The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Cheng Xiaogang - One of the best experts on this subject based on the ideXlab platform.
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approximation model of piecewise Stationary Stochastic Process autocorrelation function
Journal of Computer Applications, 2012Co-Authors: Cheng XiaogangAbstract:In order to deal with the frequently encountered non-Stationary random signals in signal Processing,they can be divided into sub-Stationary random signals,and autocorrelation function can be used to reflect the essential characteristics of sub-Stationary signals.The computation of piecewise Stationary Stochastic Process autocorrelation function was discussed.In order to reduce the amount of calculation and errors of the existing function models,a new model to approximate autocorrelation function of piecewise Stationary Stochastic Process was proposed in this paper.The computer simulation shows that the model can effectively approximate autocorrelation function.The computing speed is faster,and the errors are much fewer and smoother.Applying the model to the restoration of blurred digital images,a very good restoration effect can be got.
Marcus Pivato - One of the best experts on this subject based on the ideXlab platform.
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building a Stationary Stochastic Process from a finite dimensional marginal
arXiv: Probability, 2001Co-Authors: Marcus PivatoAbstract:If A is a finite alphabet, Z^D is a D-dimensional lattice, U is a subset of Z^D, and mu_U is a probability measure on A^U that ``looks like'' the marginal projection of a Stationary random field on A^(Z^D), then can we ``extend'' mu_U to such a field? Under what conditions can we make this extension ergodic, (quasi)periodic, or (weakly) mixing? After surveying classical work on this problem when D = 1, we provide some sufficient conditions and some necessary conditions for mu_U to be extendible for D > 1, and show that, in general, the problem is not formally decidable.
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building a Stationary Stochastic Process from a finite dimensional marginal
Canadian Journal of Mathematics, 2001Co-Authors: Marcus PivatoAbstract:If A is a finite alphabet,U⊂ ZD, and µU is a probability measure on AU that "looks like" the marginal projection of a Stationary Stochastic Process on A Z D , then can we "extend" µU to such a Process? Under what conditions can we make this extension ergodic, (quasi)periodic, or (weakly) mixing? After surveying classical work on this problem when D = 1, we provide some sufficient conditions and some necessary conditions for µU to be extendible for D > 1, and show that, in general, the problem is not formally decidable.
William A Brown - One of the best experts on this subject based on the ideXlab platform.
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fraction of time probability for time series that exhibit cyclostationarity
Signal Processing, 1991Co-Authors: W A Gardner, William A BrownAbstract:Abstract A nonStochastic alternative to the Stochastic Process framework for conceptualizing, modeling and analyzing time-series encountered in communications, radar and telemetary systems is proposed. Wold's isomorphism between a single time-series and an ergodic Stationary Stochastic Process is generalized to accomodate time-series with periodic structure and corresponding cycloergodic cycloStationary Stochastic Processes. This reveals the existence of a nonStochastic theory for single time-series with periodic structure that completely parallels the theory of cycloergodic cycloStationary Stochastic Processes. In particular, the concept of a nonStochastic Stationary fraction-of-time probability (temporal-probability) model for a single time-series, which is closely associated with Wold's isomorphism, is generalized to cycloStationary and almost cycloStationary nonStochastic temporal-probability models for time-series with periodic structure corresponding to a single period and to multiple incommensurate periods, respectively. Gaussian time-series are considered as a specific illustrative case. Applications to signal Processing are cited.
Vincent P Diego - One of the best experts on this subject based on the ideXlab platform.
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strategy and model building in the fourth dimension a null model for genotype age interaction as a gaussian Stationary Stochastic Process
BMC Genetics, 2003Co-Authors: Vincent P Diego, Laura Almasy, Thomas D Dyer, Julia Maria Pavan Soler, John BlangeroAbstract:Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype × age interaction in cardiovascular phenotypes related to the aging Process from the Framingham Heart Study. We found evidence for genotype × age interaction for fasting glucose and systolic blood pressure. There is polygenic genotype × age interaction for fasting glucose and systolic blood pressure and quantitative trait locus × age interaction for a linkage signal for systolic blood pressure phenotypes located on chromosome 17 at 67 cM.
John F Forbes - One of the best experts on this subject based on the ideXlab platform.
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control design for first order Processes shaping the probability density of the Process state
Journal of Process Control, 2004Co-Authors: M G Forbes, Martin Guay, John F ForbesAbstract:Abstract While the long term behaviour of a Stationary Stochastic Process is concisely summarized by the probability density function (PDF), many well-established regulatory control techniques focus only on two quantities derived from the PDF, the mean and variance, as key design targets. This paper presents a technique for the design of control laws that shape all aspects of the PDF of a first-order Process. By developing relationships between the moments of the PDF and the coefficients in a control law parameterization, an approximate control law is developed to shape the Process PDF as desired.