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We proposed a method of sparse signal subspace decomposition (3SD).
The method of sparse representation has been successfully used for gearbox fault diagnosis, but most of the literatures only focus on the extraction of impact modulation and always neglect the steady modulation representing the distributed faults.
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The stochastic modelling method was used as opposed to deterministic method because of sparse well distribution over the Z-2 reservoir.
We develop a new initial filtering step to avoid completely this diagonalization, thus making the CheFSI method free of sparse iterative diagonalizations at all SCF steps.
In this paper stochastic sampling as a method of frequency sparse signal acquisition is presented.
This paper proposes a novel estimation method of a sparse and doubly selective channel based on CS theory.
The feasibility of the sparse signal recovery methods of the sparse sampling model of RBSR were demonstrated in these results.
In summary, the modified multilevel cross-entropy method for SParSE comprises of 3 steps.
We also expand the sparse inverse covariance method to sparse inverse of general similarity matrices for high-order correlation.
In order to obtain such a set of solutions, we additionally propose the bag of pursuits (BOP) method for sparse approximation.
So, we design a pre-filter model which with no considerable loss of optimality and do analyses of the equalization methods of the sparse multi-path channel.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com