Adaptive denoising algorithm using peak statistics-based thresholding and novel adaptive complementary ensemble empirical mode decomposition

DOI10.1016/j.ins.2021.02.040OpenAlexW3135169846MaRDI QIDQ6086266

Mengfei Hu, Hai-Tao Liu, Wei Dong, Fengjiao Xu, Shuqing Zhang

Publication date: 9 November 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.02.040

zbMATH Keywords

multi-sensor data fusionadaptive threshold denoising methodologynovel adaptive complementary ensemble empirical mode decompositionpeak statistics (PS)-based thresholding

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (2)

Robust adaptive filtering based on M-estimation-based minimum error entropy criterion ⋮ An optimized twin support vector regression algorithm enhanced by ensemble empirical mode decomposition and gated recurrent unit

Cites Work

• LAPB: locally adaptive patch-based wavelet domain edge-preserving image denoising
• Resilient consensus of switched multi-agent systems
• Wavelet-denoising multiple echo state networks for multivariate time series prediction
• An improved EMD method with modified envelope algorithm based on \(C^2\) piecewise rational cubic spline interpolation for EMI signal decomposition
• The Identification of Multiple Outliers
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Ideal spatial adaptation by wavelet shrinkage
• Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding

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