Source Separation in Physical-Chemical Sensing

Source Separation in Physical-Chemical Sensing Master advanced signal processing for enhanced physical and chemical sensors with this essential guide In many domains (medicine, satellite imaging and remote sensing, food industry, materials science), data is obtained from large sets of physical/chemical sensors or sensor arrays. Such sophisticated measurement techniques require advanced and smart processing for extracting useful information from raw sensing data. Usually, sensors are not very selective and record a mixture of the useful latent variables. An innovative technique called Blind Source Separation (BSS) can isolate and retrieve the individual latent variables from a mixed-source data array, allowing for refined analysis that fully exploits these cutting-edged imaging and signal-sensing technologies. Source Separation in Physical-Chemical Sensing, supplies a thorough introduction to the principles of BSS, main methods and algorithms and its potential applications in various domains where data are obtained through physical or chemical sensors. Designed to bridge the gap between chemical/physical analysis and signal processing, it promises to be invaluable in many fields. Its alertness to the latest technologies and the full range of potential BSS applications make it an indispensable introduction to this cutting-edge method. Source Separation in Physical-Chemical Sensing readers will also find: BSS examples on chemical and physical sensors and devices to enhance processing and analysis. Detailed treatment of source separation in potentiometric sensors, ion-sensitive sensors, hyperspectral imaging, Raman and fluorescence spectroscopy, chromatography, and others. Thorough discussion of Bayesian source separation, nonnegative matrix factorization, tensorial methods, geometrical methods, constrained optimization, and more. Source Separation in Physical-Chemical Sensing is a must-have for researchers and engineers working in signal processing and statistical analysis, as well as for chemists, physicists or engineers looking to apply source separation in various application domains.

Source Separation in Physical-Chemical SensingMaster advanced signal processing for enhanced physical and chemical sensors with this essential guideIn many domains (medicine, satellite imaging and remote sensing, food industry, materials science), data is obtained from large sets of physical/chemical sensors or sensor arrays. Such sophisticated measurement techniques require advanced and smart processing for extracting useful information from raw sensing data. Usually, sensors are not very selective and record a mixture of the useful latent variables. An innovative technique called Blind Source Separation (BSS) can isolate and retrieve the individual latent variables from a mixed-source data array, allowing for refined analysis that fully exploits these cutting-edged imaging and signal-sensing technologies.Source Separation in Physical-Chemical Sensing, supplies a thorough introduction to the principles of BSS, main methods and algorithms and its potential applications in various domains where data are obtained through physical or chemical sensors. Designed to bridge the gap between chemical/physical analysis and signal processing, it promises to be invaluable in many fields. Its alertness to the latest technologies and the full range of potential BSS applications make it an indispensable introduction to this cutting-edge method.Source Separation in Physical-Chemical Sensing readers will also find:* BSS examples on chemical and physical sensors and devices to enhance processing and analysis.* Detailed treatment of source separation in potentiometric sensors, ion-sensitive sensors, hyperspectral imaging, Raman and fluorescence spectroscopy, chromatography, and others.* Thorough discussion of Bayesian source separation, nonnegative matrix factorization, tensorial methods, geometrical methods, constrained optimization, and more.Source Separation in Physical-Chemical Sensing is a must-have for researchers and engineers working in signal processing and statistical analysis, as well as for chemists, physicists or engineers looking to apply source separation in various application domains.

About the Editors xiiiList of Contributors xvForeword xviiPreface xxiNotation xxiii1 Overview of Source Separation 1Christian Jutten, Leonardo Tomazeli Duarte, and Saïd Moussaoui1.1 Introduction 11.2 The Problem of Source Separation 31.3 Statistical Methods for Source Separation 151.4 Source Separation Problems in Physical--Chemical Sensing 241.5 Source Separation Methods for Chemical--Physical Sensing 301.6 Organization of the Book 352 Optimization 43Emilie Chouzenoux and Jean-Christophe Pesquet2.1 Introduction to Optimization Problems 432.2 Majorization--Minimization Approaches 502.3 Primal-Dual Methods 722.4 Application to NMR Signal Restoration 832.5 Conclusion 913 Non-negative Matrix Factorization 103David Brie, Nicolas Gillis, and Saïd Moussaoui3.1 Introduction 1033.2 Geometrical Interpretation of NMF and the Non-negative Rank 1053.3 Uniqueness and Admissible Solutions of NMF 1123.4 Non-negative Matrix Factorization Algorithms 1183.5 Applications of NMF in Chemical Sensing. Two Examples of Reducing Admissible Solutions 1293.6 Conclusions 1414 Bayesian Source Separation 151Saïd Moussaoui, Leonardo Tomazeli Duarte, Nicolas Dobigeon, and Christian Jutten4.1 Introduction 1514.2 Overview of Bayesian Source Separation 1524.3 Statistical Models for the Separation in the Linear Mixing 1594.4 Statistical Models and Separation Algorithms for Nonlinear Mixtures 1734.5 Some Practical Issues on Algorithm Implementation 1774.6 Applications to Case Studies in Chemical Sensing 1824.7 Conclusion 1915 Geometrical Methods -- Illustration with Hyperspectral Unmixing 201José M. Bioucas-Dias and Wing-Kin Ma5.1 Introduction 2015.2 Hyperspectral Sensing 2025.3 Hyperspectral Mixing Models 2065.4 Linear HU Problem Formulation 2085.5 Dictionary-Based Semiblind HU 2225.6 Minimum Volume Simplex Estimation 2275.7 Applications 2395.8 Conclusions 2446 Tensor Decompositions: Principles and Application to Food Sciences 255Jérémy Cohen, Rasmus Bro, and Pierre Comon6.1 Introduction 2556.2 Tensor Decompositions 2616.3 Constraints in Decompositions 2736.4 Coupled Decompositions 2796.5 Algorithms 2866.6 Applications 297References 307Index 325