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A Wiener system is composed of a linear dynamic subsystem followed by a static nonlinearity. It is well known in the literature that the identi cation of the linear subsystem of a Wiener system can be separated from that of the output nonlinearity, if the input signal is Gaussian. In order to deal with non Gaussian inputs, two new algorithms are proposed in… (More)

We propose new methods for estimating the frontier of a set of points. The estimates are defined as kernel functions covering all the points and whose associated support is of smallest surface. They are written as linear combinations of kernel functions applied to the points of the sample. The weights of the linear combination are then computed by solving a… (More)

- Mariette Maurizot, Patrick Bouthemy, +4 authors Jean-Marc Odobez
- 1994

Digital image analysis appears to be more and more relevant to the study of physical phenomena involving uid motion, and of their evolution over time. In that context, 2D deformable motion analysis is one of the most important issues to be investigated. The interpretation of such deformable 2D ow elds can generally be stated as the characterization of… (More)

- Lennart Ljung, Rajiv Singh, Qinghua Zhang, Peter Lindskog, Anatoli Ioudiski, Anatoli Iouditski
- 2009

The paper describes additions to the System Identi cation Toolbox offered by The MathWorks, Inc, that handle the estimation of nonlinear models. Both structured grey-box models and general, exible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface (GUI) should be as close as possible to the… (More)

The paper describes additions to the Matlab System Identi cation Toolbox, that handle also the estimation of nonlinear models. Both structured grey-box models and general, exible black-box models are covered. The idea is that the look and feel of the syntax, and the graphical user interface should be as close as possible to the linear case.

We discuss new methods for the recovery of signals with block-sparse structure, based on `1-minimization. Our emphasis is on the efficiently computable error bounds for the recovery routines. We optimize these bounds with respect to the method parameters to construct the estimators with improved statistical properties. We justify the proposed approach with… (More)

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