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Currently existing state-of-the-art tempo detection algorithms are generally speaking based on methods of periodicity detection.
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Generally, the observation of life phenomena incurs certain costs and thus the number of sampling points in time is often small, as in the case of DNA microarray data. 1 For this reason, a reliable method of periodicity detection is needed for small datasets.
On a method of investigating periodicities in disturbed series with special reference to Wolfer's sunspot numbers.
Finally, for the Periodic model, we varied the periodicity from 10.0 to 12.0 bp (corresponding roughly to the range of DNA helix), and for each of these values, we also varied the amplitude parameter, which is a measure of the strength of periodicity (see Methods).
However, as noted by Ptitsyn et al. [ 32], this method allows random re-institution of periodicity unless permutations in position of size p are avoided.
However, most of the mentioned methods measure the periodicity of motion in the restricted situations like stationary camera and known environments, which are not applicable to our real-world application.
Training plan is based on the practice of method with a periodicity of 2 h per day.
In problems where the strength of periodicity is of interest, resampling methods are applicable.
The figures show that the proposed method generates the most accurate results in the sense of periodicity.
Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest.
Methods of identifying a new type of latent periodicity in DNA called latent profile periodicity, or profility, have been proposed in the present work.
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