Nurdal Watsuji

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We study recovering sparse and compressible signals using l<sub>p</sub> minimization with p &lt;; 1 when some part of the support of the signal is known a priori. Sparse reconstruction method based on l<sub>p</sub> minimization with partially known set is proposed. Recovery conditions of l<sub>p</sub> minimization with partially known support is given.(More)
In this study we presented the performance of random demodulation based analog to information converter under noisy environment. Compressive sampling (Compressed sensing) is a new area of signal processing and attracts too much attention in recent years. Compressive sampling states that if a signal having length N has a sparse representation on an(More)
In this study two algorithms are compared in order to reconstruct a block sparse signal from sparsely corrupted measurements using Compressive Sampling / Compressed Sensing (CS). Compressive sampling / Compressed sensing (CS) is a new area of signal processing and attracts too much attention in recent years. Compressive sampling states that if a signal(More)
In this paper we have studied an alternative method of determining a sparse signal from sparsely corrupted measurements using iteratively reweighted least squares (IRLS). It is well known that the theory of compressive sensing (CS) has shown that if a signal having length N has a sparse representation on an orthonormal basis, then it is possible to recover(More)
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