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On the analogue methods for power spectral density estimation†
International Journal of Control, 1970This paper examines an analogue method for power spectral density estimation which employs an asymmetrical modulation. The approximate expressions for the expected value and for the dispersion of the estimate thus obtained are worked out by means of simplifying hypotheses.
DI PILLO, Gianni, KOCH G., MARANO R.
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Calculation of Doppler spectral power density functions
IEEE Transactions on Biomedical Engineering, 1992A volume integral method for the calculation of Doppler ultrasound spectral power density (spd) functions is described. Axisymmetric flow in a circular tube with a power law velocity profile is assumed. The spd function is regarded as a probability density function for scatterer velocity, and the assumptions under which this is justified are considered.
Gepffrey K. Aldis, Rosemary S. Thompson
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Spectral power density calculations for pulsed Doppler
Ultrasonics, 2002Range-gated pulsed Doppler can be used to make localized velocity measurements within a blood vessel. A spectral flow profile can be created by stepping a sufficiently small sample volume across the lumen, but no set of spectra will correspond directly to the true velocity profile.
R S, Thompson, G K, Aldis
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Power spectral density of a subjective speckle pattern
Applied Optics, 1986L'information de deplacement dans le plan sur l'objet diffusant est obtenue a partir d'une photographie speckle a double exposition focalisee/defocalisee soit par filtrage ponctuel soit par filtrage du champ ...
R K, Mohanty, C, Joenathan, R S, Sirohi
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Smoothing of power spectral densities
Proceedings of the 33rd Midwest Symposium on Circuits and Systems, 2002Power spectral estimates are smoothed using a Kalman filtering approach. The filter is used to segment the power spectral density by separating signal-dominated regions from noise-dominated regions. In doing so, it tends to preserve the fidelity for signal-related spectral peaks while smoothing the segments dominated by the noise.
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Extract EEG Features by Combining Power Spectral Density and Correntropy Spectral Density
2019 Chinese Automation Congress (CAC), 2019The electroencephalogram(EEG)-based brain computer interface (BCI) has been applied to many fields, such as medication, old-age help, transportation and entertainment. How to extract efficient features from low signal to noise ratio (SNR) EEG signals is one of the challenges in EEG signal analysis. In EEG-based BCI systems, power spectral density (PSD)
Xuemei Qin, Yunfei Zheng, Badong Chen
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Power spectral density limitations of the wavelet-OFDM system
2016 24th European Signal Processing Conference (EUSIPCO), 2016Wavelet-OFDM based on the discrete wavelet transform is a multicarrier modulation technique of considerable interest, due to its good performance in several respects such as the peak-to-average power ratio and the interference cancellation, as investigated in the literature.
Chafii, Marwa +3 more
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On the power spectral density of self-synchronizing scrambled sequences
IEEE Transactions on Information Theory, 1998We derive a closed-form expression for the power spectral density of amplitude/phase-shift keyed bit sequences randomized through self-synchronizing scrambling when the source sequence is a stationary sequence of statistically independent bits. In addition to the dependence on the symbol pulse shape, duration, and the signal space values with which ...
Ivan J. Fair +2 more
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Calculation of the power spectral density of the wavefront
SPIE Proceedings, 2000The power spectral density (PSD) was employed to be the specific criterion of the large aperture optical components. Its definition and calculation has analyzed in detail, and the results were given consequently. Furthermore, the calculation of the averaged PSD in different situation is discussed.
Rongzhu Zhang +4 more
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Covariance interpolation and geometry of power spectral densities
2009 European Control Conference (ECC), 2009When methods of moments are used for identification of power spectral densities, a model is matched to estimated second order statistics such as, e.g., covariance estimates. There is an infinite family of power spectra consistent with such an estimate and in applications, such as identification, we want to single out the most representative spectrum ...
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