As an important part of social network analysis, Weibo user influence analysis has been concerned by researchers all the time. Concerning the timeliness shortage and neglect of the relevance between users and topics when analyzing user behaviors, a user influence analysis algorithm for Weibo topics, named Topic and Spread user Rank (TSRank), was proposed. Firstly, based on Weibo topics, the timeliness of user's forwarding behavior was analyzed to construct two topic forwarding networks, user forwarding and user blog forwarding, in order to predict the user's topic information dissemination capability. Secondly, the text contents of user's personal history Weibo and background topic Weibo were analyzed to mine the relevance between user and background topic. Finally, the influence of Weibo user was calculated by comprehensively considering user's topic information dissemination capability and relevance between user and background topic. The experiments on crawled real topic data of Sina Weibo were conducted. The experimental results show that the topic forwarding number of users with higher topic correlation is significantly greater than that of users with lower topic correlation. Compared with no forwarding timeliness, the Catch Ratio (CR) of TSRank algorithm is increased by 18.7%, which is further compared with typical influence analysis algorithms, such as WBRank, TwitterRank and PageRank, TSRank algorithm improves the precision and recall by 5.9%, 8.7%, 13.1% and 6.7%, 9.1%, 14.2% respectively, which verifies the effectiveness of TSRank algorithm. The research results can support theoretical research of social attributes and topic forwarding of social networks as well as the application research of friend recommendation and public opinion monitoring.

Iterative Shrinkage Thresholding Algorithm (ISTA) often uses fixed iteration step to solve the dynamic optimization problem of depth from defocus, which leads to poor convergence efficiency and low accuracy of reconstructed microscopic 3D shape. A method based on gradient estimation of acceleration operator and secant linear search, called Fast Linear Iterative Shrinkage Thresholding Algorithm (FL-ISTA), was proposed to optimize ISTA. Firstly, the acceleration operator, which consists of the linear combination of the current and previous points, was introduced to reestimate the gradient and update the iteration point during each iteration process. Secondly, in order to change the restriction of the fixed iteration step, secant linear search was used to determine the optimal iteration step dynamically. Finally, the improved algorithm was applied to solve the dynamic optimization problem of depth from defocus, which accelerated the convergence of the algorithm and improved the accuracy of reconstructed microscopic 3D shape. Experimental results of reconstructed standard 500 nm grid show that compared with ISTA, FISTA (Fast ISTA) and MFISTA (Monotohy FISTA), the efficiency of FL-ISTA was improved and the depth from defocus decreased by 10 percentage points, which is closer to the scale of standard 500 nm grid. Compared with ISTA, the Mean Square Error (MSE) and average error of microscopic 3D shape reconstructed by FL-ISTA were decreased by 18 percentage points and 40 percentage points respectively. The experimental results indicate that FL-ISTA can effectively improve the convergence rate of solving the dynamic optimization problem of depth from defocus and elevate the accuracy of the reconstructed microscopic 3D shape.

In order to eliminate the influence of varying correlation of Wireless Sensor Network (WSN) data caused by transmission in the performance of the current Compressed Sensing (CS) reconstruction algorithms, a correlation adaptive reconstruction algorithm for network data was proposed. Firstly, the iterative algorithm was used to estimate the correlation of the date to be reconstructed, then two-step correlation of support set were used for coordinating the non-zero value in sparse coefficient vector, and eventually a more precise reconstruction of data was realized. The simulation result shows that this algorithm can effectively restrain the effect of noises in WSN data reconstruction and improve the accuracy of reconstruction under varying correlation.