In chaotic SS communication systems, the capabilities of suppressing multipath and multiuser interference are characterized by the properties of AC and CC, which are usually evaluated using AC function (ACF), CC function (CCF), AC sidelobe, and CC root-mean-square (RMS) value.
The correlation coefficient (CC) and root mean square error (RMSE) were adopted as the quantitative indicator to assess the accuracy.
The results demonstrate that the TC method is reliable because the correlation coefficient (CC) and root mean square error (RMSE) derived from TC are close to those derived from ground observations, with only 9%and7%7% mean relative differences, respectively.
values, and the related correlation coefficients (CC) and root-mean-squared (RMS) errors of the input and estimated anomaly data.
The complex phenotypes that are exhibited by CC mice are rooted in their genetic variation (Ferris et al. 2013; Aylor et al. 2011; Durrant et al. 2011; Kelada et al. 2012) and, as such, these mice are valuable tools for genetic mapping and systems research.
This is confirmed by the very high Pearson correlation coefficient (CC) values and very small root mean squared errors (RMSEs).
We observed a negative correlation between cc(N, X) and the relative root distance r(N, X).
Results are shown in terms of correlation coefficient (CC), mean absolute error (MAE), and root-mean-square error (RMSE).
On the right, values for estimated drift parameters (Ads and A′ds), root mean square (RMS) and correlation coefficient (CC).
W1 was found at the root position, and clustered with the O. officinalis (CC) outgroup, suggesting that the root position is among the W1 lineages.
