{"product_id":"source-separation-in-physicalchemical-sensing-9781119137221","title":"Source Separation in PhysicalChemical Sensing","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eSource Separation in Physical-Chemical Sensing\u003c\/b\u003e \u003cp\u003e\u003cb\u003eMaster advanced signal processing for enhanced physical and chemical sensors with this essential guide\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eSource Separation in Physical-Chemical Sensing,\u003c\/i\u003e supplies a thorough introduction to the principles of BSS, main methods and algorithms and its potent\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAbout the Editors xiii\u003c\/p\u003e \u003cp\u003eList of Contributors xv\u003c\/p\u003e \u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eNotation xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Overview of Source Separation 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChristian Jutten, Leonardo Tomazeli Duarte, and Saïd Moussaoui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 The Problem of Source Separation 3\u003c\/p\u003e \u003cp\u003e1.3 Statistical Methods for Source Separation 15\u003c\/p\u003e \u003cp\u003e1.4 Source Separation Problems in Physical--Chemical Sensing 24\u003c\/p\u003e \u003cp\u003e1.5 Source Separation Methods for Chemical--Physical Sensing 30\u003c\/p\u003e \u003cp\u003e1.6 Organization of the Book 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Optimization 43\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEmilie Chouzenoux and Jean-Christophe Pesquet\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction to Optimization Problems 43\u003c\/p\u003e \u003cp\u003e2.2 Majorization--Minimization Approaches 50\u003c\/p\u003e \u003cp\u003e2.3 Primal-Dual Methods 72\u003c\/p\u003e \u003cp\u003e2.4 Application to NMR Signal Restoration 83\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Non-negative Matrix Factorization 103\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDavid Brie, Nicolas Gillis, and Saïd Moussaoui\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 103\u003c\/p\u003e \u003cp\u003e3.2 Geometrical Interpretation of NMF and the Non-negative Rank 105\u003c\/p\u003e \u003cp\u003e3.3 Uniqueness and Admissible Solutions of NMF 112\u003c\/p\u003e \u003cp\u003e3.4 Non-negative Matrix Factorization Algorithms 118\u003c\/p\u003e \u003cp\u003e3.5 Applications of NMF in Chemical Sensing. Two Examples of Reducing Admissible Solutions 129\u003c\/p\u003e \u003cp\u003e3.6 Conclusions 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Bayesian Source Separation 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSaïd Moussaoui, Leonardo Tomazeli Duarte, Nicolas Dobigeon, and Christian Jutten\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 151\u003c\/p\u003e \u003cp\u003e4.2 Overview of Bayesian Source Separation 152\u003c\/p\u003e \u003cp\u003e4.3 Statistical Models for the Separation in the Linear Mixing 159\u003c\/p\u003e \u003cp\u003e4.4 Statistical Models and Separation Algorithms for Nonlinear Mixtures 173\u003c\/p\u003e \u003cp\u003e4.5 Some Practical Issues on Algorithm Implementation 177\u003c\/p\u003e \u003cp\u003e4.6 Applications to Case Studies in Chemical Sensing 182\u003c\/p\u003e \u003cp\u003e4.7 Conclusion 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Geometrical Methods -- Illustration with Hyperspectral Unmixing 201\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJosé M. Bioucas-Dias and Wing-Kin Ma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 201\u003c\/p\u003e \u003cp\u003e5.2 Hyperspectral Sensing 202\u003c\/p\u003e \u003cp\u003e5.3 Hyperspectral Mixing Models 206\u003c\/p\u003e \u003cp\u003e5.4 Linear HU Problem Formulation 208\u003c\/p\u003e \u003cp\u003e5.5 Dictionary-Based Semiblind HU 222\u003c\/p\u003e \u003cp\u003e5.6 Minimum Volume Simplex Estimation 227\u003c\/p\u003e \u003cp\u003e5.7 Applications 239\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Tensor Decompositions: Principles and Application to Food Sciences 255\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJérémy Cohen, Rasmus Bro, and Pierre Comon\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 255\u003c\/p\u003e \u003cp\u003e6.2 Tensor Decompositions 261\u003c\/p\u003e \u003cp\u003e6.3 Constraints in Decompositions 273\u003c\/p\u003e \u003cp\u003e6.4 Coupled Decompositions 279\u003c\/p\u003e \u003cp\u003e6.5 Algorithms 286\u003c\/p\u003e \u003cp\u003e6.6 Applications 297\u003c\/p\u003e \u003cp\u003eReferences 307\u003c\/p\u003e \u003cp\u003eIndex 325\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406994874711,"sku":"9781119137221","price":94.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119137221.jpg?v=1730497823","url":"https:\/\/bookcurl.com\/products\/source-separation-in-physicalchemical-sensing-9781119137221","provider":"Book Curl","version":"1.0","type":"link"}