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The proposed sequential algorithm, based on the concept of sequential minimum mean square error (MSE) estimation, to determine the coefficients of the scattering matrix, guarantees the convergence and the resulting computational complexity is linear with the number of iterations.
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Moreover, it also provides a consensus matrix that can be used as a dissimilarity matrix, guaranteeing the same performance as the corresponding matrix produced by Consensus.
Fortunately, the dispersed zeolite particles in the membranes are intimately enclosed by the surrounding PDMS matrix, guaranteeing the permeate flow to be molecularly selective.
An appropriate design of the M-matrix guarantees the positivity of the turbulence equation dependent variables for any time step, without the use of any clipping.
The deterministic matrices guarantee the recovery performance that is empirically reliable, allowing fast processing and low complexity.
Setting where is the largest eigenvalue of the matrix guarantees that the largest eigenvalue of matrix will be 1, and consequently the corresponding eigenvector is the unique solution.
The new sensing matrix guarantees unique sparse reconstruction with high probability for sparse signals with uniformly distributed supports.
Based on the CS theory [6, 7], the effective channel matrix B in (3) is expected to be an effective sensing matrix which guarantees the recovery of the sparse signal x.
From the derived and proved convergence criteria, such an optimal D matrix also guarantees the convergence of algorithms.
It is demonstrated that uptake of solvent vapors in the matrix polymer guarantees the increase in a composite's resistance over a wide range of vapor concentration.
Therefore, in the next section, we will discuss how to design the LCS signatures (i.e., the design of (mathcal {F})) to ensure that the effective channel matrix B guarantees the recovery of the sparse signal x.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com