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According to the Table 2, under λ Open image in new windowσ shift in the intercept, when autocorrelation is weak (ρ = 0.1), the MEWMA method performs relatively similar to MCUSUM control chart, and they also have better performance for detecting the small, moderate, and large shifts than the T2 control chart.
Although this method performs relatively better for a channel with a large delay spread, its BER performance is still far away from the perfect channel estimation.
The MH method performs relatively better than in Scenario 1, delivering messages in just under 3 min for the largest network.
Once the amount of autocorrelation coefficient is high, MCUSUM and MEWMA methods perform uniformly better than the T2 method and also, MCUSUM method performs relatively similar to MEWMA method.
In the remaining environments where the thresholding method performs relatively
These results again demonstrate that when the information is sufficient to estimate the appropriate level of aggregation, the Surveillance Tree method performs relatively better.
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Nonetheless, the proposed method performed relatively better for the Non-linear and Medical images under Motion Blur and Gaussian white noise.
In contrast, the remaining three methods perform relatively worse.
Consequently, depth saliency methods perform relatively worse than RGB saliency methods in terms of precision.
Meanwhile, we observe that the proposed methods perform relatively worse in hand-clapping class and running class.
As shown in Fig. 12c, in the depth saliency situation, the RGB saliency methods perform relatively worse than the RGB-D saliency methods in terms of precision.
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Write better and faster with AI suggestions while staying true to your unique style.
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