Fully adaptive time-varying wave-shape model: Applications in biomedical signal processing
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2024/01/01
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Journal
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Indian UGC (journal)
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Refrences52
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Joaquin Ruiz ORCID (unauthenticated)
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Gastón Schlotthauer ORCID (unauthenticated)
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Leandro Vignolo ORCID (unauthenticated)
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Marcelo A. Colominas ORCID (unauthenticated)
Cite
Ruiz, Joaquin, et al. “Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processing”. Signal Processing, vol. 214, 2024, p. 109258, https://doi.org/10.1016/j.sigpro.2023.109258.
Ruiz, J., Schlotthauer, G., Vignolo, L., & Colominas, M. A. (2024). Fully adaptive time-varying wave-shape model: Applications in biomedical signal processing. Signal Processing, 214, 109258. https://doi.org/10.1016/j.sigpro.2023.109258
Ruiz, Joaquin, Gastón Schlotthauer, Leandro Vignolo, and Marcelo A. Colominas. “Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processing”. Signal Processing 214 (2024): 109258. https://doi.org/10.1016/j.sigpro.2023.109258.
1.
Ruiz J, Schlotthauer G, Vignolo L, Colominas MA. Fully adaptive time-varying wave-shape model: Applications in biomedical signal processing. Signal Processing. 2024;214:109258.
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Wave-shape oscillatory model for nonstationary periodic time series analysis | Foundations of Data Science | 12 | 2021 | |
| Deep learning for robust decomposition of high-density surface EMG signals | 2020 | |||
| Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation | Applied and Computational Harmonic Analysis |
| 40 | 2020 |
Deep learning for denoising | GEOPHYSICS |
| 215 | 2019 |
Convolutional neural network for gear fault diagnosis based on signal segmentation approach | Structural Health Monitoring |
| 39 | 2019 |
Refrences Analysis
The category
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics 16
is the most frequently represented among the references in this article. It primarily includes studies from Signal Processing and Applied and Computational Harmonic Analysis. The chart below illustrates the number of referenced publications per year.