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As an illustration, consider the coefficient in estimation (1).
In order to address such case, we consider the coefficient of variation of the aggregated traffic crossing each link.
Finally, we consider the coefficient estimates for functions f z) to be in the classes S p and C p.
If we consider the coefficient b ζη in Equation 13 given by the condition max{|a αβ |,|c kl |} < |b ζη |, then Equation 13 is a hyperbolic equation.
As mentioned above, in this example we consider the coefficient a as a constant, and f is an exact data function.
Similar to the first example; however, in this example we consider the coefficient a is not a constant, i.e., it is temporally dependent (a (t )=2t+1), then (A (t )-A (1 )=t^{2}+t-2); we choose g (x )=e^{3}sum_{m=1}^{3}sin (mx ),qquad varphi (t )=1.
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In this paper, we consider the coefficient-based regularized kernel regression.
The new matrix model allows to consider the coefficients transmitted by the subcarriers as the precoding of the data symbols.
Hence, it makes sense to consider the coefficients ((a,c)) in the normal form as the main bifurcation parameters.
Consider the coefficients of the symmetric midpoint (also intermediate time) and the one-sided concurrent seasonal adjustment filters of a span of length (2m+1).
In case we consider the coefficients {β ilk } perfectly known at the BS, the ideal MMSE estimator is given by [26] hat{mathbf{g}}_{iik}^{text{MMSE}} = frac{beta_{iik}}{zeta_{ik}}mathbf{z}_{ik}, (8).
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