Yuanchang Sun

Postprocessing and sparse blind source separation of positive and partially overlapped data

We study sparse blind source separation (BSS) for a class of positive and partially overlapped signals. The signals are only allowed to have nonoverlapping at certain locations, while they could overlap with each other elsewhere. For nonnegative data, a novel approach has been proposed by Naanaa and Nuzillard (NN) assuming that nonoverlapping exists for each source signal at some location of acquisition variable. However, the NN method introduces errors (spurious peaks) in the output when their nonoverlapping condition is not satisfied. To resolve this problem and improve robustness of separation, postprocessing techniques are developed in two aspects. One is to detect coherent and uncertain components from NN outputs by using multiple mixture data, then removing the uncertain portion to enhance signals. The other is to find better estimation of mixing matrix by leveraging reliable source peak structures in NN output. Numerical results on examples including NMR spectra of a ^1^3C-1-acetylated carbohydrate with overlapping proton spin multiplets show satisfactory performance of the postprocessed sparse BSS and offer promise to resolve complex spectra without using multidimensional NMR methods.

Nonnegative Sparse Blind Source Separation for NMR Spectroscopy by Data Clustering, Model Reduction, and

Motivated by applications in nuclear magnetic resonance (NMR) spectroscopy, we introduce a novel blind source separation (BSS) approach to treat nonnegative and correlated data. We consider the (over)-determined case where

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sources are to be separated from

A Recursive Sparse Blind Source Separation Method and Its Application to Correlated Data in NMR Spectroscopy of Biofluids

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A geometric blind source separation method based on facet component analysis

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Underdetermined Sparse Blind Source Separation of Nonnegative and Partially Overlapped Data

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A sparse semi-blind source identification method and its application to Raman spectroscopy for explosives detection

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Content adaptive image matching by color-entropy segmentation and inpainting

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Computational modeling of spectral data fitting with nonlinear distortions

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