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In the present implementation, an artificial neural network surrogate model is employed but the method can, in principle, be applied with any surrogate model form.
The main advantage of our method is that it is model independent, i.e., it can be applied with any classifier that provides a probability estimate for a prediction.
We also propose an enhancement technique, called Adaptive Regular Stream Triggering (ART), which can be applied with any scheduling policy to enhance stream merging.
Although the proposed scheme can be applied with any number of cells, for the homogeneous deployment, the interferences to one cell are mainly from the adjacent two cells.
Our approach can be applied with any visual sensor obeying approximately a central projection model, presents good robustness to image noise, and avoids the problem of a short baseline by exploiting the information of three views.
(iv) Although ART in principle can be applied with any scheduling policy, including PCS, negative interference exists between ART and PCS, and thus their combination generally achieves worse than any of them applied individually.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com