Numerical analysis for iterative filtering with new efficient implementations based on FFT

Cicone, Antonio; Zhou, Haomin

doi:10.1007/s00211-020-01165-5

Cited by 70 publications

(74 citation statements)

References 91 publications

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“…The key difference is in the signal moving average computed in IF, which is obtained as the convolution of the signal s(t) with an a priori chosen filter function w(t). This apparently small difference between the way IF and EMD compute the moving average opened the doors to the mathematical analysis of IF [53,56,57,[59][60][61] such as the demonstration of its a priori convergence [56,57,61] and its acceleration [59,61] in what is called the Fast Iterative Filtering (FIF) method. FIF allows us to compute the exact same decomposition as IF when the signal is extended periodically at the boundaries.…”

Section: Non-stationary Signal Decomposition and Their Multiscale Statistical Analysis: The Fast Iterative Filtering Algorithmmentioning

confidence: 99%

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Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake

et al. 2020

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The short-term prediction of earthquakes is an essential issue connected with human life protection and related social and economic matters. Recent papers have provided some evidence of the link between the lithosphere, lower atmosphere, and ionosphere, even though with marginal statistical evidence. The basic coupling is hypothesized as being via the atmospheric gravity wave (AGW)/acoustic wave (AW) channel. In this paper we analyze a scenario of the low latitude earthquake (Mw = 6.9) which occurred in Indonesia on 5 August 2018, through a multi-instrumental approach, using ground and satellites high quality data. As a result, we derive a new analytical lithospheric–atmospheric–ionospheric–magnetospheric coupling model with the aim to provide quantitative indicators to interpret the observations around 6 h before and at the moment of the earthquake occurrence.

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Section: Non-stationary Signal Decomposition and Their Multiscale Statistical Analysis: The Fast Iterative Filtering Algorithmmentioning

confidence: 99%

“…We decomposed the diurnal vTEC observations using the FIF method, which we briefly recalled in Section 2.5. The interested reader can find more details on this algorithm and its pseudo-code in [59,61] FIF code for Matlab is freely available at www.cicone.com);…”

Section: Vertical Total Electron Content Observationsmentioning

confidence: 99%

Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake

et al. 2020

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“…In particular, we mention here its low computational complexity which makes it the fastest technique of its kind; the guaranteed uniqueness of the derived decompositions; a complete mathematical framework; and a complete adaptivity to the signal under investigation ensuring that there is no need to set a priori neither the number of components to be extracted nor the basis to be used in the process. Interested readers can find more details in [2], [4], [9].…”

Section: A Fast Iterative Filtering (Fif)mentioning

confidence: 99%

“…Cross Correlation (IMXC spelled I-M-cross-C) method, for the scale-wise measurement of lags between two complex and non-stationary signals. We leverage on the Multivariate Fast Iterative Filtering (MvFIF) [2] technique, being the multivariate implementation of the Fast Iterative Filtering (FIF) technique [4]. The lags are then identified on a scale-by-scale basis by using the maximum cross correlation among homogeneous modes.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic Mode Cross Correlation: A Novel Technique to Identify Scale-Dependent Lags Between Two Signals and Its Application to Ionospheric Science

Urbář

Cicone

Spogli

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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In this work we address the following question: can we use modern, cutting edge techniques conceived for the analysis of nonlinear non-stationary signals to measure scale-wise lags? To this scope, we propose a novel technique, called Intrinsic Mode Cross Correlation method, which leverages on the decomposition of nonlinear non-stationary signals by the Multivariate Fast Iterative Filtering (MvFIF) technique and the computation of a scale by scale cross correlation. We evaluate this technique on artificial signals (whose ground truth is known) and plasma density data provided by the Langmuir probes onboard the Swarm satellites. We show that this technique allows indeed to reconstruct the lag dependence on the involved spatio/temporal scales for the artificial data set (even in presence of high levels of noise), and to estimate them in a real life signal. This can pave the way to future uses of this technique in contexts in which the causation chain can be hidden in a complex, multiscale coupling of the investigated features.

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“…The first locally adaptive data-driven method, empirical mode decomposition (EMD), proposed by Huang [3], is very suitable for dealing with non-stationary and nonlinear signals, but there are some issues such as over-envelope, mode mixing, and end effect under strong background noise [4,5]. Smith and Cicone respectively proposed local mean decomposition (LMD) [6] and adaptive local iterative filtering (ALIF) [7] recursive mode decomposition methods similar to EMD, which mitigated the influence of mode mixing and end effect.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Feature Extraction of Rolling Bearing Based on dFIF

Chen

2022

J. Phys.: Conf. Ser.

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Aiming at the problems that the traditional adaptive mode decomposition method has low decomposition efficiency, poor ability of anti-mode mixing and decomposition accuracy, a rolling bearing fault feature recognition method based on direct fast iterative filtering (dFIF) and the crest factor of envelope spectrum is proposed. dFIF first decomposes the raw rolling bearing vibration signal into a set of intrinsic mode function (IMFs), then the optimal component is chosen based on the maximal crest factor of envelope spectrum, and finally the bearing fault characteristic frequency and its frequency multiplication are extracted using envelope demodulation analysis. It is demonstrated by relevant experimental data that the proposed method has efficient decomposition speed, accurate decomposition accuracy, and the accurate selection of effective components, and can effectively realize rolling bearing fault detection.

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Numerical analysis for iterative filtering with new efficient implementations based on FFT

Cited by 70 publications

References 91 publications

Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake

Magnetospheric–Ionospheric–Lithospheric Coupling Model. 1: Observations during the 5 August 2018 Bayan Earthquake

Intrinsic Mode Cross Correlation: A Novel Technique to Identify Scale-Dependent Lags Between Two Signals and Its Application to Ionospheric Science

Research on Fault Feature Extraction of Rolling Bearing Based on dFIF

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