Aiming at the problem of reachable region search problem in urban computing, a method based on time segment tree was developed. In the method, a time segment tree structure was designed to store the local reachable regions, and a dynamic adaptive search algorithm was proposed, so as to improve the efficiency and accuracy of reachable region search. The method includes four steps. Firstly, the probability time weights of road segments were constructed on the basis of road speed distribution model and the trajectory data. Then, the short-term reachable regions were queried and stored by using the hierarchical skip list algorithm. After that, an efficient index structure for the hierarchical reachable region was built by the use of the time segment tree. Finally, the iterative search in the road network was carried out by using the time segment tree index, and the reachable region set was obtained. Extensive experiments were conducted on Beijing road network and taxi trajectory datasets. The results show that the proposed method improves the efficiency and accuracy by 18.6% and 25% respectively compared with the state-of-the-art Single-location reachability Query Maximum/minimum Bounding region search (SQMB) method.

To solve Dynamic Optimization Problems (DOP), a Differential Evolution algorithm with Competitive Strategy based on multi-population (DECS) was proposed. Firstly, one of the populations was chosen as a detection population. Whether the environment had changed was determined by monitoring the fitness values of all individuals in the population and dimension of the population. Secondly, the remaining populations were used as the search populations to search the optimal value independently. During the search, a exclusion rule was introduced to avoid the aggregation of multiple search populations in the same local optimal neighborhood. After the iteration of several generations, competitive operation was performed on all search populations. The population to which the optimal individual belong was retained and the next generation's individuals of the population were generated by using the quantum individual generation mechanism. Then other search populations were reinitialized. Finally, 49 dynamic change problems about 7 test functions were used to verify DECS, and the experimental results were compared with Artificial Immune Network for Dynamic optimization (Dopt-aiNet) algorithm, restart Particle Swarm Optimization (rPSO) algorithm, and Modified Differential Evolution (MDE) algorithm. The experimental results show that the average error mean of 34 problems for DECS is less than Dopt-aiNet and the average error mean of all problems for DECS was less than that for rPSO and MDE. Therefore, DECS is feasible to solve DOP.

In view of the problem that how to select the network dynamically in heterogeneous wireless network environment, an network selection and handover strategy based on context-awareness was proposed. A dynamic network solution and a fuzzy logic handover decision were proposed. Based on them, the strategy chose a selection index to filter those access networks that did not satisfied the requirements and designed a network score function for network ranking calculation. The simulation results show that the proposed handover strategy can select a suitable access network, and effectively use the resources.

The hard disk of cloud computing platform is not reliable. This paper proposed to use Self-Monitoring Analysis and Reporting Technology (SMART) log to predict hard disk failure based on Classification using lOcal clusterinG with Over-Sampling (COG-OS) framework. First, faultless hard disks were divided into multiple disjoint sample subsets by using DBScan or K-means clustering algorithm. And then these subsets and another sample set of faulty hard disks were mixed, and Synthetic Minority Over-sampling TEchnique (SMOTE) was used to make the overall sample set tend to balance. At last, faulty hard disks was predicted by using LIBSVM classification algorithm. The experimental results show that the method is feasible. COG-OS improves SMOTE+Support Vector Machine (SVM) on faulty hard disks' recall and overall performance, when using K-means method to divide samples of faultless hard disks and using LIBSVM method with Radial Basis Function (RBF) kernel to predict faulty hard disks.

To reduce the heavy transmission burden of multichannel color image encryption algorithms, a single-channel color image encryption algorithm based on cutting spectrum and 2D Arnold transform was presented. In the proposed algorithm, the R, G, B components of the original color image were extracted, and their spectra were obtained separately by the Fractional Fourier Transform (FrFT) of different orders, followed by cutting their spectra to construct a new spectrum, then the combined spectrum was scrambled by the 2D Arnold transform to confuse and diffuse the spectrum information well enough. The encrypted image was a gray image, thus the transmission burden was reduced apparently while the main information of the original color image was kept. The simulation results and performance analyses verify the validity and the security of the encryption algorithm.

Traditional Mean-shift target tracking algorithm is rather sensitive to background environment. The use of kernel weighted histogram for target template and target candidates can not always get exact center of the target. Therefore, an improved feature selection mechanism was proposed, in which background weighted histogram was chosen for target template and kernel weighted histogram for target candidates. The simulation results show that the method achieves more accurate target tracking in complex environments.