With the extensive application of wideband receivers, the blind detection of non-cooperation burst signal in broadband is increasingly important. It is difficult to detect burst signals with low duty cycle time and to distinguish the burst signals with high duty cycle time from continuous-time signals. The problem was solved by constructing two broadband spectral statistics including maximum spectrum and maximum difference spectrum. By keeping the maximum value of instantaneous spectrum, the maximum spectrum has the information of both burst and non-burst signals; by keeping the maximum value of difference between adjacent instantaneous spectrums, the maximum difference spectrum can extract burst information and suppress continuous-time signals. By using these two spectrums, the detection of burst signals in broadband is completed. The test results show that the proposed algorithm can handle burst signals of all the duty cycle time.

Since baseband processors for Multiple Input and Multiple Output (MIMO) equalization require high throughput and high flexibility, a parallel MIMO detector was proposed for 3GPP-LTE standard based on Software Defined Radio (SDR) methodology, which adopted Single Instruction Multiple Data (SIMD) and Very Long Instruction Word (VLIW) technology to exploit the parallelism on both inter-tone and inner-tone MIMO equalization. Each SIMD lane supported both 16 bit fixed-point and 20 bit floating-point complex vector and matrix operations, met the requirements of power, processing delay and precision for different MIMO configurations. The experimental results show that the proposed MIMO detector is much more efficient and 4×4 matrix inversion rate is up to 95 MInversion/s, which satisfies the requirement of 3GPP-LTE standard. Besides, its programmability and configurability support different algorithms of MIMO equalization.

Focusing on the issue that the communication path selected by random routing algorithm of the onion router (Tor) can not be controlled, thus leading to problems such as the abuse of anonymous techniques and the failure of tracing methods, a Tor anonymous circuit control method based on node failure was proposed. To effectively control the circuit, the fake TCP reset information was sent to mimic the node failure, so that the Tor client would not stop choosing nodes until it selected the controlled ones. The results of theoretic analysis of Tor network path selection algorithm and the real test in a private Tor network composed of 256 onion routers demonstrate the effectiveness of the proposed approach. Compared with traditional methods which deploy high bandwidth routers to attract users to select the controlled nodes, the proposed method can improve the probability of choosing controlled entry node from 4.8% to about 60%, when entry guard was generally enabled by Tor client by default. The results also show, as the length of a controlled path increases, the success rate of building path decreases. Therefore the proposed method is suitable for controlling short paths.

According to the characteristics of traditional multivariate linear regression method for long processing time and limited memory, a parallel multivariate linear regression forecasting model was designed based on MapReduce for the time-series sample data. The model was composed of three MapReduce processes which were used to solve the eigenvector and standard orthogonal vector of cross product matrix composed by historical data, to forecast the future parameter of the eigenvalues and eigenvectors matrix, and to estimate the regression parameters in the next moment respectively. Experiments were designed and implemented to the validity effectiveness of the proposed parallel multivariate linear regression forecasting model. The experimental results show multivariate linear regression prediction model based on MapReduce has good speedup and scaleup, and suits for analysis and forecasting of large data.

To improve the resource utilization, the task resource requirement features of processor, network, disk and so on were efficiently estimated through analyzing the way of the task request, measuring the performance of application program in task or simulating to run the task. Afterwards, according to the features of resource requirement, the tasks could be classified into processor bound task, communicate bound task, disk bound task and others. And then the classified tasks were integrated with specific virtual machines to make all kinds of resources to be used efficiently. The research shows that the method can classify the task efficiently. And compared to unclassified method, it can reduce the times of virtual machines migration or integration.