the i-th mobile dislocation X(i) is related to the value of F and is capable of characterizing the entire stress, strain, and displacement fields at the tip of a discrete dislocation pileup. If the interactions between dislocations are linear elastic, the measured positions of the mobile dislocations can be used to determine the value of F, which can then be used as a quantitative measure of the strength of a dislocation barrier resisting the propagation of a microslip or the nucleation of a microfracture.

Gao, Q.↗
NASA NTRS1995

Nonequilibrium transport in superconducting filaments

The step-like current-voltage characteristics of highly homogeneous single-crystalline tin and indium thin filaments has been measured. The length of the samples L approximately 1 cm was much greater than the nonequilibrium quasiparticle relaxation length Lambda. It was found that the activation of a successive i-th voltage step occurs at current significantly greater than the one derived with the assumption that the phase slip centers are weakly interacting on a scale L much greater than Lambda. The observation of 'subharmonic' fine structure on the voltage-current characteristics of tin filaments confirms the hypothesis of the long-range phase slip centers interaction.

Arutyunov, K. YU.↗
NASA NTRS1996

Multilevel Algorithm for Atmospheric Data Assimilation

A multiscale algorithm for the problem of optimal statistical interpolation of observed data has been developed. This problem includes the calculation of the vector of the 'analyzed' (best estimated) atmosphere flow field w(sup a) by the formula: w(sup a) = w(sup f) + P(sup f) H(sup T) y, where the quantity y is defined by the equation (H P(sup f) H(sup T) + R)y = w(sup o) - H w(sup f), using the given model forecast first guess w(sup f) and the vector of observations w(sup o); H is an interpolation operator from the regular grid to the observation network, P(sup f) is the forecast error covariance matrix, and R is the observation error covariance matrix. At this initial stage the case of univariate analysis of single level radiosonde height data is considered. The matrix R is assumed to be diagonal, and the matrix P(sup f) is assumed to be given by the formula P(sub ij)(sup f) = sigma(sub i)(sup f) mu(sub ij) sigma(sub j)(sub f), where mu(sub ij) is a smooth, decreasing function of the distance between the i-th and the j-th points. In this paper we describe a multiscale iterative process based on a multiresolution, simultaneous displacement technique and a localized variational calculation of iteration parameters.

Brandt, Achi↗
NASA NTRS1996

First-Order Approximation of the Ordered Binary-Symmetric Channel

This paper presents different results related to the ordering of a sequence of N received symbols with respect to their reliability measure, for BPSK transmission over the AWGN channel model. First, a tight approximation of Pe (i; N), the probability that the hard decision associated with the i-th symbol of the ordered sequence is in error, is derived. Then, it is shown that despite the fact that the random variables representing the noise at positions n 1(sub 1), n(sub 2), ..., n(sub j) of the ordering are no longer independent, the events of having a hard decision decoding error at these positions remain almost independent Pe (n(sub i), n2, ..., n(sub j); N), the probability that the hard decisions associated with the symbols at positions n(sub 1), n(sub 2), ..., n(sub j), in the ordered sequence are in error, is thus well approximated from each of the Pe (n(sub i): N), for i is a member of [1, j]. Finally, based on the independence of these events, the fully connected 2(sup N) -state BSC representing the channel after ordering is simplified by N independent time-shared 2-state BSC's. This new model allows one to easily and tightly approximate the capacity of the channel after ordering.

Fossorier, Marc P. C.↗
NASA NTRS2001

Linear and Order Statistics Combiners for Pattern Classification

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Tumer, Kagan↗
DOE OSTI2020

Materials Data on ThI2 by Materials Project

ThI2 is Molybdenite-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is two-dimensional and consists of four ThI2 sheets oriented in the (0, 0, 1) direction. Th is bonded in a 6-coordinate geometry to six equivalent I atoms. All Th–I bond lengths are 3.22 Å. I is bonded in a 3-coordinate geometry to three equivalent Th atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on ThI4 by Materials Project

ThI4 crystallizes in the monoclinic P2_1/c space group. The structure is two-dimensional and consists of one ThI4 sheet oriented in the (1, 0, 0) direction. Th4+ is bonded in a 8-coordinate geometry to eight I1- atoms. There are a spread of Th–I bond distances ranging from 3.15–3.33 Å. There are four inequivalent I1- sites. In the first I1- site, I1- is bonded in an L-shaped geometry to two equivalent Th4+ atoms. In the second I1- site, I1- is bonded in a water-like geometry to two equivalent Th4+ atoms. In the third I1- site, I1- is bonded in an L-shaped geometry to two equivalent Th4+ atoms. In the fourth I1- site, I1- is bonded in an L-shaped geometry to two equivalent Th4+ atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on ThI3 by Materials Project

ThI3 crystallizes in the orthorhombic Cccm space group. The structure is three-dimensional. there are three inequivalent Th sites. In the first Th site, Th is bonded in a body-centered cubic geometry to eight I atoms. There are four shorter (3.29 Å) and four longer (3.33 Å) Th–I bond lengths. In the second Th site, Th is bonded in a 8-coordinate geometry to eight I atoms. There are four shorter (3.25 Å) and four longer (3.27 Å) Th–I bond lengths. In the third Th site, Th is bonded in a 8-coordinate geometry to eight I atoms. There are a spread of Th–I bond distances ranging from 3.19–3.34 Å. There are four inequivalent I sites. In the first I site, I is bonded in a 2-coordinate geometry to two equivalent Th atoms. In the second I site, I is bonded in a 2-coordinate geometry to two equivalent Th atoms. In the third I site, I is bonded in a distorted trigonal non-coplanar geometry to three Th atoms. In the fourth I site, I is bonded in a distorted trigonal non-coplanar geometry to three Th atoms.

36 MATERIALS SCIENCE↗