This paper presents a novel system for application in evaluation of ashlar masonry walls inspecte... more This paper presents a novel system for application in evaluation of ashlar masonry walls inspected with Ground Penetrating Radar (GPR). The system includes the following processing of the GPR signal: elimination of the background from the backscattered signals; depth resolution enhancing; automatic gain control for visualization; and automatic generation of B-Scans (radargrams) of the internal state of the wall. Inhomogeneities in the structure of the wall are detected from the B-Scans. Several experiments were made on scale models of historic ashlar masonry walls. Controlled loads with different weights were applied on the walls, and the corresponding B-Scans were obtained. It is demonstrated the capability of the system for the detection of nook and crack flaws, and for characterizing the deformations of the walls under different steps of weight loads.
Infrared spectroscopy provides direct information on the composition of interstellar dust grains,... more Infrared spectroscopy provides direct information on the composition of interstellar dust grains, which play a fundamental role in the evolution of interstellar medium ISM, from cold, quiet and low density molecular clouds to warm, active and dense protostellar ones. The determination of these components is fundamental to predict under the appropriate environmental conditions their evolution, including the appearance of new molecules, radicals and complex organics. The absorption spectrum of the astrophysical ice can be considered the additive linear absorption spectra of the multiple molecules present in the ice, so a linear instantaneous ICA mixture model is appropriate. We present the ICA statement of the problem, discussing the convenience of the model and its advantages in front of supervised methods. We obtain the MAP estimate of the mixing matrix, including its non-negative entries as a prior. We present the results carried out with an ice analogs database, confirming the suitability of the ICA approach.
This paper presents a new method for the reconstruction of missing data in seismic signals. The m... more This paper presents a new method for the reconstruction of missing data in seismic signals. The method is based on Wiener systems considering non-Gaussian statistics in the probability density function of the seismic data. Wiener structures are proposed combining different techniques for the linear and non-linear stages. The linearity in the data is recovered using kriging and cross correlation, and the data nonlinearity is reconstructed using direct sample estimation and a third order polynomial approximation. The results by linear and Wiener structures are compared with the results of Multi-Layer Perceptron and Radial Basis Function networks. Several examples with real data demonstrate the efficiency of the method for seismic trace reconstruction. The accuracy of the recovered data is evaluated by the error of the estimates and statistics of the data density for the recovered data.
This paper presents two novel applications of ICA in Non Destructive Evaluation by ultrasounds ap... more This paper presents two novel applications of ICA in Non Destructive Evaluation by ultrasounds applied to diagnosis of the material consolidation status and to determination of the thickness material profiles in restoration of historical buildings. In those applications the injected ultrasonic pulse is buried in backscattering grain noise plus sinusoidal phenomena; this latter is analyzed by ICA. The mixture matrix is used to extract useful information concerning to resonance phenomenon of multiple reflections of the ultrasonic pulse at non consolidated zones and to improve the signals by detecting interferences in ultrasonic signals. Results are shown by real experiments at a wall of a Basilica’s restored cupola. ICA is used as pre-processor to obtain enhanced power signal B-Scans of the wall.
This paper presents an application of neural networks in pattern recognition of defects in sonic ... more This paper presents an application of neural networks in pattern recognition of defects in sonic signals from non-destructive evaluation by multichannel impact-echo. The problem approached consists in allocating parallelepiped-shape materials in four levels of classifications defining material condition (homogeneous or defective), kind of defects (holes and cracks), defect orientation, and defect dimension. Various signal features as centroid frequency, attenuation and amplitude of the principal frequency are estimated per channel and processed by PCA and feature selection methods to reduce dimensionality. Results for simulations and experiments applying Radial Basis Function, Multilayer Perceptron and Linear Vector Quantization neural networks are presented. Neural networks obtain good performance in classifying several 3D finite element models and specimens of aluminum alloy.
This article presents an ICA model for applying in Non Destructive Testing by Impact-Echo. The ap... more This article presents an ICA model for applying in Non Destructive Testing by Impact-Echo. The approach consists in considering flaws inside the material as sources for blind separation using ICA. A material is excited by a hammer impact and a convolutive mixture is sensed by a multichannel system. Obtained information is used for classifying in defective or non defective material. Results based on simulation by finite element method are presented, including different defect geometry and location.
This paper presents a novel procedure to classify data from mixtures of independent component ana... more This paper presents a novel procedure to classify data from mixtures of independent component analyzers. The procedure includes two stages: learning the parameters of the mixtures (basis vectors and bias terms) and clustering the ICA mixtures following a bottom-up agglomerative scheme to construct a hierarchy for classification. The approach for the estimation of the source probability density function is non-parametric and the minimum kullback-Leibler distance is used as a criterion for merging clusters at each level of the hierarchy. Validation of the proposed method is presented from several simulations including ICA mixtures with uniform and Laplacian source distributions and processing real data from impact-echo testing experiments.
This paper presents a novel system for application in evaluation of ashlar masonry walls inspecte... more This paper presents a novel system for application in evaluation of ashlar masonry walls inspected with Ground Penetrating Radar (GPR). The system includes the following processing of the GPR signal: elimination of the background from the backscattered signals; depth resolution enhancing; automatic gain control for visualization; and automatic generation of B-Scans (radargrams) of the internal state of the wall. Inhomogeneities in the structure of the wall are detected from the B-Scans. Several experiments were made on scale models of historic ashlar masonry walls. Controlled loads with different weights were applied on the walls, and the corresponding B-Scans were obtained. It is demonstrated the capability of the system for the detection of nook and crack flaws, and for characterizing the deformations of the walls under different steps of weight loads.
Infrared spectroscopy provides direct information on the composition of interstellar dust grains,... more Infrared spectroscopy provides direct information on the composition of interstellar dust grains, which play a fundamental role in the evolution of interstellar medium ISM, from cold, quiet and low density molecular clouds to warm, active and dense protostellar ones. The determination of these components is fundamental to predict under the appropriate environmental conditions their evolution, including the appearance of new molecules, radicals and complex organics. The absorption spectrum of the astrophysical ice can be considered the additive linear absorption spectra of the multiple molecules present in the ice, so a linear instantaneous ICA mixture model is appropriate. We present the ICA statement of the problem, discussing the convenience of the model and its advantages in front of supervised methods. We obtain the MAP estimate of the mixing matrix, including its non-negative entries as a prior. We present the results carried out with an ice analogs database, confirming the suitability of the ICA approach.
This paper presents a new method for the reconstruction of missing data in seismic signals. The m... more This paper presents a new method for the reconstruction of missing data in seismic signals. The method is based on Wiener systems considering non-Gaussian statistics in the probability density function of the seismic data. Wiener structures are proposed combining different techniques for the linear and non-linear stages. The linearity in the data is recovered using kriging and cross correlation, and the data nonlinearity is reconstructed using direct sample estimation and a third order polynomial approximation. The results by linear and Wiener structures are compared with the results of Multi-Layer Perceptron and Radial Basis Function networks. Several examples with real data demonstrate the efficiency of the method for seismic trace reconstruction. The accuracy of the recovered data is evaluated by the error of the estimates and statistics of the data density for the recovered data.
This paper presents two novel applications of ICA in Non Destructive Evaluation by ultrasounds ap... more This paper presents two novel applications of ICA in Non Destructive Evaluation by ultrasounds applied to diagnosis of the material consolidation status and to determination of the thickness material profiles in restoration of historical buildings. In those applications the injected ultrasonic pulse is buried in backscattering grain noise plus sinusoidal phenomena; this latter is analyzed by ICA. The mixture matrix is used to extract useful information concerning to resonance phenomenon of multiple reflections of the ultrasonic pulse at non consolidated zones and to improve the signals by detecting interferences in ultrasonic signals. Results are shown by real experiments at a wall of a Basilica’s restored cupola. ICA is used as pre-processor to obtain enhanced power signal B-Scans of the wall.
This paper presents an application of neural networks in pattern recognition of defects in sonic ... more This paper presents an application of neural networks in pattern recognition of defects in sonic signals from non-destructive evaluation by multichannel impact-echo. The problem approached consists in allocating parallelepiped-shape materials in four levels of classifications defining material condition (homogeneous or defective), kind of defects (holes and cracks), defect orientation, and defect dimension. Various signal features as centroid frequency, attenuation and amplitude of the principal frequency are estimated per channel and processed by PCA and feature selection methods to reduce dimensionality. Results for simulations and experiments applying Radial Basis Function, Multilayer Perceptron and Linear Vector Quantization neural networks are presented. Neural networks obtain good performance in classifying several 3D finite element models and specimens of aluminum alloy.
This article presents an ICA model for applying in Non Destructive Testing by Impact-Echo. The ap... more This article presents an ICA model for applying in Non Destructive Testing by Impact-Echo. The approach consists in considering flaws inside the material as sources for blind separation using ICA. A material is excited by a hammer impact and a convolutive mixture is sensed by a multichannel system. Obtained information is used for classifying in defective or non defective material. Results based on simulation by finite element method are presented, including different defect geometry and location.
This paper presents a novel procedure to classify data from mixtures of independent component ana... more This paper presents a novel procedure to classify data from mixtures of independent component analyzers. The procedure includes two stages: learning the parameters of the mixtures (basis vectors and bias terms) and clustering the ICA mixtures following a bottom-up agglomerative scheme to construct a hierarchy for classification. The approach for the estimation of the source probability density function is non-parametric and the minimum kullback-Leibler distance is used as a criterion for merging clusters at each level of the hierarchy. Validation of the proposed method is presented from several simulations including ICA mixtures with uniform and Laplacian source distributions and processing real data from impact-echo testing experiments.
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Papers by Addisson Salazar