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Abstract: This paper introduces the application of independent component analysis mixture modelling (ICAMM) in non-destructive testing (NDT). The application consists of discriminating patterns for material quality control from homogeneous and defective materials inspected by impact-echo testing. This problem is modelled as a mixture of independent component analysis (ICA) models, representing a class of defective or homogeneous material by an ICA model whose parameters are learned from the impact-echo signal spectrum. These parameters define a kind of particular signature for the different defects. The proposed procedure is intended to exploit to the maximum the information obtained with the cost efficiency of only a single impact. To illustrate this capability, four levels of classification detail (material condition, kind of defect, defect orientation, and defect dimension) are defined, with the lowest level of detail having up to 12 classes. The results from several 3D finite element models and lab specimens of an aluminium alloy that contain defects of different shapes and sizes in different locations are included. The performance of the classification by ICA mixtures is compared with linear discriminant analysis (LDA) and with multi-layer perceptron (MLP) classification. We demonstrate that the mass spectra from impact-echo testing fit ICAMM, and we also show the feasibility of ICAMM to contribute in NDT applications. [Copyright &y& Elsevier]

We consider a classifier based on Independent Component Analysis Mixture Modelling (ICAMM) to model the feature jointprobability density. This classifier is applied to a challenging novel application: classification of archaeological ceramics. ICAMM gathers relevant characteristics that have general interest for material classification. It can deal with arbitrary forms of the underlying probability densities in the feature vector space as nonparametric methods can do. Mutual dependences among the features are modelled in a parametric form so that ICAMM can achieve good performance even with a training set of relatively small size, which is characteristic of parametric methods. Moreover, in the training stage, ICAMM can incorporate probabilistic semisupervision (PSS): labelling by an expert of a portion of the whole available training set of samples. These properties of ICAMM are well-suited for the problem considered: classification of ceramic pieces coming from four different periods, namely, Bronze Age, Iberian, Roman, and Middle Ages. A feature set is obtained from the processing of the ultrasonic signal that is recorded in through-transmission mode using an ad hoc device. A physical explanation of the results is obtained with comparison with classical methods used in archaeology. The results obtained demonstrate the promising potential of ICAMM for material classification. [ABSTRACT FROM AUTHOR]

Conventional partial correlation coefficients (PCC) were extended to the non-Gaussian case, in particular to independent component analysis (ICA) models of the observed multivariate samples. Thus, the usual methods that define the pairwise connections of a graph from the precision matrix were correspondingly extended. The basic concept involved replacing the implicit linear estimation of conventional PCC with a nonlinear estimation (conditional mean) assuming ICA. Thus, it is better eliminated the correlation between a given pair of nodes induced by the rest of nodes, and hence the specific connectivity weights can be better estimated. Some synthetic and real data examples illustrate the approach in a graph signal processing context. [ABSTRACT FROM AUTHOR]

Recent works in signal processing on graphs have been driven to estimate the precision matrix and to use it as the graph Laplacian matrix. The normalized elements of the precision matrix are the partial correlation coefficients which measure the pairwise conditional linear dependencies of the graph. However, the non-linear dependencies inherent in any non-Gaussian model cannot be captured. We propose in this paper a generalized partial correlation coefficient which is derived by assuming an underlying multivariate Gaussian Mixture Model of the observations. Exact and approximate methods are proposed to estimate the generalized partial correlation coefficients from estimates of the Gaussian Mixture Model parameters. Thus it may find application in any non-Gaussian scenario where the Laplacian matrix is to be learned from training signals. [ABSTRACT FROM AUTHOR]