2012Unpublished venueRequires access

Introduction to a novel wavelet

Md. Shoaibur Rahman, Md. Aynal Haque

Open publisher page 2 citations

Abstract

Wavelet is a basis function used in wavelet transformation. The wavelet transformation is a strong tool, and widely used in the signal processing purposes. However, the universality of application of a particular wavelet is still restricted. Here, we have presented a novel wavelet that can be used with better performance than many of the existing wavelet family members. The new wavelet satisfies the basic properties of wavelet, and a summary of the proposed wavelet shows that it is symmetric and mesokurtic with zero mean, contains large number of vanishing moments, and can be efficiently used both for continuous and discrete type wavelet transformations with exact reconstruction of signals. The new wavelet demonstrates better performance in the analysis of biological data sets, and a similar improvement is expected when analysing many other statistical data in different sectors.

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What this paper is about

Wavelet is a basis function used in wavelet transformation. The wavelet transformation is a strong tool, and widely used in the signal processing purposes. However, the universality of application of a particular wavelet is still restricted. Here, we have presented a novel wavelet that can be used with better performance than many of the existing wavelet family members. The new wavelet satisfies the basic properties of wavelet, and a summary of the proposed wavelet shows that it is symmetric and mesokurtic with zero mean, contains large number of vanishing moments, and can be efficiently used both for continuous and discrete type wavelet transformations with exact reconstruction of signals. The new wavelet demonstrates better performance in the analysis of biological data sets, and a similar improvement is expected when analysing many other statistical data in different sectors.

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Available abstract

Wavelet is a basis function used in wavelet transformation. The wavelet transformation is a strong tool, and widely used in the signal processing purposes. However, the universality of application of a particular wavelet is still restricted. Here, we have presented a novel wavelet that can be used with better performance than many of the existing wavelet family members. The new wavelet satisfies the basic properties of wavelet, and a summary of the proposed wavelet shows that it is symmetric and mesokurtic with zero mean, contains large number of vanishing moments, and can be efficiently used both for continuous and discrete type wavelet transformations with exact reconstruction of signals. The new wavelet demonstrates better performance in the analysis of biological data sets, and a similar improvement is expected when analysing many other statistical data in different sectors.

Key concepts: Wavelet, Wavelet packet decomposition, Stationary wavelet transform, Discrete wavelet transform, Second-generation wavelet transform, Lifting scheme, Cascade algorithm, Wavelet transform

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