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
Since product metric on AdS space has played a very important role in Lorentz version of AdS/CFT correspondence, the Yang Mills equation on AdS space with this metric is considered and a static solution is obtained in...Since product metric on AdS space has played a very important role in Lorentz version of AdS/CFT correspondence, the Yang Mills equation on AdS space with this metric is considered and a static solution is obtained in this paper, which helps to understand the AdS/CFT correspondence of Yang Mills fields.展开更多
In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfe...In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis(FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.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Supported by National Key Basic Research Program of China under Grant No. 2004CB31800；National Natural Science Foundation of China under Grant No. 10375087；CUMT Foundation for Youth under Grant No. 2008A034, Qihang Project and Innovation Project of CUMT
文摘Since product metric on AdS space has played a very important role in Lorentz version of AdS/CFT correspondence, the Yang Mills equation on AdS space with this metric is considered and a static solution is obtained in this paper, which helps to understand the AdS/CFT correspondence of Yang Mills fields.
基金supported by the National Natural Science Foundation of China(No.61502256)
文摘In this paper, we propose a video searching system that utilizes face recognition as searching indexing feature. As the applications of video cameras have great increase in recent years, face recognition makes a perfect fit for searching targeted individuals within the vast amount of video data. However, the performance of such searching depends on the quality of face images recorded in the video signals. Since the surveillance video cameras record videos without fixed postures for the object, face occlusion is very common in everyday video. The proposed system builds a model for occluded faces using fuzzy principal component analysis(FPCA), and reconstructs the human faces with the available information. Experimental results show that the system has very high efficiency in processing the real life videos, and it is very robust to various kinds of face occlusions. Hence it can relieve people reviewers from the front of the monitors and greatly enhances the efficiency as well. The proposed system has been installed and applied in various environments and has already demonstrated its power by helping solving real cases.
