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The main result of this work is in reducing the number of averaging operations and enhancing estimation accuracy by using a robust wavelet de-noising method.
These findings suggest that incorporation of branching reactions can be effective in enhancing estimation accuracy, although it does not change the model error remarkably.
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This new estimation will be referred to throughout the paper as enhanced estimation.
Furthermore, the results still show enhanced estimation performance for high SNR.
Here, a novel smart estimation method based on coulomb counting is proposed in order to enhance estimation accuracy.
It will always enhance estimation accuracy, if the measurements relate the process variables in a different way from the constraints.
With consideration of SNR and channel environments, therefore, the use of different pulse shape can enhance estimation performance compared to that of the system using only one pulse.
While pooled regression enhances estimation efficiency, it assumes the equal variance of residuals by restricting the control variables to have the same coefficients across the two different excess cash sub-groups.
We find that segregating motion signals into ON/OFF channels can enhance estimation accuracy by accounting for natural light/dark asymmetries.
The use of local entropy is also found to be effective in enhancing the estimation accuracy because atmospheric light estimation from bright objects can be prevented.
We evaluated the possibility of enhancing this estimation by quantifying MRI (DWI and FLAIR) signals, and compared this approach to the visual evaluation of DWI-FLAIR mismatch.
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