In order to solve the problem of poor small target detection effect in Synthetic Aperture Radar (SAR) target ship detection, a self-adaptive anchor single-stage ship detection method was proposed. Firstly, on the basis of Feature Selective Anchor-Free (FSAF) algorithm, the optimal feature fusion method was obtained by using the Neural Architecture Search (NAS) to make full use of the image feature information. Secondly, a new loss function was proposed to solve the imbalance of positive and negative samples while enabling the network to regress the position more accurately. Finally, the final detection results were obtained by combining the Soft-NMS filtering detection box which is more suitable for ship detection. Several groups of comparison experiments were conducted on the open SAR ship detection dataset. Experimental results show that, compared with the original target detection algorithm, the proposed method significantly reduces the missed detections and false positives of small targets, and improves the detection performance for inshore ships to a certain extent.

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.

Due to the limitation of the computational power of embedded processors,the poor real-time performance has always been an urgent problem to be solved in the practical applications of Visual Inertial Simultaneous Localization And Mapping(VI-SLAM). Therefore,a real-time Simultaneous Localization And Mapping(SLAM)with keyframes determined by Inertial Measurement Unit(IMU)was proposed,which was mainly divided into three threads:tracking,local mapping and loop closing. Firstly,the keyframes were determined adaptively by the tracking thread through the IMU pre-integration, and the adaptive threshold was derived from the result of the visual inertia tight coupling optimization. Then,only the keyframes were tracked,thereby avoiding the feature processing to all frames. Finally,a more accurate Unmanned Aerial Vehicle(UAV)pose was obtained by the local mapping thread through the visual inertial bundle adjustment in the sliding window,and the globally consistent trajectory and map were output by the loop closing thread. Experimental results on the dataset EuRoC show that the algorithm can significantly reduce the tracking thread time consumption without loss of precision and robustness,and reduce the dependence of VI-SLAM on computing resources. In the actual flight test,the true trajectory of the drone with scale information can be estimated accurately by the algorithm in real time.

The trusted virtual platform constructed by the combination of virtualization technology and trusted computing and its trust chain have become a research hot spot. But at present, most of the research achievements construct the trust chain by extending the conventional trust chain model, as a result, the model is not precise and the logic is not completely reasonable. Moreover, there are two separate trust chains, one starts from the underlying virtual platform, the other starts from the top-level user Virtual Machine (VM). In order to solve this problem, a trust chain model with waterfall characteristic called TVP-QT was proposed for the trusted virtual platform. This model starts with the physical Trusted Platform Module (TPM), and adds a Trusted-Joint Point (TJP) between the chain of the underlying virtual platform and the chain of the top-level user VM. The TJP is in charge of the measurement of virtualization TPM (vTPM) for VM after the trusted chain is transmitted from the underlying virtual platform to the TJP, then the vTPM gets the control and is in charge of the measurement of the related components and applications of the top-level user VM in the starting process. The TJP which has the waterfall characteristic between the underlying virtual platform and the top-level user VM can be viewed as a connecting link, and it can satisfy the hierarchical and dynamic characteristics of the virtual platform, moreover guarantee the trust of the whole virtual platform. Finally, the correctness of the model was proved in theory, and the generality and feasibility of the proposed trust chain model in the instantiation system was analyzed and discussed. Simulation results on Xen show that the trust chain can ensure the trust and credibility of the trusted cloud platform in the whole running process.

Concerning covering loopholes and uneven local deployment, a Directional and Heterogeneous Precision Self-deployment Algorithm (DHPSA) was proposed. Autonomous deployment process was divided into two stages. Firstly, a node moved to the destination path by choosing the optimal route in real-time under virtual forces of neighbor node and specified path. Then, through autonomous rotation and autonomous moving, the location of the node was finely tuned under joint virtual force of neighbor nodes and the accurate coverage of the target path was realized finally. The contrast experiments show that, compared with the VFPSA (Virtual Force-based Precision Self-deployment Algorithm), the coverage rate of the proposed algorithm is increased by about 4.4 percent, the overlapping rate is decreased by about 3.4 percent, moving distance is reduced by about 2.1 percent and deployment time is reduced by about 4.3 percent. The simulation experiment results show that the proposed deployment algorithm can effectively increase the coverage rate, decrease the overlap rate and reduce energy consumption.

Collaborative filtering algorithm is the most widely used algorithm in personalized recommendation system. Focusing on the problem of date sparseness and poor scalability, a new clustering recommendation algorithm based on user interest and social trust was proposed. Firstly, according to user rating information, the algorithm divided users into different categories by clustering technology, and set up a user neighbor set based on interest. In order to improve the accuracy of the calculation of interest similarity, the modified cosine formula was used to eliminate the difference of user scoring criteria. Then, the trust mechanism is introduced to measure implicit trust value among users by defining the direct trust calculation method and indirect trust calculation method, converted a social network to a trust network, and set up a user neighbor set based on trust. Finally, this algorithm combined the predictive value of two neighbor sets to generate recommendations for users by weighting method. The simulation experiment was carried out to test the performance on Douban dataset, found suitable value of α and k. Compared with collaborative filtering algorithm based on users and recommendation algorithm based on trust, the Mean Absolute Error (MAE) decreased by 6.7%, precision, recall and F1 increased by 25%,40% and 37%. The proposed algorithm can effectively improve the quality of recommendation system.

Aiming at the problems of massive data analysis requirement, complex cognitive inference in big data tasks, low efficiency of random assignment algorithm and virtual property and uncertainty of Internet users, an optimization algorithm for accurately theme-aware task assignment in crowd computing on big data was proposed. Firstly, the themes in crowd computing were extracted by method which combined with theme extraction model with fuzzy-kmeans adaptation, then the correlations were computed through task model and user model. Secondly, new users' real theme and initial accuracy were tested by historical tasks with high quality answers. Lastly, the probability that a user can participate in a certain kind of task was calculated and a sequence of candidate sequences was predicted by Logistic Regression (LR), and then the appropriate workers were assigned accurately to the tasks. Compared with random algorithm, the accuracy of the proposed algorithm was more than 20 percentage points higher, which increases with the increase of the training data, and the accuracy was nearly close to 100% especially in correlation tasks through full training. The simulation results show that the proposed algorithm has a higher accuracy with more cost-effective and performance in big data environment.

To improve the performance of the co-training algorithm and expand the range of applications, a multi-view semi-supervised collaboration classification algorithm with the combination of consistent and inconsistent label rules was proposed, which aimed at providing a more effective method for the classification of the bridge structured health data. The proposed algorithm used combination of agreement and disagreement label rules for the unlabeled data by judging whether the two classifiers were consistent. Put the sample to the label set, if the label results were consistent. If the label results were inconsistent and the confidence was beyond the threshold, it put the label result of the high confidence to the label set, took full use of the unlabeled data to improve the performance of the classifier, and updated the classification model by the difference of the classifiers. The experimental results of the proposed algorithm on the bridge structured health datasets and standard UCI datasets verify the effectiveness and feasibility of the proposed model on the multi-view classification problems.

The basic notion of strand spaces model was introduced, then a equivalence formula checking secrecy of messages was given, and it was proven that a signed message by a confidential private key had its origin in a honest strand. Finally these two conclusions were used to check BAN modified version of CCITT X.509(3) protocol.