一个高性能无训练的管道,用于通过基于自适应波段的无声化和贝叶斯数字化方法进行强大的随机电报信号表征
Tonghe Bai1,2, Ayush Kapoor2, Na Young Kim3,4,5
1Institute for Quantum Computing, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.
Scientific reports
|February 5, 2026
概括
我们开发了一种快速,无需培训的管道,用于分析随机电报信号 (RTS). 我们的方法准确地描述信号波动和陷状态,即使有噪声,提高了分析速度的83倍.
科学领域:
- 物理 物理学 物理
- 化学 化学 化学
- 生物学 生物学 生物学
- 材料科学 材料科学 材料科学
背景情况:
- 随机电报信号 (RTS) 揭示了各种系统中的时间波动.
- 由于噪音和多层次信号,分析RTS是复杂的.
- 准确的RTS表征对于理解诸如电荷捕获等微观过程至关重要.
研究的目的:
- 为RTS分析开发一个高通量,无训练的信号处理管道.
- 提高RTS表征的准确性和速度,特别是在噪音条件下.
- 为自主RTS研究提供可扩展和可重复的基础.
主要方法:
- 适应式双树复杂波波变换 (DTCWT) 用于与自动参数选择进行表示.
- 轻量级贝叶斯数字化用于概率潜态推理.
- 在没有代优化的情况下进行时间规范化,以获得强大的状态分辨率.
主要成果:
- 显著提高了RTS重建精度和陷状态分辨率.
- 在各种噪音模式和多陷场景中改进了停留时间估计.
- 与经典和神经网络基线相比,实现了高达83倍的速度.
结论:
- 拟议的管道为自主RTS分析提供了一个可扩展和可重复的框架.
- 在实时和大规模实验数据分析方面展示了实用的可用性和灵活性.
- 为更复杂的RTS表征提供未来扩展的基础.
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