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Using [18, Corollary 4.5], we can obtain the following interpolation estimates: (4.16).
The following interpolation theorem plays a key role in dealing with this problem.
Before that let us first recall the following interpolation and projection results.
In order to prove Theorem 1.1, we need the following interpolation inequalities in two and three space dimensions.
And in just the same way as in [3, Theorem 2.5], we establish the following interpolation inequality.
Indeed, if the set of time-shifted sinc functions is considered a frame for, a Gramian matrix equation represents the following interpolation: (2).
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This algorithm will be used in the following temporal interpolation in our proposed algorithms.
It is resized into a 9 × 9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing.
Under these conditions the signal can be reconstructed from the uniform samples according to the following sinc interpolation: (1).
For this serum sample we therefore used the following linear interpolation of the data to estimate the endpoint cut-offs, endpoint = (mean OD of the antigen of interest at the last positive serum dilution divided by two times the negative antigen OD at the same serum dilution) multiplied by the last positive dilution.
The rest of the article is organized as follows: The following section describes the proposed interpolation method for one-dimensional vectors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com