Lévy area for Gaussian processes: A double Wiener--Itô integral approach

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Wiener type theorems for Jacobi series with nonnegative coefficients

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Implementació de l'algorisme de Wiener i Oorschot en un entorn MPI

Aquest projecte presenta una breu introducció a la criptografia. S'expliquen principis fonamentals, com què és la criptografia i el criptoanàlisi els mètodes més rellevants de cada cas. Això servirà com a base teòrica per estudiar el funcionament del criptosistema de ElGamal, la seguretat del qual es basa en la dificultat de resoldre el problema del logaritme discret. Un cop tenim clar el problema del logaritme discret, s'implementarà una aplicació que el resolgui, mitjançant l'algorisme Rho de Pollard. Aquesta aplicació contarà amb el suport de la llibreria NTL, llibreria de nombres gegants, per poder implementar-la. Per acabarl, i com a principal objectiu, el que es pretén és implementar una aplicació paral·lela que resolgui el problema del logaritme discret en un entorn multicomputador utilitzant la proposta de Wiener i Oorschot.

Variance reduction methods for simulation of densities on Wiener space

We develop a general error analysis framework for the Monte Carlo simulationof densities for functionals in Wiener space. We also study variancereduction methods with the help of Malliavin derivatives. For this, wegive some general heuristic principles which are applied to diffusionprocesses. A comparison with kernel density estimates is made.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Quasi-Nonparametric Blind Inversion of Wiener Systems

An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm can perform e ciently even in the presence of hard distortions.

Initialisation of Nonlinearities for PNL and Wiener systems Inversion

This paper proposes a very fast method for blindly initial- izing a nonlinear mapping which transforms a sum of random variables. The method provides a surprisingly good approximation even when the basic assumption is not fully satis¯ed. The method can been used success- fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener system inversion, for improving algorithm speed and convergence.

Blind Inversion of Wiener Systems

A system in which a linear dynamic part is followed by a non linear memoryless distortion a Wiener system is blindly inverted This kind of systems can be modelised as a postnonlinear mixture and using some results about these mixtures an e cient algorithm is proposed Results in a hard situation are presented and illustrate the e ciency of this algorithm