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Wavelets:     more books (100)

Fundamentals of Wavelets: Theory, Algorithms, and Applications (Wiley Series in Microwave and Optical Engineering) by Jaideva C. Goswami, Andrew K. Chan, 1999-02-16 Second Generation Wavelets and Applications by Maarten H. Jansen, Patrick J. Oonincx, 2005-04-28 Discovering Wavelets by Edward Aboufadel, Steven Schlicker, 1999-10-05 Time-Frequency/Time-Scale Analysis, Volume 10 (Wavelet Analysis and Its Applications) by Patrick Flandrin, 1998-10-05 Wavelets and Multiwavelets (Studies in Advanced Mathematics) by Fritz Keinert, 2003-11-12 Time Frequency and Wavelets in Biomedical Signal Processing (IEEE Press Series on Biomedical Engineering) Elements of Wavelets for Engineers and Scientists by Dwight F. Mix, Kraig J. Olejniczak, 2003-09-08 Bayesian Inference in Wavelet Based Models Wavelets: Theory and Applications for Manufacturing by Robert X Gao, Ruqiang Yan, 2010-11-28 Wavelets and their Applications (Digital Signal & Image Processing Series (ISTE-DSP)) Signal Processing with Fractals : A Wavelet-Based Approach by Gregory Wornell, 1996 Wavelets: Calderón-Zygmund and Multilinear Operators (Cambridge Studies in Advanced Mathematics) by Yves Meyer, Ronald Coifman, 2000-07-31 Wavelets, Vibrations and Scalings (Crm Monograph Series) by Yves Meyer, 1997-11-18 Numerical Analysis of Wavelet Methods, Volume 32 (Studies in Mathematics and its Applications) by A. Cohen, 2003-05-13

lists with details

81. Wavelet -- From MathWorld
    Wavelet, wavelets are a class of a functions used to localize a givenfunction in both space and scaling. A family of wavelets can  
    http://mathworld.wolfram.com/Wavelet.html
    
    Extractions: Wavelets are a class of a functions used to localize a given function in both space and scaling. A family of wavelets can be constructed from a function , sometimes known as a "mother wavelet," which is confined in a finite interval. "Daughter wavelets" are then formed by translation ( b ) and contraction ( a ). Wavelets are especially useful for compressing image data, since a wavelet transform has properties which are in some ways superior to a conventional Fourier transform An individual wavelet can be defined by References Benedetto, J. J. and Frazier, M. (Eds.). Wavelets: Mathematics and Applications. Boca Raton, FL: CRC Press, 1994. Chui, C. K. An Introduction to Wavelets. San Diego, CA: Academic Press, 1992. Chui, C. K. (Ed.). Wavelets: A Tutorial in Theory and Applications. San Diego, CA: Academic Press, 1992. Chui, C. K.; Montefusco, L.; and Puccio, L. (Eds.).

82. Wavelets
    wavelets the Idiots Guide. The idiot in this case being me. These functionsare known as wavelets, and this leads to a wavelet transform.  
    http://monet.me.ic.ac.uk/people/gavin/java/waveletDemos.html
    
    Extractions: The idiot in this case being me. In order to be able to understand how wavelets work I've been coding some simple routines in Java, so I thought I'd post them on the web together with some explanation. Once I've got latex2html working correctly, I aim to extend this explanation to make it more rigorous (and mathematical). The basis set for Fourier analysis consists of harmonic (ie. perfectly monochromatic) waves which extend to infinity in both directions. If we consider the 1-d case where the function being analysed is a time signal, this is a process of decomposing the function f(t) into its frequency components. Many physical problems involve waves and can be analysed in this manner (technically, the basis functions for the Fourier transform are related to the generators of the translation group, and so Fourier analysis is appropriate for problems which exhibit translational invariance). However, although we have gained information about the frequency content of f(t) we have lost all temporal information. Alternatively we can consider the function f(t) to be an expansion in terms of a basis set of Dirac functions - in other words the basis set (and hence the information gained) is localised in time but not in frequency. In many cases we would wish to examine both time and frequency information simultaneously. This can be accomplished by expanding f(t) in terms of functions that are both oscillatory and localised in time. These functions are known as wavelets, and this leads to a wavelet transform.

83. BIG - Wavelets In Medicine And Biology
    . waveletsEPFL, Research. English only, BIG Research wavelets in Medicineand Biology. wavelets in Medicine and Biology.  
    http://bigwww.epfl.ch/research/wavelets.html

84. Laboratoire Jacques-Louis Lions, Paris 6, CNRS UMR 7598
    Welcome to the TMR Project wavelets in Numerical Simulation .  
    http://www.ann.jussieu.fr/wavelet/

85. STATISTICS WEEK AT DUKE WORKSHOP ON WAVELETS AND STATISTICS
    INTERNATIONAL WORKSHOP ON wavelets AND STATISTICS. Todd Ogden, U. of South Carolina Cheng Cheng, Johns Hopkins U. Testing for Abrupt Jumps with wavelets;  
    http://www.stat.duke.edu/conferences/BV97/bv.html

86. Wavelets - Best Of The Web - Signal Processing, Algorithms
    wavelets are mathematical functions that cut up data into different frequency components,and then study each component with a resolution matched to its scale.  
    http://www.eg3.com/wavelets.htm

87. 2D And 3D Progressive Transmission Using Wavelets
    Last modified 03/25/97 Acknowledgments. Much of this presentation is derivedfrom the wavelets Course given at SIGGRAPH 96. kdisc wavelets.  
    http://www.cs.wpi.edu/~matt/courses/cs563/talks/Wavelet_Presentation/
    
    Extractions: Last modified: 03/25/97 Acknowledgments Much of this presentation is derived from the Wavelets Course The 3D surface material comes both from the above course notes and from the paper by Certain et. al. entitled, "Interactive Multiresolution Surface Viewing," presented at SIGGRAPH 96. Table of Contents Introduction Wavelets, with their roots in signal processing and harmonic analysis, have had a significant impact in several areas of computer science. They have led to a number of efficient and easy to implement algorithms for use in such fields as: The development of wavelets has been motivated primarily by the need for fast algorithms to compute compact representations of functions and data sets. They exploit the structure (if any) in the data or underlying function, reorganizing the same in a hierarchical fashion. The Haar Wavelet The Haar Wavelet is probably the simplest wavelet to understand. Consider two numbers

88. Wavelets
    wavelets. We are developing a waveletbased X-ray source detectionalgorithm which is a combination of, and contains enhancements  
    http://astro.uchicago.edu/rranch/vkashyap/Wavelet/
    
    Extractions: We are developing a wavelet-based X-ray source detection algorithm which is a combination of, and contains enhancements to, independent algorithms developed at Palermo by Francesco Damiani (1995 Wuerzberg; 1995 ADASS V) and at Chicago by Bob Nichol and Brad Holden (Nichol et al. 1996, submitted to ApJ; Holden et al. 1996, submitted to ApJ). X-Ray Source Detection Using the Wavelet Transform ADASS V , in press.

89. Auditory Wavelets
    Auditory wavelets? « The vOICe Home Page A For more material on wavelets,a good entry point is Amara Graps' Wavelet Page. Copyright  
    http://www.seeingwithsound.com/wavelet.htm

90. GDA:Wavelets:1
    One gets information on both the amplitude of any periodic signals within theseries, and how this amplitude varies with time. on to wavelets 2 .  
    http://cires.colorado.edu/geo_data_anal/topics/wavelet/wavelet1.html
    
    Extractions: The simplest method for analyzing non-stationarity of a timeseries would be to compute statistics such as the mean and variance for different time periods and see if they are significantly different. In Figure 1 we have also plotted the running 15-year variance, as a measure of total power inherent in the signal versus time. One can see that ENSO had more variance during 1880-1920 and also since 1950, with a relatively quiet period during 1920-1950. While the running variance tells us what the overall strength of the signal was at certain times, it suffers from two major defects: [Time Localization] The shape of the curve is highly dependent on the length of the window used. Fifteen years was chosen above as a compromise between either too-much smoothness (say using a 30-year window), or too-little (a 5-year window). An ideal method would allow different window sizes depending on the scales that one is interested in.

91. Surfing Wavelets On Streams One-Pass Summaries For Approximate
    Surfing wavelets on Streams One (2002) (Correct) Active bibliography (related documents)More All 4.2 Surfing wavelets on Streams OnePass Summaries for..  
    http://citeseer.nj.nec.com/gilbert01surfing.html

92. Citations Ten Lectures On Wavelets - Daubechies (ResearchIndex)
    Only retrieving 1000 documents. I. Daubechies, Ten Lectures on wavelets, SIAM,Philadelphia, 1992. wavelets in multidimensional heat equations.  
    http://citeseer.nj.nec.com/context/267/0

93. Princeton - PWB 032700 - Reality In Wavelets
    Reality in wavelets. Ingrid Daubechies (Photo by Denise Applewhite).By Ken Howard. Making sense of the world is something professors  
    http://www.princeton.edu/pr/pwb/00/0327/p/wavelet.shtml

94. Magasa's Wavelets
    An Introduction to wavelets by Amara Graps. Wavelet digest; wavelets BristolUniv. wavelets and their application to condition monitoring (Univ.  
    http://dali.korea.ac.kr/~magasa/wavelets.html

95. Detour - Wayne's Wavelets Page Is No Longer Available...
    Sorry, Wayne's wavelets page is no longer available. Wayne's new page is aboutclay animation and stop motion animation! Please update your bookmarks to  
    http://www.intrepid.net/~hollyoak/wave.htm

96. KLUWER Academic Publishers | Wavelets In Signal And Image Analysis
    Books » wavelets in Signal and Image Analysis. wavelets in Signaland Image Analysis From Theory to Practice. Add to cart. edited  
    http://www.wkap.nl/prod/b/1-4020-0053-7
    
    Extractions: Despite their novelty, wavelets have a tremendous impact on a number of modern scientific disciplines, particularly on signal and image analysis. Because of their powerful underlying mathematical theory, they offer exciting opportunities for the design of new multi-resolution processing algorithms and effective pattern recognition systems. This book provides a much-needed overview of current trends in the practical application of wavelet theory. It combines cutting edge research in the rapidly developing wavelet theory with ideas from practical signal and image analysis fields. Subjects dealt with include balanced discussions on wavelet theory and its specific application in diverse fields, ranging from data compression to seismic equipment. In addition, the book offers insights into recent advances in emerging topics such as double density DWT, multiscale Bayesian estimation, symmetry and locality in image representation, and image fusion.

97. Results
    Search Results. Search Results for wavelets IN keyword Found 35of 105,850 searched. Rerun within the Portal Search within Results  
    http://portal.acm.org/results.cfm?query=wavelets keyword&coll=portal&dl=ACM

98. Results
    Search Results. Search Results for wavelets and fractals IN CCS Found36 of 105,850 searched. Rerun within the Portal Search within Results  
    http://portal.acm.org/results.cfm?query=Wavelets and fractals CCS&coll=port

99. Wavelets Software And Applications
    wavelets Software and Applications. We created a new kind of secondgenerationwavelets on a rectangular grid, based on a red-black blocking scheme.  
    http://home.tvd.be/cr26864/PhD/

100. Wavelets As Multiresolution Signal Decomposition Tools
     wavelets as Signal Decomposition and Analysis Tools. By Elmer Smalling III.Wavelet theory is an active field of signal analysis approximation  
     http://www.connect.net/smalling/wavelet.htm
     
     Extractions: Wavelets as Signal Decomposition and Analysis Tools By Elmer Smalling III Wavelet theory is an active field of signal analysis approximation theory that describes the frequency content of a signal in time and combines powerful new mathematical methods for representing functions with software. Although rooted in work from over 30 years ago,it has undergone considerable development in the past decade. Before describing wavelets, popular transform methods like Fast Fourier Transforms (FFTs) and Discrete Cosine Transforms (DCTs) will be briefly described. For signal analysis, synthesis, and compression, simple transformation from one domain to another ( time to frequency and vice versa), has many uses. Digital signal processing requires mathematical tools to transform multi-frequency (multiresolutional) time-varying signals for data compression, pattern recognition, and digital filtering. Domain transforms such as Fast Fourier Transforms (FFTs) and Discrete Cosine Transforms (DCTs) had been at the forefront of signal transformation and analysis until the introduction of the wavelet transform in the early 1980s. Although not the solution for every signal processing challenge, wavelets are more efficient for many applications than FFT or DCT methodology. This article provides a brief background on transform methodology and an introduction to wavelets and their applications.

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