Focused on the high computational time complexity of the depth map based Synthesized View Distortion Change (SVDC) algorithm in 3D High Efficiency Video Coding (3D-HEVC), a new parallelization method of SVDC algorithm based on hybrid granularity was proposed under the reconfigurable array structure. Firstly, the SVDC algorithm was divided into two parts:Virtual View Synthesis (VVS) and distortion value calculation. Secondly, the VVS part was accelerated by pipeline operation, and the distortion value calculation part was accelerated by dividing into two levels:task level, which means dividing the synthesized image according to pixels, and instruction level, that is dividing the distortion values inside the pixel by the calculation process. Finally, a reconfigurable mechanism was used to parallelize the VVS part and distortion value calculation part. Theoretical analysis and hardware simulation results show that, in terms of execution time, the proposed method has the speedup ratio of 2.11 with 4 Process Elements (PEs). Compared with the SVDC algorithms based on Low Level Virtual Machine (LLVM) and Open Multi-Processing (OpenMP), the proposed method has the calculation time reduced by 18.56% and 21.93% respectively. It can be seen that the proposed method can mine the parallelism of the SVDC algorithm, and effectively shorten the execution time of the SVDC algorithm by combining with the characteristics of the reconfigurable array structure.

At present, the deployment of nodes in wireless sensor network mainly adopts the algorithm based on Voronoi diagram. In the process of deployment using Voronoi algorithm, due to the large number of nodes involved in the deployment and the high complexity of the algorithm, the iteration time of the algorithm is long. In order to solve the problem of long iteration time in node deployment, a Deployment Algorithm based on Basic Architecture (DABA) was proposed. Firstly the nodes were combined into basic architectures, then center position coordinates of the basic architecture were calculated, finally the node deployment was performed by using Voronoi diagram. The algorithm was still able to realize the deployment effectively under the condition that there were obstacles in the deployment area. The experimental results show that DABA can reduce the deployment time by two thirds compared with the Voronoi algorithm. The proposed algorithm can significantly reduce the iteration time and the complexity of the algorithm.

The I/O performance of new type storage devices is usually an order of magnitude higher than that of traditional Solid State Disk (SSD). However, simply replacing SSD with new type storage device will not significantly improve the performance of distributed file system. This means that the current distributed file system cannot give full play to the performance of new type storage devices. To solve the problem, the data writing process and transmission process of Hadoop Distributed File System (HDFS) were analyzed quantitatively. Through quantitative analysis of the time consumptions of different stages of HDFS writing process, the most time-consuming data transmission between nodes was found in each stage of writing data. Therefore, the corresponding optimization strategy was proposed, that is, the processes of data transmission and processing were parallelized by using asynchronous write. So that the processing stages of different data packets were parallel to each other, shortening the total processing time of data writing, thereby the write performance of HDFS was improved. Experimental results show the proposed scheme improves the HDFS write throughput by 15%-24%, and reduces the overall write execution time by 28%-36%.

To evaluate the effect of split delivery and waiting time on Vehicle Routing Problem (VRP) with stochastic travel time, by considering the waiting time under soft time window, a stochastic programming model with correction was formulated to solve the Split Delivery VRP (SDVRP) with stochastic travel time. Meanwhile, an improved Particle Swarm Optimization (PSO) was proposed for this problem. At first, an improved relative position index method was used to decode the integer type code in which some customers (the split customers) may appear more than once. Then, an adaptive selection process was used to update the velocity in which the length of the involved vectors are different from each other besause of the split delivery. At last, the path relinking method was used to update the position of the swarm to deal with the information loss caused by the transformation between the continuous space and the discrete space. The experimental results on modified Solomon's instances show that the all-in cost is averagely increased by about 3%, and customers tend to choose split delivery when considering the waiting time. The use of split delivery can effectively reduce the all-in cost (2%) and the use of vehicles (0.6); in addition, in some instances, especially in the R2 instances, the waiting time can be reduced by about 0.78%.

Because the existing Robust Principal Component Analysis (RPCA) approaches do not consider the continuity and the incompletion of sequential data, one type of low-rank matrix recovery model, named Regularized Incomplete RPCA (RIRPCA), was proposed. First, the model of RIRPCA was constructed based on a metric function for evaluating the continuity, where the model minimized a weighted combination of the matrix nuclear norm, L ₁ norm and regularized term. Then, the augmented Lagrange multipliers algorithm was employed to solve the proposed convex optimization problem. This algorithm has good scalability and low computation complexity. Finally, RIRPCA was applied to the background modeling of videos. The experimental results demonstrate that the proposed method has the superiority of recovering missing entries and separating foreground over matrix completion and incomplete RPCA.

In this paper, a new monocular vision system was proposed to improve obstacle detection capability of indoor mobile robot. In this system, firstly, the Hue, Saturation, Intensity (HSI) color space conversion of images was performed. Secondly, a small target threshold selection method was proposed to segment the images, which enhanced the precision of the image segmentation. Thirdly, the target scene matching method and target projection matching method were used to calculate the change of the target pixel and projection so as to judge whether the target is obstacles or ground graphs. The experimental results show that the monocular vision system is effective and feasible, and this system can be applied to the navigation for small indoor mobile robots.

The discrete dynamic Bayesian network is a useful tool for modeling and inferring the time series progress, and it has wide modeling application value. But its inference algorithm needs improving. Concerning the shortcomings of the discrete dynamic Bayesian network, such as its inference algorithm is hard to understand, hard to program and running slowly, this paper proposed the most suitable storage data structure of the discrete dynamic Bayesian network, deduced the fast inference algorithm for the discrete dynamic Bayesian network, and verified the inference algorithm for the discrete dynamic Bayesian networks through a sample.

The forecasting model about aviation ammunition storage-transportation system with automatic regulatory system in aircraft carrier to replenish the required aviation ammunition timely was established. This paper studied the storage and transportation system about aviation ammunition, built the causal loop diagrams and stock and flow diagrams about every element impacting the storage-transportation efficiency of aviation ammunition with the method of system dynamics and simulated by the system dynamics software Vensim PLE 5.9. The simulation results show that this model can predict the storage and transportation trend about aviation ammunition under the condition of perturbation, which correspond with the data and conclusion of exercises with aircraft ammunition storage system.

An adaptive filter was proposed to detect and remove pepper and salt noise in an image based on Pulse Coupled Neural Network (PCNN) firing matrix. The PCNN was simplified and a unidirectional decaying threshold was proposed to avoid complex parameter selection and improve processing speed. The noise-polluted pixels were detected through analyzing the PCNN firing matrix, and then only the noisy pixels were filtered by a median filter while protecting image edges and details. The window size of the filter and the filtering time were adaptively determined by calculating the noise intensity of the contaminated image. The experimental results show that the proposed method performs better in removing noise while conserving detailed information than traditional filters do.

A hot standby high available system based on Linux was introduced. It used IP address takeover to guarantee the smooth swift between the host system and the backup system when the network was failed.