Xuzhou Chen

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A major challenge in virtual network embedding is to find an efficient mapping of each virtual network (VN) to the nodes and links in the substrate network (SN) so that the residual SN can host as many VN requests as possible. This network optimization problem has been shown to be NP hard. In this paper we propose a “border matching” mechanism(More)
This paper presents a recursive procedure to compute the Moore-Penrose inverse of a matrix A. The method is based on the expression for the Moore-Penrose inverse of rank-one modified matrix. The computational complexity of the method is analyzed and a numerical example is included. A variant of the algorithm with lower computational complexity is also(More)
Singular value decomposition (SVD) is one of the most important factorizations in matrix computation. However, computing SVD is still time-consuming, especially when the dimension of matrices exceeds tens of thousands. In this paper, we present a high performance approach called “Bisection and Twisted” (BT) for solving bidiagonal SVD. As(More)
—How to efficiently map the nodes and links in a given virtual network (VN) to those in the substrate network (SN) so that the residual substrate network (RSN) can host as many VN requests as possible is a major challenge in virtual network embedding. Most research has developed heuristic algorithms with interactive or two-stage methods. These methods,(More)
In this paper, we describe an approach of using a PC based robot (PCRob) for teaching advanced topic course in pattern recognition and computer vision. Unlike most of the robots where only the microprocessors are used, the robot we design and build uses mini PC and off-the-shelf peripherals to provide the computing power in order to process some(More)
For an invertible diagonal matrix D , the convergence of the power scaled matrix sequence N N D A  is investigated. As a special case, necessary and sufficient conditions are given for the convergence of N N D T  , where T is triangular. These conditions involve both the spectrum as well as the diagraph of the matrix T. The results are then used to(More)
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