The Experts below are selected from a list of 16470 Experts worldwide ranked by ideXlab platform
K Hallatschek - One of the best experts on this subject based on the ideXlab platform.
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an ultra fast smoothing algorithm for time Frequency Transforms based on gabor functions
Applied and Computational Harmonic Analysis, 2014Co-Authors: K HallatschekAbstract:Abstract Gabor functions, Gaussian wave packets, are optimally localized in time and Frequency, and thus in principle ideal as (frame) basis functions for a wavelet, windowed Fourier or wavelet-packet transform for the detection of events in noisy signals or for data compression. A major obstacle for their use is that a tailored efficient operator acting on the transform coefficients for altering the width of the wave packets does not exist. However, by virtue of a curious property of the Gabor functions it is possible to change the width of the wave packets using just one-dimensional convolutions with very short kernels. The cost of a wavelet-type transform based on the scheme presented below is similar to that of a low order wavelet transform for a compact kernel and significantly less than the algorithme a trous. The scheme can hence easily be employed for the processing of signals in real time.
Richard Frayne - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
Robert A Brown - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a new local multiscale fourier analysis for medical imaging
Medical Physics, 2003Co-Authors: Hongmei Zhu, Robert A Brown, M L Lauzon, Bradley G Goodyear, G S Mayer, A G Law, L Mansinha, Joseph Ross MitchellAbstract:The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet Transforms; it reveals Frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with Frequency-dependent Gaussian scaling windows. This multi-scale time–Frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize,analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time–Frequency Transforms, such as the Gabor and wavelet Transforms, are discussed and demonstrated using functional magnetic resonance imaging data.
Alan S Willsky - One of the best experts on this subject based on the ideXlab platform.
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a wavelet packet approach to transient signal classification
Applied and Computational Harmonic Analysis, 1995Co-Authors: R.e. Learned, Alan S WillskyAbstract:Abstract Time–Frequency Transforms, including wavelet and wavelet packet Transforms, are generally acknowledged to be useful for studying non-stationary phenomena and, in particular, have been shown or claimed to be of value in the detection and characterization of transient signals. In many applications time–Frequency Transforms are simply employed as a visual aid to be used for signal display. Although there have been several studies reported in the literature, there is still considerable work to be done investigating the utility of wavelet and wavelet packet time–Frequency Transforms for automatic transient signal classification. This paper contributes to this ongoing investigation through the development of a non-parametric wavelet packet feature extraction procedure which identifies features to be used for the classification of transient signals for which explicit signal models are not available or appropriate. Recent literature in this area is devoted to truly ad-hoc, high-dimensional, non-parametric types of classification in which one or more time–Frequency transform forms the base from which a large number of features are determined by trial and error. In contrast, the wavelet-packet-based procedure presented in this paper was formulated to systematically adapt to any data dictionary within which several classes must be distinguished. This method is aimed at focusing the information in the data set to find the smallest number of features for robust, reliable classification. The promise of our method is illustrated by performing our procedure on a set of biologically generated underwater acoustic signals. For this example the wavelet-packet-based features obtained by our method yield excellent classification results when used as input for a neural network and a nearest neighbor rule.
M L Lauzon - One of the best experts on this subject based on the ideXlab platform.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a general description of linear time Frequency Transforms and formulation of a fast invertible transform that samples the continuous s transform spectrum nonredundantly
IEEE Transactions on Signal Processing, 2010Co-Authors: Robert A Brown, M L Lauzon, Richard FrayneAbstract:Examining the Frequency content of signals is critical in many applications, from neuroscience to astronomy. Many techniques have been proposed to accomplish this. One of these, the S-transform, provides simultaneous time and Frequency information similar to the wavelet transform, but uses sinusoidal basis functions to produce Frequency and globally referenced phase measurements. It has shown promise in many medical imaging applications but has high computational requirements. This paper presents a general transform that describes Fourier-family Transforms, including the Fourier, short-time Fourier, and S- Transforms. A discrete, nonredundant formulation of this transform, as well as algorithms for calculating the forward and inverse Transforms are also developed. These utilize efficient sampling of the time-Frequency plane and have the same computational complexity as the fast Fourier transform. When configured appropriately, this new algorithm samples the continuous S-transform spectrum efficiently and nonredundantly, allowing signals to be transformed in milliseconds rather than days, as compared to the original S-transform algorithm. The new and efficient algorithms make practical many existing signal and image processing techniques, both in biomedical and other applications.
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a new local multiscale fourier analysis for medical imaging
Medical Physics, 2003Co-Authors: Hongmei Zhu, Robert A Brown, M L Lauzon, Bradley G Goodyear, G S Mayer, A G Law, L Mansinha, Joseph Ross MitchellAbstract:The Stockwell transform (ST), recently developed for geophysics, combines features of the Fourier, Gabor and wavelet Transforms; it reveals Frequency variation over time or space. This valuable information is obtained by Fourier analysis of a small segment of a signal at a time. Localization of the Fourier spectrum is achieved by filtering the signal with Frequency-dependent Gaussian scaling windows. This multi-scale time–Frequency analysis provides information about which frequencies occur and more importantly when they occur. Furthermore, the Stockwell domain can be directly inferred from the Fourier domain and vice versa. These features make the ST a potentially effective tool to visualize,analyze, and process medical imaging data. The ST has proven useful in noise reduction and tissue texture analysis. Herein, we focus on the theory and effectiveness of the ST for medical imaging. Its effectiveness and comparison with other linear time–Frequency Transforms, such as the Gabor and wavelet Transforms, are discussed and demonstrated using functional magnetic resonance imaging data.
