The Study Of Wavelet Analyzing For Coal Thickness Detection Signal Based On DSP

This thesis mainly studies two problems, one is to deduce matrix expression of fast wavelet algorithm that suits to advance computer language C/C++ programming based on wavelet multi-resolution analysis; another is to run fast wavelet algorithm on high-speed digital processor TMS320C5402 and analyse and process fact measure coal thickness signal in simulator of integrated development environment CCS for meeting real-time demand for coal thickness measure. The concrete content is summarized as follows:First, according to analyse the basic theory of top coal thickness detecting and coal thickness detecting signal, we draw the conclusion that in theory we can use sound wave upright reflecting method to judge coal thickness availably. Through comparing for FFT phase- frequency drawing and wavelet time-frequency analysis drawing of fact measure coal thickness signal, and according to the characteristic of elastic wave propagating in coal rock, we can judge the first arriving point of reflecting wave which is included in sound wave only using time- frequency method. We provide the feasibility of wavelet multi-resolution analysis in coal thickness signal analysis. On the basis of wavelet multi-resolution analysis, we analysed fast wavelet algorithm-Mallat algorithm in detail. For implementing Mallat algorithm in computer, we deduce the matrix expression of fast wavelet algorithm possessing the characteristic of convolution operation and signal cycle expandability that suits to computer advanced language programming, and we program the algorithm using advanced language C/C++. We adopt module programming method in the process of programming, the procedure has three modules: reading data module, fast wavelet decompose module and fast wavelet reconstruct module.Secondly, In order to operate the fast wavelet algorithm in the hardware in the coal thickness detector and meet real-time character demand for coal thickness detecting signal processing, we select the high speed digital signal processor TMS320C5402 chip as the key part of fast wavelet algorithm operation. This thesis introduces the hardware construct traits and the unique assembly instruction system and the integrated developing environment (CCS2)...