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One-dimensional smoothing using extreme envelope average

机译：使用极限包络平均的一维平滑

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In this paper, a new de-noising technique called extreme envelope average (EEA) is presented. This technique is related to the energy reduction of the noisy data throughout the successive averages between the maximum and minimum extreme envelopes. The basic principle is simple and is based on the central tendency of the extreme averages after a specific number of sets of the process. Important structures of the signal, representing low-frequency components, are maintained. It is a very fast convergence method, requires a unique noisy data, and presents satisfactory results. There are no constraints about the linearity, stationary or harmonic content in relation to of the test signal, that make it a useful approach for signal that presenting abrupt changes along of its curvature. And, it can also be used for non-equally-spaced data. The present study is limited to signals numerically corrupted by white Gaussian noise with zero mean.
机译：在本文中，提出了一种新的降噪技术，称为极端包络平均（EEA）。此技术与最大和最小极端包络线之间连续平均中噪声数据的能量减少有关。基本原理很简单，并且基于特定数量的过程集之后的极端平均值的集中趋势。代表低频分量的信号的重要结构得以保持。这是一种非常快速的收敛方法，需要独特的噪声数据，并且显示出令人满意的结果。对于与测试信号有关的线性，平稳或谐波含量没有任何限制，这使其成为一种有用的方法，用于显示沿曲率突然变化的信号。并且，它也可以用于非等距数据。本研究仅限于在信号上被零高斯白高斯噪声数值破坏的信号。

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