Luis Vergara

Optimum detection of ultrasonic echoes applied to the analysis of the first layer of a restored dome

Optimum detection is applied to ultrasonic signals corrupted with significant levels of grain noise. The aim is to enhance the echoes produced by the interface between the first and second layers of a dome to obtain interface traces in echo pulse B-scan mode. This is useful information for the restorer before restoration of the dome paintings. Three optimum detectors are considered: matched filter, signal gating, and prewhitened signal gating. Assumed models and practical limitations of the three optimum detectors are considered. The results obtained in the dome analysis show that prewhitened signal gating outperforms the other two optimum detectors.

NDE ultrasonic methods to characterise the porosity of mortar

Premature damage of mortar and concrete structures, due to environmental action, demands procedures to estimate durability of this type of components. Mortar or concrete composition (e.g. grain size, type and percentage of sand) may have some influence in the durability, but it is mainly related to porosity, which determines the interaction between aggressive agents and material. In this work, several IDE ultrasonic methods to estimate porosity of mortar are presented and evaluated. In these methods, porosity is related to (1) the material structural noise, (2) sound velocity and (3) ultrasonic attenuation. In all these methods, mortar is consider to be formed by only two phases: solid and pores.

Studies on ultrasonic scattering from quasi-periodic structures

This study extends the work done on nonuniform phase statistics by including additional results based on quasi-periodic scattering. Three parameters are used to predict the presence of regular structure within the region of interest. The signal-to-noise ratio of phase and the /spl chi//sup 2/ statistic resulting from conducting a goodness of fit test are two parameters used to verify whether the phase signal followed a uniform distribution. A third parameter, the power spectral density (PSD), was studied and its ability to provide information on the level of periodicity present was analyzed. Computer simulations and experiments on tissue mimicking phantoms were carried out, the results of which indicate that the parameters introduced in this paper have good potential in providing a better understanding of scattering from a collection of quasi-periodic scatterers.

A general procedure for learning mixtures of independent component analyzers

This paper presents a new procedure for learning mixtures of independent component analyzers. The procedure includes non-parametric estimation of the source densities, supervised-unsupervised learning of the model parameters, incorporation of any independent component analysis (ICA) algorithm into the learning of the ICA mixtures, and estimation of residual dependencies after training for correction of the posterior probability of every class to the testing observation vector. We demonstrate the performance of the procedure in the classification of ICA mixtures of two, three, and four classes of synthetic data, and in the classification of defective materials, consisting of 3D finite element models and lab specimens, in non-destructive testing using the impact-echo technique. The application of the proposed posterior probability correction demonstrates an improvement in the classification accuracy. Semi-supervised learning shows that unlabeled data can degrade the performance of the classifier when they do not fit the generative model. Comparative results of the proposed method and standard ICA algorithms for blind source separation in one and multiple ICA data mixtures show the suitability of the non-parametric ICA mixture-based method for data modeling.

Estimation of velocity fluctuation in internal combustion engine exhaust systems through beamforming techniques

The knowledge of the particle velocity fluctuations associated with acoustic pressure oscillation in the exhaust system of internal combustion engines may represent a powerful aid in the design of such systems, from the point of view of both engine performance improvement and exhaust noise abatement. However, usual velocity measurement techniques, even if applicable, are not well suited to the aggressive environment existing in exhaust systems. In this paper, a method to obtain a suitable estimate of velocity fluctuations is proposed, which is based on the application of spatial filtering (beamforming) techniques to instantaneous pressure measurements. Making use of simulated pressure-time histories, several algorithms have been checked by comparison between the simulated and the estimated velocity fluctuations. Then, problems related to the experimental procedure and associated with the proposed methodology are addressed, making application to measurements made in a real exhaust system. The results indicate that, if proper care is taken when performing the measurements, the application of beamforming techniques gives a reasonable estimate of the velocity fluctuations.

Measurement of cement porosity by centroid frequency profiles of ultrasonic grain noise

In this paper, we propose a technique for material characterization by using centroid frequency profiles (CFP) of ultrasound echo signals. These echo signals are composed by grain noise due to the superposition of many small echoes from the inner microstructure plus observation noise. A CFP indicates the centroid frequency dependence on depth, corresponding to power spectrum density assessments at different depths. We show in the paper the relation between the mean and variance of the CFP and the grain-to-observation-noise-ratio (GOR) at every depth. The GOR depends on the material ultrasound attenuation, so that CFP may be used for material characterization. Although we consider here the estimation of cement paste porosity, the proposed technique may have general applicability. Cement paste is the main component of mortar and concrete. Therefore, cement porosity is an important problem because the vulnerability (and thence the durability) of these construction materials to external agents depends heavily on it. Experiments have been made to show the correlation between cement paste porosity and a penetration parameter obtained from the CFP.

Infrared image processing and its application to forest fire surveillance

This paper describes an scheme for automatic forest surveillance. A complete system for forest fire detection is firstly presented although we focus on infrared image processing. The proposed scheme based on infrared image processing performs early detection of any fire threat. With the aim of determining the presence or absence of fire, the proposed algorithms performs the fusion of different detectors which exploit different expected characteristics of a real fire, like persistence and increase. Theoretical results and practical simulations are presented to corroborate the control of the system related with probability of false alarm (PFA). Probability of detection (PD) dependence on signal to noise ration (SNR) is also evaluated.

Fast communication: On including sequential dependence in ICA mixture models

We present in this communication a procedure to extend ICA mixture models (ICAMM) to the case of having sequential dependence in the feature observation record. We call it sequential ICAMM (SICAMM). We present the algorithm, essentially a sequential Bayes processor, which can be used to sequentially classify the input feature vector among a given set of possible classes. Estimates of the class-transition probabilities are used in conjunction with the classical ICAMM parameters: mixture matrices, centroids and source probability densities. Some simulations are presented to verify the improvement of SICAMM with respect to ICAMM. Moreover a real data case is considered: the computation of hypnograms to help in the diagnosis of sleep disorders. Both simulated and real data analysis suggest the potential interest of including sequential dependence in the implementation of an ICAMM classifier.

Material grain noise analysis by using higher-order statistics

We have derived equations relating to the cumulants of the backscattered signal to material and transducer parameters. Then, we proposed a practical method to estimate the material grain moments from estimates of the cumulants at some particular values. The proposed model and techniques are verified on some phantoms having different scatterer density and grain sizes.

Combination of multiple detectors for EEG based biometric identification/authentication

The different structures of the brain of human beings produce spontaneous electroencephalographic (EEG) records that can be used to identify subjects. This paper presents a method for biometric authorization and identification based on EEG signals. The hardware uses a simple 2-signal electrode and a reference electrode configuration. The electrodes are positioned in such a way to be as unobtrusive as possible for the tested subject. Multiple features are extracted from the EEG signals that are processed by different classifiers. The system uses all the possible combinations between classifiers and features, fusing the best results. The fused decision improves the classification performance for even a small number of observation vectors. Results were obtained from a population of 50 subjects and 20 intruders, both in authentication and identification tasks. The system obtains an Equal Error Rate (EER) of 2.4% with only a few seconds for testing. The obtained performance measures are an improvement over the results of current EEG-based systems.