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Time-Frequency Filters and Time-Frequency Expansions
1998The separation of signal components occupying effectively disjoint regions of the TF plane is a fundamental problem of TF signal processing that is encountered in many applications. A related problem is the parsimonious representation (expansion) of signals located in a given TF region. In this chapter, we propose solutions to both problems.
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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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68th EAGE Conference and Exhibition incorporating SPE EUROPEC 2006, 2006
Long offset analysis of seismic data has gained interest during the past years, both for conventional AVO-analysis and time-lapse seismic data. A common problem for such analysis on marine seismic data is the presence of overburden generated refraction noise, similar to the ground roll noise problem of land data.
E. Zabihi Naeini, H. R. Siahkoohi
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Long offset analysis of seismic data has gained interest during the past years, both for conventional AVO-analysis and time-lapse seismic data. A common problem for such analysis on marine seismic data is the presence of overburden generated refraction noise, similar to the ground roll noise problem of land data.
E. Zabihi Naeini, H. R. Siahkoohi
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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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2012
The fundamental idea of time-frequency analysis is to understand and describe how the frequency content of a signal is changing in time [20, 18]. By a signal one means a function of time, for example the electric and magnetic fields, pressure, voltage etc. Time functions are called wave forms or signals. We shall denote the signal by s(t).
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The fundamental idea of time-frequency analysis is to understand and describe how the frequency content of a signal is changing in time [20, 18]. By a signal one means a function of time, for example the electric and magnetic fields, pressure, voltage etc. Time functions are called wave forms or signals. We shall denote the signal by s(t).
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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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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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IEEE Transactions on Information Theory, 1964
This paper develops a concept of duality, called time-frequency duality, which is applicable to a class of networks called communication-signal-processing networks. Such networks consist of an interconnection of basic elements such as filters, mixers, delay lines, etc.
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This paper develops a concept of duality, called time-frequency duality, which is applicable to a class of networks called communication-signal-processing networks. Such networks consist of an interconnection of basic elements such as filters, mixers, delay lines, etc.
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2018
This thesis was digitised by the British Library from microfilm. You can acquire a single copy of this thesis for research purposes by clicking on the padlock icon on the thesis file. Please be aware that the text in the supplied thesis pdf file may not be as clear as text in a thesis that was born digital or digitised directly from paper due to the ...
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This thesis was digitised by the British Library from microfilm. You can acquire a single copy of this thesis for research purposes by clicking on the padlock icon on the thesis file. Please be aware that the text in the supplied thesis pdf file may not be as clear as text in a thesis that was born digital or digitised directly from paper due to the ...
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ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
The author considers the transformation of bilinear time-frequency distributions for the case of a linear time-variant transformation of the signal. The conditions for the output to be representable, that is, for a signal to exist which gives the output distribution, are derived and related to the general question of time-frequency filtering. >
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The author considers the transformation of bilinear time-frequency distributions for the case of a linear time-variant transformation of the signal. The conditions for the output to be representable, that is, for a signal to exist which gives the output distribution, are derived and related to the general question of time-frequency filtering. >
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