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The results of a previous paper claiming the demonstration that edge localized mode (ELM) dynamics on TCV are chaotic in a number of cases has recently been called into question, because the statistical test employed was found to also identify linear auto ...
The problem of estimating and predicting Origin-Destination (OD) tables is known to be important and difficult. In the specific context of Intelligent Transportation Systems (ITS), the dynamic nature of the problem and the real-time requirements make it ev ...
This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...
We present a general Fourier-based method that provides an accurate prediction of the approximation error as a function of the sampling step T. Our formalism applies to an extended class of convolution-based signal approximation techniques, which includes ...
This paper describes inferences based on linear predictors for stationary time series. These methods are flexible, since relatively few assumptions are needed to fit a linear predictor. A confidence interval for the resulting predicted value, which takes a ...
We consider one aspect of the general problem of unicast equation based rate control in the Internet, which we formulate as follows. When a so called ``loss-event" occurs, a data source updates its sending rate by setting it to f(pn^), where $\hat{ ...
The application of methods drawn from nonlinear and stochastic dynamics to the analysis of cardiovascular time series are applied with particular interests in the identification of changes associated with aging. Scale invariant and scale dependent approach ...
This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algor ...
This thesis explores latent-variable probabilistic models for the analysis and classification of electroenchephalographic (EEG) signals used in Brain Computer Interface (BCI) systems. The first part of the thesis focuses on the use of probabilistic methods ...
This paper proposes a simple, computationally efficient 2-mixture model approach to discrimination between speech and background noise. It is directly derived from observations on real data, and can be used in a fully unsupervised manner, with the EM algor ...
