Valeriu Vrabie

A Source Separation Technique for Processing of Thermometric Data From Fiber-Optic DTS Measurements for Water Leakage Identification in Dikes

Distributed temperature sensors (DTSs) show real advantages over conventional temperature sensing technology such as low cost for long-range measurement, durability, stability, insensitivity to external perturbations, etc. They are particularly interesting for long-term health assessment of civil engineering structures such as dikes. In this paper, we address the problem of identification of leakage in dikes based on real thermometric data recorded by DTS. Formulating this task as a source separation problem, we propose a methodology based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). As the first PCA estimated source extracts an energetic subspace, other PCA sources allow to access the leakages. The energy of a leakage being very low compared to the entire data, a temporal windowing approach guarantees the presence of the leakages on these other PCA sources. However, on these sources, the leakages are not well separated from other factors like drains. An ICA processing, providing independent sources, is thus proposed to achieve better identification of the leakages. The study of different preprocessing steps such as normalization, spatial gradient, and transposition allows to propose a final scheme that represents a first step towards the automation of the leakage identification problem.

Independent component analysis of Raman spectra: Application on paraffin-embedded skin biopsies

Abstract Raman spectra provide wealthy but complex information about the chemical constituents of biological samples. Digital processing techniques are usually needed to extract the spectra of chemical constituents and their associated concentration profiles. However, spectral signatures may admit transformations from those recorded on pure constituents and these techniques require a priori knowledge of spectra to be estimated. We propose in this study to analyse paraffin-embedded skin biopsies of malignant and benign tumors dedicated to oncology researches by Raman spectroscopy and advanced signal processing methods. We show that the commonly used principal component analysis (PCA) does not give physically interpretable estimators of spectra and associated concentration profiles. Based on a linear model and taking into account the statistical properties of spectra, independent component analysis (ICA) is used to better estimate the spectra of chemical constituents. The estimators of associated concentration profiles are no longer orthogonal and have only positive values, contrary to PCA. ICA allows to model the paraffin by three Raman spectra and provides good estimators of underlying spectra of the human skin, which is of great interest in oncology since the retrieval of spectral features of different types of skin tumors is sufficient for their discrimination.

Digital Dewaxing of Raman Signals: Discrimination between Nevi and Melanoma Spectra Obtained from Paraffin-Embedded Skin Biopsies

Malignant melanoma (MM) is the most severe tumor affecting the skin and accounts for three quarters of all skin cancer deaths. Raman spectroscopy is a promising nondestructive tool that has been increasingly used for characterization of the molecular features of cancerous tissues. Different multivariate statistical analysis techniques are used in order to extract relevant information that can be considered as functional spectroscopic descriptors of a particular pathology. Paraffin embedding (waxing) is a highly efficient process used to conserve biopsies in tumor banks for several years. However, the use of non-dewaxed formalin-fixed paraffin-embedded tissues for Raman spectroscopic investigations remains very restricted, limiting the development of the technique as a routine analytical tool for biomedical purposes. This is due to the highly intense signal of paraffin, which masks important vibrations of the biological tissues. In addition to being time consuming and chemical intensive, chemical dewaxing methods are not efficient and they leave traces of the paraffin in tissues, which affects the Raman signal. In the present study, we use independent component analysis (ICA) on Raman spectral images collected on melanoma and nevus samples. The sources obtained from these images are then used to eliminate, using non-negativity constrained least squares (NCLS), the paraffin contribution from each individual spectrum of the spectral images of nevi and melanomas. Corrected spectra of both types of lesion are then compared and classified into dendrograms using hierarchical cluster analysis (HCA).

Automatic Monitoring System for Singularity Detection in Dikes By DTS Data Measurement

The development of automated monitoring systems for the detection of singularities, such as leakages in dikes, is indispensable to avoid mass disaster. An efficient solution for dike survey is the use of distributed temperature sensors (DTSs) based on optical fiber, offering a multitude of advantages such as low cost, extreme robustness, long-range measurement, etc. However, the temperature data acquired with DTSs, being not directly interpretable, require intervention of signal processing techniques. This paper addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, which is representative of the nonsingular zones, is estimated using singular value decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference, thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2-norm. To detect only the singularities in dikes, such as leakages or drains, a constant false alarm rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation-type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed detecting the presence of a real water leakage at the site, which was previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis.

Multicomponent wave separation using HOSVD/unimodal-ICA subspace method

Multicomponent sensor arrays now are commonly used in seismic acquisition to record polarized waves. In this article, we use a three-mode model polarization mode, distance mode, and temporal mode to take into account the specific structure of signals that are recorded with these arrays, providing a data-structure-preserving processing. With the suggested model, we propose a multilinear decomposition named higher-order singular value decomposition and unimodal independent component analysis HOSVD/unimodal ICA to split the recorded threemode data into two orthogonal subspaces: the signal and noise subspaces.This decomposition allows the separation and identification of polarized waves with infinite apparent horizontal propagation velocity.The HOSVD leads to a definition of a subspace method that is the counterpart of the well-known subspace method for matrices that is driven by singular value decompositionSVD,aclassictoolinmonocomponentarrayprocessing. The proposed three-mode subspace decomposition provides a multicomponentwave-separationalgorithm.Toenhancetheseparation result, when the signal-to-noise ratio is low or when orthogonality constraints are not well adapted to the recorded waves, a unimodal-ICA step is included on the temporal mode. Doingthisreplacestheclassicorthogonalityconstraintsbetween estimated waves with independence constraints that might allow better recovery of recorded seismic waves.Asimulation on realistic two-component 2C geophysical data shows qualitative and quantitative improvements for the wavefield-separation results. The relative-mean-square errors between the original and estimated signal subspaces are, respectively, 52% for SVD applied on each component separately, 27.4% for HOSVD-based technique applied to the whole three-mode dataset, and 7.3% for HOSVD/unimodal-ICA technique. The efficiency of the threemodesubspacedecompositionsalsoisshownonrealthree-component 3C geophysical data. These results emphasize the potentialoftheHOSVD/unimodal-ICAsubspacemethodformulticomponentseismic-waveseparation.

Preprocessing Methods of Raman Spectra for Source Extraction on Biomedical Samples: Application on Paraffin-Embedded Skin Biopsies

Raman spectra are classically modeled as a linear mixing of spectra of molecular constituents of the analyzed sample. Source separation methods are thus well suited to estimate these constituent spectra. However, physical distortions due to the instrumentation and biological nature of samples add nonlinearities to the Raman spectra model. These distortions are dark current, detector and optic responses, fluorescence background, and peak misalignment and peak width heterogeneity. The source separation results are thus deteriorated by these effects. We propose to develop specific preprocessing steps to correct these distortions and to retrieve a linear model. The benefits brought by these steps are studied by the application of two different source separation methods named joint approximate diagonalization of eigenmatrices and maximum likelihood positive source separation after the application of each step on a dataset acquired on a paraffin-embedded human skin biopsy. The efficacy of these methods to separate Raman spectra is also discussed.

Pre-processing and Source Separation methods for Raman spectra analysis of biomedical samples

Raman spectroscopy is a useful tool to investigate the molecular composition of biological samples. Source separation methods can be used to efficiently separate dense informations recorded by Raman spectra. Distorting effects such as fluorescence background, peak misalignment or peak width heterogeneity break the linear and instantaneous generative model needed by the source separation methods. Preprocessing steps are required to compensate these deforming effects. We show in this paper how efficiency of source separation methods is deeply dependent on preprocessing steps. Resulting improvements are illustrated through the study of the numerical dewaxing of Raman signal of a human skin biopsy. The applied source separation methods are a classical ICA algorithm named JADE and two positive source separation methods called NMF and MLPSS.

Biometric System Based on Voice Recognition Using Multiclassifiers

In this paper we present a new speaker recognition system based on the fusion of two identification classifiers followed by a verification step. The user pronounces two passwords: the first one is composed by three words uniquely combined from a set of 21 possible words, while the second password represents the name of the user. The first step of the proposed system uses the first password to feed two identification classifiers: a speaker identification system (text independent) and a isolated word identification system (speaker independent). The isolated word identification system is constructed as the fusion of three classifiers, one for each word of the first password. The aim of this first step is to identify a couple speaker/password corresponding to the first password by combining the results of the two identification classifiers. A verification system is then applied on the second password in order to confirm or infirm the identification result (speaker identity) given by the fusion above. Compared with a state of the art speaker recognition system (text dependent) that gives an EER of 4.76%, the first step of the proposed system provides an EER of 0.38%, while the second step an EER of 0.26% for a text independent verification and of 0.13% for a text dependent verification.

Monitoring and early detection of internal erosion: Distributed sensing and processing

Early detection of leakages in hydraulic infrastructures is important to ensure their safety and security. Significant flow of water through the dike can be an indicator of internal erosion and results in a thermal anomaly. Temperature measurements are therefore capable of revealing information linked to leakage. Optical fiber–based distributed temperature sensors present an economically viable and reliable solution for recording spatio-temporal temperature data over long distances, with spatial and temperature resolutions of 1 m and 0.05°C, respectively. The acquired data are influenced by several factors, among them water leakages, heat transfer through the above soil depth, seasonal thermal variations, and the geomechanical environment. Soil properties such as permeability alter the acquired signal locally. This article presents leakage detection methods based on signal processing of the raw temperature data from optical fiber sensors. The first approach based on source separation identifies leakages by separating them from the non-relevant information. The second approach presents a potential alarm system based on the analysis of daily temperature variations. Successful detection results for simulated as well as real experimental setups of Electricité de France are presented.

Effects of digital dewaxing methods on K-means-clusterized IR images collected on formalin-fixed paraffin-embedded samples of skin carcinoma

Mid-IR spectral imaging is an efficient method to analyze biological samples. Several research studies showed its potential to diagnose cancerous tissues. However, some limitations appear when formalin-fixed paraffin-embedded tissues are studied due to the intense IR contribution of paraffin, unless to perform a time-consuming and aggressive chemical dewaxing. We propose in this paper to analyze the efficiency of two digital dewaxing methods developed to remove the paraffin influence on IR images acquired on a cancerous skin sample. The first method is the extended multiplicative signal correction (EMSC), which is a preprocessing step applied to neutralize the IR contribution of paraffin. The second one, previously developed for Raman spectroscopy of paraffined tissues, is based on the independent component analysis (ICA) and the nonnegatively constrained least squares (NCLS). ICA+NCLS permits to remove the IR spectral signature from tissue spectra. Both preprocessing methods are compared on the basis of K-means-clusterized IR images in respect to a conventional histopathological staining. In conclusion, these preliminary results show the efficiency of the preprocessing methods; however ICA+NCLS has to be improved to get more relevant outcomes.