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Tai et al. [20] proposed a deblocking algorithm based on five filtering modes by considering the activity across block boundaries.
These five filtering modes include three frequency-related modes (the smooth mode, the intermediate mode, and the complex mode), one special mode, and a refined mode.
In this work, a novel deblocking algorithm is proposed based on three filtering modes in terms of the activity across block boundaries.
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In Figure 11 we present the result of the mode filtering for modes nos., (mode with very low energy), and (the mode with the highest energy).
The redundant factor of RARKF is designed as a function of the motor current for tolerating the modeling error and load-dependent filtering mode switching.
In this method, filter noises and irrelevant features were filtered using Fisher classification and information gain in the filtering mode, respectively, to obtain overlapping feature subsets and effectively reduce modeling and detection time.
Based on a harmonic drive model and a redundant adaptive robust Kalman filter (RARKF), the proposed approach can adapt torque estimation filtering optimality and robustness to the load variation by self-tuning the filtering gain and self-switching the filtering mode between optimal and robust.
Consequently, the signal becomes stationary and classical filtering tools can be used to filter modes.
In this case, one can use classical frequency filtering tools to filter modes.
Once representation of the signal is calculated, a mask filtering has to be applied to filter modes.
Creative processing and filter modes produce high quality finished images, while offering the ability to explore artistic freedom through unique special effects.
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