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The main result of this paper determines the computational order of prediction from a dynamical statistical model underpinned by the dynamical computer model.
In other words, it is the modeller's task to consider the computational order of the operations during a simulation.
However, the conventional MC-CDMA system [8] utilizes the MMSE equalizer, which requires a matrix inversion operation, resulting in a computational order of O N3).
The multiangle resolution fast beamformer was proposed as a computationally efficient approximation of DS beamforming, reducing the computational order of complexity from to.
The new method improves maximum absolute and root mean squared errors of solution values as well as their computational order of convergence.
It is clearly seen from this table that the errors decrease monotonously with the increase of the computational order k for both considered cases.
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With the new computational ordering, the pixels considered or visited at any point during computation consist of a single connected component of pixels in the image, resembling a flood-fill that adapts to the grey level landscape.
As seen from Table 3, for carefully selected optimal values of (hbar_{f}=hbar_{g}=hbar _{theta}=-1), the logarithms of the residual errors decrease dramatically with increasing of the HAM computational orders.
This new algorithm is based on a different computational ordering of the pixels which is suggested by another immersion analogy than the one corresponding to the standard connected-component algorithm.
Furthermore, it is shown that the computational orders of predictions from this dynamical statistical model and from a black box statistical model are comparable, but the likelihood optimization of the former model is more efficient.
Therefore, the per-iteration computational complexity order of the proposed algorithm is O K N d 3 + N r 3 + N b 3 + N s 2 + K + 1 3.5.
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