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end{aligned}Furthermore, all updated phase-field values ((phi _alpha ^{n+1}=phi ^{n}_alpha +Mtriangle t; rhs_alpha)), with values smaller than zero, are inadmissible and are set to zero.

ϕ t i is an inverse Fourier transform of U t i ( x, y ), which is defined in step 5 of Algorithm 2. ϕis1 is the updated phase of the semi-sparse wave field in the ith iteration.

Given a motion model p ( x t | x t − 1, x ̇ t ) and a sensing model p z t |x t ), we recursively apply a prediction phase where we incorporate motion to update the state and an update phase where the sensing data is used to compute the state's posterior distribution.

Then, a hypothesis updating phase is performed: It starts with the generation of the new possible tracks for each mobile object present in each hypothesis.

In the updating phase, we use an exponential weighted moving average (EWMA) method to update the PDR for each transmission power level and use the result to select the optimal level.

In this comparison, a fixed value of 0.2 and a fixed percent of probing packets of % were used in the updating phase.

The target of the initialization phase is to quickly populate the PDR-table and select a good transmission power level to start with and then enter the updating phase as explained in Section 4.1.

For this purpose, the harmony memory updating phase is enhanced by considering also designs that are worse than the worst design stored in the harmony memory but are far enough from local optima.

We divided the mechanism into two phases; the initialization phase and the updating phase.

The initialization phase should be very short compared to the updating phase.

The performance of the updating phase with different and is investigated in Section 6.1.2.