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Timeliness is determined by the interval between any two steps within a surveillance system.
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We show that the optimum evolutionary trajectory (fitness landscape) has the property that the relative fitness increase between any two consecutive steps is constant.
We assume a standard distributed computing model (see, e.g., [5, 21] for additional details) where n robots, p1,…,p n, are sequential processes (state machines), that run asynchronously, namely, the time between any two consecutive steps of the same robot is arbitrarily long.
In the final minutes of the match, Lethal was trying to reposition himself on a ladder, however, he got his leg stuck in-between two steps.
We alternate between these two steps until convergence.
Our main improvement is embedding the SUSC between these two steps.
A link procedure for fitting the cross sections is developed between these two steps.
The process of exchangin informations between the two steps is then iterated until convergence to a fixed point.
Therefore, the average Amean between the two steps was used to compute SFAF.
Iteration between these two steps increases the joint likelihood until convergence is reached.
We have to consume time for data processing between the two steps of the proposed method.
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