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This performance can also be approximated by setting a signal-to-noise ratio (SNR) threshold at both RNs; the RN will only forward the source data if the received SNR is larger than that threshold [9].
This decision can also be approximated by setting a signal-to-noise ratio (SNR) threshold at all the RNs; the RN will only forward the BS data if the received SNR is larger than that threshold [12, 24].
This can be approximated by setting the selection gradients acting on each trait to have the same sign, in this case positive.
Since any covariance matrix Σ can be decomposed as Σ=E λE T, where λ is a diagonal matrix of ordered eigenvalues and E an orthonormal matrix of eigenvectors, the matrix can be approximated by setting the smallest eigenvalues (in the matrix λ) to zero.
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In the first approach the stellar spottedness is approximated by set of circular spots.
The displacement and rotational functions of the plates are approximated by sets of boundary characteristic orthogonal polynomials.
After interpolation, the 2D curve with coordinates {x s),y(s) } that matches whisker geometry is approximated by set of points {x n, y n }, where n = 1…N.
In order to simplify the calculation of the intersection area, ellipses can be approximated by a set of circles [[9]] or by a set of points [[16]].
By the fuzzy approximation, the HJI in (12) can be approximated by a set of algebraic inequalities in (19).
Thus, H can be approximated by the set (B^{theta}).
In our work, we assume that the unknown joint distribution between the target samples and the associated labels can be approximated by a set of representative samples.
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