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Interpolation over censored frames was computed by least-squares spectral estimation, so that continous data could be bandpass filtered (0.009
We extend the work of Lee et al. [4] to redistance functions defined via interpolation over a regular grid.
In this paper, a new approach to construct a bivariate rational interpolation over triangulation is presented, based on scattered data in parallel lines.
For volume mesh deformation and two-way data interpolation over non-matching grids interface, a radial basis function methodology combined with a data reduction algorithm has been used.
The proposed metric favors interpolation over extrapolation through a penalty function, causing the metric to prefer a design of validation experiments near the boundaries of the domain, while simultaneously exploring inside the domain.
In our method, both inner and boundary vertices are repositioned by analytically minimizing the L1 error between a paraboloid function and its piecewise linear interpolation over the neighborhood of each vertex.
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When the depth information became available again for a later observation, a new estimation of the depth was interpolated (using cubic spline interpolation [29]) over the window during which the depth was missing.
It does not construct a global interpolation polynomial over a whole coarse cell.
The cell-integrated average is defined via the spatial integration of the interpolation function over a single grid cell and is advanced using a flux form.
From experimental results, numerical comparison confirms the superiority of the proposed interpolation algorithm over other existing methods.
By using the estimated (hat {h}_{l}(m)) at every symbol, the CIR at every sampling time (hat {h}_{l}(m,n_{2})) can be estimated by using the cubic spline interpolation method over one frame.
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