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The values of the coefficients (-498.9, 49.76, and -1.653, respectively, for cd, dc2, and dc3; respective errors of 32.6, 3.66, and 0.135, equivalent to about 6.5%, 7.3%, and 8.2%, respectively) indicate the sensitivity of the dc variable.
The respective errors thus introduced have different signs and largely cancel each other.
As for the Sabbagh model, it is appropriate for the Abeche, N'Djamena, Ati and Moussoro cities with respective errors of 3.7042, −0.3477, 0.3564 and 3.4003%.
However, both the proposed method and the Tiansi method generally handle the respective errors much better than that of Canny with the proposed method slightly doing better than that of Tiansi.
The values of the coefficients and the respective errors of the first term (without dc) and the three other terms (dc, dc2, and dc3) in Equation 1 are included.
These mean values are much smaller than those for Equation 1, whereas the respective errors are much greater (15.5% to 19.7% for volume terms as compared to 6.5%to8.2%2% for centroid depth terms).
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All experiments were done at least thrice and figures and data have been expressed along with the respective error bars and standard deviations, respectively.
Table 1 gives a synopsis of blind algorithm cost functions and their respective error functions.
Calculation of the anisotropy tensor, respective error analysis and immediate data visualization is provided by Safyr5 software.
Fundamental ideas of data acquisition and processing, AMS tensor fitting and respective error analysis are described and discussed.
Moreover, it is observed that these designs depend on the respective error covariance structure but are invariant to the above four lifetime distributions.
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