Due to the loose organization of crowdsourcing, crowdsourcing workers have deceptive behaviors in the process of completing tasks. How to identify the deceptive behaviors of workers and reduce their impact, thus ensuring the completion quality of crowdsourcing tasks, has become one of the research hotspots in the field of crowdsourcing. Based on the evaluation and analysis of the task results, a Weight Setting Algorithm Based on Generalized Pareto Distribution (GPD) (WSABG) was proposed for the unified type deceptive behaviors of crowdsourcing workers. In the algorithm, the maximum likelihood estimation of GPD was performed, and the dichotomy was used to approximate the zero point of the likehood function in order to calculate the scale parameter σ and shape parameter ε. A new weight formula was defined, and an absolute influence weight was given to each worker according to the feedback data of the crowdsourcing workers to complete the current task, and finally the GPD-based crowdsourcing worker weight setting framework was designed. The proposed algorithm can solve the problem that the difference between the task results data is small and the data are easy to be centered on the two poles. Taking the data of Yantai University students' evaluation of teaching as the experimental dataset, with the concept of interval transfer matrix proposed, the effectiveness and superiority of WSABG algorithm are proved.

In order to solve the problem of high-precise indoor positioning calculation using received signal strength, a novel WMKF (Kalman Filtering and Weighted Median) positioning algorithm was proposed. The algorithm was different from previous indoor localization algorithms. Firstly, Kalman filter method was used to smooth random error, and weighted median method was made to reduce the influence of gross error, then the log distance path loss model was used to obtain the decline curve and calculate the estimated distance. Finally, the centroid method was used to get the position of the target node. The experimental results show that, this WMKF algorithm initially improve that the poor stability of positioning in a relatively complex environment, and effectively enhanced the positioning accuracy, making the accuracy between 0.81m to 1m.

Automatic extraction of brain is an important step in the preprocessing of brain internal analysis. To improve the extraction result, a modified Brain Extraction Tool (BET) and hybrid level set model based method for automatic brain extraction was proposed. The first step of the proposed method was obtaining rough brain boundary with the improved BET algorithm. Then the morphological expansion was operated on the rough brain boundary to initialize the Region of Interest (ROI) where the hybrid active contour model was defined to obtain a new contour. The ROI and the new contour were iteratively replaced until the accurate brain boundary was achieved. Seven Magnetic Resonance Imaging (MRI) volumes from Internet Brain Segmentation Repository (IBSR) website were used in the experiment. The proposed method achieved low average total misclassification ratio of 7.89%. The experimental results show the proposed method is effective and feasible.

In order to protect Android platforms from the application-level privilege escalation attacks, this paper analyzed the XManDroid access control model, which has better ability on fighting these attacks, especially the collusion attack on the covert channel. To address the problem that XManDroid could not detect the multi-application and multi-permissions collusion attacks, this paper proposed an improved mandatory access control model which recorded the communication history of applications by building an IPC links colored diagram. At last, the test result on the prototype system show that the new model can solve the problem in the XManDroid well.

In order to reduce the complexity of the reconstruction algorithm and improve the precision of estimation, the authors proposed a new Sparsity Adaptive Matching Pursuit (SAMP) algorithm by using the adaptive threshold applied in the OFDM (Orthogonal Frequency Division Multiplexing) sparse channel estimation. The Monte Carlo simulation results show that, compared with the traditional method, the CPU run time decreased by 44.7%. And in lower SNR (SignaltoNoise Ratio), the performance achieved obvious improvements. Besides, in OFDM sparse channel estimation, a new design of pilot pattern was presented based on the mutual coherence of the measurement matrix in Compressive Sensing (CS) theory. The Monto Carlo simulation results show that, the precision of channel is increased by 2-4 dB with the new pilot pattern.

To deal with the problems of low accuracy and local convergence in conventional Particle Swarm Optimization (PSO) algorithm, the chaos algorithm and cloud model algorithm were introduced into the evolutionary process of PSO algorithm and the chaos cloud model particle swarm optimization (CCMPSO) algorithm was proposed. The particles were divided into excellent particles and normal particles when CCMPSO was in convergent status. To search the global optimum location, the cloud model algorithm as well as excellent particles was applied to local refinement in convergent area, meanwhile chaos algorithm and normal particles were used to global optimization in the outside space of convergent area. The convergence of CCMPSO was analyzed by eigenvalue method. The simulation results prove the CCMPSO has better optimization performance than other main PSO algorithms.

Based on LEACH, we proposed a novel clustering algorithm Centroidbased Distributed Clustering Scheme（CDCS） for Wireless Sensor Networks（WSNs）. In CDCS, each sensor firstly decided whether it was local tentative clusterheads on its own at any given time with a certain probability popt. The tentative clusterhead computed the centroid of cluster based on information of sensors within cluster; and then dynamically adjusted the structure of cluster, so that the total energy dissipation within the cluster was minimized. Theoretical analysis and simulation results show that CDCS prolong the lifetime of a sensor network by 32%~38% over that of LEACH in different scenes while still maintaining the simplicity of LEACH.