The experiments will consider two types of dictionaries: a dictionary with uncorrelated elements (realization of a white noise process) and a dictionary with correlated elements [realizations of a second order AutoRegressive (AR) random process].
Unfortunately, many of these methods require a correlated finite element model or mass normalized mode shapes.
Although a large number of methods exist for detecting damage in a structure using measured modal parameters, many of them require a correlated finite element model, or at least, modal data of the structure for the intact state as baseline.
KLT or PCA is a data-dependent mathematical procedure that uses an orthogonal transformation to convert possibly correlated elements of the data into a set of linearly uncorrelated components called "principal components".
The results in terms of SNR (in dB) are given in Figure 4 both for the case of a dictionary of uncorrelated (left) and correlated elements (right).
These correlations are an index of the same sources for the correlated elements: the first ones may originate from sea spray which is characterized by the presence of NaCl, the second ones could be due to crustal source mainly composed of Al, Si.
The correlations, index of the same sources for the correlated elements, allow us to identify different clusters of parameters: the first one is due to NaCl particles, the second one to aluminosilicates and the last one to the compounds with calcium and other metal elements.
3. The diet is a complex mixture of foods and nutrients including many highly correlated elements; even if the nutrient exposures of interest could be measured exactly, it may be a formidable task to estimate the relationship between a specific nutrient, such as fat or fiber, and breast cancer risk, while accommodating other dietary factors.
We have developed a novel technique called the technique of determinant inequalities to maximize the mutual information by obtaining the upper and lower bounds of the correlated elements of the covariance matrix and applied it to an experimental taste sensor to demonstrate how certain tastes can be selectively suppressed.
The L+S approach assumes that the dynamic MRI data, represented as a space-time matrix M, is a linear superposition of L and S components, where L represents highly spatially and temporally correlated elements, such as the image background, while S captures dynamic information that is sparse in an appropriate transform domain.
These correlated elements are obtained; thanks to the filter H z): with ρ = 0.9 and φ = π/4.
