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In this paper a new watermark embedding and recovery technique is proposed based on the compressed sensing framework. Both the watermark and the host signal are assumed to be sparse, each in its own domain. In recovery, the L1-minimization is used to recover the watermark and the host signal perfectly in clean conditions. The proposed technique is tested on MP3 audio where the effects of MP3 compression/decompression,...
Most of the recent compressive sensing (CS) literature has focused on sparse signal recovery based on compressive measurements. However, exact signal recovery may not be required in certain signal processing applications such as in inference problems. In this paper, we provide performance limits of classification of sparse as well as not necessarily sparse signals based on compressive measurements...
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