The effective storage mode for time series was designed on Hadoop Distributed File System (HDFS), the sub-series were distributed to the compute nodes on Hadoop cluster by applying Distributed Cache tool, and the matrix of dynamic time warping distances was partitioned into several sub-matrixes. Based on MapReduce programming mode, by parallel computing sub-matrixes in each back-diagonal iteratively, the parallel computation of dynamic time warping distances was implemented, and an efficient parallel algorithm for searching similar patterns from data streams was developed by improving pruning redundant computation. The experimental results on the data set of snow depth long time series in China show that when the length of each time series is equal to or longer than 5000, the required time of parallel computing dynamic time warping distances is less than that of the corresponding sequential computation, and when the length of each time series is equal to or longer than 9000, the more the compute nodes used, the less the required parallel computation time; furthermore, when the length of each pattern is equal to or longer than 4000 and the number of compute nodes is equal to or larger than 5, the required time of parallel searching similar sub-series from data streams is 20% of the corresponding sequential searching time.

In order to speed up the convergence of self-training AM-CNN (Associative Memories Cellular Neural Network) and enhance the performance of achieved AM-CNN, an algorithm for obtaining the space-invariant cloning templates of AM-CNN was proposed, which took the output error of objective CNN as objective function and took local searching and global searching respectively in two internals separated by a given objective function threshold, coupled with the idea of ant optimization algorithm and Particle Swarm Optimization (PSO). Concluded from the numerical simulation results, the proposed algorithm outputs the objective AM-CNN and converges quickly. Meanwhile, the performance of the achieved AM-CNN is better and more stable compared with previous methods. The achieved AM-CNN is also robust to Gauss noise of N(0,0.8) with recall rate of about 90%.

A powerful clue to human identification was furnished by gait due to the comprehensive dissimilarities in body structure and walk behavior.Automatic recognition by gait was subject to increasing interest and had the unique capability to recognize human individual at a distance when other biometrics were obscured.A comprehensive survey of recent developments of gait-based human identity recognition were provided.The main methods in each aspect of gait-based recognition were described.Some detailed discussions on research challenges and future directions in gait analysis were put forward.

An appearance-based approach to the problem of gait recognition was adopted. The width of the outer contour of the binarized silhouette of a walking person was chosen as the basic image feature. A statistical method which combined Principal Component Analysis(PCA) with Linear Discriminant Analysis(LDA) for feature transformation of spatial templates was proposed. This method was used to reduce data dimensionality and to optimize the class separability of different gait sequences. Our method is applied to three data-sets(NLPR,CMU,UMF), extensive experimental results demonstrate that the proposed algorithm performs an encouraging recognition rate with relatively lower computational cost.