...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Signal Processing, IET >Denoising method for capillary electrophoresis signal via learned tight frame
【24h】

Denoising method for capillary electrophoresis signal via learned tight frame

机译：毛细管电泳信号通过学习紧密框架的去噪方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Since capillary electrophoresis (CE) signals are always contaminated by random noise, which has negative influence on the accuracy of detection and analysis, it is necessary to remove noise before further applications of the CE signals. In this study, a tight frame learned from the data itself is applied to the removal of noise for CE signals. To achieve an effective decomposition of the CE signal, a one-dimensional discrete tight frame tailored to the input signal is first constructed by introducing tight frame constraint into the popular dictionary learning model. Then, due to each subband containing different information of the noise, an adaptive threshold is computed to shrink the detail coefficients instead of using a global threshold. Finally, the denoised CE signal is reconstructed from the thresholded coefficients by using the inverse transform of the tight frame. To evaluate the denoising efficiency, the proposed method is applied to the simulated CE signals and real CE signals. Experimental results indicate that compared with other denoising methods, the proposed method obtains a better shape preservation of the peaks as well as a higher signal-to-noise ratio.
机译：由于毛细管电泳（CE）信号始终被随机噪声污染，因此对检测和分析的准确性产生负面影响，因此必须在CE信号进一步应用之前去除噪声。在该研究中，从数据本身学习的紧密框架被应用于去除CE信号的噪声。为了实现CE信号的有效分解，首先通过将紧密的帧约束引入普遍的字典学习模型来构建对输入信号定制的一维离散紧密框架。然后，由于包含噪声的不同信息的每个子带，计算自适应阈值以缩小细节系数而不是使用全局阈值。最后，通过使用紧密框架的逆变换，从阈值的系数重建去噪CE信号。为了评估去噪效率，所提出的方法应用于模拟CE信号和真实CE信号。实验结果表明，与其他去噪方法相比，所提出的方法获得了更好的峰值保存以及更高的信噪比。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号