scholarly journals A Generalized Demodulation and Hilbert Transform Based Signal Decomposition Method

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Zhi-Xiang Hu ◽  
Wei-Xin Ren ◽  
Zuo-Cai Wang ◽  
Yue-Ling Jing ◽  
Xia Yang
Keyword(s):  
Hilbert Transform ◽  
Decomposition Method ◽  
Signal Decomposition ◽  
Dynamic Strain ◽  
Cable Stayed Bridge ◽  
Multicomponent Signal ◽  
Frequency Components ◽  
Echolocation Signal ◽  
The Hilbert Transform ◽  
Strain Signal

This paper proposes a new signal decomposition method that aims to decompose a multicomponent signal into monocomponent signal. The main procedure is to extract the components with frequencies higher than a given bisecting frequency by three steps: (1) the generalized demodulation is used to project the components with lower frequencies onto negative frequency domain, (2) the Hilbert transform is performed to eliminate the negative frequency components, and (3) the inverse generalized demodulation is used to obtain the signal which contains components with higher frequencies only. By running the procedure recursively, all monocomponent signals can be extracted efficiently. A comprehensive derivation of the decomposition method is provided. The validity of the proposed method has been demonstrated by extensive numerical analysis. The proposed method is also applied to decompose the dynamic strain signal of a cable-stayed bridge and the echolocation signal of a bat.

Identification of Linear Time-Varying Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2006 ◽  
Vol 74 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Z. Y. Shi ◽  
S. S. Law
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Degrees Of Freedom ◽  
Linear Time ◽  
Time Varying ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

This paper addresses the identification of linear time-varying multi-degrees-of-freedom systems. The identification approach is based on the Hilbert transform and the empirical mode decomposition method with free vibration response signals. Three-different types of time-varying systems, i.e., smoothly varying, periodically varying, and abruptly varying stiffness and damping of a linear time-varying system, are studied. Numerical simulations demonstrate the effectiveness and accuracy of the proposed method with single- and multi-degrees-of-freedom dynamical systems.

A high efficiency wavefield decomposition method based on Hilbert transform

Geophysics ◽  
2021 ◽  
pp. 1-68
Author(s):  
Xue Guo ◽  
Ying Shi ◽  
Weihong Wang ◽  
Xuan Ke ◽  
Hong Liu ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
High Efficiency ◽  
Computational Cost ◽  
Difference Approximation ◽  
Reverse Time ◽  
Time Migration ◽  
The Hilbert Transform ◽  
Wavefield Decomposition

Wavefield decomposition can be used to extract effective information in reverse time migration (RTM) and full waveform inversion (FWI). The wavefield decomposition methods based on the Hilbert transform (HTWD) and the Poynting vector (PVWD) are the most commonly used. The HTWD needs to save the wavefields at all time steps or introduce additional numerical simulation, which increases the computational cost. The PVWD cannot handle multi-wave arrivals, and its performance is poor in complex situations. We propose an efficient wavefield decomposition method based on the Hilbert transform (EHTWD). The EHTWD constructs two wavefields to replace the original wavefield and the wavefield after Hilbert transform. The first wavefield is obtained by using the dispersion relation to modify the frequency components. The other wavefield is obtained by time difference approximation. Therefore, there is a 90° phase change between the two wavefields. In EHTWD, we only need two wavefields at different moments, which avoids the additional numerical simulation. The EHTWD is also suitable for wavefield decomposition in arbitrary directions. Compared with HTWD, the computational complexity can be greatly reduced with the decrease of the number of imaging time slices. The numerical examples of wavefield decomposition demonstrate that the proposed method can realize wavefield decomposition in any direction. The examples of imaging decomposition and real data also show that the EHTWD suppresses the imaging noise effectively.

Decomposition and Analysis of Non-Stationary Dynamic Signals Using the Hilbert Transform

2008 ◽  
Author(s):  
Michael Feldman
Keyword(s):  
Hilbert Transform ◽  
Time Domain ◽  
Instantaneous Frequency ◽  
Decomposition Method ◽  
Initial Signal ◽  
Time Modeling ◽  
Stationary Signals ◽  
A New Technique ◽  
The Time Domain ◽  
The Hilbert Transform

This paper describes a new technique, called the Hilbert Vibration Decomposition method, dedicated to decomposition of non-stationary wideband dynamic signals. Using the Hilbert transform in the time domain, we extract a number of elementary oscillating components of the initial signal, who’s both the instantaneous frequency and envelope can vary in time. Modeling examples of decomposition of non-stationary signals are included.

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

A Bayesian View on the Hilbert Transform and the Kramers-Kronig Transform of Electrochemical Impedance Data: Probabilistic Estimates and Quality Scores

2020 ◽  
Author(s):  
Jiapeng Liu ◽  
Ting Hei Wan ◽  
Francesco Ciucci
Keyword(s):  
Power Generation ◽  
Electrochemical Impedance Spectroscopy ◽  
Hilbert Transform ◽  
Electrochemical Impedance ◽  
The Self ◽  
Impedance Data ◽  
The Hilbert Transform ◽  
Validation Tests ◽  
Self Consistency ◽  
Mathematical Relations

<p>Electrochemical impedance spectroscopy (EIS) is one of the most widely used experimental tools in electrochemistry and has applications ranging from energy storage and power generation to medicine. Considering the broad applicability of the EIS technique, it is critical to validate the EIS data against the Hilbert transform (HT) or, equivalently, the Kramers–Kronig relations. These mathematical relations allow one to assess the self-consistency of obtained spectra. However, the use of validation tests is still uncommon. In the present article, we aim at bridging this gap by reformulating the HT under a Bayesian framework. In particular, we developed the Bayesian Hilbert transform (BHT) method that interprets the HT probabilistic. Leveraging the BHT, we proposed several scores that provide quick metrics for the evaluation of the EIS data quality.<br></p>

scholarly journals A Radial Basis Function Finite Difference Scheme for the Benjamin–Ono Equation

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 65
Author(s):  
Benjamin Akers ◽  
Tony Liu ◽  
Jonah Reeger
Keyword(s):  
Radial Basis Function ◽  
Hilbert Transform ◽  
Basis Function ◽  
Differential Operators ◽  
Convergence Rates ◽  
Traveling Wave Solutions ◽  
Algebraic Decay ◽  
Pseudo Differential Operators ◽  
Radial Basis ◽  
The Hilbert Transform

A radial basis function-finite differencing (RBF-FD) scheme was applied to the initial value problem of the Benjamin–Ono equation. The Benjamin–Ono equation has traveling wave solutions with algebraic decay and a nonlocal pseudo-differential operator, the Hilbert transform. When posed on R, the former makes Fourier collocation a poor discretization choice; the latter is challenging for any local method. We develop an RBF-FD approximation of the Hilbert transform, and discuss the challenges of implementing this and other pseudo-differential operators on unstructured grids. Numerical examples, simulation costs, convergence rates, and generalizations of this method are all discussed.

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