Topic
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: The electroencephalographic spectral indexes obtained by periodogram and autoregressive modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.
Abstract: Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.
25 citations
TL;DR: The proposed method overcomes the typical limit of traditional processing techniques as coherent demodulation or spectral analysis by implementing a least mean squares estimation of the variables of interest.
Abstract: This paper describes an innovative approach for position estimation using traditional displacement inductive sensors such as linear variable transducers. In addition, the same algorithm offers an evaluation of velocity and acceleration. However, the same approach can be applied to any other kind of alternating-current-excited sensor. The proposed method overcomes the typical limit of traditional processing techniques as coherent demodulation or spectral analysis by implementing a least mean squares estimation of the variables of interest. A working prototype has been designed around a low-cost digital signal processor from Texas Instruments Inc., and an estimation time on the order of 1 ms has been obtained. In static conditions, the resolution is about 0.01% of the full scale of the considered sensor, which is on the same order as the one obtained with spectral estimation. In dynamic conditions, simulations show a performance improvement in position and velocity estimation with a sensible root mean square error (RMSE) reduction. The experimental results in dynamic conditions are difficult to quantify, owing to noise, even if the performances are better than with traditional methods.
25 citations
01 Feb 1994
TL;DR: In this paper, a power system is excited with a low-level pseudo-random probing signal and the frequency, damping, magnitude, and shape of oscillatory modes are identified using spectral density estimation and frequency-domain transfer function identification.
Abstract: A procedure is proposed where a power system is excited with a low-level pseudo-random probing signal and the frequency, damping, magnitude, and shape of oscillatory modes are identified using spectral density estimation and frequency-domain transfer function identification. Attention is focussed on identifying system modes in the presence of noise. Two example cases are studied: identification of electromechanical oscillation modes in a 16-machine power system; and turbine-generator shaft modes of a 3-machine power plant feeding a series-compensated 500-kV network. >
25 citations
Patent•
30 Jun 2015TL;DR: In this paper, a spectral estimation method using multiple, poly-energetic x-ray sources to generate x-rays and to direct the xrays towards a target object is disclosed. And the method also includes estimating cross-sectional images of the target object based on the polyenergetic measurements.
Abstract: Spectral estimation and poly-energetic reconstructions methods and x-ray systems are disclosed. According to an aspect, a spectral estimation method includes using multiple, poly-energetic x-ray sources to generate x-rays and to direct the x-rays towards a target object. The method also includes acquiring a series of poly-energetic measurements of x-rays from the target object. Further, the method includes estimating cross-sectional images of the target object based on the poly-energetic measurements. The method also includes determining path lengths through the cross-sectional images. Further, the method includes determining de-noised poly-energetic measurements and de-noised path lengths based on the acquired poly-energetic measurements and the determined path lengths. The method also includes estimating spectra for angular trajectories of a field of view based on the de-noised poly-energetic measurements and the path lengths.
25 citations
TL;DR: A new numerical expression, called the regularized resolvent transform (RRT), is presented, which is a direct transformation of the truncated time-domain data into a frequency-domain spectrum and is suitable for high-resolution spectral estimation of multidimensional time signals.
25 citations