• Instructor: Cristina Pereyra
  
• E-mail: crisp AT math . unm . edu
  
• Office: SMLC 320
  
• Phone: 277-4613 (best by e-mail)
  
• Schedule: TTh 9:30-10:45am, in room MITCH 221
  
• Office Hours:Mon 3-4:30pm, Th 2-3:30pm or by appointment
  
• Course Assistant: TBA if any xxx@math.unm.edu
  277-xxxx, SMLC xxx, Office hours: TBA
  

This class is cross-listed as:

• Math 472 - Call # 44645 - Fourier analysis and wavelets
  
• Math 572 - Call # 44646 - Fourier analysis and wavelets
  (Graduate students please register in Math 572.)

Here are quick links to the homework, and to the textbook.

This course is an introduction to Fourier Analysis and Wavelets. It has been specifically designed for engineers, scientists, statisticians and mathematicians interested in the basic mathematical ideas underlying Fourier analysis, wavelets and their applications.
This course integrates the classical Fourier Theory with its latest offspring, the Theory of Wavelets. Wavelets and Fourier analysis are invaluable tools for researchers in many areas of mathematics and the applied sciences, to name a few: signal processing, statistics, physics, differential equations, numerical analysis, geophysics, medical imaging, fractals, harmonic analysis, etc. It is their multidisciplinary nature that makes these theories so appealing.

Topics will include:

• Fourier series: pointwise convergence, summability methods, mean-square convergence.
• Discrete Fourier Transform (including Fast Fourier Transform), and Discrete Haar Transform (including Fast Haar Transform)
• Fourier transform on the line. Time-frequency diccionary. Heisenberg's Uncertainty Principle, Sampling theorems and other applications. Including excursions into Lp spaces and distributions.
• Time/frequency analysis, windowed Fourier Transform, Gabor basis, Wavelets.
• Multiresolution analysis on the line. Prime example: the Haar basis. Basic wavelets examples: Shannon's and Daubechies' compactly supported wavelets. Time permiting we will explore variations over the theme of wavelets: Biorthogonal wavelets, and two-dimentional wavelets for image processing.

Numerical experiments are necessary to fully understand the scope of the theory. We will let the students explore this realm according to their interests. The use of some Wavelet Toolbox will be encouraged. There exists a WAVELAB 850 package which is Matlab based software designed by a team at Stanford and available for free on the Internet. MATLAB 7.12.0 is available in the Mathematics and Statistics Department Computer Laboratory.

Textbook: We will be use a book that wrote with my colleague Lesley Ward from University of South Australia and just came out. The book is called Harmonic Analysis: From Fourier to Wavelets , Student Mathematical Library Series, Volume 63, American Mathematical Society 2012. I appreciate all the feedback I can get from you in terms of typos, erratas, and possible improvements for the second edition!

Grades: Grades will be based on homeworks, projects and/or take-home exams.

Prerequisites: Linear algebra and advanced calculus, or permission from the instructor.

Recommended Texts: The literature for Fourier Analysis and Wavelets is large.

• Good for undergraduates with strong linear algebra background: An Introduction to Wavelets Through Linear Algebra by Michael Frazier Springer Verlag, Feb 1999; ISBN: 0387986391.
• More appropriate for graduate students: Introduction to Fourier Analysis and Wavelets by Mark A. Pinsky. The Brooks/Cole Series in Advanced Mathematics, 2002; ISBN 0-534-37660-6
• This book is of encyclopedic nature, excellent for graduate students in engineering and also in statistics/math: A Wavelet Tour of Signal Processing. The sparse way by S. Mallat, Third Edition, Academic Press, 2008; ISBN 978-0123743701

There are many excellent books devoted to the classical theory of Fourier analysis (starting with Trigonometric Series by A. Zygmund 2nd edition, Cambridge University Press, Cambridge 1959, and following with a long list).

• Appropriate for advanced undergraduate students: Fourier Analysis: An Introduction by E. M. Stein and R. Shakarchi, Princeton lectures in Analysis I, Princeton University Press, 2003; ISBN 0-691-11384-X.
• Appropriate for advanced undergraduate students, full of historical notes and anecdotes: Fourier Analysis by T. Korner. Cambridge University Press, 1989; ISBN 0-521-38991-7
• A bit more advanced: Fourier series and integrals by H. Dym and H.P. McKean. Academic Press, 1986; ISBN: 0122264517
• A bit more advanced: An Introduction to Harmonic Analysis by Y. Katznelson. Dover Publications Inc. New York, NY 1976; ISBN o-486-63331-4

In the last 15-20 years there have been published a number of books on wavelets, as well as countless articles. Here is a limited guide:

• A classic, for graduate students: Ten lectures on wavelets, by Ingrid Daubechies, 1992.
• For graduate students: A mathematical introduction to wavelets, by P. Wojtaszczyk, 1997.
• For advanced graduate students: A first course on wavelets, by E. Hernandez and G. Weiss, 1996.
• For advanced graduate students: Wavelets and operators, by Yves Meyer, 1992.

• Wavelets and Filter Banks, by G. Strang and T. Nguyen, 1996.
• An introduction to wavelets, by C. K. Chui, 1992.
• A friendly guide to wavelets, by G. Keiser, 1994.

• The world according to wavelets, by B. Burke Hubbard, 2nd edition, 1998.
• Wavelets: Tools for science and technology, by S. Jaffard, Y. Meyer, R. D. Ryan, 2001.

Americans with Disabilities Act: Qualified students with disabilities needing appropriate academic adjustments should contact me as soon as possible to ensure your needs are met in a timely manner. Handouts are available in alternative accessible formats upon request.

Return to: Department of Mathematics and Statistics, University of New Mexico

Last updated: August 20, 2012