Focused on the issue that the traditional important node identification method for K-shell networks needs global topology during iteration and cannot be used in dynamic networks, an important node identification method for dynamic networks based on neighborhood priority asynchronous H operation was proposed. Firstly, the algorithm was proved to converge to Ks (K-shell) value; then the degree of each node was taken as the initial value of h-index, and the nodes to be updated were selected by the h-index ranking of the node and the h-index change of the neighbor nodes; meanwhile the h-index was modified to adapt to the topology change according to the number change and maximum degree of the dynamic network nodes, finally the algorithm converged to the Ks and the important nodes were found. The simulation results show that the algorithm can find important nodes effectively by local information of neighbor nodes with less convergence time. Compared with the random selection algorithm and the neighborhood-variety selection algorithm, the convergence time of the proposed algorithm decreases by 77.4% and 28.3% respectively in static networks and 84.3% and 38.8% respectively in dynamic networks.

In order to solve the problems such as low transmission efficiency and high network resource overhead in Internet of Vehicles (IoV) environment, a new routing algorithm based on node cognitive interaction, which is suitable for urban traffic environment, was proposed. Firstly, based on trust theory, a concept of cognitive interaction degree was proposed. Then, based on this, the vehicle nodes in IoV were classified and given with different initial values of cognitive interaction degree. Meanwhile, the influence factors such as interaction time, interaction frequency, physical distance, hops between nodes and the Time-To-Live of message were introduced, and a cognitive interaction evaluation model of vehicle nodes was constructed. The cognitive interaction degrees of vehicle nodes were calculated and updated by using the proposed model, and a neighbor node with higher cognitive interaction degree than others could be selected as relay node to forward the messages after the comparison between the nodes. Simulation results show that compared with Epidemic and Prophet routing algorithms, the proposed algorithm effectively increases the message delivery rate and reduces the message delivery delay, while significantly reducing the overhead of network resources and helping to improve the quality of message transmission in IoV environment

Channel congestion occurs when the vehicular traffic density increases to a certain extent in Internet of Vehicles (IoV), even if there are only beacons in the wireless channel. To solve the problem, a Distributed-Weighted Fair Power Control (D-WFPC) algorithm was proposed. Firstly, considering the actual channel characteristics in IoV, the Nakagami-m fading channel model was used to establish the random channel model. Then, the mobility of the nodes in IoV was considered, and a power control optimization problem was established based on the Network Utility Maximization (NUM) model, which kept the local channel load under the threshold to avoid congestion. Finally, a distributed algorithm was designed by solving the problem with dual decomposition and iterative method. The transmit power of each vehicle was dynamically adjusted according to the beacons from neighbor vehicles. In the simulation experiment, compared with the fixed transmit power schemes, the D-FWPC algorithm reduced the delay and packet loss ratio effectively with the increase of traffic density, the highest reduction was up to 24% and 44% respectively. Compared with the Fair distributed Congestion Control with transmit Power (FCCP) algorithm, the D-FWPC algorithm had better performance all the way and the highest reduction in delay and packet loss ratio was up to 10% and 4% respectively. The simulation results show that the D-WFPC algorithm can converge quickly and ensure messages to be transmitted with low delay and high reliability in IoV.

Focusing on the issue that edges extracted by state-of-the-art exist some deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., an edge extraction method based on graph theory was proposed, which considered the image as an undirected graph by regarding each pixel as a node and connecting two adjacent nodes in horizontal or vertical direction to constitute a side. The proposed method included three phases:in pixels similarity calculation phase, the weights were given to sides in undirected graph, which represented pixels similarity; in threshold determination phase, the mean of all the weights (the similarity of the whole image) was determined as a threshold; in edge determination phase, when weights on horizontal or vertical sides were smaller than the threshold, the left nodes of horizontal side and the upper nodes of vertical side were retained to constitute edges of the image. The experimental results show that the proposed edge extraction method based on graph theory is suitable for the images with obvious target and background, and can overcome deficiencies including non-continuity, incompleteness, incline, jitter and notches etc., and has anti-noise ability to Speckle noise and Gaussian noise.

Considering the weakness of the selective search method that needs a large number of windows to localize objects, a novel object localization method based on fusion of visual saliency and superpixels was proposed in this paper. Firstly, the visual saliency map was used to coarsely localize the objects, and then the adjacent superpixels could be merged according to the appearance features of image, starting from the above coarse positions. Furthermore, the method employed a simple background detector to avoid the over-merge. Finally, a greedy algorithm was used to iteratively combine the merged regions and generate the final bounding boxes. The experimental results on Pascal VOC 2007 show that the proposed method leads to a 20% reduction in the number of the bounding boxes on the same detection rate (recall of 0.91) compared to the selective search algorithm, and its overlap rate reaches 0.77. The presented method can keep higher overlap rate and recall scores with fewer windows because of its coarse-to-fine process.

DV-Hop algorithm uses the hop number multiplied by the average distance per hop to estimate the distance between nodes and the trilateral measurement or the maximum likelihood to estimate the node coordinate information, which has defects and then causing too many positioning errors. This paper presented an improved DV-Hop algorithm based on node density regional division (Density Zoning DV-Hop, DZDV-Hop), which used the connectivity of network and the node density to limit the hop number of the estimated node coordinate information and the weighted centroid method to estimate the positioning coordinates. Compared with the traditional DV-Hop algorithm in the same network hardware and topology environment, the result of Matlab simulation test shows that, the communication amount of nodes can be effectively reduced and the positioning error rate can be reduced by 13.6% by using the improved algorithm, which can improve the positioning accuracy.

Opportunistic network routing algorithm based on geographic location information in DIrection based Geographic routing scheme (DIG) has the problems of large delay and low success rate, which is due to that DIG algorithm makes the waiting time of the data in the cache too long and cannot guarantee the data-carrying node move to the destination node. To solve these problems, Geographic Routing algorithm based on Directional Data Transmission (GRDDT) was proposed. The algorithm used a new data forwarding mechanism and a more effective use of the neighbor list information, effectively avoiding the appearance of the above circumstances, so as to reduce data packet transmission delay and to improve the success rate. OPNET simulation results show that, the performance of transmission delay and success rate of GRDDDT algorithm are improved compared with DIG.

Most existing methods for detecting plane in point cloud cost long operation time, and the result of detection is susceptible to noise. To address these problems, this paper put forward a kind of multi-plane detection algorithm based on geometric statistical characteristics of the point clouds. The proposed method coarsely segmented point clouds according to the change rate of the volume density firstly, then used the Multi-RANSAC to fit planes, at last the authors proposed a new merge-constraint condition to combine and optimize the initial fitted planes. The experimental results show that the method in this paper is easy to realize, can effectively reduce the influence of cumulative noise to the detection results, improve the plane detection accuracy and also greatly reduce the computing time.

To study the reachability of a class of nonlinear hybrid systems, this paper presented an verification method based on polyhedron inclusion. Firstly, some notions about hybrid systems and reachability were introduced. The method based on polyhedron inclusion was proposed to compute the linear approximation of polynomial hybrid systems. Quantifier elimination and nonlinear optimization method were applied to obtain the associated linear hybrid systems. Then the over-approximation of reachable set of original polynomial hybrid systems can be computed by using SpaceEx. Furthermore, the safety properties of the systems also can be verified.

A hybrid control method based on state feedback and adaptive neural network was proposed, which considered the taking off and landing control problem under unknown mass of the Unmanned Aerial Vehicle (UAV). A state feedback controller was designed to realize the horizontal position and heading control. The accurate control of height was archived considering the vehicle's unknown load through the Radial Basis Function (RBF) neural network. The simulation analysis and experiments illustrate that the proposed control method can effectively realize the accurate control of height, and can be able to online estimate aircraft quality parameters.