Everton Z. Nadalin

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The problem of independent component analysis (ICA) was firstly formulated and studied in the context of real-valued signals and mixing models, but, recently, an extension of this original formulation was proposed to deal with the problem within the framework of finite fields. In this work, we propose a strategy to deal with ICA over these fields that(More)
This article reviews some key aspects of two important branches in unsupervised signal processing: blind deconvolution and blind source separation (BSS). It also gives an overview of their potential application in seismic processing, with an emphasis on seismic deconvolution. Finally, it presents illustrative results of the application, on both synthetic(More)
In this work, we investigate the application of the recently introduced signal decomposition method known as robust principal component analysis (RPCA) to the problem of wave separation in seismic data. The motivation of our research comes from the observation that the elements of the decomposition performed by RPCA can be associated with particular(More)
The theory of ICA over finite fields, established in the last five years, gave rise to a corpus of different separation strategies, which includes an algorithm based on the pairwise comparison of mixtures, called MEXICO. In this work, we propose an alternative version of the MEXICO algorithm, with modifications that - as shown by the results obtained for a(More)
The efforts of Yeredor, Gutch, Gruber and Theis have established a theory of blind source separation (BSS) over finite fields that can be applied to linear and instantaneous mixing models. In this work, the problem is treated for the case of convolutive mixtures, for which the process of BSS must be understood in terms of space-time processing. A method(More)
This work fits in the frames of sparse component analysis (SCA), informed source separation (ISS) and doping watermarking. The SCA relies on a strong hypothesis of sparsity of the sources. In a particular context where the original sources are available (ISS), we make the distributions of the time-frequency coefficients of the sources more sparse, through a(More)