Results 291 to 300 of about 6,746,316 (343)
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Discrete-time, discrete-frequency, time-frequency analysis
IEEE Transactions on Signal Processing, 1998Summary: A formulation of a discrete-time, discrete-frequency Wigner distribution for analysis of discrete-time, periodic signals is given using an approach involving group representation theory. This approach is motivated by a well-known connection between group theory and the continuous Wigner distribution.
Richman, Michael S. +2 more
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2003
Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e.
A. Ramachandra Rao +2 more
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Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e.
A. Ramachandra Rao +2 more
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Time--frequency analysis of biosignals
IEEE Engineering in Medicine and Biology Magazine, 2009The wavelet transform has a powerful time-frequency analysis and signal-coding tool suitable for use in the manipulation of complex nonstationary signals. This article provides an overview of the emerging role of wavelet-transform analysis in biomedical signal processing and analysis.
Addison, Paul +2 more
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Perceptually motivated time-frequency analysis
The Journal of the Acoustical Society of America, 2005This paper describes the design of a bilinear time-frequency distribution which is a joint model of temporal and spectral masking. The distribution is used to generate temporally evolving excitation patterns of nonstationary signals and systems and is conceived as a tool for acousticians and engineers for perceptual time-frequency analysis ...
Jonathan J, O'Donovan, Dermot J, Furlong
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2012
In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal.
Walter J. Freeman, Rodrigo Quian Quiroga
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In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal.
Walter J. Freeman, Rodrigo Quian Quiroga
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2016
This chapter is a logical continuation of the previous chapter on signal changes. The consideration of nonstationary signals requires an assortment of analysis tools, to highlight different aspects of importance. Many scientific and technical activities are interested on such, for medical purposes, for earthquake study, for machine maintenance, for ...
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This chapter is a logical continuation of the previous chapter on signal changes. The consideration of nonstationary signals requires an assortment of analysis tools, to highlight different aspects of importance. Many scientific and technical activities are interested on such, for medical purposes, for earthquake study, for machine maintenance, for ...
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THE LINEAR TIME FREQUENCY ANALYSIS TOOLBOX
International Journal of Wavelets, Multiresolution and Information Processing, 2012The Linear Time Frequency Analysis Toolbox is a MATLAB/Octave toolbox for computational time-frequency analysis. It is intended both as an educational and computational tool. The toolbox provides the basic Gabor, Wilson and MDCT transform along with routines for constructing windows (filter prototypes) and routines for manipulating coefficients.
Soendergaard, Peter +2 more
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2017
Fourier-analysis provides a description of a given data set in terms of monochromatic oscillations without any time information. It is thus mostly useful for stationary signals. If the spectrum changes in time it is desirable to obtain information about the time at which certain frequencies appear. This can be achieved by applying Fourier analysis to a
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Fourier-analysis provides a description of a given data set in terms of monochromatic oscillations without any time information. It is thus mostly useful for stationary signals. If the spectrum changes in time it is desirable to obtain information about the time at which certain frequencies appear. This can be achieved by applying Fourier analysis to a
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IEEE Signal Processing Magazine, 1999
It has been well understood that a given signal can be represented in an infinite number of different ways. Different signal representations can be used for different applications. For example, signals obtained from most engineering applications are usually functions of time. But when studying or designing the system, we often like to study signals and
null Shie Qian, null Dapang Chen
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It has been well understood that a given signal can be represented in an infinite number of different ways. Different signal representations can be used for different applications. For example, signals obtained from most engineering applications are usually functions of time. But when studying or designing the system, we often like to study signals and
null Shie Qian, null Dapang Chen
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2011
Let z be a signal in \(L^2(\mathbb{Z}_{N})\). Then we say that z is time-localized near n0 if all components z(n) are 0 or relatively small except for a few values of n near n0. An orthonormal basis B for \(L^2(\mathbb{Z}_{N})\) is said to be time-localized if every signal in B is time-localized.
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Let z be a signal in \(L^2(\mathbb{Z}_{N})\). Then we say that z is time-localized near n0 if all components z(n) are 0 or relatively small except for a few values of n near n0. An orthonormal basis B for \(L^2(\mathbb{Z}_{N})\) is said to be time-localized if every signal in B is time-localized.
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