In [12], a statistical AV model based on Gaussian mixture models (GMMs) is presented for measuring the coherency of audio and its corresponding video and is used for extracting speech of interest from instantaneous squared mixtures on a simple French logatoms AV corpus.
Another recent study by [ 10] has also measured the coherency of evolutionary conservation of interaction patterns in an integrated network of S. cerevisiae comprising transcriptional and protein-protein interaction data.
The former, (r_{0}), measures the spatial coherency of the point cloud, in terms of the difference between the point cloud's diameter and the radius of the point neighborhoods necessary to obtain a single connected component.
Hence, unlike the GMM model, it can capture temporal dynamics from a limited context of frames around the current frame to enhance the coherency measure.
The coherency attribute is a measure of lateral changes in acoustic impedance caused by variations in structure, stratigraphy, lithology, porosity, and fluid content.
Each domain will be individually assessed using Cronbach's alpha, the standard statistical technique for assessing the coherency of each item within each domain.
The rotating speed deviation between the measured speed and the synchronized speed should be considered in the coherency identification.
Some easy interpretations can be made for simple examples: constant areas are characterized by λ 1 = λ 2 ≈ 0, straight edges are such that λ 1 ≫ λ 2 ≈ 0, their orientation being that of e 2, corners yield λ 1 ≥ λ 2 ≫ 0. The coherency c of the local image is measured by the ratio c = λ 1 − λ 2 λ 1 + λ 2, large coherency reveals anisotropy in the texture.
Thus, the proposed the coherency identification method based on hierarchical clustering algorithm is applied to identify the coherent groups for the swing curves measured by WAMS.
The interhemispheric coherency is in general a little higher than the coherency with the AE index.
