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Low Resource-Consuming Cyber-physical System Sampling Method using Bernoulli Circulant Matrix

机译：使用Bernoulli循环矩阵的低资源消耗网络系统采样方法

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Cyber-physical System (CPS) have a high requirement on real-time property, and it is difficult to improve the sampling efficiency base on traditional sampling theory. In this paper, the compression sensing (CS) theory is applied to the sampling compression process of CPS system. The CS theory was used to the sampling compression method of CPS system. The Bernoulli circulant matrix, which is easy to be realized and stored, and its construction algorithm were designed to simplify the realization of CS theory in CPS. It is concluded that for random data set, the compression ratio increases from 14.06 % to 42.18 % and the reconstruction error decreases from 27.65 to 1.28 with increasing repetition times. Note that the sampling time are around tens of microseconds and the reconstruction time are around several milliseconds, which indicates a high real-time performance for CPS. In addition, for image data set, the compression ratios are about 42.90 % which indicates a high compression ratio and huge storage resources saving. More importantly, the sampling time and reconstruction time are only several microseconds and several seconds respectively, which indicates a high real-time performance for CPS.
机译：网络物理系统（CPS）对实时性质具有很高的要求，很难改善传统采样理论的采样效率基础。在本文中，压缩感应（CS）理论应用于CPS系统的采样压缩过程。 CS理论用于CPS系统的采样压缩方法。伯努利循环矩阵易于实现和存储，其施工算法旨在简化CPS中CS理论的实现。结论是，对于随机数据集，压缩比从14.06 ％增加到42.18 ％，重建误差从27.65降至1.28，随着重复时间的增加。请注意，采样时间围绕着数十秒左右，重建时间约为几毫秒，这表示CPS的高实时性能。另外，对于图像数据集，压缩比率为约42.90 ％，表示高压缩比和巨大的存储资源。更重要的是，采样时间和重建时间仅为几微秒和几秒，这表示CPS的高实时性能。

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