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从大规模户外羽流的气味统计时间历史中可以预测气味源距离。

Odour source distance is predictable from a time history of odour statistics for large scale outdoor plumes.

机构信息

Computer Science Engineering Department, University of Nevada, Reno, NV, USA.

Integrative Neuroscience Program, University of Nevada, Reno, NV, USA.

出版信息

J R Soc Interface. 2024 Jul;21(216):20240169. doi: 10.1098/rsif.2024.0169. Epub 2024 Jul 31.

Abstract

Odour plumes in turbulent environments are intermittent and sparse. Laboratory-scaled experiments suggest that information about the source distance may be encoded in odour signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here, we analyse odour signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odour signal statistics can yield useful estimates of distance. We show that achieving accurate estimates of distance requires integrating statistics from 5 to 10 s, with a high temporal encoding of the olfactory signal of at least 20 Hz. By combining distance estimates from a linear model with wind-relative motion dynamics, we achieved source distance estimates in a 60 × 60 m search area with median errors of 3-8 m, a distance at which point odour sources are often within visual range for animals such as mosquitoes.

摘要

在湍流环境中,气味羽流是间歇性的且稀疏的。实验室规模的实验表明,关于源距离的信息可能被编码在气味信号统计中,但在现实世界的流动条件下,是否可以做出有用且连续的距离估计尚不清楚。在这里,我们分析了在沙漠和森林环境中进行的带有传感器的户外实验的气味信号,以表明气味信号统计信息可以提供有用的距离估计。我们表明,要实现准确的距离估计,需要整合 5 到 10 秒的统计数据,并且嗅觉信号的时间编码至少为 20 Hz。通过将线性模型的距离估计与风相对运动动力学相结合,我们在 60×60 m 的搜索区域中实现了源距离估计,中位数误差为 3-8 m,在这个距离上,气味源通常在动物如蚊子的视觉范围内。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5cb0/11288670/a1c68fd1f411/rsif20240169f01.jpg

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