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
This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analy...This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance.展开更多
Remediation into film of the Danish author Hans Christian Andersen＇s many fairytales often accentuate his quaint or sentimental tendencies. In a silent film adaptation of ＂The Little Match Girl＂ the French filmmake...Remediation into film of the Danish author Hans Christian Andersen＇s many fairytales often accentuate his quaint or sentimental tendencies. In a silent film adaptation of ＂The Little Match Girl＂ the French filmmaker Jean Renoir transports us into an avant-garde world of consumerism and commodification that seems at odds with Andersen＇s original vision. Yet Renoir＇s film is remarkably resonant with Andersen＇s own pre-cinematic imagination even as it produces a different inflection of the uncanny. The match girl is turned into a femme enfant fatale and her illu- minated visions are redirected away from the comforts of home to a world of artifice and melodrama.展开更多
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Project supported by the National Natural Science Foundation of China （Nos. 61170092, 61133011, and 61103091）
文摘This paper develops a novel online algorithm, namely moving average stochastic variational inference （MASVI）, which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ＇stochastic variational inference＇ and ＇SGRLD＇, our algorithm achieves a faster convergence rate and better performance.
文摘Remediation into film of the Danish author Hans Christian Andersen＇s many fairytales often accentuate his quaint or sentimental tendencies. In a silent film adaptation of ＂The Little Match Girl＂ the French filmmaker Jean Renoir transports us into an avant-garde world of consumerism and commodification that seems at odds with Andersen＇s original vision. Yet Renoir＇s film is remarkably resonant with Andersen＇s own pre-cinematic imagination even as it produces a different inflection of the uncanny. The match girl is turned into a femme enfant fatale and her illu- minated visions are redirected away from the comforts of home to a world of artifice and melodrama.
