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Table 2 shows that the standard deviation of the differences between observations and estimates decreases as the estimation time scale increases (for both PM10 and ozone cases).
In order to accomplish both accurate TOA estimation and to reduce the estimation time, a two-step algorithm is proposed where first a coarse TOA estimate is obtained starting from received signal energy samples, then the arrival time of the first signal path is refined by considering a hypothesis testing approach.
In this study, the estimation time is not optimized and the hysteresis effect is not solved.
Intuition indicates that smoothing is best if the estimation time is the center of the observation interval.
Also, the registration times of identifications may not coincide with the estimation time of the corresponding positions.
The integration of scene structure information to our NPCS algorithm can greatly reduce the estimation time, as described in Section 2 and illustrated in Table 4.
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The proposed method was able to estimate the network including 70 genes, and the estimation times were almost the same as those of the GHC method.
As in the table, the Cond MinSad successively reduces the motion estimation time of the original Enhanced Predictive Zonal Search, and the average reduction ratio is 0.22.
With multiple robots, the field estimation time includes the time taken for sensing, computation, communication, and final fusion to recover the field density distribution.
As in the table, Cond M i n S a d,S i m successively reduces the motion estimation time, and the average reduction ratio is 0.36.
It is also remarked the motion estimation time consumption (MEtime).
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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