The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform

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

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO - SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

Bin Deng - One of the best experts on this subject based on the ideXlab platform.

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO - SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

Hongqiang Wang - One of the best experts on this subject based on the ideXlab platform.

  • sfm signal detection and parameter estimation based on pulse repetition interval transform
    European Signal Processing Conference, 2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

  • EUSIPCO - SFM signal detection and parameter estimation based on pulse-repetition-interval transform
    2012
    Co-Authors: Bin Deng, Hongqiang Wang, Xiang Li
    Abstract:

    A novel algorithm is proposed for detecting and estimating sinusoidal frequency-modulated (SFM) signals based on the pulse-repetition-interval (PRI) transform which is originally used for estimating PRIs of pulse trains. Our algorithm utilizes the resemblance between the spectrum of a SFM signal and a pulse train, and applies the PRI transform to this spectrum. It can detect SFM signals under moderate signal-to-noise ratios, and is also capable of accurately estimating the modulation frequencies even if there are multiple components. The algorithm proposed has been successfully applied to simulated data, and compared with that of the Autocorrelation Method, showing its superiority.

Tsuyoshi Shiina - One of the best experts on this subject based on the ideXlab platform.

  • clinical assessment of real time freehand elasticity imaging system based on the combined Autocorrelation Method
    IEEE Symposium on Ultrasonics, 2003
    Co-Authors: Tsuyoshi Shiina, Naotaka Nitta, Makoto Yamakawa, Ei Ueno, Takeshi Matsumura, Satoshi Tamano, Tsuyoshi Mitake
    Abstract:

    Various techniques for tissue elasticity imaging have been proposed in the last decade. For clinical applications, real-time and freehand manipulation of a probe is required. In a previous study, we developed the combined Autocorrelation Method (CAM), which produces an elasticity image with high-speed processing and accuracy, and achieves a wide dynamic range for strain estimation. In the current study, we extended the CAM clinical uses to be robust for tissue sideslip and suited to freehand compression. We achieved this imaging system by adopting its algorithm and using a commercial ultrasonic scanner and a PC. The echo signals are captured in real time and the strain image frame rate was 10 frames/s. Strain images are superimposed on B-mode images with a translucent color scale. In the clinical measurement, elasticity images for breast and prostate cancer were obtained from more than 50 subjects. Some results yielded an elasticity image, that is, a visualization of the tumor area and detected a non-invasive ductal carcinoma. These results demonstrate that the system can provide high-quality and stable elasticity images in clinical measurement.

  • real time tissue elasticity imaging using the combined Autocorrelation Method
    Journal of Medical Ultrasonics, 2002
    Co-Authors: Tsuyoshi Shiina, Naotaka Nitta, Ei Ueno, J C Bamber
    Abstract:

    The elastic properties of tissues are expected to provide novel information for use in diagnosing pathologic changes in tissues and discriminating between malignant and benign tumors. Because it is hard to directly estimate the elastic modulus distribution from echo signals, Methods for imaging the distribution of tissue strain under static compression are being widely investigated. Imaging the distribution of strain has proven to be useful for detecting disease tissues on the basis of their differences in elastic properties, although it is more qualitative than elastic modulus distribution. Many approaches to obtaining strain images from echo signals have been proposed. Most of these approaches use the spatial correlation technique, a Method of detecting tissue displacement that provides maximum correlation between the echo signal obtained before and the one obtained after compression. Those Methods are not suited for real-time processing, however, because of the amount of computation time they require. An alternative approach is a phase-tracking Method, which is analogous to Doppler blood flowmetry. Although it can realize the rapid detection of displacement, the aliasing effect prevents its application to the large displacements that are necessary to improve the S/N ratio of the strain image. We therefore developed a more useful technique for imaging tissue elasticity. This approach, which we call the combined Autocorrelation (CA) Method, has the advantages of producing strain images of high quality with real-time processing and being applicable to large displacements.

  • Real‑time Tissue Elasticity Imaging using the Combined Autocorrelation Method
    Journal of Medical Ultrasonics, 2002
    Co-Authors: Tsuyoshi Shiina, Naotaka Nitta, Ei Ueno, J C Bamber
    Abstract:

    The elastic properties of tissues are expected to provide novel information for use in diagnosing pathologic changes in tissues and discriminating between malignant and benign tumors. Because it is hard to directly estimate the elastic modulus distribution from echo signals, Methods for imaging the distribution of tissue strain under static compression are being widely investigated. Imaging the distribution of strain has proven to be useful for detecting disease tissues on the basis of their differences in elastic properties, although it is more qualitative than elastic modulus distribution. Many approaches to obtaining strain images from echo signals have been proposed. Most of these approaches use the spatial correlation technique, a Method of detecting tissue displacement that provides maximum correlation between the echo signal obtained before and the one obtained after compression. Those Methods are not suited for real-time processing, however, because of the amount of computation time they require. An alternative approach is a phase-tracking Method, which is analogous to Doppler blood flowmetry. Although it can realize the rapid detection of displacement, the aliasing effect prevents its application to the large displacements that are necessary to improve the S/N ratio of the strain image. We therefore developed a more useful technique for imaging tissue elasticity. This approach, which we call the combined Autocorrelation (CA) Method, has the advantages of producing strain images of high quality with real-time processing and being applicable to large displacements.

  • strain estimation using the extended combined Autocorrelation Method
    Japanese Journal of Applied Physics, 2001
    Co-Authors: Makoto Yamakawa, Tsuyoshi Shiina
    Abstract:

    We previously proposed the combined Autocorrelation (CA) Method to estimate the strain distribution in tissue. This Method is based on the conventional Doppler Method but overcomes the problem of aliasing, which is a weakness of the Doppler Method. The ability of the CA Method has been demonstrated with phantom experiments and in vitro measurements. However, we have to maintain the tissue displacement in the axial direction because the CA Method is based on 1-D processing, and this is difficult to perform with the conventional compression system. Therefore, in this paper, we propose an extended CA Method that is robust against sideslip. We also demonstrate the ability of this Method with computer simulations.

  • tissue elasticity imaging based on combined Autocorrelation Method and 3 d tissue model
    Internaltional Ultrasonics Symposium, 1998
    Co-Authors: Naotaka Nitta, Makoto Yamakawa, Tsuyoshi Shiina, Ei Ueno, Marvin M Doyley, J C Bamber
    Abstract:

    To obtain an accurate strain distribution under a large displacement, beyond ultrasonic wavelength, the authors propose a Method of reconstructing the strain distribution by modifying the Autocorrelation technique. They call this Method the "combined Autocorrelation Method". The Method is capable of rapidly detecting strain in the phase domain processing without aliasing. They also attempt to reconstruct the elastic modulus distribution by solving the inverse problem based on a 3-D finite element tissue model. These Methods were applied to obtaining strain and elastic modulus images of a tumor phantom and an extracted breast tissue including a cancer tumor. A tumor with a 10 mm diameter and with poor contrast in a B-mode image was clearly displayed as a region harder than the surrounding soft tissue in the strain and elastic modulus images. These results indicate that the proposed Method is a promising means for diagnosing tumors.

T Shimamura - One of the best experts on this subject based on the ideXlab platform.

  • a weighted Autocorrelation Method for pitch extraction of noisy speech
    International Conference on Acoustics Speech and Signal Processing, 2000
    Co-Authors: H Kobayashi, T Shimamura
    Abstract:

    Pitch period (or fundamental frequency) extraction plays an important role on speech processing and has a wide spread of applications in systems associated with speech. Many pitch extraction Methods have been proposed so far, but improvement in noisy environments is still a remaining subject. In this paper, we propose a modified version of the Autocorrelation Method which is well known to be robust against noise. Utilizing that the difference function (amplitude difference function) has similar characteristics with the Autocorrelation function, the Autocorrelation function is weighted by the reciprocal of the difference function. By simulation experiments based on continuous speech, it is shown that the proposed pitch extraction Method behaves more robustly than the conventional Methods against additive noise, and especially it is very effective at low signal-to-noise ratio.

  • ICASSP - A weighted Autocorrelation Method for pitch extraction of noisy speech
    2000 IEEE International Conference on Acoustics Speech and Signal Processing. Proceedings (Cat. No.00CH37100), 2000
    Co-Authors: H Kobayashi, T Shimamura
    Abstract:

    Pitch period (or fundamental frequency) extraction plays an important role on speech processing and has a wide spread of applications in systems associated with speech. Many pitch extraction Methods have been proposed so far, but improvement in noisy environments is still a remaining subject. In this paper, we propose a modified version of the Autocorrelation Method which is well known to be robust against noise. Utilizing that the difference function (amplitude difference function) has similar characteristics with the Autocorrelation function, the Autocorrelation function is weighted by the reciprocal of the difference function. By simulation experiments based on continuous speech, it is shown that the proposed pitch extraction Method behaves more robustly than the conventional Methods against additive noise, and especially it is very effective at low signal-to-noise ratio.