.png){kind=icon}
返回
Energy-efficient recurrence quantification analysis
.png){kind=icon}
OA
AI
DOI：10.1140/epjs/s11734-025-02121-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recurrence quantification analysis (RQA) is a widely used tool for studying complex dynamical systems, but its standard implementation requires computationally expensive calculations of recurrence plots (RPs) and line length histograms. This study introduces strategies to compute RQA measures directly from time series or phase space vectors, avoiding the need to construct RPs. The calculations can be further accelerated and optimised by applying a random sampling procedure, in which only a subset of line structures is evaluated. These modifications result in shorter run times, less memory use and access, and lower overall energy consumption during analysis whilst maintaining accuracy. This makes them especially appealing for large-scale data analysis and machine learning applications. The ideas are not limited to diagonal line measures, but can likewise be applied to vertical line-based measures and to recurrence network measures. By lowering computational costs, the proposed strategies contribute to energy saving and sustainable data analysis, and broaden the applicability of recurrence-based methods in modern research contexts.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
T
IF:
0
论文数:
446
被引数:
0
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏Recurrence-plot-based measures of complexity and their application to heart-rate-variability data基于递归图的复杂性度量及其在心率变异性数据中的应用OAAI
OAAI分享收藏
查看更多内容