A New Method for Fast Computation of Moments Based on 8-neighbor Chain CodeApplied to 2-D Objects Recognition

2D moment invariants have been successfully applied in pattern recognition tasks. The main difficulty of using moment invariants is the computational burden. To improve the algorithm of moments computation through an iterative method, an approach for fast computation of moments based on the 8-neighbor chain code is proposed in this paper. Then artificial neural networks are applied for 2D shape recognition with moment invariants. Compared with the method of polygonal approximation, this approach shows higher accuracy in shape representation and faster recognition speed in experiment

THE ADJUSTMENT OF LEG STIFFNESS DURING DYNAMIC EXERCISE AND DOWNWARD STEPPING FOR ELDERLY

The purpose of the present study was to evaluate the ability of leg stiffness regulation during downward stepping and maximal Counter-Movement-Jump (CMJ) for the elderly. Ten healthy aged people (age: 68.6±5 years; height: 165.3±4.4cm; mass: 61.7±9.3kg) and 10 students (age: 24.3±2years; height: 171.5±4.6cm; mass: 65.9±8kg) volunteered as subjects. Kistler force platform (1200Hz) and Peak high-speed camera (120Hz) were used synchronously to record the ground reaction force and the kinematic parameters of the subjects performing CMJ and stepping down from different heights. The results revealed that the elderly group has a smaller joint range of motion and greater leg stiffness than the young group during stepping down. The force and the leg stiffness during CMJ were significantly smaller for the elderly. The leg stiffness during downward stepping is independent of dynamic leg stiffness during CMJ. With aging, the adjustment ability of leg stiffness for maximal dynamic voluntary contraction was decreased

PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs

{\it SimRank} is a classic measure of the similarities of nodes in a graph. Given a node $u$ in graph $G =(V, E)$, a {\em single-source SimRank query} returns the SimRank similarities $s(u, v)$ between node $u$ and each node $v \in V$. This type of queries has numerous applications in web search and social networks analysis, such as link prediction, web mining, and spam detection. Existing methods for single-source SimRank queries, however, incur query cost at least linear to the number of nodes $n$, which renders them inapplicable for real-time and interactive analysis. { This paper proposes \prsim, an algorithm that exploits the structure of graphs to efficiently answer single-source SimRank queries. \prsim uses an index of size $O(m)$, where $m$ is the number of edges in the graph, and guarantees a query time that depends on the {\em reverse PageRank} distribution of the input graph. In particular, we prove that \prsim runs in sub-linear time if the degree distribution of the input graph follows the power-law distribution, a property possessed by many real-world graphs. Based on the theoretical analysis, we show that the empirical query time of all existing SimRank algorithms also depends on the reverse PageRank distribution of the graph.} Finally, we present the first experimental study that evaluates the absolute errors of various SimRank algorithms on large graphs, and we show that \prsim outperforms the state of the art in terms of query time, accuracy, index size, and scalability.Comment: ACM SIGMOD 201