Results 241 to 250 of about 4,499,288 (288)

Deconvolutive Time-frequency Distribution Based

Proceedings, 2012
The Wigner-Ville distribution is a time frequency tool widely used in seismic data analysis. Obtaining high time-frequency resolution is a fundamental significant in spectral decomposition. The Wigner-Ville distribution is a bilinear method for spectral decomposition that suffers from the cross terms problem.
Y. M. A. Mahmoodi, H. R. Siahkoohi, M. Rezaee Far   +2 more
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Minimum entropy time-frequency distributions

IEEE Signal Processing Letters, 2005
Re/spl acute/nyi entropy has been proposed as an effective measure of signal information content and complexity on the time-frequency plane. The previous work concerning Re/spl acute/nyi entropy in the time-frequency plane has focused on measuring the complexity of a given deterministic signal.
S. Aviyente, W.J. Williams
openaire   +1 more source

General time-frequency distribution series

Proceedings of IEEE-SP International Symposium on Time- Frequency and Time-Scale Analysis, 2002
In this paper, we present a new and robust method, general time-frequency distribution series (TFDS), for time-frequency analysis. We also introduce the concept of local interference and global interference and show that the local interference is important and the global interference is less important in TF analysis. The TFDS is very general. With only
null Dapang Chen, null Shie Qian
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Doppler spectral estimation using time-frequency distributions

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1999
The time-frequency distribution (TFD) of Doppler blood flow signals is usually obtained using the spectrogram, which requires signal stationarity and is known to produce large estimation variance. This paper examines four alternative, nonstationary spectral estimators: a smoothed pseudo-Wigner distribution (SPWD), the Choi-Williams distribution (CWD ...
F, Forsberg, H, Oung, L, Needleman
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The running time-frequency distributions

Circuits Systems and Signal Processing, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Visualization of EEG Using Time-Frequency Distributions

Methods of Information in Medicine, 1997
Abstract:The EEG is a time-varying or nonstationary signal. Frequency and amplitude are two of its significant characteristics, and are valuable clues to different states of brain activity. Detection of these temporal features is important in understanding EEGs. Commonly, spectrograms and AR models are used for EEG analysis.
Stiber, Bilin Zhang, Sato, S.
openaire   +2 more sources

High-spectral-resolution time-frequency distribution kernels

SPIE Proceedings, 1992
A new class of time-frequency distribution (TFD) kernels is introduced. Members in this class satisfy the desirable TFD properties and simultaneously provide local autocorrelation functions (LAF) that are amenable to high-frequency resolution modeling techniques.
Amin, Moeness G., Williams, William J.
openaire   +1 more source

Instantaneous frequency and time-frequency distributions

IEEE International Symposium on Circuits and Systems, 2003
A discussion of instantaneous frequency from the point of view of joint time-frequency distributions is presented. From this perspective, instantaneous frequency is the average frequency at a particular time. This view forces the consideration of the standard deviation of instantaneous frequency at a given time.
L. Cohen, C. Lee
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Minimum entropy time-frequency distributions

Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496), 2002
We present an approach to designing discrete time-frequency distributions that are extremely localized in the time-frequency plane. These distributions, which satisfy the marginals, are constructed recursively by transferring energy among the points in the time-frequency distribution (TFD) in a direction which decreases the entropy of the TFD.
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Time frequency distribution using neural networks

Proceedings of the IEEE Symposium on Emerging Technologies, 2005., 2005
Inthis paper wepresent amethod ofobtaining aTime Frequency Distribution (TFD) ofasignal whose frequenc) components varywithtinme. Themethod employs Neural Networks (NN)whicharetrained by usingthe spectrograms ofseveral training signals asinput and TFDs thatare highlyconcentrated alongthe instantaneous frequencies oftheindividual components present ...
I. Shafi, J. Ahmad, null Syed Ismail Shah, F.M. Kashife   +3 more
openaire   +1 more source