| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Parana, Dept Fis, BR-81531980 Curitiba, PR - Brazil
[2] Univ Fed Sao Paulo, Inst Ciencia Tecnol ICT, BR-12231280 Sao Jose Dos Campos, SP - Brazil
[3] Inst Nacl Pesquisas Espaciais, Lab Associado Comp & Math Aplicads, BR-12227010 Sao Jose Dos Campos, SP - Brazil
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | European Physical Journal-Special Topics; v. 230, n. 14-15, p. 2737-2744, OCT 2021. |
| Web of Science Citations: | 1 |
| Abstract | |
Recurrence microstates can be used to analyze many properties of stationary states of stochastic and deterministic time series, including the level of correlation of stochastic signals. Here, we show how artificially inserted data (data that does not belong to a original stationary signal) may be detected using recurrence microstates statistics. We show that the method is sensitive enough to detect the breaking of the stationary signal even when the corrupted inserted data span into the same domain of the original data. Examples of our analyses are applied to two numerically generated time series of dynamical systems, namely the logistic map, and the Lorenz equations. Finally to show results applied to experimental time series, we analyze a digital audio signal of a human speech. (AU) | |
| FAPESP's process: | 15/50122-0 - Dynamic phenomena in complex networks: basics and applications |
| Grantee: | Elbert Einstein Nehrer Macau |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/03211-6 - Non linear dynamics |
| Grantee: | Iberê Luiz Caldas |
| Support Opportunities: | Research Projects - Thematic Grants |