The spectral analysis of cyclo-non-stationary signals

The classical two-channel push-pull chirp transform spectrometer (CTS) has been widely applied in satellite-borne remote sensing systems for earth observation and deep space exploration. In this paper, we present two simplified structures with single M(l)-C(s) CTS arrangements for the spectral analysis of stationary signals. A simplified CTS system with a single M(l)-C(s) arrangement and a time delay line was firstly developed. Another simplified structure of CTS with a M(l)-C(s) arrangement and a frequency conversion channel was also developed for spectral analysis of stationary signals. Simulation and experiment results demonstrate that the two simplified arrangements can both realize spectrum measurement for the stationary signals and obtain the same frequency resolution, amplitude accuracy and system sensitivity as that of the classical two-channel push–pull CTS system. Compared to the classical CTS structure, the two simplified arrangements require fewer devices, save power consumption and have reduced mass. The matching problem between the two channels can be avoided in the two simplified arrangements. The simplified CTS arrangements may have potential application in the spectrum measurement of stationary signals in the field of aviation and spaceflight.

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A Study of Parameters Setting of the STADZT

Acta Polytechnica ◽

10.14311/1654 ◽

2012 ◽

Vol 52 (5) ◽

Author(s):

Václav Turoň

Keyword(s):

Spectral Analysis ◽

Fourier Transform ◽

Frequency Resolution ◽

Short Time Fourier Transform ◽

Time Frequency ◽

Stationary Signals ◽

Zolotarev Polynomials ◽

Non Stationary Signal ◽

Short Time ◽

Main Influence

This paper deals with the new time-frequency Short-Time Approximated Discrete Zolotarev Transform (STADZT), which is based on symmetrical Zolotarev polynomials. Due to the special properties of these polynomials, STADZT can be used for spectral analysis of stationary and non-stationary signals with the better time and frequency resolution than the widely used Short-Time Fourier Transform (STFT). This paper describes the parameters of STADZT that have the main influence on its properties and behaviour. The selected parameters include the shape and length of the segmentation window, and the segmentation overlap. Because STADZT is very similar to STFT, the paper includes a comparison of the spectral analysis of a non-stationary signal created by STADZT and by STFT with various settings of the parameters.

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The Hilbert-Huang Spectral Analysis Method Applied to VIV

Volume 4: Terry Jones Pipeline Technology; Ocean Space Utilization; CFD and VIV Symposium ◽

10.1115/omae2006-92119 ◽

2006 ◽

Cited By ~ 5

Author(s):

Celso P. Pesce ◽

Andre´ L. C. Fujarra ◽

Leonardo K. Kubota

Keyword(s):

Spectral Analysis ◽

Stationary Time Series ◽

Analysis Method ◽

Hilbert Spectrum ◽

Hilbert Huang Transform ◽

Time Modulation ◽

Mode Decomposition ◽

Highly Nonlinear ◽

Stationary Signals ◽

Spectral Analysis Method

Vortex-Induced Vibration (VIV) is a highly nonlinear dynamic phenomenon. Usual spectral analysis methods rely on the hypotheses of linear and stationary dynamics. A new method envisaged to treat nonlinear and non-stationary signals was presented by Huang et al. [1] : The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. This technique, called thereafter the Hilbert-Huang transform (or spectral analysis) method, is here applied to VIV phenomena, aiming at disclosing some hidden dynamic characteristics, such as the time-modulation and jumps of multi-branched response frequencies and their related energy spectra.

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Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

10.1101/2020.06.06.137950 ◽

2020 ◽

Author(s):

Eduardo Arrufat-Pié ◽

Mario Estévez Báez ◽

José Mario Estévez Carreras ◽

Calixto Machado Curbelo ◽

Gerry Leisman ◽

...

Keyword(s):

Spectral Analysis ◽

Main Tool ◽

Good Alternative ◽

Transform Method ◽

Hilbert Huang Transform ◽

Multivariate Empirical Mode Decomposition ◽

Healthy Humans ◽

Mode Decomposition ◽

Stationary Signals ◽

Mean Differences

AbstractThe fast Fourier transform (FFT), has been the main tool for the EEG spectral analysis (SPA). However, as the EEG dynamics shows nonlinear and non-stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non-stationary signals known as the Hilbert-Huang transform method. In this study we describe and compare the spectral analyses of the EEG using the traditional FFT approach with those calculated with the Hilbert marginal spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. Segments of continuous 60-seconds EEG recorded from 19 leads of 47 healthy volunteers were studied. Although the spectral indices calculated for the explored EEG bands showed significant statistical differences for different leads and bands, a detailed analysis showed that for practical purposes both methods performed substantially similar. The HMS showed a reduction of the alpha activity (−5.64%), with increment in the beta-1 (+1.67%), and gamma (+1.38%) fast activity bands, and also an increment in the theta band (+2.14%), and in the delta (+0.45%) band, and vice versa for the FFT method. For the weighted mean frequencies insignificant mean differences (lower than 1Hz) were observed between both methods for the delta, theta, alpha, beta-1 and beta-2 bands, and only for the gamma band values for the HMS were 3 Hz higher than with the FFT method. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non-stationarity may be present.

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