In the research of elastic wave signal detection algorithm, a method based on adaptive wavelet analysis and segmentation threshold processing of the channel noise removal methods is suggested to overcome the effect of...In the research of elastic wave signal detection algorithm, a method based on adaptive wavelet analysis and segmentation threshold processing of the channel noise removal methods is suggested to overcome the effect of noise, which is prcduced by absorption loss, scattering loss, reflection loss and multi-path effect during the elastic wave in the transmission undelgroound. The method helps to realize extraction and recovery of weak signal of elastic wave from the multi-path channel, and simulation study is carded out about wavelet de-noising effects of the elastic wave and obtained satisfactory results.展开更多
The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of unde...The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.展开更多
Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response sig...Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response signals associated with multiple input and output locations have been measured, the primary objective of this study is to apply the local or global noise removal technique for improving the modal identification based on the polyreference time domain (PTD) method. While the traditional PTD method improves modal parameter estimation by over-specifying the computational model order to absorb noise, this paper proposes an approach using the actual system order as the computational model order and rejecting much noise prior to performing modal parameter estimation algorithms. Two noise removal approaches are investigated: a "local" approach which removes noise from one signal at a time, and a "global" approach which removes the noise of multiple measured signals simultaneously. The numerical investigation in this article is based on experimental measurements from two test setups: a cantilever beam with 3 inputs and 10 outputs, and a hanged plate with 4 inputs and 32 outputs. This paper demonstrates that the proposed noise-rejection method outperforms the traditional noise-absorption PTD method in several crucial aspects.展开更多
To effectively suppress white noise and preserve more useful components of electrocardiogram(ECG) signal, a novel de-noising method based on morphological component analysis(MCA) is proposed. MCA is a method which all...To effectively suppress white noise and preserve more useful components of electrocardiogram(ECG) signal, a novel de-noising method based on morphological component analysis(MCA) is proposed. MCA is a method which allows us to separate features contained in an original signal when these features present different morphological aspects. According to the features of ECG, we used the UWT dictionary to sparsely represent mutated component, and used the DCT dictionary to sparsely represent smooth component. The experimental results of the samples choosing from MIT-BIH databases show that the MCA-based method is effective for white noise removal.展开更多
This paper proposes a robust auto-focus(AF) measure based on inner energy. In general, the inner energy of noise pixels is close to zero because the magnitude of gradient and the direction of the noise pixels are rand...This paper proposes a robust auto-focus(AF) measure based on inner energy. In general, the inner energy of noise pixels is close to zero because the magnitude of gradient and the direction of the noise pixels are random. Therefore, the inner energy can effectively eliminate the influence of noise on image quality assessment. But the gradients of near edge points are consistent with those of edge points, so the inner energy of edge pixels is relatively large, and the detail information of the image can be highlighted. Experimental results indicate that compared with traditional methods, the proposed method has higher accuracy, fewer local peaks, stronger robustness and better practicability. In particular, the evaluation results are close to the subjective evaluation of the human eyes. These results illustrate that the proposed method can be applied in automatic focusing.展开更多
文摘In the research of elastic wave signal detection algorithm, a method based on adaptive wavelet analysis and segmentation threshold processing of the channel noise removal methods is suggested to overcome the effect of noise, which is prcduced by absorption loss, scattering loss, reflection loss and multi-path effect during the elastic wave in the transmission undelgroound. The method helps to realize extraction and recovery of weak signal of elastic wave from the multi-path channel, and simulation study is carded out about wavelet de-noising effects of the elastic wave and obtained satisfactory results.
基金Supported by project of Natural Science Foundation of China(No.41174097)
文摘The ground penetrating radar （GPR） detection data is a wide band signal, always disturbed by some noise, such as ambient random noise and muhiple refleetion waves. The noise affects the target identification of underground medium seriously. A method based on principal component analysis （PCA） was proposed to ex- tract the target signal and remove the uncorrelated noise. According to the correlation of signal, the authors get the eigenvalues and corresponding eigenvectors by decomposing the covariance matrix of GPR data and make linear transformation for the GPR data to get the principal components （PCs）. The lower-order PCs stand h^r the strong correlated target signals of the raw data, and the higher-order ones present the uneorrelated noise. Thus the authors can extract the target signal and filter uncorrelated noise effectively by the PCA. This method was demonstrated on real ultra-wideband through-wall radar data and simulated GPR data. Both of the results show that the PCA method can effectively extract the GPR target signal and remove the uncorrelated noise.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51079134 and 51009124)the NSFC Major International Joint Research Project (Grant No. 51010009)+2 种基金the Program for Changjiang Scholars and Innovative Research Team in University (Grant No. PCSIRT 1086)the Natural Science Foundation of Shandong Province(Grant Nos. ZR2011EEQ022 and 2009ZRA05100)the Fundamental Research Funds for the Central Universities (Grant Nos. 27R1202008A and27R1002076A)
文摘Modal identification involves estimating the modal parameters, such as modal frequencies, damping ratios, and mode shapes, of a structural system from measured data. Under the condition that noisy impulse response signals associated with multiple input and output locations have been measured, the primary objective of this study is to apply the local or global noise removal technique for improving the modal identification based on the polyreference time domain (PTD) method. While the traditional PTD method improves modal parameter estimation by over-specifying the computational model order to absorb noise, this paper proposes an approach using the actual system order as the computational model order and rejecting much noise prior to performing modal parameter estimation algorithms. Two noise removal approaches are investigated: a "local" approach which removes noise from one signal at a time, and a "global" approach which removes the noise of multiple measured signals simultaneously. The numerical investigation in this article is based on experimental measurements from two test setups: a cantilever beam with 3 inputs and 10 outputs, and a hanged plate with 4 inputs and 32 outputs. This paper demonstrates that the proposed noise-rejection method outperforms the traditional noise-absorption PTD method in several crucial aspects.
基金Natural Science Foundatoin of Fujian Province of Chinagrant number:2012J01280
文摘To effectively suppress white noise and preserve more useful components of electrocardiogram(ECG) signal, a novel de-noising method based on morphological component analysis(MCA) is proposed. MCA is a method which allows us to separate features contained in an original signal when these features present different morphological aspects. According to the features of ECG, we used the UWT dictionary to sparsely represent mutated component, and used the DCT dictionary to sparsely represent smooth component. The experimental results of the samples choosing from MIT-BIH databases show that the MCA-based method is effective for white noise removal.
基金supported by the National Natural Science Foundation of China(Nos.U1509207 and 61325019)
文摘This paper proposes a robust auto-focus(AF) measure based on inner energy. In general, the inner energy of noise pixels is close to zero because the magnitude of gradient and the direction of the noise pixels are random. Therefore, the inner energy can effectively eliminate the influence of noise on image quality assessment. But the gradients of near edge points are consistent with those of edge points, so the inner energy of edge pixels is relatively large, and the detail information of the image can be highlighted. Experimental results indicate that compared with traditional methods, the proposed method has higher accuracy, fewer local peaks, stronger robustness and better practicability. In particular, the evaluation results are close to the subjective evaluation of the human eyes. These results illustrate that the proposed method can be applied in automatic focusing.
