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MSE has been proposed as a biologically meaningful complexity measure by quantifying the entropy over multiple time scales inherent in physiologic signals.
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Since it is believed that a high differential entropy is a necessary property of secure image hashes, we evaluate the security in terms of unpredictability of FJLT hashing by quantifying the differential entropy of the FJLT hash vector, as proposed in [8].
However, they suffer from the following shortcomings: 1. IC evaluates a motif by quantifying the relative entropy of the motif PFM (Positional Frequency Matrix) under assumption of model independence.
ENS is calculated by taking the exponential of the Shannon Wiener index (H′), which quantifies the entropy in a set of samples (Shannon, 1948).
The various algorithms typically used to quantify the complexity (entropy) of CoP may yield very different results, particularly when sampling frequency and noise are different.
Specifically, we quantified the conditional entropy, Ĥ, of an IS given a CS.
With regard to energy, it is useful to think of entropy as quantifying the concentration (or availability) of the form of energy.
The multiscale entropy (MSE) is a novel method for quantifying the intrinsic dynamical complexity of physiological systems over several scales.
Several other measures (such as singular value decomposition, entropy, joint entropy and mutual information) applied to time series generated by the phase flows utilized, have been tested in quantifying the functional modes' complexity, always giving converging results.
In the present paper, we studied interrelations between classical and novel entropy measures to quantify the structural information content of networks.
This parameter quantifies the amount of entropy in the movement of a neuron by comparing the length of the total path of the cell between two different time points and the straight distance between them.
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