Sentence examples for filtering approximation from inspiring English sources

In this study, a least squares optimal deformable filtering approximation is introduced as an efficient tool for linear SV filtering, in the context of restoring SV-degraded images.

In this study, we propose for the first time (to the best of our knowledge), to use this LS-optimal deformable filtering approximation as an efficient tool for restoring linearly SV-degraded images.

In this study, we propose using the least squares optimal deformable filtering approximation as an efficient tool for linear shift variant (SV) filtering, in the context of restoring SV-degraded images.

Compute inverse 3D WT of the filtered approximation and the denoised detail coefficients to obtain the estimate of Isq (see Section 2.2).

(6) Compute inverse 3D WT of the filtered approximation and the denoised detail coefficients to obtain the estimate of Isq (see Section 2.2).   (7) The square root of the resultant gives the denoised magnitude MR image.  .

The authors redefine the sensor localization problem within a graphical model framework and present the use of a recent generalization of particle filtering for approximation of sensor locations.

As the observation processing progresses, the weight variance increases until it gets to a point where the random measure resembles a very poor filtering distribution approximation.

The obtained FIR filter approximation formula of the digital ideal fractional delay operator z−α is similar to the well known Grünwald-Letnikov FIR digital filter approximation formula of the ideal analog fractional differentiator sα.

In this letter we present a closed form solution of some equiripple linear-phase half-band FIR filter approximation.

The design technique is based on the MacLaurin series expansion formula which is applied to a given function to obtain a closed form FIR digital filter approximation of the digital ideal fractional delay operator z−α.

We derive indirect estimators of conditionally heteroskedastic factor models in which the volatilities of common and idiosyncratic factors depend on their past unobserved values by calibrating the score of a Kalman-filter approximation with inequality constraints on the auxiliary model parameters.