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This volume is a selection of written notes corresponding to
courses taught at the CIMPA School: "New Trends in Applied Harmonic
Analysis: Sparse Representations, Compressed Sensing and
Multifractal Analysis". New interactions between harmonic analysis
and signal and image processing have seen striking development in
the last 10 years, and several technological deadlocks have been
solved through the resolution of deep theoretical problems in
harmonic analysis. New Trends in Applied Harmonic Analysis focuses
on two particularly active areas that are representative of such
advances: multifractal analysis, and sparse representation and
compressed sensing. The contributions are written by leaders in
these areas, and cover both theoretical aspects and applications.
This work should prove useful not only to PhD students and postdocs
in mathematics and signal and image processing, but also to
researchers working in related topics.
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New Trends in Applied Harmonic Analysis, Volume 2 - Harmonic Analysis, Geometric Measure Theory, and Applications (Hardcover, 1st ed. 2019)
Akram Aldroubi, Carlos Cabrelli, Stephane Jaffard, Ursula Molter
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R3,117
R1,894
Discovery Miles 18 940
Save R1,223 (39%)
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Ships in 12 - 17 working days
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This contributed volume collects papers based on courses and talks
given at the 2017 CIMPA school Harmonic Analysis, Geometric Measure
Theory and Applications, which took place at the University of
Buenos Aires in August 2017. These articles highlight recent
breakthroughs in both harmonic analysis and geometric measure
theory, particularly focusing on their impact on image and signal
processing. The wide range of expertise present in these articles
will help readers contextualize how these breakthroughs have been
instrumental in resolving deep theoretical problems. Some topics
covered include: Gabor frames Falconer distance problem Hausdorff
dimension Sparse inequalities Fractional Brownian motion Fourier
analysis in geometric measure theory This volume is ideal for
applied and pure mathematicians interested in the areas of image
and signal processing. Electrical engineers and statisticians
studying these fields will also find this to be a valuable
resource.
This volume is a selection of written notes corresponding to
courses taught at the CIMPA School: "New Trends in Applied Harmonic
Analysis: Sparse Representations, Compressed Sensing and
Multifractal Analysis". New interactions between harmonic analysis
and signal and image processing have seen striking development in
the last 10 years, and several technological deadlocks have been
solved through the resolution of deep theoretical problems in
harmonic analysis. New Trends in Applied Harmonic Analysis focuses
on two particularly active areas that are representative of such
advances: multifractal analysis, and sparse representation and
compressed sensing. The contributions are written by leaders in
these areas, and cover both theoretical aspects and applications.
This work should prove useful not only to PhD students and postdocs
in mathematics and signal and image processing, but also to
researchers working in related topics.
This contributed volume collects papers based on courses and talks
given at the 2017 CIMPA school Harmonic Analysis, Geometric Measure
Theory and Applications, which took place at the University of
Buenos Aires in August 2017. These articles highlight recent
breakthroughs in both harmonic analysis and geometric measure
theory, particularly focusing on their impact on image and signal
processing. The wide range of expertise present in these articles
will help readers contextualize how these breakthroughs have been
instrumental in resolving deep theoretical problems. Some topics
covered include: Gabor frames Falconer distance problem Hausdorff
dimension Sparse inequalities Fractional Brownian motion Fourier
analysis in geometric measure theory This volume is ideal for
applied and pure mathematicians interested in the areas of image
and signal processing. Electrical engineers and statisticians
studying these fields will also find this to be a valuable
resource.
|
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