The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Dima Bykhovsky - One of the best experts on this subject based on the ideXlab platform.
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Mathematica code for numerical generation of random process with given distribution and Exponential Autocorrelation function
Elsevier, 2018Co-Authors: Dima BykhovskyAbstract:Stochastic simulations commonly require random process generation with a predefined probability density function (PDF) and an Exponential Autocorrelation function (ACF). Such processes may be represented as a solution of a stochastic differential equation (SDE) of the first order. The numerically-stable solution of this SDE may be provided by a discrete-time differential equation. Both the generation of the required SDE and the implementation of the differential equation may be effectively done by Mathematica software for most of the typical distributions. Moreover, the required implicit Milstein method for positive domain distributions is not supplied by built-in SDE-related Mathematica functions. Keywords: Stochastic differential equation, Exponential Autocorrelation, Numerical generation of random proces
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Mathematica code for numerical generation of random process with given distribution and Exponential Autocorrelation function
SoftwareX, 2018Co-Authors: Dima BykhovskyAbstract:Abstract Stochastic simulations commonly require random process generation with a predefined probability density function (PDF) and an Exponential Autocorrelation function (ACF). Such processes may be represented as a solution of a stochastic differential equation (SDE) of the first order. The numerically-stable solution of this SDE may be provided by a discrete-time differential equation. Both the generation of the required SDE and the implementation of the differential equation may be effectively done by Mathematica software for most of the typical distributions. Moreover, the required implicit Milstein method for positive domain distributions is not supplied by built-in SDE-related Mathematica functions.
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On the numerical generation of positive-axis-defined distributions with an Exponential Autocorrelation function
Digital Signal Processing, 2017Co-Authors: Dima Bykhovsky, Vladimir LyandresAbstract:Abstract Stochastic modeling commonly requires random process generation with an Exponential Autocorrelation function (ACF). These random processes may be represented as a solution of a stochastic differential equation (SDE) of the first order and usually have one-sided (positive-axis-defined) distributions. However, adoption of the SDE-based method faces serious limitations due to difficulties with the numerical solution. To overcome this issue we propose a tractable general numerical solution of the above-mentioned SDE that preserves solution positivity and accuracy, and validate it with numerical simulations.
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comments on simple generation of gamma gamma gamma and k distributions with Exponential Autocorrelation function
Journal of Lightwave Technology, 2016Co-Authors: Dima BykhovskyAbstract:This paper is a comment on "Simple generation of gamma, gamma-gamma and K distributions with Exponential Autocorrelation function”. The original article states that the simulation of a free-space optical (FSO) communication channel in the presence of strong turbulence typically requires the generation of channel states with a K or gamma-gamma distribution and a predefined Autocorrelation function. In the comment it is concluded that the resulting Autocorrelation function for gamma-gamma and K processes may be approximated as a simple exponent only under certain constraints on distribution parameters. Whenever these constraints are not met, the resulting Autocorrelation function is still analytically tractable, but it is no longer a simple exponent.
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Simple Generation of Gamma, Gamma–Gamma, and K Distributions With Exponential Autocorrelation Function
Journal of Lightwave Technology, 2016Co-Authors: Dima BykhovskyAbstract:The simulation of a free-space optical (FSO) communication channel in the presence of strong turbulence typically requires the generation of channel states with a K or gamma–gamma distribution and a predefined Autocorrelation function. In this paper, we propose a simple and effective simulator of the strong-turbulence FSO channel that addresses the influence of the temporal covariance effect. Specifically, the proposed simulator provides K and gamma–gamma distributed values with the Exponential Autocorrelation function and a prescribed correlation time. This simulator is based on the numerical solution of the first-order stochastic differential equation. The simulated channel states are generated by a simple discrete-time differential equation and the simulator performance is analyzed in the paper.
Federico Milano - One of the best experts on this subject based on the ideXlab platform.
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sde based wind speed models with weibull distribution and Exponential Autocorrelation
Power and Energy Society General Meeting, 2016Co-Authors: Rafael Zarateminano, Francesca Madia Mele, Federico MilanoAbstract:This paper discusses three approaches to construct wind speed models based on Stochastic Differential Equations (SDEs). The methods are applied to construct models able to simulate wind speed trajectories that are statistically described by means of the Weibull distribution and the Exponential Autocorrelation. The ability of the three models to reproduce stochastic processes with the above indicated statistical properties is duly studied and compared. With this aim, wind speed measurements recorded in a weather station located in Ireland are analyzed. The parameters obtained in this analysis are used to set up the developed models. Finally, the statistical properties of the trajectories generated by the three models are compared with the statistical properties of the considered wind speed data set.
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Construction of SDE-based wind speed models with Exponentially decaying Autocorrelation
Renewable Energy, 2016Co-Authors: Rafael Zárate-miñano, Federico MilanoAbstract:This paper provides a systematic method to build wind speed models based on stochastic differential equations (SDEs). The resulting models produce stochastic processes with a given probability distribution and Exponentially decaying Autocorrelation function. The only information needed to build the models is the probability density function of the wind speed and its Autocorrelation coefficient. Unlike other methods previously proposed in the literature, the proposed method leads to models able to reproduce an exact Exponential Autocorrelation even if the probability distribution is not Gaussian. A sufficient condition for the property above is provided. The paper includes the explicit formulation of SDE-based wind speed models obtained from several probability distributions used in the literature to describe different wind speed behaviors. All models are validated through numerical simulations. Finally, the proposed procedure is applied to model the wind speed observed at a meteorological station in New Zealand. A comparison of the statistical properties of the wind speed measurements and of the stochastic process generated by the SDE model is also provided.
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construction of sde based wind speed models with Exponential Autocorrelation
arXiv: Applications, 2015Co-Authors: Rafael Zarate Minano, Federico MilanoAbstract:This paper provides a systematic method to build wind speed models based on stochastic differential equations (SDEs). The resulting models produce stochastic processes with a given probability distribution and Exponential decaying Autocorrelation function. The only information needed to build the models is the probability density function of the wind speed and its Autocorrelation coefficient. Unlike other methods previously proposed in the literature, the proposed method leads to models able to reproduce an exact Exponential Autocorrelation even if the probability distribution is not Gaussian. A sufficient condition for the property above is provided. The paper includes the explicit formulation of SDE-based wind speed models obtained from several probability distributions used in the literature to describe different wind speed behaviors.
Yan Yao - One of the best experts on this subject based on the ideXlab platform.
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An analytically tractable model for video conference traffic
IEEE Transactions on Circuits and Systems for Video Technology, 2000Co-Authors: Zailu Huang, Yan YaoAbstract:We propose an analytically tractable approach to model compressed video traffic called C-DAR(1). The C-DAR(1) model combines an approach utilizing a discrete-time Markov chain with a continuous-time Markov chain. We show that this approach accurately models the distribution and Exponential Autocorrelation characteristics of video conferencing traffic. Also, we show that by comparing our analytical results against a simulation using actual video conferencing data, our model provides realistic results. In addition to presenting this new approach, we address the effects of long-range dependencies (LRD) in the video traffic. Based on our analytical and simulation results, we are able to conclude that the LRD have minimal impact on videoconference traffic modeling.
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ICC (2) - A theoretic analysis model for VBR video traffic in ATM networks
Proceedings of ICC'97 - International Conference on Communications, 1Co-Authors: Zailu Huang, Yan YaoAbstract:Up to now, there is no analysis model suitable to describe the Gamma distribution and Exponential Autocorrelation characteristics presented in the literature, which is very important for video traffic. In this paper, we propose such a video model called the C-DAR(1) model. We believe that it is the first model meeting the distribution and correlation characteristics of video conference traffic. A scheme is given to link the continuous-time Markov chain model with a discrete-time one. A doubtful conclusion in the work of Skelly et al. (1993) is discussed.
Alan D. Sokal - One of the best experts on this subject based on the ideXlab platform.
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Dynamic critical behavior of the Swendsen-Wang algorithm for the three-dimensional Ising model
Nuclear Physics B, 2004Co-Authors: Giovanni Ossola, Alan D. SokalAbstract:We have performed a high-precision Monte Carlo study of the dynamic critical behavior of the Swendsen-Wang algorithm for the three-dimensional Ising model at the critical point. For the dynamic critical exponents associated to the integrated Autocorrelation times of the "energy-like" observables, we find z_{int,N} = z_{int,E} = z_{int,E'} = 0.459 +- 0.005 +- 0.025, where the first error bar represents statistical error (68% confidence interval) and the second error bar represents possible systematic error due to corrections to scaling (68% subjective confidence interval). For the "susceptibility-like" observables, we find z_{int,M^2} = z_{int,S_2} = 0.443 +- 0.005 +- 0.030. For the dynamic critical exponent associated to the Exponential Autocorrelation time, we find z_{exp} \approx 0.481. Our data are consistent with the Coddington-Baillie conjecture z_{SW} = \beta/\nu \approx 0.5183, especially if it is interpreted as referring to z_{exp}.Comment: LaTex2e, 39 pages including 5 figure
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Dynamic critical behavior of a Swendsen-Wang-Type algorithm for the Ashkin-Teller model
Journal of Statistical Physics, 1996Co-Authors: Jesús Salas, Alan D. SokalAbstract:We study the dynamic critical behavior of a Swendsen-Wang-type algorithm for the Ashkin-Teller model. We find that the Li-Sokal bound on the Autocorrelation time (τ_int.δ≥ const x C _ H ) holds along the self-dual curve of the symmetric Ashkin-Teller model, and is almost, but not quite sharp. The ratio τ_int.δ/ C _ H appears to tend to infinity either as a logarithm or as a small power (0.05≲ p ≲0.12). In an appendix we discuss the problem of extracting estimates of the Exponential Autocorrelation time.
Zailu Huang - One of the best experts on this subject based on the ideXlab platform.
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An analytically tractable model for video conference traffic
IEEE Transactions on Circuits and Systems for Video Technology, 2000Co-Authors: Zailu Huang, Yan YaoAbstract:We propose an analytically tractable approach to model compressed video traffic called C-DAR(1). The C-DAR(1) model combines an approach utilizing a discrete-time Markov chain with a continuous-time Markov chain. We show that this approach accurately models the distribution and Exponential Autocorrelation characteristics of video conferencing traffic. Also, we show that by comparing our analytical results against a simulation using actual video conferencing data, our model provides realistic results. In addition to presenting this new approach, we address the effects of long-range dependencies (LRD) in the video traffic. Based on our analytical and simulation results, we are able to conclude that the LRD have minimal impact on videoconference traffic modeling.
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ICC (2) - A theoretic analysis model for VBR video traffic in ATM networks
Proceedings of ICC'97 - International Conference on Communications, 1Co-Authors: Zailu Huang, Yan YaoAbstract:Up to now, there is no analysis model suitable to describe the Gamma distribution and Exponential Autocorrelation characteristics presented in the literature, which is very important for video traffic. In this paper, we propose such a video model called the C-DAR(1) model. We believe that it is the first model meeting the distribution and correlation characteristics of video conference traffic. A scheme is given to link the continuous-time Markov chain model with a discrete-time one. A doubtful conclusion in the work of Skelly et al. (1993) is discussed.
