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The metric distribution can be extracted by.
Once again, the analysis in [36] can be followed in order to extract the metric distribution.
In order to calculate the metric distribution, the characteristic function for is used.
In order to extract the metric distribution, it is necessary to calculate the correlation coefficient for the energy between the outputs of two adjacent filter paths.
After the extraction of the metric distribution under ℋ 1 and for a given threshold, the probability of correct detection PD can be calculated.
It is also noticed that when noise frequency selectivity increases, PD error also increases due to the fact that under ℋ 1, the metric distribution is affected by the validity of the approximately flat assumption for both PS signal and noise.
Similar(52)
A very similar figure can be extracted for the Neyman-Pearson detector for uniform DFT filter banks, since both optimal detectors have identical metric distributions.
Exposure metric distributions and correlations.
To calculate the skewness of the metric distributions, the package e1701 (version 1.6 4) [ 43] was used.
Experimental results show the effectiveness of AIMOES in terms of the root mean square deviation (RMSD) metric, the distribution diversity of the obtained Pareto front and the success rate of mutation operators.
Spacing metric (SM) Distribution of solutions in Pareto frontier as denoted in (19).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com