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Source Separation in Physical-Chemical Sensing (Hardcover)
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Source Separation in Physical-Chemical Sensing (Hardcover)
Series: IEEE Press
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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 are obtained from large set 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 makes 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.
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