Limitation of the Lateral Angled Broadband Low Frequency Impact Excitation on the Non-Destructive Condition Assessment of the Timber Utility Poles

Timber utility poles play a significant role in the infrastructure of Australia as well as many other countries for power distribution and communication networks. Due to the advanced age of Australia’s timber pole infrastructure, substantial efforts are undertaken on maintenance and asset management to avoid any failures of the utility lines. Nevertheless, the lack of reliable tools for assessing the condition of in-service poles seriously jeopardizes the maintenance and asset management. For instance, each year approximately 300,000 poles are replaced in the Eastern States of Australia with up to 80% of them still being in a very good condition, resulting in major waste of natural resources and money. Non-destructive testing (NDT) methods based on stress wave propagation can potentially offer simple and cost-effective tools for identifying the in-service condition of timber poles. Nonetheless, most of the currently available methods are not appropriate for condition assessment of timber poles in-service due to presence of uncertainties such as complicated material properties, environmental conditions, interaction of soil and structure, and an impact excitation type. In order to address these complexities, advanced digital signal processing methodologies are needed to be employed. Deterministic signal separation, blind signal separation, and frequency-wavenumber velocity filtering are the three groups of methodologies, which could most probably provide solutions. In this paper applicability and effectiveness of the blind signal separation methods is investigated through a numerical data obtained from of a timber pole modelled with both isotropic and orthotropic material properties. Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and K-means clustering algorithms are the blind signal separation methodologies that are employed in this research work

Desacoplamento de sistemas de controle multivariáveis por ICA com modificação do branqueamento.

A utilização de sensores em sistemas de controle de processos é de vital importância para o monitoramento e operação adequada das plantas industriais. Por sua vez, os sinais podem apresentar interferências de outras fontes, além de que em certos casos, não é possível observar diretamente os sinais individuais das fontes. Diante disso, as técnicas de processamento e separação de sinais são utilizadas no intuito de extrair as informações das fontes contidas nos sinais misturados. As principais técnicas de separação de sinais estão associadas a técnica ICA (Independent Component Analysis), que sofreu significativa evolução desde sua criação nos anos 80. Tal evolução teve contribuição também da técnica PCA (Principal Component Analysis) e do desenvolvimento da capacidade de processamento computacional. No entanto, essas técnicas apresentam dois problemas básicos, a saber: desvio de amplitude e mudança de fase, sendo tais problemas limitantes quanto a sua utilização em sistemas de controle. Sendo assim, esse trabalho tem como objetivo apresentar uma solução do problema de amplitude das técnicas ICA’s para utilização na redução do acoplamento de sistemas multivariáveis. A correção proposta, baseada na correção da etapa de branqueamento dos algoritmos ICA, gerando a técnica MOD-ICA, foi utilizada como alternativa para a quebra da correlação entre variáveis dos sistemas multivariados. Essa técnica foi utilizada para o projeto e obtenção dos pares de controle de uma planta de produção de etanol anidro modelada na plataforma Aspen Dynamics. No estudo de caso proposto, foi observada uma redução significativa no número condicional dos pares de controle propostos, e a matriz de separação foi utilizada como parâmetro de desacoplamento do sistema de controle. Dessa forma, a técnica proposta MOD-ICA apresentada pode ser utilizada como ferramenta de geração de projeto de sistemas de controle, podendo a matriz de separação ser considerada como modelo de redução de acoplamento, resultando assim na obtenção de um sistema de controle mais robusto às variações inerentes do processo.The use of sensors in process control systems is of vital importance for the proper operation and monitoring of industrial plants. In turn, process signals may have interference from other sources and, in some cases, it is not possible to observe directly the individual signals of the sources. In view of this, signal processing and separation techniques are used in order to extract the information from the sources contained in mixed signals. The main signal separation techniques are associated with the Independent Component Analysis (ICA), which has undergone significant evolution since its creation in the 1980s. Such evolution also had the contribution of the Principal Component Analysis (PCA) and the development of computational processing power. However, these techniques have two basic glitches: deviation of amplitude and phase change, which limit their use in control systems. Therefore, this research aims to present a solution to the problem of amplitude in ICA techniques for use in decoupling reduction in multivariate systems. The proposed correction, based on the stage of whitening ICA algorithms, which generated the technique MOD-ICA, was used as an alternative to breaking the correlation between variables in multivariate systems. Such technique was used for projecting and obtaining controlling pairs in an industrial plant of anhydrous ethanol production modeled on the Aspen Dynamics platform. In the case study proposed in this research, a significant reduction in the conditional numbers of the proposed controlling pairs was observed, and the separating matrix was used as a parameter of decoupling for the control system. As a result, the proposed MOD-ICA technique can be used as a tool for generating control systems, and its separating matrix can be considered as a model for decoupling reduction, which results in a more robust control system for process variation

Signal processing techniques for the enhancement of marine seismic data

This thesis presents several signal processing techniques applied to the enhancement of marine seismic data. Marine seismic exploration provides an image of the Earth's subsurface from reflected seismic waves. Because the recorded signals are contaminated by various sources of noise, minimizing their effects with new attenuation techniques is necessary. A statistical analysis of background noise is conducted using Thomson’s multitaper spectral estimator and Parzen's amplitude density estimator. The results provide a statistical characterization of the noise which we use for the derivation of signal enhancement algorithms. Firstly, we focus on single-azimuth stacking methodologies and propose novel stacking schemes using either enhanced weighted sums or a Kalman filter. It is demonstrated that the enhanced methods yield superior results by their ability to exhibit cleaner and better defined reflected events as well as a larger number of reflections in deep waters. A comparison of the proposed stacking methods with existing ones is also discussed. We then address the problem of random noise attenuation and present an innovative application of sparse code shrinkage and independent component analysis. Sparse code shrinkage is a valuable method when a noise-free realization of the data is generated to provide data-driven shrinkages. Several models of distribution are investigated, but the normal inverse Gaussian density yields the best results. Other acceptable choices of density are discussed as well. Finally, we consider the attenuation of flow-generated nonstationary coherent noise and seismic interference noise. We suggest a multiple-input adaptive noise canceller that utilizes a normalized least mean squares alg orithm with a variable normalized step size derived as a function of instantaneous frequency. This filter attenuates the coherent noise successfully when used either by itself or in combination with a time-frequency median filter, depending on the noise spectrum and repartition along the data. Its application to seismic interference attenuation is also discussed

Modified singular value decomposition by means of independent component analysis

International audienceIn multisensor signal processing (underwater acoustics, geophysics, etc.), the initial dataset is usually separated into complementary subspaces called signal and noise subspaces in order to enhance the signal-to-noise ratio. The singular value decomposition (SVD) is a useful tool to achieve this separation. It provides two orthogonal matrices that convey information on normalized wavelets and propagation vectors. As signal and noise subspaces are on the whole well evaluated, usually the SVD procedure cannot correctly extract only the source waves with a high degree of sensor to sensor correlation. This is due to the constraint given by the orthogonality of the propagation vectors. To relax this condition, exploiting the concept of independent component analysis (ICA), we propose another orthogonal matrix made up of statistically independent normalized wavelets. By using this combined SVD-ICA procedure, we obtain a better separation of these source waves in the signal subspace. Efficiency of this new separation procedure is shown on synthetic and real datasets

Représentations parcimonieuses pour les signaux multivariés

Dans cette thèse, nous étudions les méthodes d'approximation et d'apprentissage qui fournissent des représentations parcimonieuses. Ces méthodes permettent d'analyser des bases de données très redondantes à l'aide de dictionnaires d'atomes appris. Etant adaptés aux données étudiées, ils sont plus performants en qualité de représentation que les dictionnaires classiques dont les atomes sont définis analytiquement. Nous considérons plus particulièrement des signaux multivariés résultant de l'acquisition simultanée de plusieurs grandeurs, comme les signaux EEG ou les signaux de mouvements 2D et 3D. Nous étendons les méthodes de représentations parcimonieuses au modèle multivarié, pour prendre en compte les interactions entre les différentes composantes acquises simultanément. Ce modèle est plus flexible que l'habituel modèle multicanal qui impose une hypothèse de rang 1. Nous étudions des modèles de représentations invariantes : invariance par translation temporelle, invariance par rotation, etc. En ajoutant des degrés de liberté supplémentaires, chaque noyau est potentiellement démultiplié en une famille d'atomes, translatés à tous les échantillons, tournés dans toutes les orientations, etc. Ainsi, un dictionnaire de noyaux invariants génère un dictionnaire d'atomes très redondant, et donc idéal pour représenter les données étudiées redondantes. Toutes ces invariances nécessitent la mise en place de méthodes adaptées à ces modèles. L'invariance par translation temporelle est une propriété incontournable pour l'étude de signaux temporels ayant une variabilité temporelle naturelle. Dans le cas de l'invariance par rotation 2D et 3D, nous constatons l'efficacité de l'approche non-orientée sur celle orientée, même dans le cas où les données ne sont pas tournées. En effet, le modèle non-orienté permet de détecter les invariants des données et assure la robustesse à la rotation quand les données tournent. Nous constatons aussi la reproductibilité des décompositions parcimonieuses sur un dictionnaire appris. Cette propriété générative s'explique par le fait que l'apprentissage de dictionnaire est une généralisation des K-means. D'autre part, nos représentations possèdent de nombreuses invariances, ce qui est idéal pour faire de la classification. Nous étudions donc comment effectuer une classification adaptée au modèle d'invariance par translation, en utilisant des fonctions de groupement consistantes par translation.In this thesis, we study approximation and learning methods which provide sparse representations. These methods allow to analyze very redundant data-bases thanks to learned atoms dictionaries. Being adapted to studied data, they are more efficient in representation quality than classical dictionaries with atoms defined analytically. We consider more particularly multivariate signals coming from the simultaneous acquisition of several quantities, as EEG signals or 2D and 3D motion signals. We extend sparse representation methods to the multivariate model, to take into account interactions between the different components acquired simultaneously. This model is more flexible that the common multichannel one which imposes a hypothesis of rank 1. We study models of invariant representations: invariance to temporal shift, invariance to rotation, etc. Adding supplementary degrees of freedom, each kernel is potentially replicated in an atoms family, translated at all samples, rotated at all orientations, etc. So, a dictionary of invariant kernels generates a very redundant atoms dictionary, thus ideal to represent the redundant studied data. All these invariances require methods adapted to these models. Temporal shift-invariance is an essential property for the study of temporal signals having a natural temporal variability. In the 2D and 3D rotation invariant case, we observe the efficiency of the non-oriented approach over the oriented one, even when data are not revolved. Indeed, the non-oriented model allows to detect data invariants and assures the robustness to rotation when data are revolved. We also observe the reproducibility of the sparse decompositions on a learned dictionary. This generative property is due to the fact that dictionary learning is a generalization of K-means. Moreover, our representations have many invariances that is ideal to make classification. We thus study how to perform a classification adapted to the shift-invariant model, using shift-consistent pooling functions.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Geophysical methods for the investigation of soils

The aim of the work at hand is the development and enhancement of geophysical processing techniques for pedological mapping. The work is concentrating on (1) the applicability of known and the development of new GPTFs using laboratory measurements under controlled conditions, (2) the areal mapping of the electrical conductivity of topsoil and subsoil using an inversion, (3) the separation of the influences of water and clay content on the electrical conductivity and (4) the development and first application of approaches for pedological mapping with geophysical methods.Das Ziel der vorliegenden Arbeit ist die Entwicklung und Verbesserung geophysikalischer Auswertemethoden zur flächenhaften bodenkundlichen Kartierung. Dabei konzentriert sich die Arbeit auf (1) die Anwendbarkeit von bekannten und die Entwicklung von neuen GPTFs anhand von Labormessungen unter kontrollierten Bedingungen, (2) die flächenhafte Kartierung der elektrischen Leitfähigkeit von Ober- und Unterboden mit Hilfe einer Inversion, (3) die flächenhafte Trennung von Wassergehalts- und Tongehaltseinfluss auf die elektrische Leitfähigkeit und (4) die Entwicklung und erste Anwendung von Ansätzen zur bodenkundlichen Kartierung mit Hilfe geophysikalischer Methoden

Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks

The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm. The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT). Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs. In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation. First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced. A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented. Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition. In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted. Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations. Then, sparse non-negative blind energy separation is realized using multiplicative updates. Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation. Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner. In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source. This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising. Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed. A robust and regularized $t_{\nu}M$-estimation of the covariance matrix of the mixed energies is employed. This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN. The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement. The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns. In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation. Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented. Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem. In this way, the strongest sparse right singular vectors using a robust $\ell_1$-norm and stability selection are the set of basis vectors that describe the input data efficiently. Active/non-active source classification is achieved based on a robust Mahalanobis classifier. For this, a robust $M$-estimator of the covariance matrix in the Mahalanobis distance is utilized. Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach. Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction. With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals. A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array. The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source. The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source. Reliability is defined with respect to the median across frequencies. All source-specific frequency bands that contribute to correct estimated angles are selected. A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle. Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging

Seismic Wave Separation By Means Of Robust Principal Component Analysis

In this work, we investigate the application of the recently introduced signal decomposition method known as robust principal component analysis (RPCA) to the problem of wave separation in seismic data. The motivation of our research comes from the observation that the elements of the decomposition performed by RPCA can be associated with particular structures that often arise in seismic data. Results obtained considering two different situations, the separation of crossing events and the separation of diffracted waves from reflected ones, confirms that RPCA is a promising tool in seismic signal processing, outperforming the classical singular value decomposition (SVD) and the extension of the SVD based on independent component analysis in most cases. © 2012 EURASIP.14941498Glangeaud, F., Mari, J.-L., (1994) Wave Separation, , Technip EditionsSheriff, R.E., Geldart, L.P., (1995) Exploratory Seismology, , Cambridge University PressYilmaz, O., (2001) Seismic Data Analysis: Processing, Inversion and Interpretation of Seismic Data, 1. , SEG, second editionFreire, S.L.M., Ulrych, T.J., Application of singular value decomposition to vertical seismic profiling (1988) Geophysics, 53, pp. 778-785Kirlin, R.L., Done, W.J., (1999) Covariance Analysis for Seismic Signal Processing, , Society of Exploration GeophysicistsYedlin, M., Jones, I.F., Narod, B.B., Application of the karhunen-love transform to diffraction separation (1987) IEEE Transactions on Acoustics, Speech and Signal Processing, 35 (1), pp. 2-8Porsani, M.J., Silva, M.G., Melo, P.E.M., Ursin, B., Svd filtering applied to ground-roll attenuation (2010) Journal of Geophysics and Engineering, 7, pp. 284-289Vrabie, V.D., Mars, J.I., Lacoume, J.-L., Modified singular value decomposition by means of independent component analysis (2004) Signal Processing, (84), pp. 645-652Bekara, M., Van Der Baan, M., Local svd/ica for signal enhancement of pre-stack seismic data (2006) 68th EAGE Conference & ExhibitionCandes, E.J., Li, X., Ma, Y., Wright, J., Robust principal component analysis? (2011) Journal of the ACM, 58, pp. 1-37Chandrasekaran, V., Sanghavi, S., Parrilo, P.A., Willsky, A.S., Sparse and low-rank matrix decompositions (2009) Proc. 47th Ann. Allerton Conf. Communication, Control, and Computing Allerton, pp. 962-967Huang, P.-S., Chen, S.D., Smaragdis, P., Hasegawa-Johnson, M., Singing-voice separation from monaural recordings using robust principal component analysis (2012) Proc. of the IEEE ICASSPZhou, T., Tao, D., Godec: Randomized low-rank & sparse matrix decomposition in noisy case (2011) Proceeding of the International Conference on Machine Learning (ICML)Ding, X., He, L., Carin, L., Bayesian robust principal component analysis (2011) IEEE Transactions on Image Processing, 20 (12), pp. 3419-343

Unsupervised Processing Of Geophysical Signals: A Review Of Some Key Aspects Of Blind Deconvolution And Blind Source Separation

Unsupervised signal processing has been an exciting theme of research for at least three decades. It finds the potential application in practically all fields where well-established techniques of digital signal processing have been employed, including telecommunications; speech and audio processing; image, radar, and sonar; and biomedical signals. Among these classical problems, geophysical signal processing has played a prominent role in the development of unsupervised methods. In fact, the field of unsupervised processing can be said to have started with the early application of Wiener's theories to seismology. © 2012 IEEE.2942735Kaplan, S.T., Ulrych, T.J., Blind deconvolution and ICA with a banded mixing matrix (2003) Proc. 4th Int. Symp. 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Publishing, 19 (6), pp. R27-R83Guitton, A., Verschuur, D.J., Adaptive subtraction of multiples using the L 1-norm (2004) Geophysical Prospecting, 52 (1), pp. 27-38. , DOI 10.1046/j.1365-2478.2004.00401.xLu, W., Adaptive multiple subtraction using independent component analysis (2006) Geophysics, 71 (5), pp. 179-184Lu, W., Liu, L., Adaptive multiple subtraction based on constrained independent component analysis (2009) Geophysics, 74 (1), pp. V1-V7Kaplan, S.T., Innanen, K.A., Adaptive separation of free-surface multiples through independent component analysis (2008) Geophysics, 73 (3), pp. V29-V36. , DOI 10.1190/1.2890407Dragoset, B., Verschuur, E., Moore, I., Bisley, R., A perspective on 3D surfacerelated multiple elimination (2010) Geophysics, 75 (5), pp. 75A245-75A261Mari, J.L., Seismic wave separation by SVD and (F-K) combined filters (2006) Proc. Extended Abstracts 2006 2nd Int. Symp. Communications, Control and Signal Processing, , Marrakech, MoroccoVrabie, V.D., Mars, J.I., Lacoume, J.-L., Modified singular value decomposition by means of independent component analysis (2004) Signal Process., 84 (3), pp. 645-652Freire, S.L.M., Ulrych, T.J., Application of singular value decomposition to vertical seismic profiling (1988) Geophysics, 53 (6), pp. 778-785Porsani, M.J., Silva, M.G., Melo, P.E.M., Ursin, B., SVD filtering applied to ground-roll attenuation (2010) J. Geophys. Eng., 7 (3), pp. 284-289Bekara, M., Van Der Baan, M., Local singular value decomposition for signal enhancement of seismic data (2007) Geophysics, 72 (2), pp. V59-V65. , DOI 10.1190/1.2435967Al-Qaisi, A., Woo, W.L., Dlay, S.S., Sparsity exploitation of mixing matrix and reflectivity sequence for ICA-blind seismic deconvolution (2008) Proc. 3rd Int. Conf. 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