TY - JOUR
T1 - A Review of Wavelet Analysis and Its Applications
T2 - Challenges and Opportunities
AU - Guo, Tiantian
AU - Zhang, Tongpo
AU - Lim, Enggee
AU - Lopez-Benitez, Miguel
AU - Ma, Fei
AU - Yu, Limin
N1 - Funding Information:
This work was supported in part by the Research Enhancement Fund of Xi'an Jiaotong-Liverpool University (XJTLU) under Grant REF-19-01-04, in part by the National Natural Science Foundation of China (NSFC) under Grant 61501380, in part by the AI University Research Center (AI-URC), in part by the XJTLU Laboratory for Intelligent Computation and Financial Technology through the XJTLU Key Program Special Fund under Grant KSF-P-02, in part by the Jiangsu Data Science and Cognitive Computational Engineering Research Centre, and in part by the ARIES Research Centre.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/6/1
Y1 - 2022/6/1
N2 - As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.
AB - As a general and rigid mathematical tool, wavelet theory has found many applications and is constantly developing. This article reviews the development history of wavelet theory, from the construction method to the discussion of wavelet properties. Then it focuses on the design and expansion of wavelet transform. The main models and algorithms of wavelet transform are discussed. The construction of rational wavelet transform (RWT) is provided by examples emphasizing the advantages of RWT over traditional wavelet transform through a review of the literature. The combination of wavelet theory and neural networks is one of the key points of the review. The review covers the evolution of Wavelet Neural Network (WNN), the system architecture and algorithm implementation. The review of the literature indicates the advantages and a clear trend of fast development in WNN that can be combined with existing neural network algorithms. This article also introduces the categories of wavelet-based applications. The advantages of wavelet analysis are summarized in terms of application scenarios with a comparison of results. Through the review, new research challenges and gaps have been clarified, which will serve as a guide for potential wavelet-based applications and new system designs.
KW - multiresolution analysis
KW - rational wavelets
KW - wavelet neural network
KW - wavelet transform
KW - Wavelets
KW - Wavelet transforms
KW - Continuous wavelet transforms
KW - Transforms
KW - Wavelet packets
KW - Discrete wavelet transforms
KW - Multiresolution analysis
KW - Signal resolution
UR - http://www.scopus.com/inward/record.url?scp=85131729485&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3179517
DO - 10.1109/ACCESS.2022.3179517
M3 - Article
AN - SCOPUS:85131729485
SN - 2169-3536
VL - 10
SP - 58869
EP - 58903
JO - IEEE Access
JF - IEEE Access
ER -