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We note that according to the choice of optimization algorithms, an optimal configuration (widehat {mathbf {x}}) that satisfies the optimization problem in (21) can vary because there is a trade of the costs F x) and H(n).
Compared to the maximal power consumption, the EAF-MR optimization found an optimal configuration that has a power consumption in a reasonable range of energy efficiency.
In the second phase, starting from the found configuration, the multi-resolution optimization finds an optimal configuration that has the lowest power consumption satisfying the SNR specification for a given communication standard.
While the multi-resolution optimization method took 2172 simulations for finding an optimal configuration, our EAF-MR optimization method required only two simulations.
Using a systematic optimization approach results in an optimal configuration and size for the electromagnetic components of the self-powered sensor.
The optimization problem for searching an optimal configuration in a small-scale reconfigurable RF front-end was previously defined in [9 11] as follows, begin{array}rcl@ begin{array}{ll} min quad F(mathbf{x}), mathbf{x} text{scriptsize s.t.
The EAF optimization method was shown to reduce the computational cost significantly, finding an optimal configuration after only five iterations instead of searching all possible configurations exhaustively.
The researchers then crunched through the 700 different shapes to produce an optimal configuration that would work at all speeds.
Consequently, an optimal configuration for a faster consensus requires a careful balance between those two factors.
We can now propose a model for choosing an optimal configuration sequence.
However, as an optimal configuration exists, finding a global optimum is not necessary.
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