Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging (Data Handling in Science and Technology): Volume 30

Cyril Ruckebusch

ISBN 10: 0444636382 / ISBN 13: 9780444636386

Verlag: Elsevier 2016-09-01, 2016

Beschreibung

Bestandsnummer des Verkäufers 6666-ELS-9780444636386

Bibliografische Details

• Titel: Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging (Data Handling in Science and Technology): Volume 30
• Verlag: Elsevier 2016-09-01
• Erscheinungsjahr: 2016
• Sprache: Englisch
• ISBN-10: 0444636382
• ISBN-13: 9780444636386
• Einband: Hardcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem―from multivariate curve resolution and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions from individual unknown components with limited prior information is a complex problem that has attracted continuous interest for almost four decades. Spectral unmixing is a topic of interest in statistics, chemometrics, signal processing, and image analysis. For decades, researchers from these fields were often unaware of the work in other disciplines due to their different scientific and technical backgrounds and interest in different objects or samples. This led to the development of quite different approaches to solving the same problem. This multi-authored book will bridge the gap between disciplines with contributions from a number of well-known and strongly active chemometric and signal processing research groups. Among chemists, multivariate curve resolution methods are preferred to extract information about the nature, amount, and location in time (process) and space (imaging and microscopy) of chemical constituents in complex samples. In signal processing, assumptions are usually around statistical independence of the extracted components. However, the chapters include the complexity of the spectral data to be unmixed as well as dimensionality and size of the data sets. Advanced spectroscopy is the key thread linking the different chapters. Applications cover a large part of the electromagnetic spectrum. Time-resolution ranges from femtosecond to second in process spectroscopy and spatial resolution covers the submicronic to macroscopic scale in hyperspectral imaging.

Über die Autorin bzw. den 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.