Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function

Yuan Zhen ZHOU Jian Bin ZHENG

引用本文: Yuan Zhen ZHOU,  Jian Bin ZHENG. Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function[J]. Chinese Chemical Letters, 2006, 17(3): 380-382. shu
Citation:  Yuan Zhen ZHOU,  Jian Bin ZHENG. Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function[J]. Chinese Chemical Letters, 2006, 17(3): 380-382. shu

Fourier Self-deconvolution Using Approximation Obtained from Frequency Domain Wavelet Transform as a Linear Function

  • 基金项目:

    This work was supported by the National Natural Science Foundation of China (No. 20275030) and the Natural Science Foundation of Shaanxi Province in China (No. 2004B20).

摘要: A new method of resolving overlapped peak, Fourier self-deconvolution (FSD) using approximation CN obtained from frequency domain wavelet transform of F(ω) yielded by Fourier transform of overlapped peak signals f(t) as the linear function, was presented in this paper.Compared with classical FSD, the new method exhibits excellent resolution for different overlapped peak signals such as HPLC signals, and have some characteristics such as an extensive applicability for any overlapped peak shape signals and a simple operation because of no selection procedure of the linear function. Its excellent resolution for those different overlapped peak signals is mainly because F(ω) obtained from Fourier transform of f(t) and CN obtained from wavelet transform of F(ω) have the similar linearity and peak width. The effect of those fake peaks can be eliminated by the algorithm proposed by authors. This method has good potential in the process of different overlapped peak signals.

English

  • 加载中
计量
  • PDF下载量:  0
  • 文章访问数:  0
  • HTML全文浏览量:  0
文章相关
  • 收稿日期:  2005-08-22
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

返回文章