A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities
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 wave...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9785993/ |
| _version_ | 1818238402785443840 |
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| author | Tiantian Guo Tongpo Zhang Enggee Lim Miguel Lopez-Benitez Fei Ma Limin Yu |
| author_facet | Tiantian Guo Tongpo Zhang Enggee Lim Miguel Lopez-Benitez Fei Ma Limin Yu |
| author_sort | Tiantian Guo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T12:41:05Z |
| format | Article |
| id | doaj.art-109a44431301463a951a66ee6150f3a6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T12:41:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-109a44431301463a951a66ee6150f3a62022-12-22T00:24:12ZengIEEEIEEE Access2169-35362022-01-0110588695890310.1109/ACCESS.2022.31795179785993A Review of Wavelet Analysis and Its Applications: Challenges and OpportunitiesTiantian Guo0https://orcid.org/0000-0003-4918-7542Tongpo Zhang1https://orcid.org/0000-0001-5469-6670Enggee Lim2https://orcid.org/0000-0003-0199-7386Miguel Lopez-Benitez3https://orcid.org/0000-0003-0526-6687Fei Ma4https://orcid.org/0000-0001-6099-480XLimin Yu5https://orcid.org/0000-0002-6891-0604Department of Communications and Networking, School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, Jiangsu, ChinaDepartment of Communications and Networking, School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, Jiangsu, ChinaDepartment of Communications and Networking, School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, Jiangsu, ChinaDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool, Merseyside, U.K.Department of Applied Mathematics, School of Science, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, Jiangsu, ChinaDepartment of Communications and Networking, School of Advanced Technology, Xi’an Jiaotong-Liverpool University (XJTLU), Suzhou, Jiangsu, ChinaAs 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.https://ieeexplore.ieee.org/document/9785993/Waveletsmultiresolution analysiswavelet transformrational waveletswavelet neural network |
| spellingShingle | Tiantian Guo Tongpo Zhang Enggee Lim Miguel Lopez-Benitez Fei Ma Limin Yu A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities IEEE Access Wavelets multiresolution analysis wavelet transform rational wavelets wavelet neural network |
| title | A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities |
| title_full | A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities |
| title_fullStr | A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities |
| title_full_unstemmed | A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities |
| title_short | A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities |
| title_sort | review of wavelet analysis and its applications challenges and opportunities |
| topic | Wavelets multiresolution analysis wavelet transform rational wavelets wavelet neural network |
| url | https://ieeexplore.ieee.org/document/9785993/ |
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