1. The Wavelet Digest :: Index Bringing together the Wavelet Community. This site hosts a free monthly newsletter on wavelets. Covers theory and applications. Includes all archives since it was founded in 1992 by Wim Sweldens. http://www.wavelet.org/

Return to the homepage Search the complete Wavelet Digest database Help about the Wavelet Digest mailing list About the Wavelet Digest The Digest The Community Latest Issue Back Issues Current submissions New submission ... Gallery What's here? This site offers several services intended to foster the exchange of knowledge and viewpoints related to theory and applications of wavelets. The Wavelet Digest Online subscription here The free and non-commercial electronic newsletter since , linking together the multi-disciplinary wavelet community. You can submit your own contributions The Wavelet Discussion Forum The discussion forum enables rapid communication between members of the wavelet community. The Wavelet Calendar of Events The calendar includes the most interesting conferences, meetings, and workshops for wavelet researchers. The Wavelet Gallery The gallery contains links to the most essential resources related to wavelets: books, software, demos, tutorials, and so on. Announcements Editorial: Surfing into the new year 12 Jan 2006 Surfing the Wavelets 31 Aug 2004 Editorial: Browsing through the Wavelet Discussion Forum 22 Dec 2003 Editorial: Adapting to the needs of the community 30 Jun 2003 This site is hosted by the Swiss Federal Institute of Technology Lausanne webmaster@wavelet.org

2. WAVELETS Wavelet history, bayesian inference, statistical modeling. http://www.isye.gatech.edu/~brani/wavelet.html

Jacket's Wavelets LOCAL DENSITY ESTIMATION WHEN DATA ARE SIZE-BIASED: WAVELET-BASED MATLAB TOOLBOX Often researchers need to estimate the density in the presence of size-biased data. The wavelet-based MATLAB toolbox biased.zip performs debiasing and estimattes density by smoothed linear projection wavelet esimator. The toolbox supports the manuscript: Wavelet Density Estimation for Stratified Size-Biased Sample, by Pepa Ramirez and Brani Vidakovic. LPM: Bayesian Wavelet Thresholding based on Larger Posterior Mode This project explores the thresholding rules induced by a variation of the Bayesian MAP principle. The MAP rules are Bayes actions that maximize the posterior. Under the proposed model the posterior is neither unimodal or bimodal. The proposed rule is thresholding and always picks the mode of the posterior larger in absolute value, thus the name LPM. We demonstrate that the introduced shrinkage performs comparably to several popular shrinkage techniques. Exact risk properties of the thresholding rule are explored. We provide extensive simulational analysis and apply the proposed methodology to real-life experimental data coming from the field of Atomic Force Microscopy (AFM). You could try the LPM thresholding if your MATLAB has access to WaveLab Module.

3. Wavelet Analysis; Significance Levels; Confidence Intervals; Introduction to wavelet analysis and Monte Carlo, interactive wavelet plotting, FAQ, and software. http://paos.colorado.edu/research/wavelets/

A Practical Guide to Wavelet Analysis With significance and confidence testing Christopher Torrence ITT Visual Info. Solutions Boulder, Colorado chris[AT]ittvis[DOT]com Gilbert P. Compo NOAA/CIRES Climate Diagnostics Center, Boulder, Colorado compo[AT]colorado[DOT]edu Software for Fortran, IDL, and Matlab Frequently Asked Questions (FAQ) Article: "A Practical Guide to Wavelet Analysis" , C. Torrence and G. P. Compo, 1998. Permission to place a copy of this work on this server has been provided by the American Meteorological Society The AMS does not guarantee that the copy provided here is an accurate copy of the published work. Wavelet Coherency and Phase Featured in: Internet Scout The Wavelet Digest The Math Forum Internet Mathematics Library

4. Wavelet - Wikipedia, The Free Encyclopedia Jump to Discrete wavelets Beylkin (18); BNC wavelets Coiflet (6, 12, 18, 24, 30) referred to as CDF N/P or Daubechies biorthogonal wavelets) http://en.wikipedia.org/wiki/Wavelet

Wavelet From Wikipedia, the free encyclopedia Jump to: navigation search A wavelet is a wave -like oscillation with an amplitude that starts out at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor . Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing . Wavelets can be combined, using a "shift, multiply and sum" technique called convolution , with portions of an unknown signal to extract information from the unknown signal. For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly a 32nd note . If this wavelet were to be convolved at periodic intervals with a signal created from the recording of a song, then the results of these convolutions would be useful for determining when the Middle C note was being played in the song. Mathematically, the wavelet will resonate if the unknown signal contains information of similar frequency - just as a tuning fork physically resonates with sound waves of its specific tuning frequency. This concept of resonance is at the core of many practical applications of wavelet theory. As wavelets are a mathematical tool they can be used to extract information from many different kinds of data, including - but certainly not limited to - audio signals and images. Sets of wavelets are generally needed to analyze data fully. A set of "complementary" wavelets will deconstruct data without gaps or overlap so that the deconstruction process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet based compression/decompression algorithms where it is desirable to recover the original information with minimal loss.

5. An Introduction To Wavelets May 12, 2004 Article by Amara Graps (in HTML, PDF or Postscript) to the interested technical person outside of the digital signal processing field. http://www.amara.com/IEEEwave/IEEEwavelet.html

A n I ntroduction to W avelets Abstract Keywords: Wavelets, Signal Processing Algorithms, Orthogonal Basis Functions, Wavelet Applications Contents: Overview Historical Perspective Sidebar- What are Basis Functions? Fourier Analysis ... Wavelet Analysis Wavelet Applications Computer and Human Vision FBI Fingerprint Compression Denoising Noisy Data Detecting Self-Similarity in a Time Series ... References Click HERE to download a gzipped, Postscript copy (compressed to 222 Kbytes, expands to 1.5Mbytes). Or click HERE to download a PDF version (360 Kbytes) The original version of this work appears in IEEE Computational Science and Engineering, Summer 1995, vol. 2, num. 2, published by the IEEE Computer Society, 10662 Los Vaqueros Circle, Los Alamitos, CA 90720, USA, TEL +1-714-821-8380, FAX +1-714-821-4010. Wavelet Resources Home Last Modified by Amara Graps on 12 May 2004.

6. Wavelets wavelets are a relatively recent arrival on the scene, starting to make their mark in both the mathematical and applied communities in the mid 1980s. http://www.spelman.edu/~colm/wav.html

Wavelets Wavelets are a relatively recent arrival on the scene, starting to make their mark in both the mathematical and applied communities in the mid 1980s. They provide an alternative to classical Fourier methods for one- and multi-dimensional data analysis and synthesis, and have numerous applications both within mathematics (e.g., to partial differential operators) and in areas as diverse as physics, seismology, medical imaging, digital image processing, signal processing and computer graphics and video. The most popular and accessible application of wavelets is probably to image compression. Emmy Noether (original, 25 to 1 and 100 to 1 normalized Haar wavelet compressed images). See Matlab M-files if you want to generate similar compression pictures for yourself. Unlike their Fourier cousins, wavelet methods make no assumptions concerning periodicity of the data at hand. As a result, wavelets are particularly suitable for studying data exhibiting sharp changes or even discontinuities. Wavelets allow information to be encoded according to "levels of detail" - in one sense this parallels the way in which we often process information in our everyday lives. Contrary to popular belief, wavelet basics can be explored keeping the mathematical prerequisites to a minimum - namely, familiarity with the elements of linear algebra. In particular, no knowledge of Fourier analysis is necessary to grasp the main concepts. We first became aware of this, in the case of simple Haar wavelets, via the wonderful paper

7. Bibliographies On Wavelets Bibliographies on wavelets at the University of Karlsruhe. http://liinwww.ira.uka.de/bibliography/Theory/Wavelets/

The Collection of Computer Science Bibliographies Collection Home Up: Bibliographies on Theory/Foundations of Computer Science Bibliographies on Wavelets Query: in any author title field Publication year : in: , since: , before: (four digit years) Options: Results as Citation Results in BibTeX 10 results per page 40 results per page 100 results per page 200 results per page sort by score year online papers only You may use Lucene syntax , available fields are: ti (title), au (author), yr (publications year). #Refs Bibliography Date Signal processing and wavelet related bibliography Bibliography of Wavelet, Time Series, and Related Works Bibliography on wavelets and chirplets Bibliography on Wavelets ... List of papers concerning wavelets in tomography Total number of references in this section Other Bibliographies on Wavelets This service is brought to you by Alf-Christian Achilles and Paul Ortyl Please direct comments to liinwwwa@ira.uka.de

8. Amara's Wavelet Page Introduction to wavelets with links to many other resources by Amara Graps. http://www.amara.com/current/wavelet.html

Wavelet Overview Wavelet Digest Wavelet Blogsphere Wavelet Patents ... Changes to the Page W avelet O verview The fundamental idea behind wavelets is to analyze according to scale. Indeed, some researchers in the wavelet field feel that, by using wavelets, one is adopting a whole new mindset or perspective in processing data. Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. This idea is not new. Approximation using superposition of functions has existed since the early 1800's, when Joseph Fourier discovered that he could superpose sines and cosines to represent other functions. However, in wavelet analysis, the scale that one uses in looking at data plays a special role. Wavelet algorithms process data at different scales or resolutions. If we look at a signal with alarge "window," we would notice gross features. Similarly, if we look at a signal with a small "window," we would notice small discontinuities. The result in wavelet analysis is to "see the forest and the trees." Can you see why these features make wavelets interesting and useful? For many decades, scientists have wanted more appropriate functions than the the sines and cosines which comprise the bases of Fourier analysis, to approximate choppy signals. By their definition, these functions are non-local (and stretch out to infinity), and therefore do a very poor job in approximating sharp spikes. But with wavelet analysis, we can use approximating functions that are contained neatly in finite domains. Wavelets are well-suited for approximating data with sharp discontinuities.

9. Wavelets wavelets Internet Sources (last update April 11th, 2000) WWW Server . NEW MT-WICE Wavelet based Image Compression (Software) Wavelet Digest Homepage http://www.mat.sbg.ac.at/~uhl/wav.html

[an error occurred while processing this directive] [an error occurred while processing this directive] WAVELETS Internet Sources (last update: April 11th, 2000 WWW - Server NEW: MT-WICE: Wavelet based Image Compression (Software) Wavelet Digest Homepage Steven Baum's Wavelet page The Imager Wavelet Library ... MathSoft Wavelet Resources page : links to many papers Wavelets at Imager Wavelets and their application to condition monitoring (Univ. Aberdeen, UK) Wavelet page at the European Southern Observatory Aware's product information (WaveTool, image compression, etc.) Multiresolution Signal Processing , Univ. of Minnesota An Introduction to Wavelets by Amara Graps. Wavelet resources page by Amara Graps. UC Berkeley Wavelet Group Numerical Harmonic Analysis Group , Univ. Vienna, Austria Ricoh's wavelet still image compression system Blue devil's wavelets , Duke University Mac A. Cody Associates Homepage Multirate Signal Processing Group , University of Wisconsin - Madison WavBox Software by C. Taswell HARC-C Wavelet Image Compression Lightning Strike Wavelet Image Compression MegaWave1+2 Software at CEREMADE The Wavelet Seismic InversionLab ... C. Brislawns fingerprint page (FBI standard)

10. Wavelets On Myspace Music - Free Streaming MP3s, Pictures & Music Downloads Myspace Music profile for wavelets. Download wavelets music singles, watch music videos, listen to free streaming mp3s, read wavelets s blog. http://www.myspace.com/wavelets

11. A Really Friendly Guide To Wavelets Nov 15, 2003 Wavelet tutorial for engineers in three parts friendly introduction, lifting, and EZW. http://polyvalens.pagesperso-orange.fr/clemens/wavelets/wavelets.html

UP PART 2 PART 3 Yes ! I've done it ! Recognition at last ! This site has won an award ! A Really Friendly Guide to Wavelets (C) C. Valens, 1999-2010 Sometimes people don't know how to address me when writing me. They call me professor Valens, doctor Valens, mr/miss Valens, Valens or professor Clemens, doctor Clemens, mr/miss Clemens, Clemens, or simply nothing. So then, how should you address me, you wonder? Well, I don't care, but if you must know, I am (a) male. NEW! * Replaced "Dirac pulse" by "Kronecker delta". * Checked, corrected and deleted WWW references. * I finally invested some time to learn how to make PDF files and updated my wavelet tutorial PDF file * Made it all more printer friendly. Some images were to large to print correctly. * Fixed missing symbols (forgot to transform some GIF files to PNG). * Added little note about two-dimensional transform to the introduction. * New email address. The folks at iName.com now want money so I decided to let good old mindless go. * All GIF files have been replaced by PNG files. These files are a bit smaller than GIF files and so this page should load a bit faster. (Saved over 15KB on this page!)

12. Gerald Kaiser Harmonic analysis, Partial Differential Equations, wavelets, physicsbased signal and image processing. http://www.wavelets.com/

Gerald Kaiser Austin, TX kaiser (at) wavelets (dotcom Kielce, Poland (1942) Austin, TX (2005) A poem Lindy Hop ... Two rescue stories Languages: English, German, Polish, Russian, Portuguese, French, Hebrew Ph.D. (Mathematics) 1977, University of Toronto Thesis: Phase-Space Approach to Relativistic Quantum Mechanics Ph.D. (Physics) 1970, University of Wisconsin - Madison Thesis: Application of Continuous-Moment Sum Rules B.Sc. (Mathematics) 1962, Case Institute of Technology Employment/Positions: 1977-1998 Professor of Mathematical Sciences, Univ. of Massachusetts (now Emeritus) Since 1998: Founder and Head, Since 1999: Editor-in-Chief (with Anne Boutet de Monvel, Univ. Paris 7), Progress in Mathematical Physics Book Series Since 2005: Visiting Scholar, Center for Relativity, University of Texas, Austin Research interests Harmonic analysis, potential theory, PDE, wavelets Real and complex geometry, Clifford analysis Electromagnetics, optics, acoustics Quantum physics, relativity, black holes Efficient representations of waves and information Physics-based signal and image processing Radar, remote sensing, communication theory

13. Discovering Wavelets: Home Page A center of activity for incorporating wavelets into the undergraduate curriculum. Tutorials, projects and resources. http://faculty.gvsu.edu/aboufade/web/dw.htm

The Discovering Wavelets Web Site maintained by Edward Aboufadel and Steven Schlicker Our goal for this site is to make it a center of activity for incorporating wavelets into the undergraduate curriculum. We believe that wavelets can be accessible to people other than research mathematicians that anyone with a background in basic linear algebra (for example, graduate and undergraduate students, and nonspecialists) can learn about and work with wavelets. Undergraduates at GVSU, many who are prospective mathematics teachers, have completed projects based on wavelets, along with undergraduate research projects. The challenge to mathematics professors is to present wavelets in way that is accessible without being trivial. New and Noteworthy December 2009: Some minor housekeeping done to the site. However, we are not updating this site regularly. Many summers at Grand valley State University, students work on projects related to wavelets. Learn about projects from 2000, 2002-2006, and 2008-09 on the student projects web page . The next opportunity to work with Prof. Aboufadel is the summer of 2011 at the GVSU Math Research Experiences for Undergraduates Program A few years ago, Oxford University Press published

14. 18.327 - 1.130 18.327 and 1.130 Joint course on wavelets, FILTER BANKS AND APPLICATIONS http://web.mit.edu/18.327/

and Joint course on WAVELETS, FILTER BANKS AND APPLICATIONS 18.327 - Wavelets and Filter Banks Gilbert Strang 1.130 - Wavelets and Multiscale Methods in Engineering Computation and Information Processing Kevin Amaratunga NEWS 18.327/1.130 has been published through MIT OpenCourseWare: OCW version of 18.327/1.130 COURSE INFORMATION Instructors: Gilbert Strang e-mail: gs@math.mit.edu Office: Room 2-240. Kevin Amaratunga e-mail: kevina@mit.edu Office: Room 1-274. Schedule for Spring 2004 : Monday and Wednesday 1:30-3:00 in Room 1-390. Text: WAVELETS AND FILTER BANKS by Strang and Nguyen, Wellesley-Cambridge Press , 2nd Ed. (1997.) Syllabus: Please see the class schedule . Also, see the course announcement for general course overview. ANNOUNCEMENTS Posted 04/20/2004 : Solutions to Problem Set 3 have been posted. Look under Solutions Posted 04/05/2004 : Solutions for Problem Set 2 have been posted. Look under Solutions Posted 03/10/2004 : Problem Set 2 is due today in class and Problem Set 3 is out (Look under Problem Sets Posted 03/03/2004 : Solutions to Problem Set 1 have been posted. Look under

15. Wavelets And Filter Banks wavelets AND FILTER BANKS by Gilbert Strang and Truong Nguyen (1996) Table of Contents. Guide to the Book. Ordering Information. Homepage for the Book. http://www-math.mit.edu/~gs/books/wfb.html

WAVELETS AND FILTER BANKS by Gilbert Strang and Truong Nguyen Table of Contents Guide to the Book Ordering Information ... Book review by Dr. Davis.

16. Daniel Lemire's Blog A friendly posting board on wavelets. http://www.ondelette.com/indexen.html

@import url( http://www.daniel-lemire.com/blog/wp-content/themes/lemiretheme/style.css ); Who is going to need a database engine in 2020? Given the Big Data phenomenon, you might think that everyone is becoming a database engineer. Unfortunately, writing a database engine is hard: Concurrency is difficult. Whenever a data structure is modified by different processes or threads, it can end up in an inconsistent state. Database engines cope with concurrency in different ways: e.g., through locking or multiversion concurrency control . While these techniques are well known, few programmers have had a chance to master them. Persistence is also difficult. You must somehow keep the database on a slow disk, while keeping some of the data in RAM. At all times, the content of the disk should be consistent. Moreover, you must avoid data loss as much as possible. So, developers almost never write their own custom engines. Some might say that it is an improvement over earlier times when developers absolutely had to craft everything by hand, down to the B-trees .  The result was often expensive projects with buggy results. However, consider that even a bare-metal language like C++ is getting support for

17. Gerald Kaiser Harmonic analysis, Partial Differential Equations, wavelets, physicsbased signal and image processing. http://wavelets.com/

Gerald Kaiser Austin, TX kaiser (at) wavelets (dotcom Kielce, Poland (1942) Austin, TX (2005) A poem Lindy Hop ... Two rescue stories Languages: English, German, Polish, Russian, Portuguese, French, Hebrew Ph.D. (Mathematics) 1977, University of Toronto Thesis: Phase-Space Approach to Relativistic Quantum Mechanics Ph.D. (Physics) 1970, University of Wisconsin - Madison Thesis: Application of Continuous-Moment Sum Rules B.Sc. (Mathematics) 1962, Case Institute of Technology Employment/Positions: 1977-1998 Professor of Mathematical Sciences, Univ. of Massachusetts (now Emeritus) Since 1998: Founder and Head, Since 1999: Editor-in-Chief (with Anne Boutet de Monvel, Univ. Paris 7), Progress in Mathematical Physics Book Series Since 2005: Visiting Scholar, Center for Relativity, University of Texas, Austin Research interests Harmonic analysis, potential theory, PDE, wavelets Real and complex geometry, Clifford analysis Electromagnetics, optics, acoustics Quantum physics, relativity, black holes Efficient representations of waves and information Physics-based signal and image processing Radar, remote sensing, communication theory

18. Wavelets And Partial Differential Equations wavelets and Partial Differential Equations. Martin J. Mohlenkamp (Department of Mathematics, College of Arts Sciences, Ohio University) A course presented at the II Pan http://www.math.ohiou.edu/~mjm/20044/PASIII/

Wavelets and Partial Differential Equations Martin J. Mohlenkamp Department of Mathematics Ohio University A course presented at the II Pan-American Advanced Studies Institute in Computational Science and Engineering, Universidad Nacional Aut�noma de Honduras, Tegucigalpa, Honduras, June 14-18, 2004. Course Description In this course we will discuss the basics of wavelets. Including multiresolution analysis, lifting techniques, and basic applications to data compression, denoising, and signal and image processing. We will discuss the interplay between wavelets and differentiation. In particular we will discuss efficient representation in terms of wavelets of derivatives and products, and how to handle boundary terms as well as irregular data. These are all cornerstones of many non-linear PDE solvers. Course Outline The official course outline, which was created (by Cristina Pereyra) some months earlier, is: Lecture 1: Time/Frequency Analysis Fourier analysis. Windowed Fourier transform.

19. University Of St. Thomas :: Wavelets Webpage St. Thomas Center for Applied Mathematics research projects, Welcome! Welcome to the wavelets Webpage at the University of St. Thomas. http://cam.mathlab.stthomas.edu/wavelets/

20. Guide To Wavelet Sources Links to tutorials, software and other wavelet sites. Compressed using gzip and cannot be rendered by all browsers. http://www-ocean.tamu.edu/~baum/wavelets.html.gz

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