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An extension of Independent Component Analysis (ICA) to the situation when the mixture of signals is contaminated by multiplicative noise is proposed in this paper. The ICA methods search for the most independent output after a linear transformation of the data vector. If the ICA model is followed by these data, the result of this search is the inverse of the unknown mixture. On the other hand, if...
We have revised the Markov model used in the analysis of microarray time-series data to uncover the gene regulatory network. Previous linear models establishes genetic relations between the microarray data which are assumed to have noise. We propose a new model to distinguish between observed data and real expression levels. The new model does not overestimate the noise and fits better the nature...
In this contribution, the relation between the principal components of the covariance matrix of a hyperspectral image and the spectra of the endmembers is studied. When the data satisfy the spectral mixing model, from this relation the spectra of the endmembers and the abundance of each endmember in the pixels of the image can be theoretically obtained through a non-lineal minimization process. The...
We present a new contribution to the single-mode extraction pulse (S-pulse) technique. The S-pulse technique uses the natural frequencies of the target as discrimination features in such a way that the field scattered by a target after it passes through a S-pulse designed for this specific target has the form of a damped sinusoid. The S-pulses are usually constructed as a linear combination of cosine...
We have revised the Markov lineal model used in the analysis of microarray time-series data. According to this model, the expression level of a given gene at any specific time is a linear combination of the measured expression levels of other genes at previous time instants, plus noise. The problem of uncovering such relationships can be solved using variational Bayesian methods. The linear model...
Global localization methods deal with the estimation of a mobile robot's pose assuming no prior state information about it and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existent algorithms are based on the minimization of a L2-norm loss function. However, the use of a L1-norm offers some alternative advantages. The present work explores...
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