This paper presents the analysis of spatial correlation in MIMO channels, calculated from data measured in office environments at 5.2GHz. Results are compared with those from channels generated using a stochastic MIMO channel model and the effect of different comparison metrics is shown. The suitability of the stochastic model under different propagation conditions is also investigated.

We use direct and stochastic numerical simulations of the magnetohydrodynamic equations to explore the influence of turbulence on the dynamo threshold. In the spirit of the Kraichnan-Kazantsev model, we model the turbulence by a noise, with given amplitude, injection scale and correlation time. The addition of a stochastic noise to the mean velocity significantly alters the dynamo threshold. Wh...

We use direct and stochastic numerical simulations of the magnetohydrodynamic equations to explore the influence of turbulence on the dynamo threshold. In the spirit of the Kraichnan-Kazantsev model, we model the turbulence by a noise, with given amplitude, injection scale, and correlation time. The addition of a stochastic noise to the mean velocity significantly alters the dynamo threshold an...

Recurrent sigmoidal neural networks with asymmetric weight matrices and recurrent neural networks with nonmonotone transfer functions can exhibit ongoing uctuations rather than settling into point attractors. It is, however, an open question if these uctuations are the sign of low dimensional chaos or if they can be considered as close to stochastic. We report on the calculation of the correlat...

it is known that a stochastic dierential equation (sde) induces two probabilisticobjects, namely a diusion process and a stochastic ow. while the diusion process isdetermined by the innitesimal mean and variance given by the coecients of the sde,this is not the case for the stochastic ow induced by the sde. in order to characterize thestochastic ow uniquely the innitesimal covariance give...

Running correlations between pairs of stochastic time series are typically characterized by low-frequency evolution. This simple result of sampling variability holds for climate time series but is not often recognized for being merely noise. As an example, this paper discusses the historical connection between El Niño–Southern Oscillation (ENSO) and average Indian rainfall (AIR). Decades of str...

The paper presents the definition of rules for the medical algorithms in some infectious diseases and also the mathematical functions for modeling: interrogation rules to elaborate specific concepts and work methods in HIV/AIDS infection, interrogation rules to elaborate specifically concepts and work methods in SEPSIS, initial conditions regarding the introduction of the notion of stochastic c...

The Kraichnan rapid advection model is recast as the stochastic dynamics of tracer trajectories. This framework replaces the random fields with a small set of stochastic ordinary differential equations. Multiscaling of correlation functions arises naturally as a consequence of the geometry described by the evolution of N trajectories. Scaling exponents and scaling structures are interpreted as ...

PolyPacFit is an advanced fitting program for time-differential perturbed angular correlation (PAC) spectroscopy. It incorporates stochastic models and provides robust options for customization of fits. Notable features of the program include platform independence and support for (1) fits to stochastic models of hyperfine interactions, (2) user-defined constraints among model parameters, (3) fi...

Predicting the peak distribution of stochastic stress responses is critical for various fatigue analysis problems with stochastic uncertainties. In this paper, a method, based on the first-order reliability method (FORM), is proposed for estimating the peak distribution of stochastic response. The method linearizes the stochastic process at the associated most probable point. After linearizatio...