Sentence examples for sum approximation from inspiring English sources

The geometric moments G nm of an image with the size N×M pixels are defined using the discrete sum approximation as follows: G_{nm}= sum_{x=0}^{N-1}sum_{y=0}^{M-1} x^{n}y^{m}f x,y).

The area under the truncated curve was then calculated using the Riemann sum approximation technique (i.e., dividing the area under a curve into small rectangles, calculating the area of each rectangle, and then summing up the areas to approximate the area under the curve (Shilov et al, 1977)).

The log-sum approximation is obtained by the Jacobian logarithm with a correction function.

These estimation functions are obtained using the log-sum approximation, then taking the exponent of the result.

An analytical numerical technique based on the Laplace transformation and the Riemann-sum approximation is used to calculate the temperature, displacement and stress distributions within the rod.

Our method is based on a Gaussian-sum approximation of the singular convolution kernel combined with a Taylor expansion of the density.

Here, we have used the log-sum approximation, i.e., and this bit metric is therefore termed as max-log MAP bit metric.

We consider application of the offset-min-sum approximation [12] to the TDMP algorithm, which yields floating-point decoder performance comparable to the original TDMP algorithm with substantially lower decoding complexity.

Starting from the convolution formulation of the nonlocal potential, for smooth and fast decaying densities, we make a full use of the Fourier pseudospectral (plane wave) approximation of the density and a separable Gaussian-sum approximation of the kernel in an interval where the singularity (the origin) is excluded.

S0 represents the subset of 1 2 M N T vectors s for which the j-th bit of the corresponding symbol is equal to bit 0. The above metric can be simplified as max-sum approximation which eliminates calculation of logarithm [9].

In order to derive the sum rate approximation, let us first approximate the two variable function f x, y : = log 1 + x 1 γ + y using first-order Taylor series around E { x } and E { y }. f x, y ≈ f E { x }, E { y } + f x E { x }, E { y } x − E { x } + f y E { x }, E { y } y − E { y }.