Gabor Coefficient

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Zhiyong Hao - One of the best experts on this subject based on the ideXlab platform.

  • doi:10.1155/2011/507215 Research Article An Improved Flowchart for Gabor Order Tracking with Gaussian Window as the Analysis Window
    2015
    Co-Authors: Yang Jin, Zhiyong Hao
    Abstract:

    which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Based on simulations on the ability of the Gaussian-function windowed Gabor Coefficient spectrum to separate order components, an improved flowchart for Gabor order tracking (GOT) is put forward.With a conventional GOT flowchart with Gaussian window, successful order waveform reconstruction depends significantly on analysis parameters such as time sampling step, frequency sampling step, and window length in point number. A trial-and-error method is needed to find such parameters. However, an automatic search with an improved flowchart is possible if the speed-time curve and order difference between adjacent order components are known. The appropriate analysis parameters for a successful waveform reconstruction of all order components within a given order range and a speed range can be determined. 1

  • An improved flowchart for Gabor order tracking with gaussian window as the analysis window
    EURASIP Journal on Advances in Signal Processing, 2010
    Co-Authors: Yang Jin, Zhiyong Hao
    Abstract:

    Based on simulations on the ability of the Gaussian-function windowed Gabor Coefficient spectrumto separate order components, an improved flowchart for Gabor order tracking (GOT) is put forward. With a conventional GOT flowchart with Gaussian window, successful order waveform reconstruction depends significantly on analysis parameters such as time sampling step, frequency sampling step, and window length in point number. A trial-and-error method is needed to find such parameters. However, an automatic search with an improved flowchart is possible if the speed-time curve and order difference between adjacent order components are known. The appropriate analysis parameters for a successful waveform reconstruction of all order components within a given order range and a speed range can be determined.

Yang Jin - One of the best experts on this subject based on the ideXlab platform.

  • doi:10.1155/2011/507215 Research Article An Improved Flowchart for Gabor Order Tracking with Gaussian Window as the Analysis Window
    2015
    Co-Authors: Yang Jin, Zhiyong Hao
    Abstract:

    which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Based on simulations on the ability of the Gaussian-function windowed Gabor Coefficient spectrum to separate order components, an improved flowchart for Gabor order tracking (GOT) is put forward.With a conventional GOT flowchart with Gaussian window, successful order waveform reconstruction depends significantly on analysis parameters such as time sampling step, frequency sampling step, and window length in point number. A trial-and-error method is needed to find such parameters. However, an automatic search with an improved flowchart is possible if the speed-time curve and order difference between adjacent order components are known. The appropriate analysis parameters for a successful waveform reconstruction of all order components within a given order range and a speed range can be determined. 1

  • An improved flowchart for Gabor order tracking with gaussian window as the analysis window
    EURASIP Journal on Advances in Signal Processing, 2010
    Co-Authors: Yang Jin, Zhiyong Hao
    Abstract:

    Based on simulations on the ability of the Gaussian-function windowed Gabor Coefficient spectrumto separate order components, an improved flowchart for Gabor order tracking (GOT) is put forward. With a conventional GOT flowchart with Gaussian window, successful order waveform reconstruction depends significantly on analysis parameters such as time sampling step, frequency sampling step, and window length in point number. A trial-and-error method is needed to find such parameters. However, an automatic search with an improved flowchart is possible if the speed-time curve and order difference between adjacent order components are known. The appropriate analysis parameters for a successful waveform reconstruction of all order components within a given order range and a speed range can be determined.

Vindas Diaz Jasson - One of the best experts on this subject based on the ideXlab platform.

  • Gabor frames and asymptotic behavior of Schwartz distributions
    'National Library of Serbia', 2016
    Co-Authors: Kostadinova Sanja, Saneva Katerina, Vindas Diaz Jasson
    Abstract:

    We obtain characterizations of asymptotic properties of Schwartz distribution by using Gabor frames. Our characterizations are indeed Tauberian theorems for shift asymptotics (S-asymptotics) in terms of short-time Fourier transforms with respect to windows generating Gabor frames. For it, we show that the Gabor Coefficient operator provides (topological) isomorphisms of the spaces of tempered distributions $\mathcal{S}'(\mathbb{R}^d)$ and distributions of exponential type $\mathcal{K}'_{1}(\mathbb{R}^{d})$ onto their images.Comment: 14 pages. Results extended to the multidimensional cas

  • Gabor frames and asymptotic behavior of Schwartz distributions
    'National Library of Serbia', 2016
    Co-Authors: Kostadinova Sanja, Saneva Katerina, Vindas Diaz Jasson
    Abstract:

    We obtain characterizations of asymptotic properties of Schwartz distribution by using Gabor frames. Our characterizations are indeed Tauberian theorems for shift asymptotics (S-asymptotics) in terms of short-time Fourier transforms with respect to windows generating Gabor frames. For it, we show that the Gabor Coefficient operator provides (topological) isomorphisms of the spaces of tempered distributions $\mathcal{S}'(\mathbb{R}^d)$ and distributions of exponential type $\mathcal{K}'_{1}(\mathbb{R}^{d})$ onto their images

Xiang-gen Xia - One of the best experts on this subject based on the ideXlab platform.

  • On the rank of the discrete Gabor transform matrix
    Signal Processing, 2001
    Co-Authors: Xiang-gen Xia, Shie Qian
    Abstract:

    By using the discrete Gabor transform or expansion, the time domain sequences are mapped into joint time-frequency domain matrices or vice versa. In many applications, it is more effective to process signals, i.e., two-dimensional matrices, in the joint time-frequency domain than in the time or frequency domain alone. From the mathematical point of view, the processing of the discrete Gabor Coefficients is no more than matrix computation. So it is beneficial to understand the properties of the Gabor Coefficient matrix. In this letter, we shall investigate the rank of the Gabor Coefficient matrix of a one-dimensional time domain signal, which is one of the most important matrix properties.

  • Gabor Transforms: Some New Properties on the Gabor
    1998
    Co-Authors: Xiang-gen Xia, Shie Qiant
    Abstract:

    By using the discrete Gabor transform or expansion, the time domain sequences are mapped into the joint time-frequency domain matrices or vice versa. In many applications, it is more effective to process signals, i.e., two dimensional matrices, in the joint timefrequency domain than in the time or frequency domain alone. From the mathematical point of view, the processing of the discrete Gabor Coefficients is no more than the matrix computation. So it is beneficial to understand the properties of the Gabor Coefficient matrix. In this paper, we shall investigate the rank of the Gabor Coefficient matrix of a one dimensional time domain signal, which is one of the most important matrix properties.

  • Gabor transforms: some new properties on the Gabor transform matrix
    Conference Record of Thirty-Second Asilomar Conference on Signals Systems and Computers (Cat. No.98CH36284), 1
    Co-Authors: Xiang-gen Xia, Shie Qian
    Abstract:

    By using the discrete Gabor transform or expansion, the time domain sequences are mapped into the joint time-frequency domain matrices or vice versa. In many applications, it is more effective to process signals, i.e., two dimensional matrices, in the joint time-frequency domain than in the time or frequency domain alone. From the mathematical point of view, the processing of the discrete Gabor Coefficients is no more than the matrix computation. So it is beneficial to understand the properties of the Gabor Coefficient matrix. In this paper, we investigate the rank of the Gabor Coefficient matrix of a one dimensional time domain signal, which is one of the most important matrix properties.

Shie Qian - One of the best experts on this subject based on the ideXlab platform.

  • On the rank of the discrete Gabor transform matrix
    Signal Processing, 2001
    Co-Authors: Xiang-gen Xia, Shie Qian
    Abstract:

    By using the discrete Gabor transform or expansion, the time domain sequences are mapped into joint time-frequency domain matrices or vice versa. In many applications, it is more effective to process signals, i.e., two-dimensional matrices, in the joint time-frequency domain than in the time or frequency domain alone. From the mathematical point of view, the processing of the discrete Gabor Coefficients is no more than matrix computation. So it is beneficial to understand the properties of the Gabor Coefficient matrix. In this letter, we shall investigate the rank of the Gabor Coefficient matrix of a one-dimensional time domain signal, which is one of the most important matrix properties.

  • Gabor transforms: some new properties on the Gabor transform matrix
    Conference Record of Thirty-Second Asilomar Conference on Signals Systems and Computers (Cat. No.98CH36284), 1
    Co-Authors: Xiang-gen Xia, Shie Qian
    Abstract:

    By using the discrete Gabor transform or expansion, the time domain sequences are mapped into the joint time-frequency domain matrices or vice versa. In many applications, it is more effective to process signals, i.e., two dimensional matrices, in the joint time-frequency domain than in the time or frequency domain alone. From the mathematical point of view, the processing of the discrete Gabor Coefficients is no more than the matrix computation. So it is beneficial to understand the properties of the Gabor Coefficient matrix. In this paper, we investigate the rank of the Gabor Coefficient matrix of a one dimensional time domain signal, which is one of the most important matrix properties.