The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Huansheng Wang - One of the best experts on this subject based on the ideXlab platform.
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wavelet based ecg Data Compression system with linear quality control scheme
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Kingchu Hung, Huansheng WangAbstract:Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG Data Compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear Compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG Data Compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a Compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear Compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. Following the use of the MIT-BIH arrhythmia Database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better reconstruction quality control than other wavelet-based methods.
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a high efficient quality control strategy for wavelet based ecg Data Compression system
BioMedical Engineering and Informatics, 2008Co-Authors: Kingchu Hung, Huansheng WangAbstract:Maintaining retrieved signal with desired quality is crucial for ECG Data Compression. In this paper, a high efficient quality control strategy is proposed for wavelet-based ECG Data Compression. The strategy is based on a modified non-linear quantization scheme that can obtain a linear distortion behavior with respective to a control variable. The linear distortion characteristic supports the design of a linear control variable prediction algorithm. By using the MIT-BIH arrhythmia Database, the experimental results show that the linear control variable prediction method can effectively improve the convergence speed than the previous literatures.
Kingchu Hung - One of the best experts on this subject based on the ideXlab platform.
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ep based wavelet coefficient quantization for linear distortion ecg Data Compression
Medical Engineering & Physics, 2014Co-Authors: Kingchu Hung, Hsiehwei Lee, Tungkuan LiuAbstract:Reconstruction quality maintenance is of the essence for ECG Data Compression due to the desire for diagnosis use. Quantization schemes with non-linear distortion characteristics usually result in time-consuming quality control that blocks real-time application. In this paper, a new wavelet coefficient quantization scheme based on an evolution program (EP) is proposed for wavelet-based ECG Data Compression. The EP search can create a stationary relationship among the quantization scales of multi-resolution levels. The stationary property implies that multi-level quantization scales can be controlled with a single variable. This hypothesis can lead to a simple design of linear distortion control with 3-D curve fitting technology. In addition, a competitive strategy is applied for alleviating Data dependency effect. By using the ECG signals saved in MIT and PTB Databases, many experiments were undertaken for the evaluation of Compression performance, quality control efficiency, Data dependency influence. The experimental results show that the new EP-based quantization scheme can obtain high Compression performance and keep linear distortion behavior efficiency. This characteristic guarantees fast quality control even for the prediction model mismatching practical distortion curve.
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wavelet based ecg Data Compression system with linear quality control scheme
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Kingchu Hung, Huansheng WangAbstract:Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG Data Compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear Compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG Data Compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a Compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear Compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. Following the use of the MIT-BIH arrhythmia Database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better reconstruction quality control than other wavelet-based methods.
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a high efficient quality control strategy for wavelet based ecg Data Compression system
BioMedical Engineering and Informatics, 2008Co-Authors: Kingchu Hung, Huansheng WangAbstract:Maintaining retrieved signal with desired quality is crucial for ECG Data Compression. In this paper, a high efficient quality control strategy is proposed for wavelet-based ECG Data Compression. The strategy is based on a modified non-linear quantization scheme that can obtain a linear distortion behavior with respective to a control variable. The linear distortion characteristic supports the design of a linear control variable prediction algorithm. By using the MIT-BIH arrhythmia Database, the experimental results show that the linear control variable prediction method can effectively improve the convergence speed than the previous literatures.
Cong Liu - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based Data Compression technique for smart grid
IEEE Transactions on Smart Grid, 2011Co-Authors: Jiaxin Ning, Wenzhong Gao, Jianhui Wang, Cong LiuAbstract:This paper proposes a wavelet-based Data Compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its Data Compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor Data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.
Jiaxin Ning - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based Data Compression technique for smart grid
IEEE Transactions on Smart Grid, 2011Co-Authors: Jiaxin Ning, Wenzhong Gao, Jianhui Wang, Cong LiuAbstract:This paper proposes a wavelet-based Data Compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its Data Compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor Data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.
Wenzhong Gao - One of the best experts on this subject based on the ideXlab platform.
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a wavelet based Data Compression technique for smart grid
IEEE Transactions on Smart Grid, 2011Co-Authors: Jiaxin Ning, Wenzhong Gao, Jianhui Wang, Cong LiuAbstract:This paper proposes a wavelet-based Data Compression approach for the smart grid (SG). In particular, wavelet transform (WT)-based multiresolution analysis (MRA), as well as its properties, are studied for its Data Compression and denoising capabilities for power system signals in SG. Selection of the Order 2 Daubechies wavelet and scale 5 as the best wavelet function and the optimal decomposition scale, respectively, for disturbance signals is demonstrated according to the criterion of the maximum wavelet energy of wavelet coefficients (WCs). To justify the proposed method, phasor Data are simulated under disturbance circumstances in the IEEE New England 39-bus system. The results indicate that WT-based MRA can not only compress disturbance signals but also depress the sinusoidal and white noise contained in the signals.
