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In the present study, we analysed, by means of computer simulation, the convergence property of a modified version of the pose estimation algorithm proposed by Lavalleé and Szelinsky [33] and based on adaptive distance maps (ADM), in order to better understand the influence of local minima and to optimize the pose estimation in terms of accuracy and precision.
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An alternative method is based on adaptive weights corresponding to colour and distance of the supporting pixels to the anchor pixel.
We propose optimizations based on adaptive techniques.
Here, we proposed the load balancing for Adaptive Position Update (LAPU) routing protocol based on adaptive transmission techniques of beacons.
Rather, learning support systems are evaluated on the premise of the unevenness based on adaptive learning.
It is based on adaptive calculation to reach the solution.
Hypotheses posited in behavioural ecology are generally based on adaptive principles of conservation, optimization or efficiency.
The algorithm was originally proposed by Lavallée and Szeliski [33] for biplanar projection, and is based on 3D adaptive distance maps.
Two geographic distance matrices were used, one based on the linear distance and one based on the ln-transformed distance.
Two gene definitions were compared: one based on physical distance and the other based on genetic distance.
A distance matrix based on Jaccard's distance among sector × period centroids was then obtained.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com