A lossless denoising algorithm for high dimensional data measured in EML

摘要：

　　Fast and low noise measurement are essential demands in EML experiments, such as magnetic field, voltage, current, speed, spectrum and so on.But the huge transient magnetic field, current and nonlinear responsibility of load (EML) make the measurement results always unexpectedly noisy.We report the discovery that performance of measured high-dimensional data can be improved by a reversible transformation, self-multiplexing.By multiplexing the positive correlational adjacent elements, the self-multiplexing could improve the SNR (signal-to-noise ratio) of the data.It is experimentally show that the transformations could work fine along with other denoising algorithms,to be a great potential method for further performance boost of high-dimensional data.

关键词： Reversible transformation Lossless Denoising High-dimensional data

作者: Jiang YUE Long LI Hui TIAN Gang WAN Yong JIN Hong-e LUO Bao-ming LI

作者单位: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing,

会议类型: 国际会议

会议名称: 2018 International Conference on Defence Technology (2018国际防务技术会议)

会议地点: 北京

会议语种:英文

页码: 103-105

在线出版日期: 2018-10-21（万方平台首次上网日期，不代表论文的发表时间）