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A method to automatically determine the level-crossing thresholds in non-uniform sampling and processing

机译:一种自动确定非均匀采样和处理中的交叉阈值的方法

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Data processing is usually based on uniformly sampled data in time. This sampling scheme is often unnecessary for non-stationary signals because samples are also taken in inactive regions. In case of embedded system, this useless samples significantly increase the power consumption. One solution to avoid this useless power consumption is the level crossing sampling scheme (LCSS). This method is not taking samples according to the time but according to amplitudes, therefore data are not sampling during inactive region. To optimize LCSS, thresholds need to be appropriately placed along the amplitude range of the input signal. In this paper, we present an algorithm to determine automatically these thresholds. This algorithm has been tested on experimental biomedical signals. We demonstrate that we could ensure the information and in the same time drastically decrease the number of processed samples.
机译:数据处理通常基于及时的统一采样数据。对于非平稳信号来说,这种采样方案通常是不必要的,因为样本也是在非活动区域中进行的。对于嵌入式系统,这种无用的采样会大大增加功耗。避免这种无用功耗的一种解决方案是电平交叉采样方案(LCSS)。该方法不是根据时间而是根据幅度进行采样,因此,在非活动区域期间不对数据进行采样。为了优化LCSS,需要在输入信号的幅度范围内适当放置阈值。在本文中,我们提出了一种自动确定这些阈值的算法。该算法已在实验性生物医学信号上进行了测试。我们证明了我们可以确保信息,同时大大减少了处理样本的数量。

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