In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the d...In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.展开更多
The virtual reality based motion simulation of the guide wire and the catheter inside specific vascular structures can benefit a lot for the endovascular intervention. A fast and well-performed collision cancellation ...The virtual reality based motion simulation of the guide wire and the catheter inside specific vascular structures can benefit a lot for the endovascular intervention. A fast and well-performed collision cancellation algorithm is proposed based on the geometric analysis and the angular propagation （AP）, and a 3-D real-time interactive system is developed for the motion simulation of the guide wire and the catheter inside the specific patient vascular. The guide wire or the catheter is modeled as the ＂multi-body＂ representation and properties are defined by its intrinsic characteristics. The motion of the guide wire or the catheter inside the vascular is guided by the collision detection and the collision cancellation algorithm. Finally, a relaxation procedure is used to achieve more realistic status. Experimental results show that the behavior of the guide wire or the catheter depends on the defined parameters. The real-time simulation can be achieved. The result shows that the simulation system is effective and promising.展开更多
An algorithm for recovering the quaternion signals in both noiseless and noise contaminated scenarios by solving an L1-norm minimization problem is presented. The L1-norm minimization problem over the quaternion numbe...An algorithm for recovering the quaternion signals in both noiseless and noise contaminated scenarios by solving an L1-norm minimization problem is presented. The L1-norm minimization problem over the quaternion number field is solved by converting it to an equivalent second-order cone programming problem over the real number field, which can be readily solved by convex optimization solvers like SeDuMi. Numerical experiments are provided to illustrate the effectiveness of the proposed algorithm. In a noiseless scenario, the experimental results show that under some practically acceptable conditions, exact signal recovery can be achieved. With additive noise contamination in measurements, the experimental results show that the proposed algorithm is robust to noise. The proposed algorithm can be applied in compressed-sensing-based signal recovery in the quaternion domain.展开更多
基金The National Natural Science Foundation of China(No.6120134461271312+7 种基金6140108511301074)the Research Fund for the Doctoral Program of Higher Education(No.20120092120036)the Program for Special Talents in Six Fields of Jiangsu Province(No.DZXX-031)Industry-University-Research Cooperation Project of Jiangsu Province(No.BY2014127-11)"333"Project(No.BRA2015288)High-End Foreign Experts Recruitment Program(No.GDT20153200043)Open Fund of Jiangsu Engineering Center of Network Monitoring(No.KJR1404)
文摘In order to classify nonlinear features with a linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network（ KPCANet） is proposed. First, the data is mapped into a higher-dimensional space with kernel principal component analysis to make the data linearly separable. Then a two-layer KPCANet is built to obtain the principal components of the image. Finally, the principal components are classified with a linear classifier. Experimental results showthat the proposed KPCANet is effective in face recognition, object recognition and handwritten digit recognition. It also outperforms principal component analysis network（ PCANet） generally. Besides, KPCANet is invariant to illumination and stable to occlusion and slight deformation.
文摘The virtual reality based motion simulation of the guide wire and the catheter inside specific vascular structures can benefit a lot for the endovascular intervention. A fast and well-performed collision cancellation algorithm is proposed based on the geometric analysis and the angular propagation （AP）, and a 3-D real-time interactive system is developed for the motion simulation of the guide wire and the catheter inside the specific patient vascular. The guide wire or the catheter is modeled as the ＂multi-body＂ representation and properties are defined by its intrinsic characteristics. The motion of the guide wire or the catheter inside the vascular is guided by the collision detection and the collision cancellation algorithm. Finally, a relaxation procedure is used to achieve more realistic status. Experimental results show that the behavior of the guide wire or the catheter depends on the defined parameters. The real-time simulation can be achieved. The result shows that the simulation system is effective and promising.
基金The National Basic Research Program of China(973 program)(No.2011CB707904)the National Natural Science Foundation of China(No.61073138,61271312,61201344,81101104,60911130370)+1 种基金the Research Fund for the Doctoral Program of Higher Education of Ministry of Education of China(No.20110092110023,20120092120036)the Natural Science Foundation of Jiangsu Province(No.BK2012329,BK2012743)
文摘An algorithm for recovering the quaternion signals in both noiseless and noise contaminated scenarios by solving an L1-norm minimization problem is presented. The L1-norm minimization problem over the quaternion number field is solved by converting it to an equivalent second-order cone programming problem over the real number field, which can be readily solved by convex optimization solvers like SeDuMi. Numerical experiments are provided to illustrate the effectiveness of the proposed algorithm. In a noiseless scenario, the experimental results show that under some practically acceptable conditions, exact signal recovery can be achieved. With additive noise contamination in measurements, the experimental results show that the proposed algorithm is robust to noise. The proposed algorithm can be applied in compressed-sensing-based signal recovery in the quaternion domain.
基金基金支持：本研究受世界卫生组织双年项目（WHO Reference2014/435380-0）、十三五国家重点研发计划项目重大慢性非传染性疾病防控研究“糖尿病肾病发生发展的危险因素及机制研究”项目（项目编号2016YFC1305400）、以及University of Michigan Health System-Peking University Health Science Center Joint Institute for Translational and Clinical Research（BMU20140479）项目支持.
