we give some new results on sparse signal recovery in the presence of noise, forweighted spaces. traditionally, were used dictionaries that have the norm equal to 1, but, forrandom dictionaries this condition is rarely satised. moreover, we give better estimationsthen the ones given recently by cai, wang and xu.

Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines...

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of the Dissertation Fast and Robust Algorithms for Compressive Sensing and Other Applications

Article history: Available online 2 October 2013

based on the compressed sensing theory, if a signal is sparse in a suitable space, by using the optimization methods, signal could be accurately reconstructed from measurements that are significantly less than the theoretical shannon requirements. the sparse representation may exist for the signal and it is not available for the noise; this could be used to distinguish these two. on the other h...

the focus of this paper is to consider the compressed sensing problem. it is stated that the compressed sensing theory, under certain conditions, helps relax the nyquist sampling theory and takes smaller samples. one of the important tasks in this theory is to carefully design measurement matrix (sampling operator). most existing methods in the literature attempt to optimize a randomly initiali...

The reconstruction of an unknown temporal-spatial profile from participatory sensing data poses a number of challenges due to uncoordinated user movement and possibly low user involvement. This paper considers the problem of reconstructing such a profile from participatory sensing data by exploiting the theory of compressive sensing. In particular we study the impact of the number of users and ...