In order to improve the real-time ability of obstacle avoidance using image processing technology for quad-rotor helicopter, an obstacle avoidance path planning algorithm was proposed based on Bayesian estimation and region division traversal. Firstly, Bayesian estimation was used to preprocess the video images collected by quad-rotor helicopter. Secondly, obstacle probability analysis was performed to obtain key frames from video images, so as to maximize the real-time performance of the helicopter. Finally, the background difference was carried out on these selected image frames to identify the obstacles, and the pixel point traversal algorithm based on region division was implemented in order to improve the accuracy of obstacle identification. Experimental results show that with the use of the proposed algorithm, the real-time performance of quad-rotor helicopter obstacle avoidance is improved with guaranteeing the obstacle avoidance identification ability, and the maximum distance between the ideal trajectory and the actual flight trajectory of the quad-rotor helicopter is 25.6 cm, while the minimum distance is 0.2 cm. The proposed obstacle avoidance path plan algorithm can provide an efficient solution for quad-rotor helicopter to avoid obstacles by using video images collected by camera.

Concerning the problem that traditional trajectory pattern mining methods have the problems of slow mining and large maximum amount of memory, a method of mining trajectory interval patterns based on spatial proximity searching was proposed. The implementation of the proposed method consists of five phases:1) Space-time discretization is performed on the trajectories, and space-time cell sequences corresponding to trajectories are achieved. 2) All the space-time cell sequences are scanned to get all no-duplication spatial cells, and all frequent spatial cells are obtained by the inclusion operation of the spatial cells and the cell sequences. 3) Frequent spatial cells are transformed into frequent interval patterns of length one. 4) Candidate interval patterns with the frequent spatial cells as units are generated by spatial proximity searching, and the support value of the candidate patterns are calculated by matching the patterns and the space-time cell sequences. 5) Based on the set support threshold, all frequent interval patterns are obtained. The experimental results show that the proposed method has the advantages of faster mining and less maximum amount of memory than traditional methods. Furthermore, in terms of running time, the proposed method has better stability and scalability performance than traditional methods. This method is helpful to the trajectory pattern mining methods to increase the mining speed and reduce the maximum amount of memory.

Louvain Method (LM) is an algorithm to detect community in complex network based on modularity optimization. Since there is no method to calculate the gain of modularity after nodes leave their community in the existing research, a method was presented to calculate the modularity-gain after nodes leave their community based on the definition of modularity and the method for calculating the modularity-gain after nodes merge. Secondly, aiming at the problem that LM requires large memory space, an improved algorithm was proposed with the strategy of separating isolated nodes. In each iteration of the algorithm, isolated nodes of the input network were separated in advance, only the connected nodes of the input network can actually participate in the iterative process. Isolated nodes and non-isolated nodes were stored respectively when storing communities detected. The experimental results based on real networks showed that the requirement of memory space was reduced by more than 40% in the improved algorithm, and the running time of the algorithm was further reduced. Experimental results indicate that the improved algorithm has more advantages in dealing with real networks.

Aiming at the problem that the existing spherical embedding algorithm cannot effectively embed the data into the low-dimensional space in the case that the distances between points far apart are inaccurate or absent, a new spherical embedding method was proposed, which can take the distances between the nearest neighbor points as input, and embeds high dimensional data of any scale onto the unit sphere, and then estimates the radius of the sphere which fit the distribution of the original data. Starting from a randomly generated spherical distribution, the Kullback-Leibler (KL) divergence was used to measure the difference of the normalized distance between each pair of neighboring points in the original space and the spherical space. Based on the difference, the objective function was constructed. Then, the stochastic gradient descent method with momentum was used to optimize the distribution of the points on the sphere until the result is stable. To test the algorithm, two types of spherical distribution data sets were simulated: which are spherical uniform distribution and Kent distribution on the unit sphere. The experimental results show that, for the uniformly distributed data, the data can be accurately embedded in the spherical space even if the number of neighbors is very small, the Root Mean Square Error (RMSE) of the embedded data distribution and the original data distribution is less than 0.00001, and the spherical radius of the estimated error is less than 0.000001; for spherical normal distribution data, the data can be embedded into the spherical space accurately when the number of neighbors is large. Therefore, in the case that the distance between points far apart are absent, the proposed method can still be quite accurate for low-dimensional data embedding, which is very helpful for the visualization of data.

To solve the problem of extension state recognition, an extension pattern distinguishing model was proposed. First, an extension pattern distinguishing definition was built; second, the characteristics of both static state and dynamic state were analyzed for universe of discourse; furthermore, a general framework of extension pattern discrimination was designed, and the formulas to calculate the degree of quantitative change and qualitative change were given in the paper; finally, both general and extension states of a given case were distinguished by using the proposed method. The experimental results demonstrate the feasibility of the proposed model for expression, analysis and discrimination of extension state. The extension pattern distinguishing model can effectively solve the pattern recognition problem of extension and states transformation which is inextricable for traditional extension pattern classifiers.

Concerning the safe problem of digital image information hiding, a scrambling algorithm based on bitwise layered Arnold transform was proposed. The secret image was stratified by bit-plane, taking into account the location and pixel gray transform, each bit-plane was scrambled for different times with Arnold transforma, and the pixel was cross transposed, and adjacent pixels were bitwise XOR to get a scrambling image. The experimental results show that the secret image histogram is more evenly distributed after stratification scrambling, its similarity with the white noise is around 0.962, and the scrambling image can be restored and extracted almost lossless, which improves the robustness. Compared with other scrambling algorithms, the proposed algorithm is more robust to resist attack, and improves the spatial information hiding security.

In order to improve the coverage rate of the sensor node deployment, concerning the coveragetrap, nodes redundancy and no reoptimization, a senor nodes deployment based on glowworm swarm optimization was proposed when the detection area was known. And the optimization had been improved. In this algorithm, each senor node was considered as a glowworm, and the intensity of signs was the intensity of luciferin. Firstly, the initial deployment of nodes was done. Then, after calculating the value of the movement probability, the direction of movement was determined as well as the direction of movement. Finally, the deployment of sensor nodes was finished. The simulation results show that this way of deployment is appropriate to the huge amounts of sensor nodes deployment, and has such characteristics as high coverage rate and strong flexibility.