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Spectral density estimation
About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.
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TL;DR: It is shown that the dual problem can be seen as a new parametric spectral estimation problem, which implies that the THREE-like solution is optimal in terms of closeness to the correlogram over a certain parametric class of spectral densities describing ARMA models.
Abstract: Spectral estimation can be preformed using the so called THREE-like approach. Such method leads to a convex optimization problem whose solution is characterized through its dual problem. In this paper, we show that the dual problem can be seen as a new parametric spectral estimation problem. This interpretation implies that the THREE-like solution is optimal in terms of closeness to the correlogram over a certain parametric class of spectral densities, enriching in this way its meaningfulness.
26 citations
TL;DR: In this article, the S-transform spectrum has similar characteristics to the Fourier spectrum of the derivative of the waveform and is used to compute seismic interpretive attributes, such as peak frequency and bandwidth.
Abstract: The S-transform is one way to transform a 1D seismogram into a 2D time-frequency analysis. We have investigated its use to compute seismic interpretive attributes, such as peak frequency and bandwidth. The S-transform normalizes a frequency-dependent Gaussian window by a factor proportional to the absolute value of frequency. This normalization biases spectral amplitudes toward higher frequency. At a given time, the S-transform spectrum has similar characteristics to the Fourier spectrum of the derivative of the waveform. For narrowband signals, this has little impact on the peak frequency of the time-frequency analysis. However, for broadband seismic signals, such as a Ricker wavelet, the S-transform peak frequency is significantly higher than the Fourier peak frequency and can thus be misleading. Numerical comparisons of spectra from a variety of waveforms support the general rule that S-transform peak frequencies are equal to or greater than Fourier-transform peak frequencies. Comparisons on re...
26 citations
TL;DR: For spectral lines with combined Doppler and pressure broadening, the Fourier transform of the line shape is calculated analytically in an isothermal layer in which both the pressure and absorber concentrations vary along the line of sight.
Abstract: For spectral lines with combined Doppler and pressure broadening, the Fourier transform of the line shape is calculated analytically in an isothermal layer in which both the pressure and absorber concentrations vary along the line of sight. Use of the Cooley-Tukey fast Fourier transform algorithm allows efficient computation of the optical depth of such layers containing a large number of absorption lines of the same shape. The computation time is almost independent of the number of absorption lines. In many cases, this method allows increased speed and accuracy compared with conventional line-by-line methods.
26 citations
07 Aug 2002
TL;DR: In this article, three signal processing techniques are considered: the discrete Fourier transforms, the wavelet filters and the discrete short-time Fourier transform (DTFT) for power quality analysis.
Abstract: This paper presents the application of signal processing tools for power quality analysis. Three signal processing techniques are considered: the discrete Fourier transforms, the wavelet filters and the discrete short-time Fourier transforms. It is designed an adjustable speed drive with a six-pulse converter using EMTP/ATP and it is presented normal energizing of utility capacitors. Finally, each kind of electrical disturbance is analyzed with example representing each tool. A qualitative comparison of results shows the advantages and drawbacks of each signal processing technique applied to power quality analysis.
26 citations
TL;DR: A novel method for the identification of characteristic components in frequency domain based on singularity analysis is proposed, in which Lipschitz exponent function is constructed from the signal through wavelet-based singularityAnalysis.
Abstract: In rotating machinery condition monitoring, identification of characteristic components is fundamental in many engineering applications so as to obtain fault sensitive features for fault detection and diagnosis. This paper proposed a novel method for the identification of characteristic components in frequency domain based on singularity analysis. In this process, Lipschitz exponent function is constructed from the signal through wavelet-based singularity analysis. In order to highlight the periodic phenomena, autocorrelation transform is employed to extract the periodic exponents and Fourier transform is used to map the time-domain information into frequency domain. Case study with rolling element bearing vibration data shows that the proposed has very excellent capability for the identification of characteristic components compared with traditional methods.
26 citations