This paper introduces decimated filter banks for the one-dimensional empirical mode decomposition （1D-EMD）. These filter banks can provide perfect reconstruction and allow for an arbitrary tree structure. Since the ...This paper introduces decimated filter banks for the one-dimensional empirical mode decomposition （1D-EMD）. These filter banks can provide perfect reconstruction and allow for an arbitrary tree structure. Since the EMD is a data driven decomposition, it is a very useful analysis instrument for non-stationary and non-linear signals. However, the traditional 1D-EMD has the disadvantage of expanding the data. Large data sets can be generated as the amount of data to be stored increases with every decomposition level. The 1D-EMD can be thought as having the structure of a single dyadic filter. However, a methodology to incorporate the decomposition into any arbitrary tree structure has not been reported yet in the literature. This paper shows how to extend the 1D-EMD into any arbitrary tree structure while maintaining the perfect reconstruction property. Furthermore, the technique allows for downsampling the decomposed signals. This paper, thus, presents a method to minimize the data-expansion drawback of the 1D-EMD by using decimation and merging the EMD coefficients. The proposed algorithm is applicable for any arbitrary tree structure including a full binary tree structure.展开更多
针对现有的M通道过采样图滤波器组整体性能较差的问题,该文提出一种过采样图滤波器组设计的新算法。在新算法中,分两步来设计图滤波器组。首先,从频谱特性方面考虑来设计分析滤波器,以分析滤波器的通带波纹和阻带能量为目标函数,以3 d ...针对现有的M通道过采样图滤波器组整体性能较差的问题,该文提出一种过采样图滤波器组设计的新算法。在新算法中,分两步来设计图滤波器组。首先,从频谱特性方面考虑来设计分析滤波器,以分析滤波器的通带波纹和阻带能量为目标函数,以3 d B约束为约束条件,通过半正定规划求解出频谱选择性较好的分析滤波器;然后,从完全重构特性方面考虑来设计综合滤波器,以综合滤波器的阻带能量为目标函数,以完全重构条件为约束函数。上述两个约束优化问题都是半正定规划问题,都可有效地求解。新算法综合考虑了滤波器组的重构特性和频率特性,因此可以设计得到整体性能良好的M通道双正交过采样的图滤波器组。仿真对比表明,与已有的设计算法相比,新算法设计所得的图滤波器组具备更小的重构误差。展开更多
基金supported in part by an internal grant of Eastern Washington University
文摘This paper introduces decimated filter banks for the one-dimensional empirical mode decomposition （1D-EMD）. These filter banks can provide perfect reconstruction and allow for an arbitrary tree structure. Since the EMD is a data driven decomposition, it is a very useful analysis instrument for non-stationary and non-linear signals. However, the traditional 1D-EMD has the disadvantage of expanding the data. Large data sets can be generated as the amount of data to be stored increases with every decomposition level. The 1D-EMD can be thought as having the structure of a single dyadic filter. However, a methodology to incorporate the decomposition into any arbitrary tree structure has not been reported yet in the literature. This paper shows how to extend the 1D-EMD into any arbitrary tree structure while maintaining the perfect reconstruction property. Furthermore, the technique allows for downsampling the decomposed signals. This paper, thus, presents a method to minimize the data-expansion drawback of the 1D-EMD by using decimation and merging the EMD coefficients. The proposed algorithm is applicable for any arbitrary tree structure including a full binary tree structure.
文摘针对现有的M通道过采样图滤波器组整体性能较差的问题,该文提出一种过采样图滤波器组设计的新算法。在新算法中,分两步来设计图滤波器组。首先,从频谱特性方面考虑来设计分析滤波器,以分析滤波器的通带波纹和阻带能量为目标函数,以3 d B约束为约束条件,通过半正定规划求解出频谱选择性较好的分析滤波器;然后,从完全重构特性方面考虑来设计综合滤波器,以综合滤波器的阻带能量为目标函数,以完全重构条件为约束函数。上述两个约束优化问题都是半正定规划问题,都可有效地求解。新算法综合考虑了滤波器组的重构特性和频率特性,因此可以设计得到整体性能良好的M通道双正交过采样的图滤波器组。仿真对比表明,与已有的设计算法相比,新算法设计所得的图滤波器组具备更小的重构误差。
