Your English writing platform
Discover LudwigSuggestions(5)
Exact(12)
However, when the node is closer to a specific reference, its mean location error achieves a lower value.
Therefore, given that the real node position is determinant for the error in range estimation, it is reasonable to assume that the location ordering may affect the mean location error.
Figure 3 Mean location error for different node positions.
Figure 1 Mean location error with respect to node density.
Figure 5 Mean location error with respect to node deployment.
Figure 3 Mean location error with respect to anchor ratio.
Similar(47)
Figure 9 plots the root mean square location error in terms of size for the three algorithms.
In the minimalist case involving four receivers, the HPB perimeter-pairs variation localizes a transmitting device with a root mean square location error of 107 meters.
In a vehicular scenario, we achieve a root mean square location error of 107 meters with four receivers and of 96 meters with eight receiving devices.
For example in the presence of 32 receivers, for confidence level, only 50% of executions yield a candidate area containing a malicious transmitter, as shown in Figure 5. Yet the same scenario localizes a transmitter with a root mean square location error of 45 meters of its true location, whether it lies within the corresponding candidate area or not.
Over all source locations and methods, the mean localization error when using the inner skull boundary was significantly lower than when using the scalp surface (scalp/inner skull = 9.08 mm/6.76 mm P < 0.0002).
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