This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MR...This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.展开更多
Virtual routers are gaining increasing attention in the research field of future networks. As the core network device to achieve network virtualization, virtual routers have multiple virtual instances coexisting on a ...Virtual routers are gaining increasing attention in the research field of future networks. As the core network device to achieve network virtualization, virtual routers have multiple virtual instances coexisting on a physical router platform, and each instance retains its own forwarding information base （FIB）. Thus, memory scalability suffers from the limited on-chip memory. In this paper, we present a splitting-after-merging approach to compress the FIBs, which not only improves the memory efficiency but also offers an ideal split position to achieve system refactoring. Moreover, we propose an improved strategy to save the time used for system rebuilding to achieve fast refactoring. Experiments with 14 real-world routing data sets show that our approach needs only a unibit trie holding 134 188 nodes, while the original number of nodes is 4 569 133. Moreover, our approach has a good performance in scalability, guaranteeing 90 000 000 prefixes and 65 600 FIBs.展开更多
基金Natural Science Foundation of Chinagrant number:81371635+3 种基金Research Fund for the Doctoral Program of Higher Education of Chinagrant number:20120131110062Shandong Province Science and Technology Development Plangrant number:2013GGX10104
文摘This paper aims to meet the requirements of reducing the scanning time of magnetic resonance imaging （MRI）, accelerating MRI and reconstructing a high quality image from less acquisition data as much as possible. MRI method based on compressed sensing （CS） with multiple regularizations （two regularizations including total variation （TV） norm and L1 norm or three regularizations consisting of total variation, L1 norm and wavelet tree structure） is proposed in this paper, which is implemented by applying split augmented lagrangian shrinkage algorithm （SALSA）. To solve magnetic resonance image reconstruction problems with linear combinations of total variation and L1 norm, we utilized composite spht denoising （CSD） to split the original complex problem into TV norm and L1 norm regularization subproblems which were simple and easy to be solved respectively in this paper. The reconstructed image was obtained from the weighted average of solutions from two subprohlems in an iterative framework. Because each of the splitted subproblems can be regarded as MRI model based on CS with single regularization, and for solving the kind of model, split augmented lagrange algorithm has advantage over existing fast algorithm such as fast iterative shrinkage thresholding（FIST） and two step iterative shrinkage thresholding （TWIST） in convergence speed. Therefore, we proposed to adopt SALSA to solve the subproblems. Moreover, in order to solve magnetic resonance image reconstruction problems with linear combinations of total variation, L1 norm and wavelet tree structure, we can split the original problem into three subproblems in the same manner, which can be processed by existing iteration scheme. A great deal of experimental results show that the proposed methods can effectively reconstruct the original image. Compared with existing algorithms such as TVCMRI, RecPF, CSA, FCSA and WaTMRI, the proposed methods have greatly improved the quality of the reconstructed images and have better visual effect.
基金Project supported by the National Basic Research Program （973） of China （No. 2012CB315805）, the National Natural Science Foundation of China （Nos. 61173167 and 61472130）, the Prospective Research Project on Future Networks of Jiangsu Future Networks Innovation Institute, China （No. 2013095-1-05）, the Hunan Provincial Innovation Foundation for Postgraduate, China （No. CX2014B150）, and the State Scholarship Fund of China （No. 201406130048）
文摘Virtual routers are gaining increasing attention in the research field of future networks. As the core network device to achieve network virtualization, virtual routers have multiple virtual instances coexisting on a physical router platform, and each instance retains its own forwarding information base （FIB）. Thus, memory scalability suffers from the limited on-chip memory. In this paper, we present a splitting-after-merging approach to compress the FIBs, which not only improves the memory efficiency but also offers an ideal split position to achieve system refactoring. Moreover, we propose an improved strategy to save the time used for system rebuilding to achieve fast refactoring. Experiments with 14 real-world routing data sets show that our approach needs only a unibit trie holding 134 188 nodes, while the original number of nodes is 4 569 133. Moreover, our approach has a good performance in scalability, guaranteeing 90 000 000 prefixes and 65 600 FIBs.
