Detecting signals from data with noise: Theory and applications

NCUIR > Research Center > Cognitive Intelligence & Precision Healthcar > journal & Dissertation > Item 987654321/107786

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107786

Title:	Detecting signals from data with noise: Theory and applications
Authors:	黃鍔;Chen, Xianyao;Wang, Meng;Zhang, Yuanling;Feng, Ying;Wu, Zhaohua;Huang, Norden E.
Contributors:	認知智慧與精準健康照護研究中心
Keywords:	Ambient noise;Atmospherics;Background noise;Data analysis;Decomposition;Earth, ocean, space;Economic models;Empirical analysis;Exact sciences and technology;External geophysics;Fourier analysis;Hypotheses;Interpolation;Meteorology;Noise;Null hypothesis;Oscillations;Physics of the high neutral atmosphere;Probability distribution;Resampling;Sampling;Sea surface;Sea surface temperature;Signal detection;Spectrum analysis;Statistical methods;Statistical significance;Stochastic processes;Studies;Surface temperature;Theory;Time series
Date:	2013-05-01
Issue Date:	2026-04-23 14:26:01 (UTC+8)
Publisher:	American Meteorological Society;Boston, MA: American Meteorological Society
Abstract:	摘要： Signal detection from noisy data by rejecting a noise null hypothesis depends critically on a priori assumptions regarding the background noise and the associated statistical methods. Rejecting one kind of noise null hypothesis cannot rule out the possibility that the detected oscillations are generated from the stochastic processes of another kind. This calls for an adaptive null hypothesis based on general characteristics of the noise that is present. In this paper, a new method is developed for identifying signals from data based on the finding that true physical signals in a well-sampled time series cannot be destroyed or eliminated by resampling the time series with fractional sampling rates through linear interpolation. Therefore, the significance of signals could be tested by checking whether the signals persist in the true time–frequency spectral representation during resampling. This hypothesis is based on the general characteristics of noise as revealed by empirical mode decomposition, an adaptive data analysis method without linear or stationary assumptions, and without any predefinition of the background noise. Applications of this method to synthetic time series, solar spot number, and sea surface temperature time series illustrate its power in identifying characteristics of background noise without any a priori knowledge. 出版者： Boston, MA: American Meteorological Society 出版日期： 2013-05-01 出處： Journal of the atmospheric sciences, 2013-05, Vol.70 (5), p.1489-1504 資源來源： EBSCOhost OmniFile Full Text Select 版權： 2014 INIST-CNRS 版權： Copyright American Meteorological Society May 2013 版權： Copyright American Meteorological Society 2013 識別號： ISSN: 0022-4928 識別號： EISSN: 1520-0469 識別號： DOI: 10.1175/JAS-D-12-0213.1 識別號： CODEN: JAHSAK
Appears in Collections:	[Cognitive Intelligence & Precision Healthcar] journal & Dissertation

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