In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on...In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.展开更多
. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet c.... This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.展开更多
In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the ...In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.展开更多
The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wav...The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise.The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech.The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.展开更多
In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature fil...In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.展开更多
文摘In the ultrasonic detection of defects in friction welded joints, it is difficult to exactly detect some weak bonding defects because of the noise pollution. This paper proposed an improved threshold function based on the multi-resolution analysis wavelet threshold de-noising method which was put forward by Donoho and Johnstone, and applied this method in the de-noising of the defective signals. This threshold function overcomes the discontinuous shortcoming of the hard-threshold function and the disadvantage of soft threshold function which causes an invariable deviation between the estimated wavelet coeffwients and the decomposed wavelet coefficients. The improved threshold function is of simple expression and convenient for calculation. The actual test results of defect noise signal show that this improved method can get less mean square error （ MSE ） and higher signal-to-noise ratio of reconstructed signals than those calculated from hard threshold and soft threshold methods. The improved threshold function has excellent de-noising effect.
文摘. This paper proposes a novel remote sensing signal de-noising algorithm based on neural networks and tensor analysis. The defects exist in a constant deviation between the wavelet coeffi cients and that the wavelet coefficients of the noisy signal to estimate the discontinuity of hard threshold function and soft threshold function, limiting its further application in order to overcome this shortcoming, this paper proposes a new threshold function, compared with the original threshold function, a new threshold function is simple and easy to calculate, not only with the soft threshold function is continuous. To deal with this drawback, we integrate the NN to enhance the model. Neural network belongs to the basic unsupervised learning of neural networks, the principle of competition based on the mechanism of learning and biological and the memory capacity can be increased as the number of learning patterns increases, not only offi ine learning can also be carried out on-line ＂learning while learning＂ type. The integrated algorithm can host better performance.
基金The Key Program of National Natural Science of China(No.U1261205)Shandong University of Science and Technology Research Fund(No.2010KYTD101)
文摘In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.
文摘The problem of speech enhancement using threshold de-noising in wavelet domain was considered.The appropriate decomposition level is another key factor pertinent to de-noising performance.This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise.The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech.The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.
文摘In this paper, a different method for de-noising of ECG signals using wavelets is presented. In this strategy, we will try to design the best wavelet for de-nosing. Genetic algorithm tests wide range of quadrature filter banks and the best of them will be chosen that minimize the Signal-to-Noise Ratio (SNR). Furthermore, the wavelet function and scaling function related to these filters are reported as the best wavelet for de-noising. Simulation results for de-noising of a noisy ECG signal show that using obtained wavelet by proposed method improves the SNR of about 2.5 dB.
