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Beyond Wavelets

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Wavelets

Abstract

In previous chapters, we have introduced the theoretical foundation and practical applications related to the wavelet transform. The ability of wavelet transform in adaptive time-scale representation and decomposition of a signal into different subfrequency band presents an efficient signal analysis method without introducing calculation burden (Sweldens 1998). Consequently, it has become a prevailing tool for nonstationary signal processing (e.g., transient pattern identification and location). Given, however, the great variety of signals that appear in real-world applications, there remains plenty of room for continued advancement in the theory of the classical wavelet transform. For example, one of the limitations of the wavelet transform is to modify the base wavelet function to better analyze signals of finite length or duration, instead of infinite or periodic signals (Sweldens 1997).

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Connecticut, Storrs, CT, 06269-3139, USA

    Robert X. Gao

  2. School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China, People’s Republic

    Ruqiang Yan

Authors
  1. Robert X. Gao
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  2. Ruqiang Yan
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Correspondence to Robert X. Gao .

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Gao, R.X., Yan, R. (2011). Beyond Wavelets. In: Wavelets. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1545-0_12

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  • DOI: https://doi.org/10.1007/978-1-4419-1545-0_12

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-1544-3

  • Online ISBN: 978-1-4419-1545-0

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