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中文摘要：
现有1维信号趋势项提取算法效率低、并且缺乏适应性和灵活性。该文提出基于多尺度极值的1维信号趋势项快速提取方法,充分利用时间序列信号极值点信息,建立信号极值点的二叉树结构,避免了传统经验模式分解（EMD）方法逐层筛选求取内蕴模式函数（IMF）分量的耗时过程,在获得与现有方法趋势项提取精度相当的情况下,极大地提高了计算速度,并且可以直接提取不同层次的趋势。仿真和实际数据实验结果表明：与传统EMD趋势分解方法和趋势滤波方法相比较,计算速度可提高1到2个数量级。
英文摘要：
Current 1D signal trend extracting methods have such disadvantages as low efficiency,poor flexibility and so on.To overcome these problems,a new method of 1D signal fast trend extracting based on multi-scale extrema is proposed.By making full use of time sequence extrema information to establish a binary tree of multi-scale extrema,it avoids the time-consuming process of obtaining Intrinsic Mode Functions（IMFs） via iteratively sifting in traditional Empirical Mode Eecomposition（EMD） method.While obtaining similar results,it greatly improves the computation speed,and it could extract the trend of different scales directly.Simulated and practical signal experiments demonstrates the effectiveness of this approach.By comparing with traditional EMD method and trend filtering method,the results show that the approach could achieve 1 or 2 order of magnitude speedups.
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Improved bi-dimensional empirical mode decomposition based on 2D-assisted signals: analysis and appl
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