Abstract:Fluorescence analysis is an important means of detecting mineral oil in water pollutants because of high sensitivity, selectivity, ease of design, etc. Noise generated from Photo detector will affect the sensitivity of fluorescence detection system, so the elimination of fluorescence signal noise has been a hot issue. For the fluorescence signal, due to the length increase of the branch set, it produces some boundary issues. The dbN wavelet family can flexibly balance the border issues, retain the useful signals and get rid of noise, the de-noising effects of dbN families are compared, the db7 wavelet is chosen as the optimal wavelet. The noisy fluorescence signal is statically decomposed into 5 levels via db7 wavelet, and the thresholds are chosen adaptively based on the wavelet entropy theory. The pure fluorescence signal is obtained after the approximation coefficients and detail coefficients quantified by thresholds reconstructed. Compared with the DWT, the signal de-noised via SWT has the advantage of information integrity and time translation invariance.
王玉田,程朋飞, 侯培国*,杨 哲 . 静态小波变换荧光检测水中矿物油信号的去噪方法 [J]. 光谱学与光谱分析, 2015, 35(05): 1286-1289.
WANG Yu-tian, CHENG Peng-fei, HOU Pei-guo*, YANG Zhe . A De-Noising Algorithm for Fluorescence Detection Signal of Mineral Oil in Water by SWT . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(05): 1286-1289.
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