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Therefore, this paper proposes a new threshold selection method based on wavelet decomposition and kurtosis analysis, that is, the UWB signal is decomposed by wavelet transform, the threshold is obtained based on the kurtosis of energy blocks of low-frequency wavelet coefficient, and then the first energy block exceeding the threshold is treated as the TOA of signal.
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Quantification analysis was performed by the comparative CT method, which mathematically transforms the threshold cycle (CT) into the relative expression levels of genes (Perkin-Elmer User Bulletin).
By transforming the threshold-based sequence to a probability-based one, the sequence with minimum expected cost is proven to consist of either a single search round or an infinite sequence of increasing thresholds.
The regression line between the dk parameter and the 5′/3′-intensity ratio of the controls allows to transform the quality threshold of the latter ratio into a dk threshold.
At the second or more decomposition of wavelet transform, the new threshold proposed in this article controls the MSNP for image de-noising.
This standard curve was used to transform the critical threshold cycle (Ct) values to the relative number of S. mutans cells.
A standard curve was used to transform the cycle threshold into nanograms of DNA.
This allowed us to transform the logarithmic cycle threshold (CT) values into linear values.
Simulation results show that the sequential method outperforms the parameter estimation technique based on the chirp-Fourier transform since the threshold effect for this method is delayed when compared to the chirp transform-based method.
Similar to the case of the continuous, probabilistic model of survival, lower thresholds result in a neutral performance trade-off giving rise to a weak fitness tradeoff, whereas higher thresholds transform the same performance trade-off into a strong fitness trade-off.
This means that after transforming the scores, the threshold can be fixed independent of the pirate strategy.
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