Abstract.We consider the iterative solution of a quadratic matrix equation with special coefficient matrices which arises in the quasibirth and death problem. In this paper, we show that the elementwise minimal positive solvent of the quadratic matrix equations can be obtained using Newton's method if there exists a positive solvent and the convergence rate of the Newton iteration is quadratic if the Fréchet derivative at the elementwise minimal positive solvent is nonsingular. Although the Fréchet derivative is singular, the convergence rate is at least linear. Numerical experiments of the convergence rate are given.
This paper proposes an object based vision (O-BV) system to implement visual servoing for autonomous mobile manipulators using two charge-coupled device (CCD) cameras. Conventional stereo vision (C-SV) system estimates the depth based on the disparity between two camera images for the same object. However, the disparity is not an effective cue for a small disparity at a long distance. To resolve this problem, in the proposed O-BV system, the individual camera tracks the object independently, and the angles of the two cameras are used to estimate the distance to the object. This depth estimation technique is applied for an autonomous mobile robot to approach to a target object precisely. The O-BV system is experimentally compared to the C-SV system in terms of computing time and depth estimation accuracy. Also the two cameras which are attached on the top of the autonomous mobile manipulator have been utilized for the mobile manipulator to approach to a target object precisely through the visual servoing. Through the experiments, it is demonstrated that the fast and precise depth estimation is a critical factor for the successful visual servoing.
Abstract. As a promising decoding algorithm for turbo codes in terms of relatively low BER, the maximum a posteriori (MAP) algorithm is most widely used. However, the conventional MAP algorithm requires a large number of computations. A modified MAP algorithm is therefore proposed for reduction of the associated memory size and ultimately power saving. A newly introduced block combing is performed for the memory efficiency such that two branch metrics (BMs) are merged into one branch metric. When calculating FSM (Forward State Metric) of the associated state transition, BM is included in the subsequent FSM, and thus when calculating APP (A Posteriori Probability), the BM is exempted and the number of computations for LLR (Log Likelihood Ratio) is reduced. Simulation results demonstrate reduced memory size in use and equivalent performance, compared to the conventional MAP algorithm.
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