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A new cross-spectral analysis procedure is proposed for the parametric estimation of the relationship between two time sequences in the frequency domain.
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There are few simple algorithms for extracting similar segment pairs between two time sequence datasets.
The algorithm proposed in [1] can extract similar sections between two time-sequence datasets, or in a single time-sequence dataset.
Figure 1 illustrates the difference between the two distance metrics in matching among points to calculate the distance between two time-series sequences C and Q.
The above equation is used to calculate the distance between two time series of similar length of time sequence n.
Comparing component trees by computing tree assignments yields a cosegmentation of two images; for cell tracking, cosegmentations between two time frames in a video sequence are of particular relevance.
A sequence of configurations between two time steps t - 1 and t is denoted by S t-1 t t-1 tontands temporal containsndences betemporalnsecorrespondences
OZANIAN: Between two and three times net revenue.
For example, accounting for the hypermutability of certain motifs may improve the accuracy of our estimates of the divergence time between two homologous sequences [ 16].
However, an interesting difference between the two time scale invariant transitions is an opposite order of the high and low-flux phase in the time-scale invariant sequence.
The both methods conduct data normalization before the DTW distance between two normalized time-series sequences is computed.
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