Abstract
The wavelet analysis is the development based on Fourier transform. And it is a breakthrough of the Fourier analysis. The paper reviews the development history of wavelet analysis. Then it discusses the similarities and differences of Fourier transform and wavelet theory. As a new transform domain signal processing methods, wavelet transform is particularly good at dealing with non-stationary signal analysis. 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. In the end the paper summarizes and discusses applications of wavelet in various domains in detail.
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© 2011 Springer-Verlag Berlin Heidelberg
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Feng, Hy., Wang, Jp., Li, Yc., Chen, J. (2011). Wavelet Theory and Application Summarizing. In: Liu, B., Chai, C. (eds) Information Computing and Applications. ICICA 2011. Lecture Notes in Computer Science, vol 7030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25255-6_43
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DOI: https://doi.org/10.1007/978-3-642-25255-6_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25254-9
Online ISBN: 978-3-642-25255-6
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