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Furthermore, the algorithm incorporates a correction for slant-based sectional length measurements using the Pythagorean theorem and eliminates the need to predict heights for a given age by the use of a linear interpolation procedure.
Intention-to-treat analysis was used to compare patients in the groups to which they were randomly assigned, with missing data being imputed using the SPSS linear interpolation procedure.
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We propose an algorithm that efficiently combines quantization and linear interpolation procedures.
The image correction was carried out in two steps: (1) map each pixel of the corrected image representation onto its distorted image according to the distortion mapping; (2) interpolate the pixel intensity in the distorted image using its neighboring points via a bi-linear interpolation procedure.
Moreover, it allows normalization of encoded sequences to a specific length with five different linear or non-linear interpolation procedures.
About one-third of the participants were missing data on household expenditure, therefore imputation using a linear interpolation computation procedure was used.
Results were compared to those ones obtained by means of an infrared digital video camera and a linear based interpolation procedure.
The novelty of the resulting scheme lies in the nature of the preprocessing of the cell averaged data, which is accomplished via a nearest neighbor linear interpolation followed by a slope limiting procedure.
For this normalization procedure we used a linear interpolation as previously described.
In the averaging procedure, missing data were filled by linear interpolation.
Since generally does not belong to the given mesh, we have to employ an interpolation (standard procedures are piecewise constant and piecewise linear interpolation).
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