Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913470315757568 |
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| author | Su, Yan-Wei Liu, Gi-Ren Sheu, Yuan-Chung Wu, Hau-Tieng |
| author_facet | Su, Yan-Wei Liu, Gi-Ren Sheu, Yuan-Chung Wu, Hau-Tieng |
| contents | We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive mode decomposition-based multiple harmonic ridge detection} (\textsf{SAMD-MHRD}). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \textsf{SAMD-MHRD} through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06673 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications Su, Yan-Wei Liu, Gi-Ren Sheu, Yuan-Chung Wu, Hau-Tieng Numerical Analysis Methodology We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive mode decomposition-based multiple harmonic ridge detection} (\textsf{SAMD-MHRD}). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \textsf{SAMD-MHRD} through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject. |
| title | Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications |
| topic | Numerical Analysis Methodology |
| url | https://arxiv.org/abs/2309.06673 |