It is difficult for traditional algorithms to accurately find out the first direct path in indoor Ultra Wide Band (UWB) Time Of Arrival (TOA) positioning system, resulting in low positioning accuracy. In order to solve the problem, a TOA indoor UWB positioning algorithm based on Time Reversal (TR) was proposed. Firstly, the spatial-temporal focusing characteristic of TR processing was used to determine the first direct path, so as to estimate the TOA of this path. Then, the Weighted Least Squares (WLS) positioning algorithm was used to assign the corresponding weights to different estimation components for improving the positioning accuracy. The simulation results show that, compared with the traditional TOA positioning, the proposed scheme has the Root Mean Square Error (RMSE) reduced by 28.6% under the low signal-to-noise ratio condition. It can be seen that the proposed scheme improves the system positioning accuracy significantly.

Wind power prediction is an important basis for the monitoring and information management of wind farms. Ultra-short-term wind power prediction is often used to balance load and optimize scheduling and requires high prediction accuracy. Due to the complex environment of wind farm and many uncertainties of wind speed, the wind power time series signals are often non-stationary and random. Recurrent Neural Network (RNN) is suitable for time series tasks, but the non-periodic and non-stationary time series signals will increase the difficulty of network learning. To overcome the interference of non-stationary signal in the prediction task and improve the prediction accuracy of wind power, an ultra-short-term wind power prediction method combining empirical model decomposition and multi-branch neural network was proposed. Firstly, the original wind power time series signal was decomposed by Empirical Mode Decomposition (EMD) to reconstruct the data tensor. Then, the convolution layer and Gated Recurrent Unit (GRU) layer were used to extract the local features and trend features respectively. Finally, the prediction results were obtained through feature fusion and full connection layer. Experimental results on the dataset of a wind farm in Inner Mongolia show that compared with AutoRegressive Integrated Moving Average (ARIMA) model, the proposed method improves the prediction accuracy by nearly 30%, which verifies the effectiveness of the proposed method.

Software-defined Wireless Local Area Network (WLAN) is a trend for managing wireless networks. Aiming at the problems of frequent handover and access failure in AP(Access Point)-intensive environment, an access control method based on global association memory retention of penalty factors was proposed. First, OpenFlow protocol was extended. Second, the extended data messages were used to report network quality, load, throughput, and utilization indicators to the controller through AP. Then, the network index parameters determined by introducing variation coefficient method were used to construct AP access weights. Finally, a global penalty factor was introduced to record frequent back-and-forth handovers in the network, and AP access weights and transmitting powers were modified based on the penalty factor retained by the global memory. Experimental comparison with the strongest signal access method and load balancing access method shows that when the STAtion (STA) moves in a complex network environment, the proposed method can effectively reduce the “ping-pong effect” of the handover, and reduce the number of network handovers and the handover delay, so as to improve the success rate of network handover. Compared with the traditional strongest signal access method, the proposed method has the number of handover requests reduced by 21.7%, which enhances the stability of network access performance.

Aiming at the problem that the traditional artificial potential field method is easy to fall into trap area and local minimum in the path planning process, an improved artificial potential field method was proposed. Firstly, the concept of safe distance was proposed to avoid unnecessary paths, so as to solve the problems of long path length and long algorithm running time. Then, in order to avoid the robot being trapped in the local minimum and trap area, the predictive distance was introduced into the algorithm, so that the algorithm was able to react before the robot being trapped in the local minimum or trap area. Finally, the robot was guided to avoid the local minimum and trap area by setting the virtual target points reasonably. The experimental results show that, the improved algorithm can effectively solve the problem that the traditional algorithm is easy to fall into the local minimum and trap area. At the same time, compared with those of the traditional artificial potential field method, the path length planned by this proposed algorithm is reduced by 5.2% and its speed is increased by 405.56%.

Concerning insufficient and blind use of the Received Signal Strength Indicator (RSSI) information among unknown nodes, a new RSSI collaborative location algorithm of selecting preference accuracy for Wireless Sensor Network (WSN) was proposed. Firstly, the nodes with high locating accuracy were selected from coarsely located unknown nodes based on the RSSI thresholds. Secondly, subset judgment method was used to seek out the unknown nodes which were less affected by the environment as the second collaboration backbone nodes. Then, based on the positioning errors of the anchor nodes, anchor node replacement criterion was used to further extract the high-precision node from the secondary selected cooperative nodes as the optimal cooperative backbone nodes. Finally, the collaborative backbone nodes were used as the cooperative objects, and the unknown nodes were modified according to the precision priorities. In the simulation experiments, the average localization accuracy of the proposed algorithm was within 1.127 m in 100 m*100 m grids. In terms of locating accuracy, the average locating accuracy of the proposed algorithm is improved by 15% compared with the improved WSN locating algorithm using RSSI model. In terms of time efficiency, compared with the traditional RSSI collaborative location algorithm, the proposed algorithm improves the time efficiency by 20% under the same condition. It can be seen that the proposed algorithm can effectively enhance the locating accuracy, reduce computational complexity and improve time efficiency.

Personalized recommendation as an effective means of information gathering has been successfully applied to e-commerce, music and film and other fields. Most of the studies have focused on the recommended accuracy, lack of consideration of the diversity of recommended results, and neglected the process characteristics of the recommended items in the application area (e. g. "Internet of Things plus E-government"). Aiming at this problem, an e-government recommendation algorithm Combining User Community and Associated Sequence mining (CAS-UC) was proposed to recommend the items most associated with users. Firstly, the static basic attributes and dynamic behavior attributes of the users and items were modeled separately. Secondly, based on the user's historical record and attribute similarity for user community discovery, the user set most similar to the target user was pre-filtered to improve the diversity of the recommended results and reduce the computational amount of the core recommendation process. Finally, the associated sequence mining of the items was taken full account of the business characteristics of e-government, and the item sequence mining with time dimension was added to further improve the accuracy of the recommended results. The simulation experiments were carried out with the information after desensitization of users on the Spark platform of ewoho.com in Wuhu. The experimental results show that CAS-UC is suitable for the recommendation of items with sequence or process characteristics, and has higher recommendation accuracy compared with traditional recommendation algorithms such as cooperative filtering recommendation, matrix factorization and recommendation algorithm based on semantic similarity. The multi-community attribution factor of the user increases the diversity of the recommended results.

In the field of dense crowd scene, in order to improve the defects of present anomaly detection methods in real-time performance and applicability, a real-time method was proposed based on the optical flow feature and Kalman filtering. Firstly, the global optical flow value was extracted as the movement feature. Then the Kalman filtering was used to process the global optical flow value. The residual was analyzed based on the assumption that the residual obeyed a Gauss distribution in normal condition which was validated by the hypothesis testing. Then the parameter of the residual probability distribution was calculated through the Maximum Likelihood (ML) estimation. Finally, under a certain confidence coefficient level, the confidence interval of normal condition and the judgment formula of abnormal condition were obtained, which could be used to detect the abnormal events. The experimental result shows that, for the videos with the size of 320×240, the average detection time of the proposed method can be as low as 0.023 s/frame and the accuracy can reach above 95%. As a result, the proposed method has high detection efficiency and good real-time performance.

In view of the friend recommendation problem in social networks, a friend recommendation algorithm based on the theory of three-degree influence was proposed. The relationships between social network users include not only the mutual friends, but also the other connecting relations with different lengths. By introducing the theory of three-degree influence, the algorithm took all the relationships within three-degree between users into account, while not only considering the number of mutual friends between users as the main basis of the friend recommendation. By assigning corresponding weights to connections with different distances, the strength of friend relationship between users could be calculated, which would be used as the standard for recommendation. The experimental results on Sina microblog and Facebook show that the precision and recall rate of the proposed algorithm are improved by about 5% and 0.8% respectively than that merely based on mutual friends, which indicates the better recommendation performance of the improved recommendation algorithm. It can be helpful for the social platform to improve the recommendation system and enhance the user experience.

To solve the gesture segmentation deviation problem under the interference of other skins and overlapping objects, a method of using depth data and skeleton tracking to segment gesture accurately was proposed. The minimum circumscribed circle, the average and the maximal inscribed circle of convexity defect, were combined to improve the detection of palm and the palm region's radius of various gesture. A fingertip candidate set was got through integrating the finger arc with convex hull, then real fingertips were obtained with three-step filtering. Six gestures have been tested in four transform cases, the recognition rate of flip, parallel, overlapping are all higher than 90% but the rate decreases obviously when tilting more than 70 degree and yawing more than 60 degree. The experimental results show that the accuracy of the proposed method is high in a variety of real scenes.

An evaluation algorithm based on HRank was proposed to evaluate the users' influence in microblog social networking platform. By introducing H parameter which used for judging the scientific research achievements of scientists and considering the user's followers and their microblog forwarding numbers, two new H-index models of followers H-index and microblog-forwarded H-index were given. Both of them could represent the users' static characters and their dynamic activities in microblog, respectively. And then the HRank model was established to make comprehensive assessment on users' influence. Finally, the experiments were conducted on Sina microblog data using the HRank model and the PageRank model, and the results were analyzed by correlation on users' influence rank and compared to the results given by Sina microblog. The results show that user influence does not have strong correlation with the number of fans, and the HRank model outperforms the PageRank model. It indicates that the HRank model can be used to identify users influence effectively.

According to the problem of premature convergence and local optimum in Firefly Algorithm (FA), this paper came up with a kind of multi-group firefly algorithm based on simulated annealing mechanism (MFA_SA), which equally divided firefly populations into many child populations with different parameter. To prevent algorithm fall into local optimum, simulated annealing mechanism was adopted to accept good solutions by the big probability, and keep bad solutions by the small probability. Meanwhile, variable distance weight was led into the process of population optimization to dynamically adjust the "vision" of firefly individual. Experiments were conducted on 5 kinds of benchmark functions between MFA_SA and three comparison algorithms. The experimental results show that, MFA_SA can find the global optimal solutions in 4 testing function, and achieve much better optimal solution, average and variance than other comparison algorithms. which demonstrates the effectiveness of the new algorithm.

In order to improve the real-time response ability of massive data processing, Storm distributed real-time platform was introduced to process data mining, and the Density-Based Spatial Clustering of Application with Noise (DBSCAN) clustering algorithm based on Storm was designed to deal with massive data. The algorithm was divided into three main steps: data collection, clustering analysis and result output. All procedures were realized under the pre-defined component of Storm and submitted to the Storm cluster for execution. Through comparative analysis and performance monitoring, the system shows the advantages of low latency and high throughput capacity. It proves that Storm suits for real-time processing of massive data.

The conflicts between the real-time, efficient intelligent analysis and the inefficient, laborious trouble shooting, which are faced by most of video surveillance systems, can be resolved by Intelligent Video Quality Detection System (IVQDS). As a part of IVQDS, video jitter detection algorithm was focused in this paper. In the proposed method, sparse optical flow features were fused together with interest point matching algorithm; correctly matched point-set which was reliably detected according to the forward-backward error criterion, was used to estimate the global motion parameters, from which motion entropy was computed to measure the motion homogeneity of the video fragment. The experimental results tested on realistic surveillance video records have shown that the proposed algorithm can work under real-time environment against the effects from big movements with high detection performance.

Many current Radio Frequency IDentification （RFID） authentication protocols cannot conform with the EPC Class 1 Gen 2 (EPC Gen2) standards or cannot meet the requirements of low-cost tags for the RFID authentication protocol. A new RFID authentication protocol based on the EPC Class 1 Gen 2 (EPC Gen2) standards was proposed and the security proof was given with BAN logic. After analyzing the security, the proposed protocol can meet the RFID security demands: information confidentiality, data integrity and identity authentication.

For effectively ensuring users' privacy and data security of the Radio Frequency Identification (RFID) system, a new RFID mutual authentication protocol was proposed in this paper, which was designed by the method of combining elliptic curves and Weil pairing. In this protocol, the mutual authentication and anonymous authentication were realized, and the traffic analysis attack, impersonation attack and replay attack were resisted. Compared with the random Hash lock, Hash chain and New-Gen2, this protocol can resist most of the attacks which have been discovered.

The definition of proximate rule was proposed based on the characteristic of text classification rule extraction. Based on the CHI values, the features of text set were selected firstly and feature significance information was provided to the further feature selection. Then rough set was used to select further the attributes on the discrete decision table. Finally precise rules or proximate rules were extracted using rough set theory. The method combined CHI value feature selection and rough set theory fully so as to avoid both feature reduction on a large scale decision table and the discretization of the decision table. The method improved the effectiveness and the practicability of extracting text rule greatly. Experiment results demonstrate the effectiveness of the method.