3,571 research outputs found
A Novel Approach for Ridge Detection and Mode Retrieval of Multicomponent Signals Based on STFT
Time-frequency analysis is often used to study non stationary multicomponent
signals, which can be viewed as the surperimposition of modes, associated with
ridges in the TF plane. To understand such signals, it is essential to identify
their constituent modes. This is often done by performing ridge detection in
the time-frequency plane which is then followed by mode retrieval.
Unfortunately, existing ridge detectors are often not enough robust to noise
therefore hampering mode retrieval. In this paper, we therefore develop a novel
approach to ridge detection and mode retrieval based on the analysis of the
short-time Fourier transform of multicomponent signals in the presence of
noise, which will prove to be much more robust than state-of-the-art methods
based on the same time-frequency representation
Spacetime Hall-MHD turbulence at sub-ion scales: structures or waves?
Spatiotemporal properties of two-dimensional (2D) Hall-magnetohydrodynamic
turbulence at intermediate plasma are studied by means of Fast
Iterative Filtering, a new technique for the decomposition of nonstationary
nonlinear signals. Results show that the magnetic energy at sub-ion scales is
concentrated in perturbations with frequencies smaller than the ion-cyclotron
(IC) frequency and with polarization properties that are incompatible with both
kinetic Alfv\'en waves (KAWs) and IC waves. At higher frequencies, we clearly
identify signatures of both whistler waves and KAWs, however their energetic
contribution to the magnetic power spectrum is negligible. We conclude that the
dynamics of 2D Hall-MHD turbulence at sub-ion scales is mainly driven by
localized intermittent structures, with no significant contribution of wavelike
fluctuations.Comment: 10 pages, 5 figures. Accepted for publication on The Astrophysical
Journal Letter
Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications
We introduce a novel ridge detection algorithm for time-frequency (TF)
analysis, particularly tailored for intricate nonstationary time series
encompassing multiple non-sinusoidal oscillatory components. The algorithm is
rooted in the distinctive geometric patterns that emerge in the TF domain due
to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive
mode decomposition-based multiple harmonic ridge detection}
(\textsf{SAMD-MHRD}). A swift implementation is available when supplementary
information is at hand. We demonstrate the practical utility of
\textsf{SAMD-MHRD} through its application to a real-world challenge. We employ
it to devise a cutting-edge walking activity detection algorithm, leveraging
accelerometer signals from an inertial measurement unit across diverse body
locations of a moving subject
Time-Frequency Ridge Analysis Based on the Reassignment Vector
International audienceThis paper considers the problem of detecting and estimating AM/FM components in the time-frequency plane. It introduces a new algorithm to estimate the ridges corresponding to the instantaneous frequencies of the components, and to segment the time-frequency plane into different `basins of attraction', each basin corresponding to one mode. The technique is based on the structure of the reassignment vector, which is commonly used for sharpening time-frequency representations. Compared with previous approaches, this new method does not need extra parameters, exhibits less sensitivity to the choice of the window and shows better reconstruction performance. Its effectiveness is demonstrated on simulated and real datasets
A Quantitative Measure of Mono-Componentness for Time-Frequency Analysis
Joint time-frequency (TF) analysis is an ideal method for analyzing non-stationary signals, but is challenging to use leading to it often being neglected. The exceptions being the short-time Fourier transform (STFT) and spectrogram. Even then, the inability to have simultaneously high time and frequency resolution is a frustrating issue with the STFT and spectrogram. However, there is a family of joint TF analysis techniques that do have simultaneously high time and frequency resolution – the quadratic TF distribution (QTFD) family. Unfortunately, QTFDs are often more troublesome than beneficial. The issue is interference/cross-terms that causes these methods to become so difficult to use. They require that the “proper” joint distribution be selected based on information that is typically unavailable for real-world signals. However, QTFDs do not produce cross-terms when applied to a mono-component signal.
Clearly, determining the mono-componentness of a signal provides a key piece of information. However, until now, the means for determining if a signal is a monocomponent or a multi-component has been to choose a QTFD, generate the TF representation (TFR), and visually examine it. The work presented here provides a method for quantitatively determining if a signal is a mono-component. This new capability provides an important step towards finally allowing QTFDs to be used on multi-component signals, while producing few to no interference terms through enabling the use of the quadratic superposition property. The focus of this work is on establishing the legitimacy for “measuring” mono-componentness along with its algorithmic implementation. Several applications are presented, such as quantifying the quality of the decomposition results produced by the blind decomposition algorithm, Empirical Mode Decomposition (EMD).
The mono-componentness measure not only provides an objective means to validate the outcome of a decomposition algorithm, it also provides a practical, quantitative metric for their comparison. More importantly, this quantitative measurement encapsulates mono-componentness in a form which can actually be incorporated in the design of decomposition algorithms as a viable condition/constraint so that true mono-components could be extracted. Incorporating the mono-component measure into a decomposition algorithm will eventually allow interference free TFRs to be calculated from multi-component signals without requiring prior knowledge
ECG Signal Analysis: Enhancement and R-Peak Detection
The project has been inspired by the need to find an efficient method for ECG Signal Analysis which is simple and has good accuracy and less computation time.
The initial task for efficient analysis is the removal of noise. It actually involves the extraction of the required cardiac components by rejecting the background noise. Enhancement of signal is achieved by the use of Empirical Mode Decomposition method. The use of EMD was inspired by its adaptive nature.
The second task is that of R peak detection which is achieved by the use of Continuous Wavelet Transform. Efficiency of the method is measured in terms of detection error rate.
Various other methods of R peak detection like Hilbert Transform and Difference Operation Method are implemented and the results when compared with the Continuous Wavelet Transform prove that CWT is a better method.
The simulation is done in MATLAB environment. The experiments are carried out on MIT-BIH database.
The results show that our proposed method is very effective and an efficient method for fast computation of R peak detection
Enhancing Missing Data Imputation of Non-stationary Signals with Harmonic Decomposition
Dealing with time series with missing values, including those afflicted by
low quality or over-saturation, presents a significant signal processing
challenge. The task of recovering these missing values, known as imputation,
has led to the development of several algorithms. However, we have observed
that the efficacy of these algorithms tends to diminish when the time series
exhibit non-stationary oscillatory behavior. In this paper, we introduce a
novel algorithm, coined Harmonic Level Interpolation (HaLI), which enhances the
performance of existing imputation algorithms for oscillatory time series.
After running any chosen imputation algorithm, HaLI leverages the harmonic
decomposition based on the adaptive nonharmonic model of the initial imputation
to improve the imputation accuracy for oscillatory time series. Experimental
assessments conducted on synthetic and real signals consistently highlight that
HaLI enhances the performance of existing imputation algorithms. The algorithm
is made publicly available as a readily employable Matlab code for other
researchers to use
On the Mode Synthesis in the Synchrosqueezing Method
Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201
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