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• W3138269711 abstract "Due to resilience to background noise, stability of sparse reconstruction, and ability to capture local time-frequency information, the frame theory is becoming a dynamic forefront topic in data science. In this study, we overcome the disadvantages in the construction of traditional framelet packets derived by frame multiresolution analysis and square iterative matrices. We propose two novel approaches: One is to directly split known framelets again and again; the other approach is based on a generalized scaling function whose shifts are not a frame of some space. In these two approaches, the iterative matrices used are not square and the number of rows in the iterative matrix can be any integer number." @default.
• W3138269711 created "2021-03-29" @default.
• W3138269711 creator A5047580482 @default.
• W3138269711 date "2021-03-24" @default.
• W3138269711 modified "2023-09-25" @default.
• W3138269711 title "Splitting of Framelets and Framelet Packets" @default.
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• W3138269711 doi "https://doi.org/10.3390/math9070697" @default.
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