[1]李圣普,王小辉,时合生.基于阈值的Mallat变换法的设计与仿真[J].计算机技术与发展,2014,24(04):107-109.
　LI Sheng-pu,WANG Xiao-hui,SHI He-sheng.Design and Simulation of Mallat Transform Method Based on Threshold[J].,2014,24(04):107-109.
点击复制

基于阈值的Mallat变换法的设计与仿真()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年04期
页码:
107-109
栏目:
智能、算法、系统工程
出版日期:
2014-04-30

文章信息/Info

Title:
Design and Simulation of Mallat Transform Method Based on Threshold
文章编号:
1673-629X（2014）04-0107-03
作者:
李圣普; 王小辉; 时合生
平顶山学院 计算机科学与技术学院
Author(s):
LI Sheng-pu; WANG Xiao-hui; SHI He-sheng
关键词:
小波分析; Mallat法; SURF阈值法; 信号去噪
Keywords:
wavelet analysis; Mallat method; SURF threshold; signal denoising
分类号:
TP391.9
文献标志码:
A
摘要:
小波分析对于信号处理具有十分重要的作用。在已知噪声频率范围情况下使用传统Mallat小波变换方法对信号处理效果较好，但这种方法无法消除信号中的大量未知白噪声。文中讨论在Mallat变化法基础上引入SURF阈值，对小波进行过滤处理，实现有效过滤白噪声。文中设计了基于SURF阈值改进的Mallat变换法，并在Matlab环境下进行去噪实验，通过将实验结果与单一Mallat小波变换法结果进行对比，结果显示改进后的Mallat小波变换法可以去除大量白噪声，使信号更加光滑、保真。
Abstract:
Wavelet analysis has plays a very important role for the signal processing. Mallat wavelet transform method can be better signal processing in the case of the noise frequency range are known,but not eliminate the unknown white noise signal. Introduce SURF thresh-old based on Mallat transform method for wavelet filter processing,which can effectively filter white noise. Improved Mallat transform method is designed based on the SURF threshold,and carry out the denoising experiment under the Matlab environment. Compared the experiment results with a single Mallat wavelet transformation method results,indicate that improved Mallat wavelet transform method can remove a lot of white noise,making the signal smoother.

相似文献/References:

[1]王西锋 高岭 张晓孪.自相似网络流量预测的分析和研究[J].计算机技术与发展,2007,(11):42.
　WANG Xi-feng,GAO Ling,ZHANG Xiao-luan.Analysis and Research on Self-Similar Network Traffic Forecast[J].,2007,(04):42.
[2]淮文军 王明芳 汪梅[].基于小波分析的电缆故障特征提取方法研究[J].计算机技术与发展,2007,(11):209.
　HUAI Wen-jun,WANG Ming-fang,WANG Mei.Cable Fault Feature Extraction Method Research Based on Wavelet Analysis[J].,2007,(04):209.
[3]韩志艳,伦淑娴,王健.基于遗传小波神经网络的语音情感识别[J].计算机技术与发展,2013,(01):75.
　HAN Zhi-yan,LUN Shu-xian,WANG Jian.Speech Emotion Recognition Based on Genetic Wavelet Neural Network[J].,2013,(04):75.
[4]谈苗苗,成孝刚,周凯,等. 基于ARIMA和灰色模型加权组合的短期交通流预测[J].计算机技术与发展,2016,26(11):77.
　TAN Miao-miao,CHENG Xiao-gang,ZHOU Kai,et al. Short-term Traffic Flow Forecasting Based on Combination of ARIMA and Gray Model[J].,2016,26(04):77.
[5]杨祎玥[],伏潜[],万定生[]. 基于深度循环神经网络的时间序列预测模型[J].计算机技术与发展,2017,27(03):35.
　YANG Yi-yue[],FU Qian[],WAN Ding-sheng[]. A Prediction Model for Time Series Based on Deep Recurrent Neural Network[J].,2017,27(04):35.
[6]温金玉,宣士斌,黄亚武,等.多尺度多类中性模糊聚类图像分割算法[J].计算机技术与发展,2019,29(07):65.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 013]
　WEN Jin-yu,XUAN Shi-bin,HUANG Ya-wu,et al.Image Segmentation Algorithm Based on Multi-scale Feature and Multi-class of Fuzzy Mean Clustering[J].,2019,29(04):65.[doi:10. 3969 / j. issn. 1673-629X. 2019. 07. 013]

更新日期/Last Update: 1900-01-01