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Justus, Kristine A.; Murlis, John; Jones, Chris D.; Cardé, Ring T.

doi:10.1023/a:1016227601019

Cited by 68 publications

(40 citation statements)

References 35 publications

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“…Interestingly, the intermittent nature of a gas plume itself contains information about source distance that is independent of concentration. It has been shown that the rate of concentration fluctuation in a turbulent environment correlates well with the distance to the gas source in the open field [2,4] and in wind-tunnel experiments [5,6]. Generally, fast fluctuations dominate the signal close to the source, while slower components become more prominent further away.…”

Section: Introductionmentioning

confidence: 97%

Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Schmuker

Bahr

Huerta

2016

Sensors and Actuators B: Chemical

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?? 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This is a pre-copyedited, author-produced PDF of an article accepted for publication in Sensors and Actuators B: Chemical, following peer review. Under embargo. Embargo end date: 18 May 2018. The version of record [Michael Schmuker, Viktor Bahr, & Ramon Huerta, ???Exploiting plume structure to decode gas source distance using metal-oxide gas sensors???, Sensors and Actuators B: Chemical, Vol. 235: 636-646, November 2016, first published on line May 19, 2016] is available online via doi: http://dx.doi.org/10.1016/j.snb.2016.05.098Estimating the distance of a gas source is important in many applications of chemical sensing, like e.g. environmental monitoring, or chemically-guided robot navigation. If an estimation of the gas concentration at the source is available, source proximity can be estimated from the time-averaged gas concentration at the sensing site. However, in turbulent environments, where fast concentration fluctuations dominate, comparably long measurements are required to obtain a reliable estimate. A lesser known feature that can be exploited for distance estimation in a turbulent environment lies in the relationship between source proximity and the temporal variance of the local gas concentration - the farther the source, the more intermittent are gas encounters. However, exploiting this feature requires measurement of changes in gas concentration on a comparably fast time scale, that have up to now only been achieved using photo-ionisation detectors. Here, we demonstrate that by appropriate signal processing, off-the-shelf metal-oxide sensors are capable of extracting rapidly fluctuating features of gas plumes that strongly correlate with source distance. We show that with a straightforward analysis method it is possible to decode events of large, consistent changes in the measured signal, so-called 'bouts'. The frequency of these bouts predicts the distance of a gas source in wind-tunnel experiments with good accuracy. In addition, we found that the variance of bout counts indicates cross-wind offset to the centreline of the gas plume. Our results offer an alternative approach to estimating gas source proximity that is largely independent of gas concentration, using off-the-shelf metal-oxide sensors. The analysis method we employ demands very few computational resources and is suitable for low-power microcontrollers

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Section: Introductionmentioning

confidence: 97%

Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Schmuker

Bahr

Huerta

2016

Sensors and Actuators B: Chemical

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“…Second, whiff durations and blank durations are also distributed as a power law over a wide range of time scales (Celani et al, 2014). The encoding problem is aggravated by shifting local statistics of odor encounters, which change with wind speed (Nagel and Wilson, 2016, 2011), position (Justus et al, 2002), or environment (Murlis et al, 2000). How does the olfactory system manage to encode whiffs of odors whose intensities and timing can vary over such wide ranges?…”

Section: Introductionmentioning

confidence: 99%

Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli

Gorur-Shandilya

Demir

Long

et al. 2017

eLife

174

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Insects find food and mates by navigating odorant plumes that can be highly intermittent, with intensities and durations that vary rapidly over orders of magnitude. Much is known about olfactory responses to pulses and steps, but it remains unclear how olfactory receptor neurons (ORNs) detect the intensity and timing of natural stimuli, where the absence of scale in the signal makes detection a formidable olfactory task. By stimulating Drosophila ORNs in vivo with naturalistic and Gaussian stimuli, we show that ORNs adapt to stimulus mean and variance, and that adaptation and saturation contribute to naturalistic sensing. Mean-dependent gain control followed the Weber-Fechner relation and occurred primarily at odor transduction, while variance-dependent gain control occurred at both transduction and spiking. Transduction and spike generation possessed complementary kinetic properties, that together preserved the timing of odorant encounters in ORN spiking, regardless of intensity. Such scale-invariance could be critical during odor plume navigation.DOI: http://dx.doi.org/10.7554/eLife.27670.001

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“…Wind-tunnel Bioassays A wind tunnel (1 m wide × 1 m high × 3 m long), described in Justus et al (2002), was used for all experiments. Because male A. transitella often ascend 15 cm or more after take-off in a pheromone plume, the 1 m height of this tunnel was essential for the bioassay.…”

Section: Methodsmentioning

confidence: 99%

Attractiveness of a Four-component Pheromone Blend to Male Navel Orangeworm Moths

et al. 2010

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The attractiveness to male navel orangeworm moth, Amyelois transitella, of various combinations of a four-component pheromone blend was measured in wind-tunnel bioassays. Upwind flight along the pheromone plume and landing on the odor source required the simultaneous presence of two components, (11Z,13Z)-hexadecadienal and (3Z,6Z,9Z,12Z,15Z)-tricosapentaene, and the addition of either (11Z,13Z)-hexadecadien-1-ol or (11Z,13E)-hexadecadien-1-ol. A mixture of all four components produced the highest levels of rapid source location and source contact. In wind-tunnel assays, males did not seem to distinguish among a wide range of ratios of any of the three components added to (11Z,13Z)-hexadecadienal. Dosages of 10 and 100 ng of the 4-component blend produced higher levels of source location than dosages of 1 and 1,000 ng.Electronic supplementary materialThe online version of this article (doi:10.1007/s10886-010-9799-x) contains supplementary material, which is available to authorized users.

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Cited by 68 publications

References 35 publications

Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli

Attractiveness of a Four-component Pheromone Blend to Male Navel Orangeworm Moths

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