Recently, sparse component analysis has become one of the most powerful tools for the blind source separation problem, under the hypothesis that the sources are sparse in a given domain. The multidisciplinary characteristic of this problem and its comprehensive formulation allows its applicability in several areas of interest such as hyperspectral images, speech, audio, seismic reflection, chemical sensors, biomedical signals and communications.In his PhD thesis, the candidate has been working on relevant contributions in the processing of signals with a certain degree of sparsity, with emphasis on reflection seismic applications. For that, it has been proposed new methods for the deconvolution and blind source separation problems. However, the proposed methods are limited to structurally simpler models such as the mono channel convolution case and the linear instantaneous mixture model.As a natural evolution of his work, this postdoctoral plan of work deals with the blind source separation of more structurally complex models in the context of sparse component analysis. More specifically, this plan of work deals with a particular case of convolutive mixture, the multichannel convolution, and two different nonlinear mixture models: the linear-quadratic and the post-nonlinear mixture models.
News published in Agência FAPESP Newsletter about the scholarship: