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• W2017265514 abstract "In this paper, we consider the 1-bit compressive sensing reconstruction problem in a scenario that the sparsity level of the signal is unknown and time variant, and the binary measurements are contaminated with the noise. We introduce a new reconstruction algorithm which we refer to as Noise-Adaptive Restricted Step Shrinkage (NARSS). NARSS is superior in terms of performance, complexity and speed of convergence to the algorithms already introduced in the literature for 1-bit compressive sensing reconstruction from the noisy binary measurements." @default.
• W2017265514 created "2016-06-24" @default.
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• W2017265514 creator A5054563831 @default.
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• W2017265514 date "2014-05-01" @default.
• W2017265514 modified "2023-09-23" @default.
• W2017265514 title "Recovering signals with variable sparsity levels from the noisy 1-bit compressive measurements" @default.
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• W2017265514 doi "https://doi.org/10.1109/icassp.2014.6854847" @default.
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