Most existing air quality prediction methods focus on simple time series data for trend prediction， and ignore the pollutant transport and diffusion laws and corresponding classified pattern features. In order to solve the above problem， a PM_2.5 diffusion characteristic extraction method based on Candlestick Pattern Matching （CPM） was proposed. Firstly， the basic periodic candlestick charts from a large number of historical PM_2.5 sequences were generated by using the convolution idea of Convolutional Neural Network （CNN）. Then， the concentration patterns of different candlestick chart feature vectors were clustered and analyzed by using the distance formula. Finally， combining the unique advantages of CNN in image recognition， a hybrid model integrating graphical features and time series features sequences was formed， and the trend reversal that would be caused by candlestick charts with reversal signals was judged. Experimental results on the monitoring time series dataset of Guilin air quality online monitoring stations show that compared with the VGG （Visual Geometry Group）-based method which uses the single time series data， the accuracy of the CPM-based method is improved by 1.9 percentage points. It can be seen that the CPM-based method can effectively extract the trend features of PM_2.5 and be used for predicting the periodic change of pollutant concentration in the future.

Aiming at the unstructured characteristics of speech signal, a method of speaker authentication based on quantum tunneling effect was proposed. Based on quantum tunneling effect, the quantum properties of speech signal framing analyzed, and each speech signal frame was regarded as a quantum state, and the quantization of the algorithm was realized. And then the potential barrier was used to separate the energy characteristics. The barrier group was constructed to extract the energy spectrum characteristics of the signal and used it as the characteristic parameter. The speech signal modeling was finally carried out by the Gaussian Mixture Model (GMM) to complete the authentication of the speaker. The simulation results show that compared with the traditional method, the use of quantum tunneling theory to achieve speaker identification can reduce the complexity of algorithm effectively, improve the discrimination and provide a new direction for speaker authentication and quantum information theory.

To improve the positioning accuracy of Wireless Sensor Network (WSN), the relationship between the positioning accuracy and the distribution of anchor nodes was studied, and a new anchor node selection algorithm based on the Degree of Aggregation-Collinearity (DAC) and Node Degree (ND) of anchors was proposed, namely DAC-ND. Firstly, the experimental analysis showed that the anchor nodes in collinear or concentrated distribution have a large influence on positioning accuracy. Secondly, by analyzing and comparing the anchor node selection algorithms based on Degree of Collinearity (DC), it was found that two kind of anchor node selection algorithms based on the minimum angle (DC-A) or minimum height (DC-H) have some disadvantages. Finally, combining the advantages of the above both algorithms, the new concept of DAC was proposed, and the DAC-ND algorithm was put forward based on DAC and ND. The Matlab simulation results illustrated that, the average position error of DAC-ND can be greatly reduced by 54%-73% compared with the random selection algorithm, and it also can be reduced by 15%-23% and 12%-23% compared with the anchor selection algorithms based on DC-A and DC-H respectively. The experimental results demonstrate that the DAC-ND algorithm can obtain much higher positioning accuracy compared with algorithms based on DC-A or DC-H, which proves the effectiveness of the DAC-ND algorithm.

In the centralized network control environment of Software-Defined Network (SDN), the problem of a single point of failure exists in the controlling plane. In order to solve the problem, a kind of controller architecture was proposed based on intrusion tolerance ideology to improve the availability and reliability of network by using the redundant and diverse central controller platform. In the proposed architecture, the intruded controllers were detected by comparing their messages. Firstly, the key message types and fields needing to be compared were defined. Then, different controller messages were compared using a consistency judgement algorithm. Finally, the controllers with abnormal messages would be isolated and restored. The Mininet-based intrusion tolerance reliability test demonstrated that the controller architecture based on intrusion tolerance could detect and filter the abnormal controller messages. The Mininet-based response-delay test showed that the requirement-delay of underlying network increased by 16% and 42% while the tolerance degree was 1 and 3 respectively. In addition, the Cbench-based response-delay and throughput tests showed that the performance of the intrusion tolerance controller lay among the subsidiary controllers, such as Ryu and Floodlight, and approached the advanced one. In practical application, the quantity and type of the subsidiary controllers can be configured according to the security level of application scenarios, and the proposed intrusion tolerance controller can satisfy the application requirements of response rate and intrusion tolerance degree.

This paper researched the fast networking Media Access Control (MAC) of mobile sensor networks based on unslotted CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) random access algorithm. This paper analyzed and optimized the networking process by preassigning the channels, simplified the association process using direct transmission instead of indirect transmission, and improved the CSMA/CA mechanism in order to reduce the collision. The simulation and field test results show that the proposed method can reduce the collision and the networking time effectively compared with the IEEE 802.15.4 protocol.

Div + CSS is popular in Web page layout. In this layout, a lot of data records of Web pages gather in a layer in the form of repetition structure. To mine data from Web well, this paper proposed a new kind of Web data mining algorithm, computed tree edit distance through string edit distance, improved string edit distance algorithm,used string edit distance to access similarity between one tree and another, and then found repeated patterns in Web pages and mined data. By testing pages of different features of repeated patterns, this algorithm is proved to extract Web data successfully with the feature whether the root and upper layer nodes are the same or the lowest layer nodes are the same.

Conventional algorithm of three dimensional surfaces reconstruction is not very effective when the contours are complex. The paper proposed a new algorithm based on matching shape of adjacent contours: first, got the keypoints of shape contours, and then matched the key-points according to shape of the neighboring layers, connected the key-points, got several independent parts, at last, reconstructed the segments respectively. The experiment proves that the algorithm is effective despite the contours are convex changeable and very complex.