Exact(1)
Here we use a linear filter of taps to estimate 's and 's in the iteration (23).
Similar(59)
Three improved-accuracy models were used in the simulations to replace the corresponding standard KIVA-3 models: (1) a droplet-vaporization model including multiple components and real-gas effects; (2) a droplet-dispersion model, using a linear filter; and (3) a swirl correction to the k-ϵ turbulence model.
Two specific stochastic models are proposed, a simple widely used seasonal model with short memory to which long-term persistence is imposed using a linear filter, and a combination of two sub-models, a stationary one with long memory and a cyclostationary one with short memory.
In a nutshell, the BLUE uses a linear filter F to produce an unbiased estimate c ^ = F y, whose mean squared-error (MSE) w.r.t.
(b) y (during several cycles) after filtering x using a linear filter (solid black line) and the linear trends obtained if only one cycle is used (red dashed line), two (green dashed line), three (blue dashed line), and so on (from Adler and Elias 2008).
In particular, the signal x m (k) can be predicted from the signal x n (k) using a linear filter matrix W n,m such that: mathbf{x}_{m}(k) = mathbf{W}_{n,m}^{T} mathbf{x}_{n} k), quad m=1, 2, 3, ldots, N, (9). with W n,n =I L. The prediction matrices can be concatenated so as to form the L×N L matrix: mathbf{W}_{n} = left[mathbf{W}_{n,1}; mathbf{W}_{n,2}; ldots; mathbf{W}_{n,N} right], (10).
The purpose of this article is to present evidence that when judiciously grouped, the OTC data show time-dependent correlations with clinical data, and that the latter can be reconstructed from the former using a linear filter.
Turns typically last <4 s, which limits the length of stimulus history that can be used in a linear filter, which thus puts a ∼4-s upper bound on the length of stimulus response that can be predicted.
These intermediate position estimates are then filtered using a linear Kalman filter (KF) to produce the final target position estimates.
Assume that the random process has a finite bandwidth or has been filtered using a linear phase filter that does not disturb the higher order spectral parameters of interest and is thus made band limited.
Butterworth-Heinemann, New York] is that the gaussian field is directly generated from its autocorrelation function and the use of a linear filter transform is avoided.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com