Speech pitch determination based on Hilbert-Huang transform
Signal Processing
Extraction of signals buried in noise part II: experimental results
Signal Processing - Fractional calculus applications in signals and systems
Extraction of signals buried in noise: part I: fundamentals
Signal Processing - Signal processing in UWB communications
Fuzzy model validation using the local statistical approach
Fuzzy Sets and Systems
Fault detection and isolation based on fuzzy automata
Information Sciences: an International Journal
Optimal selection of wavelet basis function applied to ECG signal denoising
Digital Signal Processing
Expert Systems with Applications: An International Journal
A dual-threshold up-down counter for GPS acquisition
Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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In the present study, a weak signal detection methodology based on the improved Hilbert-Huang transform (HHT) was proposed. Aiming to restrain the end effects of empirical mode decomposition (EMD), wavelet analysis was embedded in iteration procedures of HHT to remove iterative errors as well as noise signal in the sifting process. Meanwhile, a new stopping criterion based on correlation analysis was proposed to remove undesirable intrinsic mode functions (IMFs). Results of analyzing synthetic signal, incipient rotor imbalance fault of Bently test-rig and weak electrocardiogram (ECG) signal show that the improved HHT combined with wavelet analysis have excellent weak signal detecting performance whilst achieving robustness against low signal-to-noise ratio (SNR). Furthermore, comparative studies of the proposed method, the classical EMD method, and other four generally acknowledged improved EMD methods, as well as a widely used stopping criterion demonstrate that the proposed method significantly reduces end effects and removes undesirable IMFs.