Aiming at the problem that most existing directed network link prediction methods only focus on the directed local and reciprocal link information and ignore the directed global structure information， a High-order Self-included Collaborative Filtering （HSCF） link prediction framework was proposed. Firstly， random walk method was used to calculate the high-order similarity matrix to preserve the high-order path information of the directed network. Secondly， an HSCF framework was constructed by combining the high-order similarity matrix with collaborative filtering method. Finally， the proposed framework was integrated with four typical directed structure similarity indices including Directed Common Neighbor （DCN）， Directed Adamic-Adar （DAA）， Directed Resource Allocation （DRA） and potential theory （Bifan）， and four directed network prediction indices HSCF-DCN， HSCF-DAA， HSCF-DRA and HSCF-Bifan were proposed on this basis. Compared with the baseline indices on ten real directed networks， the experimental results show that the AUC （Area Under Curve of Receiver Operating Characteristic （ROC）） values of HSCF-DCN， HSCF-DAA， HSCF-DRA and HSCF-Bifan are increased by an average of 8.16%， 8.85%， 9.64% and 10.33% respectively and the F-score values of them are increased by an average of 66.62%， 68.32%， 68.95% and 76.18% respectively.

Concerning the low recommendation accuracy of personalized commodity recommendation methods, a Commodity Recommendation Method Integrating User Trust and Brand Recognition (TBCRMI) was proposed. By analyzing the user's purchase behavior and evaluation behavior, the user's recognition of brands and the activities of users themselves were calculated. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used to cluster the users, based on which the user trust relationship was fused, and the nearest neighbors were obtained by Top- K method. Finally, the target user commodity recommendation list was generated based on the nearest neighbors. In order to verify the effectiveness of the algorithm, two datasets (Amazon Food and Unlocked Mobile Phone) were used, User based Collaborative Filtering (UserCF) algorithm, Collaborative Filtering recommendation algorithm with User trust (SPTUserCF) and Merging Trust in Collaborative Filtering (MTUserCF) algorithm were chosen, and the accuracy, recall and F1 value were compared and analyzed. The experimental results show that TBCRMI is superior to the commonly used personalized commodity recommendation methods in either multi-brand comprehensive recommendation or single brand recommendation.

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.

The traditional nonweighted least squares Support Vector Machine (SVM) and weighted least square SVM have a few disadvantages of processing low Signal-to-Noise Ratio (SNR) laser echo in the field of lidar bathymetry, a filtering method named HW-LS-SVM was proposed by combining robust least square and weighted least square SVM. Firstly, strong prior weight function, residual error and mean square error were calculated by elimination weight function, then the weight of least square SVM was computed by weight function. Finally, the echo signal was filtered by iterative computation. The simulation results show that HW-LS-SVM algorithm is more robust than least square SVM, Bayes least square SVM and the traditional weighted least square SVM. The results were satisfactory when the noise rate reached to 45%, and the correct rate of the extracted water surface and bottom was 100%. The extracted water depths from 4 groups of laser echoes in deep area and 4 groups in shallow area all agree with the background data. The proposed method has better anti-noise performance and is more suitable for the filtering processing of the low SNR lidar bathymetry signal.

Concerning the phenomenon that common parking service could not satisfy the increasing demand of the private vehicle owners, an intelligent parking guidance system based on ZigBee network and geomagnetic sensors was designed. Real-time vehicle position or related traffic information was collected by geomagnetic sensors around parking lots and updated to center sever via ZigBee network. On the other hand, out-door Liquid Crystal Display (LCD) screens controlled by center sever displayed information of available parking places. In this paper, guidance strategy was divided into 4 levels, which could provide clear and effective information to drivers. The experimental results prove that the distance detection accuracy of geomagnetic sensors was within 0.4m, and the lowest loss packet rate of the wireless network in the range of 150m is 0%. This system can possibly provide solution for better parking service in intelligent cities.

Considering the basic need of the attack resistance test for trustworthiness, controllability and effectiveness of attack operation, a Denial of Service (DoS) Attack Script Language (DASL) was designed based on Domain Specific Language (DSL), which could be used to develop DoS attacks simply, quickly and conveniently. In this article, attack unit was defined, the domain specific syntactic was constructed based on the analysis of attack samples, the semantic function was realized based on LIBNET, and the interpreter of DASL was designed on the basis of ANTLR. The experimental results show that, attacks developed by DASL were effective and controllable. And DASL can lower the complexity of development, reduce the amount of code to write, increase the efficiency of development and provide powerful support for DoS penetration testing.

The reduction and storage of enormous point cloud data is a crucial link in reverse model reconstruction. Considering the features of point cloud data by single station laser scanning, a new method — grid sector method was put forward for its reduction and storage. Point cloud data could be filtered and stored only by traversal. This method was realized on VC++ 6.0. Multi-station scanning of point cloud registration and stitching would be more quickly and efficiently, if the site goes through the fan in a single grid after treatment. Based on the contrast with traditional compressing methods, this paper analyzed its characteristics and proved its applicability in battlefield terrain digitization.

The requirements to hardware and software of smart phones are comparatively high.In the software aspect,it needs a GUI platform that can support Java programs at least.QT/Embedded is a very good embedded GUI that supports Java programs for smart phones.The implementation of the Java graphic platform on smart phone was introduced.