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The wavelet method produces higher correlation, but its entropy is lower than that of the EMD-fused image, indicating a limited improvement of the spatial resolution.
Compared to the LCMV, subspace projection, and Bayesian method of [28], the proposed method produces higher output SINR and better beampattern shape.
In addition, by comparing with the reference methods, the BDSAE method produces higher average PESQ scores for various input SNR conditions.
The simulation results validate that our method produces higher probability of sparsity pattern recovery and better approximation recovery compared to two state-of-the-art Lasso methods.
Results show that the system with the proposed method produces higher output power than the system with PnO method; the difference is approximately 15.45%.
The comprehensive comparison with the computed order analysis further shows that the proposed method produces higher accurate results in less computation time.
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And although both are developing rapidly, translators say that only the CAT method produces high quality results.
The method produces high porosity microparticles with diameters of 5 12 μm and conversion to PA4 of 56 65%.
In Section 4, the results of Monte Carlo simulations generated from the bivariate Pareto distribution show that the proposed weighted method produces high efficiencies relative to existing methods.
The method produces high-resolution images thanks to its sparsity-driven nature and simultaneously removes phase errors causing defocusing in the cross-range direction.
This method produces high quality fitting results on incomplete data, which are hard to be offered by the surface fitting based methods.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com