UID:
almafu_9961367875702883
Format:
1 online resource
ISBN:
9781119137276
,
1119137276
,
9781119137290
,
1119137292
,
111913725X
,
9781119137252
Series Statement:
IEEE Press Series
Content:
"With the advent of more affordable, higher resolution or innovative data acquisition techniques, chemical analysis has been using progressively advanced signal and image processing tools. Indeed, both specialities (analytical chemistry and signal processing) share similar values of best practice in carrying out identifications and comprehensive characterizations, be they of chemical samples or of numerical data. Signal and image processing, for instance, often breaks down data into atoms, molecules, with specific decompositions and priors, as common in chemistry. Many problems in chemical engineering can be addressed with classical or advanced methods of signal and image processing, through topics such as chemical analysis leading to PARAFAC/tensor methods, hyper spectral imaging, ion-sensitive sensors, artificial noise, chromatography, mass spectrometry, TEP imaging, etc."--
Note:
Intro -- Table of Contents -- Title Page -- Copyright -- About the Editors -- List of Contributors -- Foreword -- Preface -- Notation -- 1 Overview of Source Separation -- 1.1 Introduction -- 1.2 The Problem of Source Separation -- 1.3 Statistical Methods for Source Separation -- 1.4 Source Separation Problems in Physical-Chemical Sensing -- 1.5 Source Separation Methods for Chemical-Physical Sensing -- 1.6 Organization of the Book -- References -- Notes -- 2 Optimization -- 2.1 Introduction to Optimization Problems -- 2.2 Majorization-Minimization Approaches -- 2.3 Primal-Dual Methods
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2.4 Application to NMR Signal Restoration -- 2.5 Conclusion -- References -- Notes -- 3 Non-negative Matrix Factorization -- 3.1 Introduction -- 3.2 Geometrical Interpretation of NMF and the Non-negative Rank -- 3.3 Uniqueness and Admissible Solutions of NMF -- 3.4 Non-negative Matrix Factorization Algorithms -- 3.5 Applications of NMF in Chemical Sensing. Two Examples of Reducing Admissible Solutions -- 3.6 Conclusions -- References -- 4 Bayesian Source Separation -- 4.1 Introduction -- 4.2 Overview of Bayesian Source Separation -- 4.3 Statistical Models for the Separation in the Linear Mixing
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4.4 Statistical Models and Separation Algorithms for Nonlinear Mixtures -- 4.5 Some Practical Issues on Algorithm Implementation -- 4.6 Applications to Case Studies in Chemical Sensing -- 4.7 Conclusion -- Appendix 4.AImplementation of Function postsourcesrnd via Metropolis-Hasting Algorithm -- References -- Notes -- 5 Geometrical Methods -- Illustration with Hyperspectral Unmixing -- 5.1 Introduction -- 5.2 Hyperspectral Sensing -- 5.3 Hyperspectral Mixing Models -- 5.4 Linear HU Problem Formulation -- 5.5 Dictionary-Based Semiblind HU -- 5.6 Minimum Volume Simplex Estimation -- 5.7 Applications
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5.8 Conclusions -- References -- Notes -- 6 Tensor Decompositions: Principles and Application to Food Sciences -- 6.1 Introduction -- 6.2 Tensor Decompositions -- 6.3 Constraints in Decompositions -- 6.4 Coupled Decompositions -- 6.5 Algorithms -- 6.6 Applications -- References -- Notes -- Index -- End User License Agreement
Additional Edition:
Print version: Source separation in physical-chemical sensing Hoboken, NJ : Wiley, 2024 ISBN 9781119137221
Language:
English
DOI:
10.1002/9781119137252
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119137252
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119137252
