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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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Resource inventories require samples with geographical scatter, sometimes not as widely spaced as would be hoped. A simple model of correlation over distances is used to create a minimum variance unbiased estimate population means. The fitting procedure is illustrated from data used to estimate Missouri corn acreage.
Time-sequential series of IUE spectra for ten F, G and K dwarfs were obtained in 1980 and 1981 to study the rotational dependence of chromospheric flux in the ultraviolet. An interactive computational method using unbiased estimators was developed to measure emission line fluxes free of arbitrary judgement concerning the behavior of the underlying spectrum and shapes of the line profiles. Due to the limited number of observational samples per star, we have used special techniques to analyze the sparsely and anharmonically sampled emission line flux data. Two different autocorrelation measures were computed for each emission line as a function of temporal frequency. Examples and results of this analysis now in progress are given for several stars.
The grouped optimal aggregation technique produces minimum variance, unbiased estimates of acreage and production for countries, zones (states), or any designated collection of acreage strata. It uses yield predictions, historical acreage information, and direct acreage estimate from satellite data. The acreage strata are grouped in such a way that the ratio model over historical acreage provides a smaller variance than if the model were applied to each individual stratum. An optimal weighting matrix based on historical acreages, provides the link between incomplete direct acreage estimates and the total, current acreage estimate.
A real-time algorithm for measuring three-dimensional movement of the human eye, especially torsional movement, is presented. As its input, the system uses images of the eyeball taken at video rate. The amount of horizontal and vertical movement is extracted using a pupil tracking technique. The torsional movement is then measured by computing the discrete cross-correlation function between the circular samples of successive images of the iris patterns and searching for the position of the peak of the function. A local least square interpolation around the peak of the cross-correlation function is used to produce nearly unbiased estimates of torsion angle with accuracy of about 3-4 arcmin. Accuracies of better than 0.03 deg are achievable in torsional measurement with SNR higher than 36 dB. Horizontal and vertical rotations of up to + or - 13 deg can occur simultaneously with torsion without introducing any appreciable error in the counterrolling measurement process.
In order that sub-lithospheric density variations be revealed with the geoid, the regional geoid anomalies associated with bathymetric variations must first be removed. Spectral techniques were used to generate a synthetic geoid by filtering the residual bathymetry assuming an Airy-type isostatic compensation model. An unbiased estimated of the admittances show that for region under study, no single compensation mechanism will explain all of the power in the geoid. Nevertheless, because topographic features are mainly coherent with the geoid, to first order an isostationally compensated lithosphere cut by major E-W fracture zones accounts for most of the power in the high degree and other SEASAT geoid in the Pacific.
The principal advantage of the HIRS cloud height retrieval is to provide an unbiased estimate of cloud height for optically thin, high cloud. This capability is employed to examine the relationship of cirrus visible reflectance to thermal IR emittance. By combining data from the HIRS-2 and AVHRR instruments on the NOAA operational satellites the proposed method attempts to improve the ability of existing satellite radiometers to recover information on middle-level and high-level clouds. This combination of data sets leads to an improved cloud recovery algorithm for the detection and analysis of cirrus clouds.
This paper addresses the statistical design of CMOS integrated circuits for improved parametric yield. The work uses the Monte Carlo technique of circuit simulation to obtain an unbiased estimation of the yield. A simple graphical analysis tool, the yield factor histogram, is presented. The yield factor histograms are generated by a new computer program called SPICENTER. Using the yield factor histograms, the most sensitive circuit parameters are noted, and their nominal values are changed to improve the yield. Two basic CMOS example circuits, one analog and one digital, are chosen and their designs are 'centered' to illustrate the use of the yield factor histograms for statistical circuit design.
Calculations of the exact Cramer-Rao Bound (CRB) for unbiased estimates of the mean frequency, signal power, and spectral width of Doppler radar/lidar signals (a Gaussian random process) are presented. Approximate CRB's are derived using the Discrete Fourier Transform (DFT). These approximate results are equal to the exact CRB when the DFT coefficients are mutually uncorrelated. Previous high SNR limits for CRB's are shown to be inaccurate because the discrete summations cannot be approximated with integration. The performance of an approximate maximum likelihood estimator for mean frequency approaches the exact CRB for moderate signal to noise ratio and moderate spectral width.
Constraints on the clustered mass density of the universe derived from the observed population mean intracluster gas fraction of x-ray clusters may be biased by reliance on a single-phase assumption for the thermodynamic structure of the intracluster medium (ICM). We propose a descriptive model for multiphase structure in which a spherically symmetric ICM contains isobaric density perturbations with a radially dependent variance. Fixing the x-ray emission and emission weighted temperature, we explore two independently observable signatures of the model in the parameter space. For bremsstrahlung dominated emission, the central Sunyaev-Zel'dovich (SZ) decrement in the multiphase case is increased over the single-phase case and multiphase x-ray spectra in the range 0.1-20 keV are flatter in the continuum and exhibit stronger low energy emission lines than their single-phase counterpart. We quantify these effects for a fiducial 10e8 K cluster and demonstrate how the combination of SZ and x-ray spectroscopy can be used to identify a preferred location in the plane of the model parameter space. From these parameters the correct value of mean intracluster gas fraction in the multiphase model results, allowing an unbiased estimate of clustered mass density to he recovered.
This study quantified architectural and population changes in the rat retinal vasculature after proton irradiation using stereology. A 100 MeV conformal proton beam delivered 8, 14, 20 and 28 Gy as single and split doses to the whole eye. The vascular networks were prepared from retinal digests. Stereological methods were used to obtain the area of the retina and unbiased estimates of microvessel/artery/vein endothelial, pericyte and smooth muscle population, and vessel length. The retinal area increased progressively in the unirradiated, age-matched controls and in the retinas irradiated with 8 and 14 Gy, indicating uniform progressive retinal growth. No growth occurred after 20 and 28 Gy. Regression analysis of total endothelial cell number in all vessels (arteries, veins and capillaries) after irradiation documented a progressive time- and dose-dependent cell loss occurring over 15 to 24 months. The difference from controls was significant (P<0.01) after 28 Gy given in single and split doses and after 20 Gy given as a split dose (P<0.05). Total vessel length in microvessel was significantly shortened at 20 and 28 Gy compared to that of controls (P<0.05). No evident dose recovery was observed in the endothelial populations after split doses. At 10 Gy, the rate of endothelial cell loss, a dose parameter used to characterize the time- and dose-dependent loss of the endothelial population, was doubled.
Scattered data interpolation is a problem of interest in numerous areas such as electronic imaging, smooth surface modeling, and computational geometry. Our motivation arises from applications in geology and mining, which often involve large scattered data sets and a demand for high accuracy. The method of choice is ordinary kriging. This is because it is a best unbiased estimator. Unfortunately, this interpolant is computationally very expensive to compute exactly. For n scattered data points, computing the value of a single interpolant involves solving a dense linear system of size roughly n x n. This is infeasible for large n. In practice, kriging is solved approximately by local approaches that are based on considering only a relatively small'number of points that lie close to the query point. There are many problems with this local approach, however. The first is that determining the proper neighborhood size is tricky, and is usually solved by ad hoc methods such as selecting a fixed number of nearest neighbors or all the points lying within a fixed radius. Such fixed neighborhood sizes may not work well for all query points, depending on local density of the point distribution. Local methods also suffer from the problem that the resulting interpolant is not continuous. Meyer showed that while kriging produces smooth continues surfaces, it has zero order continuity along its borders. Thus, at interface boundaries where the neighborhood changes, the interpolant behaves discontinuously. Therefore, it is important to consider and solve the global system for each interpolant. However, solving such large dense systems for each query point is impractical. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. The problems arise from the fact that the covariance functions that are used in kriging have global support. Our implementations combine, utilize, and enhance a number of different approaches that have been introduced in literature for solving large linear systems for interpolation of scattered data points. For very large systems, exact methods such as Gaussian elimination are impractical since they require 0(n(exp 3)) time and 0(n(exp 2)) storage. As Billings et al. suggested, we use an iterative approach. In particular, we use the SYMMLQ method, for solving the large but sparse ordinary kriging systems that result from tapering. The main technical issue that need to be overcome in our algorithmic solution is that the points' covariance matrix for kriging should be symmetric positive definite. The goal of tapering is to obtain a sparse approximate representation of the covariance matrix while maintaining its positive definiteness. Furrer et al. used tapering to obtain a sparse linear system of the form Ax = b, where A is the tapered symmetric positive definite covariance matrix. Thus, Cholesky factorization could be used to solve their linear systems. They implemented an efficient sparse Cholesky decomposition method. They also showed if these tapers are used for a limited class of covariance models, the solution of the system converges to the solution of the original system. Matrix A in the ordinary kriging system, while symmetric, is not positive definite. Thus, their approach is not applicable to the ordinary kriging system. Therefore, we use tapering only to obtain a sparse linear system. Then, we use SYMMLQ to solve the ordinary kriging system. We show that solving large kriging systems becomes practical via tapering and iterative methods, and results in lower estimation errors compared to traditional local approaches, and significant memory savings compared to the original global system. We also developed a more efficient variant of the sparse SYMMLQ method for large ordinary kriging systems. This approach adaptively finds the correct local neighborhood for each query point in the interpolation process.
A new semi-empirical model is proposed for extracting the quality (Q) factors of arrays of superconducting microwave kinetic inductance detectors (MKIDs). The determination of the total internal and coupling Q factors enables the computation of the loss in the superconducting transmission lines. The method used allows the simultaneous analysis of multiple interacting discrete resonators with the presence of a complex spectral baseline arising from reflections in the system. The baseline removal allows an unbiased estimate of the device response as measured in a cryogenic instrumentation setting.
Multi Model Monte Carlo with Python (\mxmc {}) is a software package developed as a general capability for computing the statistics of outputs from an expensive, high-fidelity model by leveraging faster, low-fidelity models for speedup. Motivated by uncertainty propagation problems where classical Monte Carlo (MC) simulation is computationally intractable, various multi-model MC approaches have recently emerged that yield unbiased estimators with significantly reduced variance relative to MC for the same cost. These existing methods include multi-level Monte Carlo (MLMC), multi-fidelity Monte Carlo (MFMC), and approximate control variates (ACV). Given a fixed computational budget and a collection of models with varying cost/accuracy, each method seeks a sample allocation strategy across the models that results in an estimator with optimal variance reduction. \mxmc {} is a versatile tool that enables convenient access to many existing multi-model MC approaches within one modular and extensible package. With \mxmc {}, users can easily compare existing methods to determine the best choice for their particular problem, while developers have a basis for implementing and sharing new variance reduction approaches. This report introduces the \mxmc {} software, providing a summary of the problem-solving workflow for users as well as a brief overview of the code layout for developers.
Abstract—Frequency domain (FD) multi-user detection (MUD) and precoding has been shown to be an effective means of approaching the theoretical per-user capacity for single carrier modulation (SCM) schemes in massive MIMO scenarios with highly dispersive channels. When a cyclic prefix is added to the SCM waveform, the circulant structures of the resulting convolutional channel matrix allows for relatively simple expressions for the FD detection and precoding. In this paper, we further refine the computationally efficient minimum mean squared error (MMSE) FD-MUD technique in a time-division duplexing (TDD) massive MIMO setup by pre-computing the scale factor needed to produce an unbiased estimate. The resulting scale factor and the matrix inverse that was calculated for the uplink is then reused for the downlink to form a computationally efficient precoding scheme with superior performance compared to zero-forcing (ZF) precoding. Performance analysis is provided and confirmed through simulation results.
The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.
Evaluations of remote sensing procedures developed specifically to estimate non-U.S. spring small grains area show accuracies of less than 10 percent relative difference to reference statistics for North Dakota in 1978 and good comparison with 9000 square miles of observations over four states and Saskatchewan, Canada during the years 1976-79. Processing a 5 x 6-nautical-mile sample site requires a few minutes manual time and a few minutes central processing unit time on an AS-3000 computer. Evaluations of summer crop, corn, and soybeans area estimates show unbiased summer crops estimates in the U.S. central corn belt but significant bias in one of two years for area estimates of corn and soybeans. Based on results to date, a highly automated corn/sorghum/soybean area estimation procedure should be achieved that is applicable to Argentina.
An on-line parameter identification method is presented. The method is based on a recursive formulation of the maximum likelihood method, with a significant modification on the gains of the state estimator. In the conventional maximum likelihood method, the Kalman gains are used in the state estimator. This produces unbiased, minimum variance parameter estimates in the presence of process noise and measurement noise, but it also slows the convergence rate when the identification is done on-line. Here we suggest choosing the gains to maximize a measure of the sensitivities of the state estimates to parameter variations. One such criterion is to minimize the trace of the inverse information matrix. This increases the convergence rate significantly. After one or two time constants, the gains are switched to the Kalman values to assure unbiased, minimum-variance estimates. The state estimate will initially be nonoptimal, and may not be adequate for control purposes. In this case, a parallel Kalman filter which uses the identifier's parameter estimates can be used. This method is applied here for the identification of a simple first-order system, and for the identification of short-period stability derivatives of an F-8 aircraft from simulated data.
A simple power law model consisting of a single spectral index, a is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at the knee energy, E(sub k), to a steeper spectral index alpha(sub 2) greater than alpha(sub 1) above E(sub k). The Maximum likelihood (ML) procedure was developed for estimating the single parameter alpha(sub 1) of a simple power law energy spectrum and generalized to estimate the three spectral parameters of the broken power law energy spectrum from simulated detector responses and real cosmic-ray data. The statistical properties of the ML estimator were investigated and shown to have the three desirable properties: (P1) consistency (asymptotically unbiased). (P2) efficiency asymptotically attains the Cramer-Rao minimum variance bound), and (P3) asymptotically normally distributed, under a wide range of potential detector response functions. Attainment of these properties necessarily implies that the ML estimation procedure provides the best unbiased estimator possible. While simulation studies can easily determine if a given estimation procedure provides an unbiased estimate of the spectra information, and whether or not the estimator is approximately normally distributed, attainment of the Cramer-Rao bound (CRB) can only he ascertained by calculating the CRB for an assumed energy spectrum-detector response function combination, which can be quite formidable in practice. However. the effort in calculating the CRB is very worthwhile because it provides the necessary means to compare the efficiency of competing estimation techniques and, furthermore, provides a stopping rule in the search for the best unbiased estimator. Consequently, the CRB for both the simple and broken power law energy spectra are derived herein and the conditions under which they are attained in practice are investigated. The ML technique is then extended to estimate spectra information from an arbitrary number of astrophysics data sets produced by vastly different science instruments. This theory and its successful implementation will facilitate the interpretation of spectral information from multiple astrophysics missions and thereby permit the derivation of superior spectral parameter estimates based on the combination of data sets.