Aiming at the problem of influence of the channel on the characteristics of the vowel formant， a systematic experiment was carried out. Firstly， the standard recordings of 8 volunteers were collected. Then， the standard recordings were played with the mouth simulator， and 104 channel recordings were recorded using 13 different channels. Finally， the characteristic voice segments were extracted， and chi-square test analysis was used in the qualitative analysis of the spectral characteristics， and one-sample t-test was used in the quantitative analysis of acoustic parameters. The statistical results show that about 69% of the channels have a significant influence on the overall form of the high-order formants， and about 85% of the channels have significant differences in the relative intensity of the formants. The one-sample t-test results show that there is no significant difference between the standard recordings and the channel recordings in center frequency of the formant. Experimental results show that the frequency characteristics of formants should be paid more attention to when processing the identification of voices in different channels.

Since the small combined stock market has less noise information, a random matrix denoising method amended by Monte Carlo simulation was proposed. Firstly, random matrix was generated by simulation; secondly, the lower and upper bounds of the noise were corrected simultaneously by using a large number of simulated data; finally, the range of noise was determined precisely. The Dow Jones China 88 Index and the Hang Seng 50 Index were used for empirical analysis. The simulation results show that, compared with LCPB (Laloux-Cizeau-Potters-Bouchaud), PG+(Plerou-Gopikrishnan) and KR (RMT denoising method based on correlation matrix eigenvector's Krzanowski stability), rationality and validity of the noise range corrected by Monte Carlo simulation denoising method are greatly improved in eigenvalue, eigenvector and inverse participation ratio. Investment portfolio of the correlation matrix before and after denoising was given, and the results indicate that the Monte Carlo simulation denoising method has the smallest value at risk under the same expected rate of return, which can provide a certain reference for the portfolio selection, risk management and other financial applications.

Because the base stations can only be placed at the border of the monitored area, the border placement problem was formally defined. For the goal to place the minimum number of base stations to cover as much as possible the monitored areas, an improved placement algorithm with polynomial time was proposed. The coverage percentage of initial algorithm was analyzed first. When initial coverage percentage is larger than guaranteed coverage percentage, it is possible to reduce the size of initial placement set. Finally, placement set was gradually improved to achieve the minimun of placement set. The results indicate that the coverage percentage and placement set of the proposed algorithm are superior to random algorithm in different test environments.

Coverage problem is one of the most fundamental problems in wireless sensor networks since it reflects the sensing quality. The present studys mostly concentrates in Omni-directional sensors which is not suitable in many applications such as video surveillance systems consisting of directional video sensors. This paper present a new (k,ω)-angle coverage problem which study directional sensors deployment. The goal is to deploy minimal number of sensors to k-angle cover all the targets. It present a greedy algorithm to solve this problem. For this algorithm, it define three contribution functions to determine the location to deploy sensor. The proposed method greedily selects a maximal contribution location to deploy a sensor until the entire targets are k-angle covered. Simulation results exhibit the characteristic and performance of this algorithm.

To extract good background image from complex traffic scenes, a comprehensive dynamic background updating method was proposed. It used the information of both frame-difference and high-level object state to update the mixed Gaussian background model. It could avoid the effect of the prolonged traffic standstill on the background and eliminate the ghosts caused by the move-away of background objects. The results of experiment on the real-time traffic video show that the method is self-adaptive and robust.