Form of presentation | Articles in international journals and collections |
Year of publication | 2022 |
Язык | английский |
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Bikchentaeva Leysan Maratovna, author
Sachenkov Oskar Aleksandrovich, author
Tagirova Irina Sergeevna, author
Yafarova Guzel Gulusovna, author
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Bikchentaeva Leysan Maratovna, postgraduate kfu
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Dakinova Margarita Vitalevna, author
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Bibliographic description in the original language |
Dakinova M. V. Spectral analysis of stabilographic signals by Fourier and Hilbert – Huang methods/ M. V. Dakinova, L. M. Bikchentaeva, I. S. Tagirova, T. V. Baltina, G. G. Yafarova and O. A. Sachenkov//IEEE Xplore 2022: VIII International Conference on Information Technology and Nanotechnology (ITNT). - 2022. - pp. 1-4, doi: 10.1109/ITNT55410.2022.9848704. |
Annotation |
Spectral analysis is an important pipeline step in digital signal processing and significantly effects on received information. Methodical distortions in the information leads to corruptions of properties contained in the signal, and these is an origin of mistaken analysis. The Fourier transform is widespread as such pipeline step, but it limited for processing non-stationary and nonlinear signals. The spectral analysis of the Hilbert-Huang transform has no such limits. The transform is based on the Hilbert spectrum and empirical mode decomposition. The research is focused on the application of the Hilbert-Huang transform to stabilogram data analyses. The method to exarticulate the control factor of the human stability mechanism is developed. |
Keywords |
Hilbert-Huang transform, empirical mode decomposition, Fourier transform, stabilogram, spectral analysis |
The name of the journal |
IEEE Xplore
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Please use this ID to quote from or refer to the card |
https://repository.kpfu.ru/eng/?p_id=270855&p_lang=2 |
Resource files | |
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Full metadata record |
Field DC |
Value |
Language |
dc.contributor.author |
Bikchentaeva Leysan Maratovna |
ru_RU |
dc.contributor.author |
Sachenkov Oskar Aleksandrovich |
ru_RU |
dc.contributor.author |
Tagirova Irina Sergeevna |
ru_RU |
dc.contributor.author |
Yafarova Guzel Gulusovna |
ru_RU |
dc.contributor.author |
Dakinova Margarita Vitalevna |
ru_RU |
dc.contributor.author |
Bikchentaeva Leysan Maratovna |
ru_RU |
dc.date.accessioned |
2022-01-01T00:00:00Z |
ru_RU |
dc.date.available |
2022-01-01T00:00:00Z |
ru_RU |
dc.date.issued |
2022 |
ru_RU |
dc.identifier.citation |
Dakinova M. V. Spectral analysis of stabilographic signals by Fourier and Hilbert – Huang methods/ M. V. Dakinova, L. M. Bikchentaeva, I. S. Tagirova, T. V. Baltina, G. G. Yafarova and O. A. Sachenkov//IEEE Xplore 2022: VIII International Conference on Information Technology and Nanotechnology (ITNT). - 2022. - pp. 1-4, doi: 10.1109/ITNT55410.2022.9848704. |
ru_RU |
dc.identifier.uri |
https://repository.kpfu.ru/eng/?p_id=270855&p_lang=2 |
ru_RU |
dc.description.abstract |
IEEE Xplore |
ru_RU |
dc.description.abstract |
Spectral analysis is an important pipeline step in digital signal processing and significantly effects on received information. Methodical distortions in the information leads to corruptions of properties contained in the signal, and these is an origin of mistaken analysis. The Fourier transform is widespread as such pipeline step, but it limited for processing non-stationary and nonlinear signals. The spectral analysis of the Hilbert-Huang transform has no such limits. The transform is based on the Hilbert spectrum and empirical mode decomposition. The research is focused on the application of the Hilbert-Huang transform to stabilogram data analyses. The method to exarticulate the control factor of the human stability mechanism is developed. |
ru_RU |
dc.language.iso |
ru |
ru_RU |
dc.subject |
Hilbert-Huang transform |
ru_RU |
dc.subject |
empirical mode decomposition |
ru_RU |
dc.subject |
Fourier transform |
ru_RU |
dc.subject |
stabilogram |
ru_RU |
dc.subject |
spectral analysis |
ru_RU |
dc.title |
Spectral analysis of stabilographic signals by Fourier and Hilbert – Huang methods |
ru_RU |
dc.type |
Articles in international journals and collections |
ru_RU |
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