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W. Sprößig In this paper, we propose a new adaptive method for frequency‐domain identification problem of discrete LTI systems. It is based on a dictionary that is consisting of normalized reproducing kernels. We prove that the singular values of the matrix generated by this dictionary converge to zero rapidly; this makes it quite efficient in representing the original systems with only a few elements...
The compressive sensing (CS) theory has shown that sparse signals can be reconstructed exactly from much fewer measurements than traditionally believed. What's more, using ℓp-norm minimization with p < 1 can do so with much fewer measurements than with p=1. In this paper, a novel algorithm is proposed for computing local minima of the nonconvex problem in the block-sparse system. A series of experiments...
In this paper, we propose a new method to optimize the sensing matrix and the overcomplete dictionary simultaneously in a block-sparse system. This method mainly includes two parts: the optimization of the sensing matrix for a given dictionary and the optimization of the overcomplete dictionary with a block structure for a predefined sensing matrix. Simulation results show that our novel method can...
Traditionally, the projection matrix in compressive sensing (CS) is chosen as a random matrix. In recent years, we have seen that the performance of CS systems can be improved by using a carefully designed projection matrix rather than a random one. In particular, we can reduce the coherence between the columns of the equivalent dictionary thanks to a well-designed projection matrix. Then, we can...
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