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The test carried out in this work demonstrates that this approximation reduces in one order of magnitude the processing time of the standard OpenCV bilateral filter function.

For the sake of simplicity, and for carrying out analytical calculations, we consider

Using this approximation reduces the computational cost drastically.

In our framework, this approximation reduces the full reference set to a custom panel for each study haplotype in each part of the genome.

This approximation reduces computational cost because it uncouples the Laplace equation in the torso from the bidomain equations in the heart, but numerical evidence has shown that it can compromise the accuracy of the ECG signals (see, e.g. Lines et al., 2003b; Pullan et al., 2005; Boulakia et al., 2010).

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This proposed approximation reduces these kinds of nonlinear Hammerstein integral equations of mixed type to a nonlinear system of algebraic equations.

The approximation reduces the required number of simulation trials.

When the method holds, each successive approximation reduces the relative error on the potential by roughly a factor of 10.

The surrogate model constructed using Proper Orthogonal Decomposition (POD) and Diffuse Approximation reduces the computational effort for solving the microscopic boundary value problems.

Although self consistent approximation reduces the dimensionality of the system, for the multi-dimension conditions, it is still hard to solve diffusion equations directly.

The integral localized approximation reduces computational time in the sense that it eliminates the undesirable and time consuming quadratures with double or triple integration.

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