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Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master's or PhD students, senior researchers, or scientists working in industrial settings, where wavelets are used to model real-world phenomena and data needs (such as finance, medicine, engineering, transport, images, signals, etc.). Features: Offers a self-contained discussion of wavelet theory | Suitable for a wide audience of post-graduate students, researchers, practitioners, and…mehr
Wavelet Analysis: Basic Concepts and Applications provides a basic and self-contained introduction to the ideas underpinning wavelet theory and its diverse applications. This book is suitable for master's or PhD students, senior researchers, or scientists working in industrial settings, where wavelets are used to model real-world phenomena and data needs (such as finance, medicine, engineering, transport, images, signals, etc.).
Features:
Offers a self-contained discussion of wavelet theory
Suitable for a wide audience of post-graduate students, researchers, practitioners, and theorists
Provides researchers with detailed proofs
Provides guides for readers to help them understand and practice wavelet analysis in different areas
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Autorenporträt
Sabrine Arfaoui is the assistant professor of mathematics at the Faculty of Sciences, University of Monastir. Her main interests include wavelet harmonic analysis, especially in the Clifford algebra/analysis framework and their applications in other fields such as fractals, PDEs, bio-signals/bio-images. Currently Dr. Arfaoui is associated with the University of Tabuk, Saudi Arabia in a technical cooperation project.
Anouar Ben Mabrouk is currently working as the professor of mathematics. He is also the associate professor of Mathematics at the University of Kairouan, Tunisia, the Faculty of Sciences, University of Monastir. His main research interests are are wavelets, fractals, probability/statistics, PDEs and related fields such as financial mathematics, time series, image/signal processing, numerical and theoretical aspects of PDEs. Dr. Ben Mabrouk is currently associated with the University of Tabuk, Saudi Arabia in a technical cooperation project.
Carlo Cattani is currently the professor of Mathematical Physics and Applied Mathematics at the Engineering School (DEIM) of University of Tuscia. His scientific interests include but are not limited to wavelets, dynamical systems, fractals, fractional calculus, numerical methods, number theory, stochastic integro-differential equations, competition models, time-series analysis, nonlinear analysis, complexity of living systems, pattern analysis, computational biology, biophysics, history of science. He has (co)authored more than 150 scientific articles on international journals as well as several books.
Inhaltsangabe
Chapter 1. Introduction. Chapter 2. Wavelets on Euclidean Spaces. 2.1. Introduction. 2.2. Wavelets on R. 2.3. Multi-Resolution Analysis. 2.4. Wavelet Algorithms. 2.5. Wavelet Basis. 2.6. Multidimensional Real Wavelets. 2.7. Examples of Wavelet Functions and MRA. 2.8. Exercise. 3. Wavelets Extended. 3.1. Affine Group Wavelets. 3.2. Multiresolution Analysis on The Interval. 3.3 Wavelets on The Sphere. 3.4. Exercise. 4. Clifford Wavelets. 4.1. Introduction. 4.2. Different Constructions of Clifford Algebra. 4.3. Graduation in Clifford Algebra. 4.4. Some useful operations of Clifford Algebra. 4.5. Clifford Functional Analysis. 4.6. Existence of Monogenic Extensions. 4.7. Clifford-Fourier Transform. 4.8. Some Experimentations. 4.9. Exercise. 5. Quantum Wavelets. 5.1. Introduction. 5.2. Bessel Functions. 5.3. Bessel Wavelets. 5.4. Fractional Bessel Wavelets. 5.5. Quantum Theory Toolkit. 5.6. Some Quantum Special Functions. 5.7. Quantum Wavelets. 5.8. Exercise. 6. Wavelets in Statistics. 6.1 Introduction. 6.2. Wavelet Analysis of Time Series. 6.3. Wavelet Variance and Covariance. 6.4. Wavelet Decimated and Stationary Transforms. 6.5. Wavelet Density Estimation. 6.6. Wavelet Thresholding. 6.7. Application to Wavelet Density Estimations. 6.8. Exercise. 7. Wavelets for Partial Differential Equations. 7.1. Introduction. 7.2. Wavelet Collocation Method. 7.3. Wavelet Galerkin Approach. 7.4. Reduction of the Connection Coefficients Number. 7.5. Two Main Applications for Solving PDEs. 7.6. Appendix. 7.7. Exercise. 8. Wavelets for Fractal and Multifractal Functions. 8.1. Introduction. 8.2. Hausdorff Measure and Dimension. 8.3. Wavelets for The Regularity Of Functions. 8.4. The Multifractal Formalism. 8.5. Similar Type Functions. 8.6. Application to Financial Index Modeling. 8.7. Appendix. 8.8. Exercise.
Chapter 1. Introduction.Chapter 2. Wavelets on Euclidean Spaces. 2.1. Introduction. 2.2. Wavelets on R. 2.3. Multi-Resolution Analysis. 2.4. Wavelet Algorithms. 2.5. Wavelet Basis. 2.6. Multidimensional Real Wavelets. 2.7. Examples of Wavelet Functions and MRA. 2.8. Exercise. 3. Wavelets Extended. 3.1. Affine Group Wavelets. 3.2. Multiresolution Analysis on The Interval. 3.3 Wavelets on The Sphere. 3.4. Exercise. 4. Clifford Wavelets. 4.1. Introduction. 4.2. Different Constructions of Clifford Algebra. 4.3. Graduation in Clifford Algebra. 4.4. Some useful operations of Clifford Algebra. 4.5. Clifford Functional Analysis. 4.6. Existence of Monogenic Extensions. 4.7. Clifford-Fourier Transform. 4.8. Some Experimentations. 4.9. Exercise. 5. Quantum Wavelets. 5.1. Introduction. 5.2. Bessel Functions. 5.3. Bessel Wavelets. 5.4. Fractional Bessel Wavelets. 5.5. Quantum Theory Toolkit. 5.6. Some Quantum Special Functions. 5.7. Quantum Wavelets. 5.8. Exercise. 6. Wavelets in Statistics. 6.1 Introduction. 6.2. Wavelet Analysis of Time Series. 6.3. Wavelet Variance and Covariance. 6.4. Wavelet Decimated and Stationary Transforms. 6.5. Wavelet Density Estimation. 6.6. Wavelet Thresholding. 6.7. Application to Wavelet Density Estimations. 6.8. Exercise. 7. Wavelets for Partial Differential Equations. 7.1. Introduction. 7.2. Wavelet Collocation Method. 7.3. Wavelet Galerkin Approach. 7.4. Reduction of the Connection Coefficients Number. 7.5. Two Main Applications for Solving PDEs. 7.6. Appendix. 7.7. Exercise. 8. Wavelets for Fractal and Multifractal Functions. 8.1. Introduction. 8.2. Hausdorff Measure and Dimension. 8.3. Wavelets for The Regularity Of Functions. 8.4. The Multifractal Formalism. 8.5. Similar Type Functions. 8.6. Application to Financial Index Modeling. 8.7. Appendix. 8.8. Exercise.
