Resolving Spectral Mixtures: Volume 30

Informationen zum Autor Cyril Ruckebusch is currently a Professor at Ecole PolytechLille, Université de Lille - Sciences et Technologies. He is doing his research at LASIR, a mixed CNRS-University Lille research unit. Cyril was previously Associate Professor at University of Lille since 2008 when he obtained the qualification for full-professorship (habilitation in physical chemistry). He received his PhD in Engineering Science in 2000. His current research focuses mainly on the development and application of chemometrics in advanced spectroscopy and imaging. He has published over 70 papers in international journals and coordinated international scientific collaboration research programs and industrial and technological projects. He is Associate Editor for reviews of the Journal of Chemometrics and Editorial Adviser of Analytica Chimica Acta.

Autorentext
Cyril Ruckebusch is currently a Professor at Ecole PolytechLille, Université de Lille - Sciences et Technologies. He is doing his research at LASIR, a mixed CNRS-University Lille research unit. Cyril was previously Associate Professor at University of Lille since 2008 when he obtained the qualification for full-professorship (habilitation in physical chemistry). He received his PhD in Engineering Science in 2000. His current research focuses mainly on the development and application of chemometrics in advanced spectroscopy and imaging. He has published over 70 papers in international journals and coordinated international scientific collaboration research programs and industrial and technological projects. He is Associate Editor for reviews of the Journal of Chemometrics and Editorial Adviser of Analytica Chimica Acta.

Inhalt

1. Introduction
2. Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data
3. Spectral Unmixing Using the Concept of Pure Variables
4. Ambiguities in Multivariate Curve Resolution
5. On the Analysis and Computation of the Area of Feasible Solutions for Two-, Three-, and Four-Component Systems
6. Linear and Nonlinear Unmixing in Hyperspectral Imaging
7. Independent Components Analysis: Theory and Applications
8. Bayesian Positive Source Separation for Spectral Mixture Analysis
9. Multivariate Curve Resolution of Wavelet Compressed Data
10. Chemometric Resolution of Complex Higher Order Chromatographic Data with Spectral Detection
11. Multivariate Curve Resolution of (Ultra)Fast Photoinduced Process Spectroscopy Data
12. Experimental and Data Analytical Approaches to Automating Multivariate Curve Resolution in the Analysis of Hyperspectral Images
13. Multiresolution Analysis and Chemometrics for Pattern Enhancement and Resolution in Spectral Signals and Images
14. A Smoothness Constraint in Multivariate Curve Resolution-Alternating Least Squares of Spectroscopy Data
15. Super-Resolution in Vibrational Spectroscopy: From Multiple Low-Resolution Images to High-Resolution Images
16. Multivariate Curve Resolution for Magnetic Resonance Image Analysis: Applications in Prostate Cancer Biomarkers Development
17. Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data: Potential, Challenges, and Applications
18. Spectral-Spatial Unmixing Approaches in Hyperspectral VNIR/SWIR Imaging
19. Sparse-Based Modeling of Hyperspectral Data