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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Linear and one-bit compressive sensing with subsampled random convolutions
Linear and one-bit compressive sensing with subsampled random convolutionsAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact INI IT. This talk has been canceled/deleted Compressive sensing predicts that sparse vectors can recovered from incomplete linear measurements with efficient algorithms in a stable way. While many theoretical results work with Gaussian random measurement matrices, practical applications usually demand for structure. The talk covers the particular case of structured random measurements defined via convolution with a random vector and subsampling (deterministic or random as well). We will give an overview on the corresponding theory and will cover also recent results concerning recovery from one-bit measurements arising in quantized compressive sensing. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:This talk is not included in any other list Note that ex-directory lists are not shown. |
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