There are large differences in shape, texture and color of images in remote sensing image datasets, and the classification accuracy of remote sensing scenes is low due to the scale differences cased by different shooting heights and angles. Therefore, a Feature Aggregation Compensation Convolution Neural Network (FAC-CNN) was proposed, which used active rotation aggregation to fuse features of different scales and improved the complementarity between bottom features and top features through bidirectional gated method. In the network, the image pyramid was used to generate images of different scales and input them into the branch network to extract multi-scale features, and the active rotation aggregation method was proposed to fuse features of different scales, so that the fused features have directional information, which improved the generalization ability of the model to different scale inputs and different rotation inputs, and improved the classification accuracy of the model. On NorthWestern Polytechnical University REmote Sensing Image Scene Classification (NWPU-RESISC) dataset, the accuracy of FAC-CNN was increased by 2.05 percentage points and 2.69 percentage points respectively compared to those of Attention Recurrent Convolutional Network based on VGGNet (ARCNet-VGGNet) and Gated Bidirectional Network (GBNet); and on Aerial Image Dataset (AID), the accuracy of FAC-CNN was increased by 3.24 percentage points and 0.86 percentage points respectively compared to those of the two comparison networks. Experimental results show that FAC-CNN can effectively solve the problems in remote sensing image datasets and improve the accuracy of remote sensing scene classification.

To deal with the problem that the localization result of Distance Vector-Hop (DV-HOP) may be rendered far from precision by the Sybil attack in Wireless Sensor Network (WSN), two hop difference based secure localization algorithms, namely HDDV-HOP and EHDDV-HOP, were proposed. Firstly, neighbor node lists of other nodes were got by the detection nodes through the controlled flooding mechanism. Secondly, the neighbor node lists were analyzed to detect fake nodes and white node lists were established. Finally, packets were selectively relayed based on white node lists and the unknown nodes were securely localized. The two algorithms differ in the techniques they used to detect fake nodes. In HDDV-HOP, whether or not neighbor node lists were the same was checked; while in EHDDV-HOP, the ratio of the amount of elements in the intersection of two neighbor node lists to that of elements in the union of the two was analyzed. The simulation results show that, compared with DV-HOP without the Sybil attack, when the ratio of beacon nodes to normal nodes reaches 20% and signal coverage is asymmetric,the localization error of HDDV-HOP is increased by 133.4%, while the error of EHDDV-HOP is increased by 7.3% when the similarity threshold is suitable, but the localization errors of the both algorithms are smaller than that of DV-HOP with the Sybil attack. Both of HDDV-HOP and EHDDV-HOP can defend against the Sybil attack, however EHDDV-HOP outperforms HDDV-HOP.

To achieve efficient software testing under cloud computing environment, a method of generating parallel test cases automatically for functional testing of Web application system was proposed. First, parallel test paths were obtained by conducting depth-first traversal algorithm on scene flow graph; then parallel test scripts were assembled from test scripts referred by the test paths, and parameterized valid test data sets that can traverse target test paths and replace test data in script were generated using Search Based Software Testing (SBST) method. A vast number of automatic distributable parallel test cases were generated by inputting test data into parallel test scripts. Finally, a prototype system of automatic testing in cloud computing environment was built for examination of the method. The experimental results show that the method can generate a large number of valid test cases rapidly for testing in cloud computing environment and improve the efficiency of testing.