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在电分析化学研究过程中,当被测物浓度很低,采样数据序列常常含有非常严重的白噪声,严重影响检出限和输出波形,同时也给伏安波的进一步分析带来困难。在众多的提高信噪比方法中,以Fourier为基础的数字处理方法得到广泛的研究和应用,它主要把时间域的测量变换成频率域的测量,但是,Fourier方法有几个无法克服的弱点。近几年来,小波分析在工程界和理论界获得了广泛的研究和应用。它的主要特点为:可以把信号按频率直接分解,容易求出信号频域分布状态的时域表示,这种表示方法同时具有时频局部化的特点;另外,在某一段频域范围(小波波谱)的信号,在不了解原始信号的情况下,可选择适合于该曲线特性的小波基,这样能更准确地处理所描述的信号。
In the process of electroanalytical chemistry, when the concentration of the analyte is very low, the sampled data sequence often contains very serious white noise, seriously affecting the detection limit and the output waveform, and also brings difficulties to the further analysis of the voltammetry. Among the many ways to improve the signal-to-noise ratio, the Fourier-based digital processing method has been widely studied and applied. It mainly transforms the time domain measurement into the frequency domain measurement. However, the Fourier method has several insurmountable weaknesses . In recent years, wavelet analysis has gained extensive research and application in engineering and theory. Its main features are: the signal can be directly decomposed according to the frequency, easy to find the time domain representation of the signal frequency domain distribution, this representation method also has the characteristics of time-frequency localization; In addition, in a certain frequency domain range Spectrum) signal, without knowing the original signal, a wavelet base suitable for the characteristics of the curve can be selected so that the described signal can be processed more accurately.