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Both the parameters of the Morlet wavelet associated with the maximal correlation value and the cyclic period are effective in feature detection of the impulse response.
Figure 3(c) gives the waveform of the correlation value which is the result of correlation filtering, in which the maximal correlation value is marked.
The correlation coefficient between constructed cyclic Morlet wavelet and the vibration signal under different parameter period is given in Figure 3(d), in which the maximal correlation value is marked and the corresponding period is s.
Correlation filtering, enlightened from matching pursuit, is used based on Laplace wavelet to identify the parameters of impulse response by calculating the maximal correlation value, which is employed by Freudinger et al. to identify the modal parameters of a flutter for aerodynamic and structural testing [12].
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Data from Affymetrix and Illumina arrays were also highly concordant with minimal and maximal correlation values ranging between 0.795 and 0.889, respectively.
The maximal cross correlation value of the mean waveforms from the two epochs' clusters (Fig. 7B) was used to confirm that multiple clusters corresponded to the same unit.
We examined the two separate regions within 500 bp upstream and downstream of each TSS, and calculated the optimal periodicity associated with the maximal auto-correlation value in each region.
Next, the optimal value for the maximal correlation threshold ρmax, used in the proposed CCI mitigation technique, was investigated.
If the maximal correlation is above a preset threshold value (CorrThr), it is accepted that both ECGs belong to one and the same person and the identity of the tested person is verified.
As this value is relatively close to the maximal possible correlation (81% of the maximal correlation), this indicates that the difference pattern is the major component encoded by the neurons.
If such correlation value is greater than previous correlation value, the new gene is added.
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