The great advantage of our approximation (beta (n)) consists in its simple form and its accuracy.
It also uses the Harris operator for edge filtering and takes advantage of an approximation to the Laplace operators for better scale selection.
An advantage of this approximation is that it can neglect the sound waves, which often highly influence the rarefied CFL condition.
A significant advantage of this approximation is that it is not necessary to specify which links are overloaded, critically loaded, or underloaded.
It exhibits advantage of stronger approximation property, faster convergence rate, greater storage capacity and higher fault tolerance than low-order neural networks.
The advantage of this approximation is that the contribution of each biotic niche variable, Bi, is independent of the rest, so that, in the case where abiotic variables are also explicitly included, the relative importance of both biotic and abiotic factors can be studied.
The main advantage of this approximation is that the light propagator for the first process of light backscattering from the host medium, i.e., the Green's function G0(ρ d,0), is exactly the clean image, I c ¯, which has been computed directly from the measured data set as outlined above without assumption of any particular light propagation model in the medium.
This can be an important advantage of MEM-approximation over other methods for systems where it is difficult to estimate the non-resonant background.
The advantage of the bootstrap approximation is explained.
We will take advantage of the first approximation to determine (bar{u}_{1}(t)).
