As current saliency detection algorithms based on deep convolutional network have problems of incomplete target and noisy background detected from complex scene images, a new algorithm of deep feature-oriented saliency detection composed with basic feature extraction and high-level feature which guided cross-level aggregating delivery was proposed. It was based on the improvement of an extant Encoded Low level distance map with Deep features (ELD) model. Firstly, according to the characteristics of convolutional features at different levels, a cross-level feature fusion network model of high-level feature guidance was established. Then, saliency clustering propagation by using high-level feature guidance on initial saliency map that generated by improved neural network was implemented. Finally, final saliency map with more details and less noise was generated by using fully-connected conditional random field after saliency propagation. The experimental results on ECSSD and DUT-ORMON data sets show that, the Precision-Recall (PR) performance of the proposed algorithm is better than ELD algorithms, and F-measure(F) is increased by 7.5% and 11%, respectively, while its Mean Average Errors (MAE) are decreased by 16% and 15%, respectively,which also can obtain more robust results in complex image scene fields of target recognition, pattern recognition, image indexing, and so on.

To solve the problem that infected nodes move out of the system during the process of rumor propagation, a new SIR (Susceptible-Infective-Removal) rumor propagation model on dynamic homogeneity network was proposed by improving the normalization conditions of classical SIR rumor propagation model. Firstly, according to the propagation rules and mean field theory, the rumor propagation dynamics equation was established on the homogenous network. Then the steady state and infection peak of the rumor propagation process were theoretically analyzed. Finally, the influence of factors on rumor propagation was studied through numerical simulation, which including infection rate, immune rate, real immune coefficient and average degree of network. Research indicates that, as infected nodes move out of the system, steady state value decreases and infection peak slightly increases, compared with the classical SIR rumor propagation model. The study also shows that the peak value of rumor infection increases as the infection probability increases and the immune probability decreases. As the real immune coefficient increases, the steady state value of immune nodes increases. The network average degree has no influence on the steady state of rumor propagation. The larger the average degree is, the earlier the arrival time of the infection peak. This research expands the application scope of SIR propagation model from a closed system to a non-closed system, providing guidance theory and numerical support for making rumor prevention measures.

In the process of rumor spreading, nodes with different degrees of recognition have different recognition abilities. A evolution model of dynamic complex network was proposed based on game theory, in which a new game gain was defined according to node degree. In this model, considering the fact that rumor propagation was often related to node interests, the non-uniform propagation rates of different nodes and propagation dynamics of rumors were described by introducing the recognition ability, and two rumor suppression strategies were proposed. The simulation were conducted on two typical network models and verified in the Facebook real network data. The research demonstrates that the fuzzy degree of rumor has little effect on the rumor propagation rate and the time required to reach steady state in BA scale-free network and Facebook network. As rumors are increasingly fuzzy, the scope of rumor in the network is expanding. Compared with Watts-Strogtz (WS) small-world network, rumors spread more easily in BA scale-free network and Facebook network. The study also finds out that immune nodes in the WS small-world network grow more rapidly than immune nodes in BA scale-free network and Facebook network with the same added value of immune benefits. In addition, there is a better rumor suppression effect by suppressing the degree of node hazard than by suppressing the game gain.

To reduce the impact of Programmed I/O (PIO) write latency in PCI express (PCIe) transmission process, too many times of interaction between the host and the embedded processing system and other issues on transmission bandwidth, a Direct Memory Access (DMA) controller based on command buffering mechanism was designed to improve the transmission bandwidth utilization. Using the internal command buffer of the Field-Programmable Gate Array (FPGA), the DMA controller could cache the data transfer request of the PC. The FPGA could dynamically access the storage space of the PC according to its own requirements and enhance the transmission flexibility. At the same time, a dynamic mosaic DMA scheduling method was proposed to reduce the times of host-to-hardware interaction and interrupt generation by merging the access requests of adjacent storage areas. In the system transmission rate test, the maximum write speed of DMA was 1631 MB/s, the maximum rate of DMA read was up to 1582 MB/s, the maximum of bandwidth was up to 85.4% of the theoretical bandwidth of PCIe bus. Compared with the traditional PIO mode DMA transfer method, DMA read bandwidth increased by 58%, DMA write bandwidth increased by 36%. The experimental results show that the proposed design can effectively improve the DMA transfer efficiency, and is significantly better than PIO method.

In order to reveal the evolution rules of supply chain network with the core of manufacturers, a kind of five-level local world network model was put forward. This model used the BA model and the multi-local world theory as the foundation, combined with the reality of network node generation and exit mechanism. First of all, the intrinsic characteristics and evolution mechanism of network were studied. Secondly, the topology structure and evolution rules of the network were analyzed, and the simulation model was established. Finally, the changes of network characteristic parameters were simulated and analyzed in different time step and different critical conditions, including nodes number, clustering coefficient and degree distribution, then the evolution law of the network was derived. The simulation results show that the supply chain network with the core of manufacturers has the characteristics of scale-free and high concentration. With the increase of time and the growth rate of the network nodes, the degree distribution of overall network approaches to the power-law distribution with the exponent three. The degree distribution of the network at all levels is different, sub-tier suppliers and retailers obey power-law distribution, suppliers and distributors obey exponential distribution, manufacturers generally obey the Poisson distribution.

Navigation exists potential safety hazard when autonomous mobile robot moves in dynamic uncertain environments. In order to improve the navigation safety, a representation method of navigation environments using time of contact was proposed, namely time of contact space. As risk index in navigation environments, the time of contact between two points of an arbitrary was computed by using linear velocity and rotation velocity, and the configuration space was mapped into time of contact space when robot moved in the navigation environment. The time of contact space was applied to classic behavior dynamic navigation method. Compared with the classical behavior dynamics method and behavior dynamics adding velocity obstacles, the simulation results prove that the time of contact space can guarantee safety navigation of autonomous mobile robot.

To raise the safety and efficiency of algorithm on Elliptic Curve Cryptography (ECC), a new multi-base scalar multiplication algorithm was presented based on original side-channel attack and scalar multiplication algorithm. In order to enhance the algorithm's security, random number and the masking technology of base point were introduced to hide the related side-channel informations of the algorithm. Meanwhile, fast point halving and the multi-base representation of scalar were conbined to improve the algorithm's efficiency. According to security analysis, the algorithm can resist various side-channel attacks well. Results of the actual experiments also show that the efficiency of the new method was improved about 36%-42% over the Purohit's method and about 8%-11% over the Lai's method (LAI Z, ZHANG Z, TAO D. Algorithm for directly computing 7P elliptic curves and its application[J]. Journal of Computer Applications, 2013,33(7):1870-1874.) on the elliptic curves recommended by National Institute of Standards and Technology (NIST) including NIST B-163, NIST B-233, NIST B-283, when the number of pre-computation points were 2 and 5 respectively. The new algorithm can be applied to the domains of smart cards and other limited storage resources, making it more secure and efficient to the encryption and decryption of sensitive data.

Considering the problem that the abnormal features have great similarity so that simple structure and high precision modeling cannot be achieved, a control chart pattern recognition method based on Kernel Principal Component Analysis (KPCA) and neural network was proposed. Firstly, the kernel method was used to translate the nonlinear feature into a higher dimensional linear feature space. Secondly this feature was projected to lower dimensional feature space. Finally the BP neural network classifier was introduced to identify the control chart pattern. This method was verified through stochastic simulation. The result demonstrates that the model can cluster each control chart pattern effectively and improve recognition accuracy.