Hence, the objective becomes to reach a terminating state in minimum expected time by choosing the next state.
Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem.
Constructal optimizations of the models with constant and discrete variable cross-sectional high conductivity channels (HCCs) are performed by choosing minimum maximum temperature difference (MTD) as optimization objective.
Indeed, with the minimum arc distance method, by choosing the sampling frequency carefully, the harmonic frequency band caused by nonlinearity has been separated from the signal frequency band, so the contamination to the signal frequency band is greatly eliminated.
Ghanekar [30] detects candidates by comparing pixel with extreme values in a 3 × 3 window, then performs the final detection by choosing minimum and maximum locations among locations of four maximum differences, and compares pixel with intensity values at those minimum and maximum locations.
The algorithms are then compared over a large application space (finding, for different numbers of tags, the minimum energy consumption and minimum read out delay possible by choosing the best coefficient and the best ICW).
However, their values can be favorably compared with the results presented for these quantities in the work of Lima et al. [41], where the minima has been obtained by choosing a parameter that enforces that behavior.
