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In order to implement the above stopping rule, we have to evaluate the norm of the residual error for each OFDM symbol a certain number of stages till the L-shaped curve is obtained and calculate the curvature information to ultimately determine the optimal stopping stage.
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Convergence of the multi-variable function f can be observed dynamically by evaluating the norm of the gradient |∇f|.
We begin by evaluating the norm of the difference between both points of iteration n + 1: A n + 1 - B n + 1 = γ ∑ k = 1 K λ k · a k A n A n + λ k - B n B n + λ k (34) = γ A n - B n ∑ k = 1 K a k λ k 2 A n + λ k B n + λ k (35).
Evaluating the norms in the right-hand side, and further estimating using the maximal Lebesgue measure of the elements of the partition yields ∥ I D k, n ∥ H − α 2 ≤ { K d / ( d / 2 − α ) 2 | D k, n | v + ( n ) 2 α / d if 0 < α < d / 2, K ∞ 2 | D k, n | v + ( n ) if d / 2 < α.
Here, we adopt LMIs to numerically evaluate the H 2 norm based on the next lemma.
To evaluate the status of norms and standards and their applicabilities especially in obstetric and neonatal emergencies in Benin.
In the first part of the paper, different SDA formulations (denoted as SOS, KOS and SKON) are assessed numerically based upon different error norms including a norm to evaluate the variational consistency of SDA elements.
To evaluate the effect of social norms on intended fruit and vegetable intake.
It evaluates the shared norms and expectations that guide thinking and behaviour of group members.
We used this norm to evaluate the pattern of stress generated.
In this context, the cosine dissimilarity for approximating real data was replaced by the Frobenius norm to evaluate the reconstruction error of complex approximation.
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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