基于小波变换的二氧化硫污染气体检测

Research on the Detecting of the Sulfur Dioxide Polluted Gas Based on Wavelet Transform

目的提出一种采用小波变换的Mallet分解算法,解决二氧化硫污染气体检测中消除各种复杂的噪声问题.方法从小波变换特性出发,将信号用Mallat分解算法分解为不同频带信息.在信号检测及去噪过程中,设定一个检测信息频带,使频率处于此频带的信息分离出来,而其他空间向量全部置为零.结果通过将小波变换去噪技术应用于荧光信号的滤波处理,可使埋于噪声中的荧光信号有效地测量出来.结论该信号处理方法可达到有用频带信号检测而其他频率信号去除的作用,为精确确定二氧化硫气体浓度提供了良好的基础.

Wavelet transform and the Mallat decomposing algorithm are presented in this paper, with which a variety of noises produced in the process of the sulfur dioxide polluted gas detecting are removed. Owing to the characteristic of wavelet transform, the detecting signal could be decomposed into different frequency bands by the Mallat decomposing algorithm. In the process of the efficient signal detecting and denoising, a measuring frequency band was set up, and the information within this band was separated and other space vectors were set to zero. So, the useful information with the frequency could be extracted while the rest were removed. The application of wavelet transform technology to the fluorescence signal filter extracted the fluorescence signal buried in noises efficiently. The method provides a better foundation for the accurate consistence determination of the sulfur dioxide.