A new approach to sparse decomposition of nonstationary signals with multiple scale structures using self-consistent nonlinear waves

DOI10.1016/j.physa.2017.04.009zbMath1495.94016OpenAlexW2605231826MaRDI QIDQ2147704

Thi-Thao Tran, Ke-Hsin Hsu, Van-Truong Pham, Hsu-Wen Vincent Young, Men-Tzung Lo

Publication date: 20 June 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2017.04.009

zbMATH Keywords

optimizationtime-frequency analysissparse representationsadaptive signal decompositionself-consistent nonlinear equations

Mathematics Subject Classification ID

Medical applications (general) (92C50) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• Unnamed Item
• Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool
• Self-oscillation
• Multimodal pressure-flow analysis: application of Hilbert-Huang transform in cerebral blood flow regulation
• The inner structure of empirical mode decomposition
• Data-driven time-frequency analysis
• Analysis of the intrinsic mode functions
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Null Space Pursuit: An Operator-based Approach to Adaptive Signal Separation
• Variational Mode Decomposition
• Compressed sensing

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