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Creators/Authors contains: "Parada-Mayorga, Alejandro"

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  1. ; ; (, IEEE Transactions on Signal Processing)
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  2. ; (, IEEE)
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  3. ; (, IEEE Transactions on Signal Processing)
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  4. ; ; ; ; (, IEEE Signal Processing Magazine)
    null (Ed.)
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  5. ; ; ; (, IEEE Transactions on Signal and Information Processing over Networks)
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  6. ; ; ; (, 2019 IEEE Data Science Workshop (DSW))
    In this paper, we calculate the optimal sampling sets for bandlimited signals on cographs. We take into account the tree structure of the cograph to derive closed form results for the uniqueness sets of signals with a given bandwidth. These results do not require expensive spectral decompositions and represent a promising tool for the analysis of signals on graphs that can be approximated by cographs. 
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  7. ; ; ; (, 2019 13th International conference on Sampling Theory and Applications (SampTA))
    In this work, we introduce the concept of blue noise sampling, traditionally used in imaging applications, for band limited signals on graphs. We show how the spectral and vertex domain characterization of these patterns is connected with results about the quality of the sampling sets already existing in the literature. We provide numerical evidence that shows that these patterns are also competitive with respect to the state of the art sampling techniques in terms of the reconstruction error. 
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  8. ; ; ; (, 2019 IEEE Data Science Workshop)
    This paper discusses the generalization of the concept of blue noise sampling from traditional halftoning to signal processing on graphs. Making use of the spatial properties of blue noise, we generate sampling patterns that provide reconstruction errors that are similar to the ones obtained with state of the art approaches. This sampling scheme presents an alternative to those techniques that require spectral decompositions. 
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