Results 241 to 250 of about 1,433,403 (294)

Positive time-frequency distribution functions

IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
We demonstrate the existence of positive joint distributions of time and frequency for arbitrary signals. A method is given to readily generate an infinite number of them for any signal. General properties of these distribution functions are derived and specific examples for some common signals are presented.
L. Cohen, T. Posch
openaire   +1 more source

Zero-tracking time-frequency distributions

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002
The zero-tracking time-frequency distribution (TFD) is introduced. The local autocorrelation function of the TFD, defined by an appropriate kernel, is used to form a polynomial whose roots correspond to the instantaneous frequencies of the multicomponent signal. Two techniques for zero-tracking based on the TFD are presented.
null Chenshu Wang, M.G. Amin
openaire   +1 more source

Multiple view time-frequency distributions

Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 2002
We propose a new approach for constructing time-frequency distributions and spectra which achieves improved localisation in the time-frequency plane while retaining the non-negativity property of an energy distribution. The approach is to construct a composite distribution from multiple views of the Wigner-Ville distribution.
G.J. Frazer, B. Boashash
openaire   +1 more source

Multitaper marginal time–frequency distributions

Signal Processing, 2006
Time-frequency distributions (TFDs) belonging to Cohen's class yield a frequency marginal that is equivalent to the periodogram of the signal. It is well-known that the periodogram is not a good spectral estimator since it is not a consistent estimate, i.e. its variance does not decrease with the sample size. Thomson addressed this issue by introducing
Selin Aviyente, William J. Williams
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

The running time-frequency distributions

Circuits Systems and Signal Processing, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

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