Results 201 to 210 of about 638,913 (261)
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Filtering and frequency interpretations of Singular Spectrum Analysis
Physica D: Nonlinear Phenomena, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Harris, T. J., Yuan, Hui
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Frequency spectrum analysis for spectrum stabilization in airborne gamma-ray spectrometer
Applied Radiation and Isotopes, 2014Abnormal multi-crystal spectral drifts often can be observed when power on the airborne gamma-ray spectrometer. Currently, these spectral drifts of each crystal are generally eliminated through manual adjustment, which is time-consuming and labor-ineffective.
Guoqiang Zeng +4 more
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Wavelet frequency spectrum analysis of oscillograms
The Analyst, 1999By combining wavelet transform with frequency spectrum analysis, wavelet frequency spectrum analysis of an oscillogram was achieved, based on the wavelet property of extracting useful information. Compared with Fourier frequency spectrum analysis of an oscillogram, this method presents many advantages.
Jianbin Zheng +3 more
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The Instantaneous Spectrum: A General Framework for Time-Frequency Analysis
IEEE Transactions on Signal Processing, 2018This paper is a contribution to the old problem of representing a signal in the coordinates of time and frequency. We review the fundamental Hilbert transform relationship in systems analysis and argue that the dual relationship assumed in signal analysis, i.e. spectral single-sidedness is not necessarily justifiable. Therefore, we abandon the analytic
Steven Sandoval, Phillip L. De Leon
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An Analysis of Frequency Rendezvous for Decentralized Dynamic Spectrum Access
IEEE Transactions on Vehicular Technology, 2010In this paper, we propose an analysis of frequency rendezvous techniques employing three different scanning rules by combining analytical results with computer simulations. Frequency rendezvous is a critical step in bootstrapping a wireless network that does not possess centralized control.
Di Pu +2 more
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Modified Pisarenko spectrum for frequency analysis
2018 7th Mediterranean Conference on Embedded Computing (MECO), 2018We present a parametric spectral analysis algorithm that aims to visualize sinusoidal components in a sampled signal. The algorithm represents a signal as the moments of a rational weight function defined on the unit circle. We also propose an averaging procedure for visualizing the weight function.
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The study of ciliary frequencies with an optical spectrum analysis system
Experimental Cell Research, 1981Abstract The cilia of rabbit tracheal epithelial cells beat at the rate of from 6 to 21 cycles/sec at physiological temperatures. The cilia of each cell show a frequency response which fluctuates within a frequency range specific to that cell. Cells from the same area of the trachea tend to fluctuate in the same frequency range.
J R, Kennedy, K E, Duckett
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Analysis of an estimator of the center frequency of a power spectrum
IEEE Transactions on Information Theory, 1968A method proposed by Pickard for estimating the center frequency of the power spectrum of a Gaussian process is analyzed in terms of the mean and variance of the estimator. Through limiting and differentiation, this method consists essentially of counting zero-crossings with positive and negative weights so that our analysis of the center-frequency ...
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Frequency-domain characterization of Singular Spectrum Analysis eigenvectors
2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2016Singular Spectrum Analysis (SSA) is a nonparametric approach used to decompose a time series into meaningful components, related to trends, oscillations and noise. SSA can be seen as a spectral decomposition, where each term is related to an eigenvector derived from the trajectory matrix.
Leles, M. C. R. +11 more
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Observational Data: Analysis of the Frequency Spectrum
2017This chapter is an analysis of the frequency-power spectrum. It first discusses the probabilistic methods for detecting signatures of individual modes in the spectrum, including schemes that incorporate a Bayesian framework for the analysis. The chapter then turns to the automated detection of the ensemble signatures of the oscillations.
Sarbani Basu, William J. Chaplin
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