The current public datasets of Pedestrian Attribute Recognition （PAR） have the characteristics of complicated attribute annotations and various collection scenarios， leading to the large variations of the pedestrian attributes in different datasets， so that it is hard to directly utilize the existing labeled information in the public datasets for PAR in practice. To address this issue， a multi-source and multi-label PAR method based on domain adaptation was proposed. Firstly， to transfer the styles of the different datasets into a unified one， the features of the samples were aligned by the domain adaption method. Then， a multi-attribute one-hot coding and weighting algorithm was proposed to align the labels with the common attribute in multiple datasets. Finally， the multi-label semi-supervised loss function was combined to perform joint training across datasets to improve the attribute recognition accuracy. The proposed feature alignment and label alignment algorithms were able to effectively solve the heterogeneity problem of attributes in multiple PAR datasets. Experimental results after aligning three pedestrian attribute datasets PETA， RAPv1 and RAPv2 with PA-100K dataset show that the proposed method improves the average accuracy by 1.22 percentage points， 1.62 percentage points and 1.53 percentage points respectively， compared to the method StrongBaseline， demonstrating that this method has a strong advantage in cross dataset PAR.

The texture of power line inspection images with parallax variation is complex， the number of paired matching points obtained by traditional algorithms is less and the registration accuracy is low， which seriously affect the stitching effect of power line inspection unmanned aerial vehicle image. In order to solve the problems， a new image stitching method based on improved Order-Aware Network （OANet） was proposed. Firstly， the Accelerated KAZE （AKAZE） algorithm was adopted to match the power line inspection images to be stitched roughly. Secondly， the Squeeze-and-Excitation Networks （SENet） was added to the Order-Aware module in OANet， which helped to enhance the grasping ability of the network for both the local and global context information， and more accurate paired matching points were obtained. Then， the Mesh-based Photometric Alignment （MPA） algorithm was used to register the images to be stitched. Finally， the optimal suture line in the overlapping area was calculated by the content compressed sensing algorithm to complete image stitching. The number of correct matching points of the improved OANet network is about 10% higher than that of the original OANet network with time consumption increased by 10 ms on average. Compared with the registration stitching algorithms such as As-Projective-As-Possible （APAP） algorithm， Adaptive As-Natural-As-Possible （AANAP） algorithm and MPA algorithm， the proposed algorithm has the highest stitching quality with the root mean square error of the overlapping area of the images to be stitched is 0 and no distortion in the non-overlapping area. Experimental results show that， the proposed algorithm can stitch the aerial images of power line inspection quickly and stably.

The virtual machines in cloud computing platform exchange data in the shared memory of physical machine. In view of the problem that the traffic cannot be captured and detected in firewall or other security components, the OpenFlow technology was analyzed, and a traffic redirection method based on OpenFlow was presented. To control traffic forwarding process and redirect it to security components, the method provided network connection for virtual machines with OpenFlow controller and virtual switches instead of physical switches, and built a traffic detection system composed of four modules including virtual switch, control unit, intrusion detection and system configuration management. The experimental results show that the proposed scheme can realize traffic redirection and the subsequent detection processing, and the system can provide switch-level and host-level control granularity. It also solves traffic detection problem under cloud computing environment in traditional scene by traffic redirection, and provides great expansion of the traffic processing based on OpenFlow.

For the traditional player skill estimation algorithms based on probabilistic graphical model neglect the first-move advantage (or home play advantage) which affects estimation accuracy, a new method to model the first-move advantage was proposed. Based on the graphical model, the nodes of first-move advantage were introduced and added into player's skills. Then, according to the game results, true skills and first-move advantage of palyers were caculated by Bayesian learning method. Finally, predictions for the upcoming matches were made using those estimated results. Two real world datasets were used to compare the proposed method with the traditional model that neglect the first-move advantage. The result shows that the proposed method can improve average estimation accuracy noticeably.

Large and very large matrix cannot be dealt by current matrix multiplication algorithms. With the development of MapReduce programming frame, parallel programs have become the main approaches for matrix computing. The matrix multiplication algorithms based on MapReduce were summarized, and an improved strategy for large matrix was proposed, which had a tradeoff in the data volume between the computation on single work node and the network transmission. The experimental results prove that the parallel algorithms outperform the traditional ones on the large matrix, and the performance will improve with the increase of the clusters.

In Mobile Ad Hoc Network (MANET), each node forwards a message in the traditional routing protocol such as flooding and expanding ring search, which results in heavy overhead and long latency of routing. In order to improve the performance of routing protocol, a scheme of probabilistic forwarding algorithm was provided based on directional prediction. The information such as ID and time was extracted from data packets by monitoring network, and a table was established to store these records which can hint the distance to the destination node. Based on these records, the node's forwarding probability was calculated and adaptively adjusted according to the network. Whether some node should forward a packet depended on the forwarding probability, which was high enough only for sustaining the routing process towards the destination. The simulation results show that the routing overhead declined up to 70% compared with flooding algorithm and 20% compared with the classical probabilistic forwarding algorithm. The new scheme significantly improved the performance of the network.

The common routing algorithms tremendously dissipate energy in largescale wireless sensor networks, which goes against the maximization of the network lifetime. A routing model about mobile Agent in sensor networks was drawn out, and then an optimization problem of mobile Agent static route was derived. A chaotic simulated annealing with memory ability and various neighborhood search methods were proposed to optimize the route of mobile Agent in largescale sensor network. The theoretical analysis and experimental results show that the proposed algorithm is superior to other intelligent algorithms in terms of the solutions, the convergence speed, and the computation time. It proves that the proposed approach has obviously prolonged the network lifetime.

The device driver must have quick response and flexible data structure for the device driver development of high speed data capture based on PCI bus. A multi-slot and multi-frame data structure was presented in this paper after the bottleneck of high speed data capture was analyzed, and a guideline to the design of the device driver was also presented. The data capture speed can be 85MB per second and it is independent of the computers' configuration.

Ontology mapping is a preliminary work to realize the communication between different Ontologies. The current studies on ontology mapping approaches mainly focus on fulfilling mapping process automatically or semi-automatically and enhancing the precision of concept similarity. The key of ontology mapping is to compute the concept similarity, single concept similarity computing approach is not in favor of improving precision of the result. In view of the above shortcomings, an improved concept similarity computing approach was proposed and described in detail. The property semantic similarity of this approach improved the existing approaches to the similarities of both data type property and object type property. It is proved that this approach is effective and has high precision.

The de-noising method based on the stationary wavelet tranform was proposed. According to the different source and the trait of frequency distributing, different de-noising methods for the detail signal transformed by selecting coiflets wavelet to decompose the noisy of ECG signal for several levels were used. The experiment shows that this method can avoid the Gibbs phenomena which often occurs in traditional DWT de-nosing method.

aving analyzed some security problems in e-commerce, a kind of security model of mobile agent in e-commerce was presented. In this security model, different access patterns, security policies and reputation management were used by mobile agent to reduce the risk in e-commerce as much as possible and the transactions of e-commerce more efficient was made.