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arxiv: 2604.19626 · v1 · submitted 2026-04-21 · 📡 eess.SP

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Odour sensing in turbulent plumes with high-speed electronic nose and non-invasive ground truth

Aaron True, Andreas T. G\"untner, Andr\'e van Schaik, Elle Stark, John Crimaldi, Lars Larson, Michael Schmuker, Nik Dennler, Saimon Collaku

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Pith reviewed 2026-05-10 01:38 UTC · model grok-4.3

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keywords odour sensingturbulent plumeselectronic nosemetal-oxide sensorsplanar laser-induced fluorescencewind tunnelsensor dynamicsbenchmark dataset
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The pith

A synchronized dataset pairs high-resolution laser imaging of turbulent acetone plumes with kilohertz electronic nose recordings.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a dataset that combines quantitative two-dimensional concentration fields from planar laser-induced fluorescence of an acetone tracer plume with simultaneous recordings from a custom high-speed MEMS metal-oxide electronic nose in a wind tunnel. This addresses the difficulty of measuring how sensor responses distort rapid, fluctuating concentration signals in real environments. The aligned data supports assessment of sensor dynamics, testing of reconstruction algorithms, and modeling of plume structures. All files are released openly to aid researchers in robotics, environmental monitoring, and neuromorphic sensing.

Core claim

The authors provide a dataset combining planar laser-induced fluorescence measurements of an acetone tracer plume with synchronized recordings from a custom kilohertz-rate MEMS MOx electronic nose deployed in a laboratory wind tunnel. The PLIF system provides quantitative two-dimensional concentration fields at high spatial and temporal resolution, while the co-located e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. The dataset enables quantitative assessment of sensor dynamics, development and benchmarking of reconstruction and deconvolution algorithms, and data-driven modelling of plume structure.

What carries the argument

The synchronized PLIF and electronic nose dataset, which supplies quantitative concentration fields as ground truth alongside time-resolved sensor responses.

If this is right

  • The effects of MOx sensor thermal and surface kinetics as low-pass filters on turbulent signals can be measured directly.
  • Reconstruction and deconvolution algorithms can be developed and tested against the aligned concentration fields.
  • Data-driven models of odour plume structure can incorporate realistic sensor inputs verified by imaging.
  • High-speed sensing systems for robotics and monitoring can be evaluated using the benchmark recordings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The dataset could support direct tests of how sensor placement or wind speed alters distortion patterns.
  • Machine learning compensation models trained on these recordings might generalize to correct lag in field deployments.
  • The concentration fields offer validation data for simulations of scalar mixing in turbulence.
  • Similar synchronized setups with varied tracers could extend the approach to other chemical environments.

Load-bearing premise

The PLIF images give undistorted quantitative concentrations exactly at the sensor location and time, with timestamps aligned precisely enough for dynamic analysis.

What would settle it

Independent point measurements at the sensor site showing concentrations that differ from the PLIF-derived values, or synchronization offsets larger than the sensor response timescale, would invalidate the dataset for quantitative dynamics studies.

Figures

Figures reproduced from arXiv: 2604.19626 by Aaron True, Andreas T. G\"untner, Andr\'e van Schaik, Elle Stark, John Crimaldi, Lars Larson, Michael Schmuker, Nik Dennler, Saimon Collaku.

Figure 1
Figure 1. Figure 1: Experimental setup for odour measurements in turbulent flow. a Simultaneous monitoring of acetone in a wind tunnel using planar laser-induced fluorescence (PLIF) and a high-speed electronic nose (e-nose). b PLIF output (source-normalised concentration), including e-nose locations: centre (C), 5 cm upstream (U), and 5 cm right (R). c E-nose printed circuit board (PCB), including pressure-humidity-temperatur… view at source ↗
Figure 2
Figure 2. Figure 2: Data post-processing methods. a Spatial alignment of six SGX MiCS-6814 elements in two housings (sensor 1,2,3 in zone A and sensor 5,6,7 in zone B) and two ScioSense CCS801 units (sensor 4 in zone C and sensor 8 in zone D), based on acquired flat-field image. Scale bar shows 2 mm x 2 mm. b Area-averaged PLIF data, oversampled via piecewise cubic Hermite interpolating polynomial (PCHIP)56 . read back simult… view at source ↗
Figure 3
Figure 3. Figure 3: Technical validation. a PLIF data, time-averaged signal-to-noise ratio (SNR). b PLIF data, instantaneous SNR at different sensor locations. c MOx hotplate temperature during experiment ‘r56’, black solid and dotted lines correspond to mean and standard deviation, respectively. d Spatiotemporal alignment of PLIF concentration and sensor conductance measurements. Arrows indicate prominent turbulence features… view at source ↗
Figure 4
Figure 4. Figure 4: Usage notes: Time-series comparison between the PLIF-derived relative concentration (black) and co-located e-nose measurements (red). Top: raw MOx conductance, middle: temporal derivative of the conductance + phase alignment, bottom: output of a supervised deconvolution approach40 + phase alignment. 8/12 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Chemical sensing in real-world environments requires resolving rapidly fluctuating and spatially heterogeneous concentration fields. However, these dynamics are strongly distorted by widely used, low-cost metal-oxide (MOx) gas sensors, whose thermal and surface-kinetic response acts as a low-pass filter on the underlying concentration signal. Quantifying and compensating for these effects remains challenging, largely due to the lack of benchmark datasets that simultaneously capture the spatiotemporal structure of turbulent odour fields and the time-resolved response of point sensors. Here, we present a dataset combining planar laser-induced fluorescence (PLIF) measurements of an acetone tracer plume with synchronised recordings from a custom, kilohertz-rate microelectromechanical (MEMS) MOx electronic nose deployed in a laboratory wind tunnel. The PLIF system provides quantitative, two-dimensional concentration fields at high spatial and temporal resolution, while the co-located e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. The dataset enables quantitative assessment of sensor dynamics, development and benchmarking of reconstruction and deconvolution algorithms, and data-driven modelling of plume structure. All recordings, metadata, calibration files, and example analysis scripts are released in open, platform-independent formats. Together, these provide a valuable reference for researchers working in odour-guided robotics, environmental monitoring, computational fluid dynamics, and neuromorphic sensing, supporting the design and evaluation of high-speed odour-sensing systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript describes the release of an open dataset that pairs planar laser-induced fluorescence (PLIF) measurements of an acetone tracer plume with synchronized high-rate recordings from a custom MEMS MOx electronic nose in a laboratory wind tunnel. The PLIF provides quantitative two-dimensional concentration fields, while the e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. Calibration files, metadata, and analysis scripts are also released.

Significance. This work is significant as it provides a valuable benchmark dataset for studying the dynamics of low-cost gas sensors in turbulent odour plumes, which is currently lacking. The combination of non-invasive quantitative ground truth with high-speed sensor data will support the development of compensation algorithms and data-driven models, benefiting fields like odour-guided robotics and environmental monitoring. The open release enhances reproducibility and accessibility. The stress-test concern on PLIF accuracy and temporal alignment does not land as a problem given the described co-located setup, standard acetone PLIF processing, and release of calibration files.

minor comments (2)
  1. [Abstract] The abstract would benefit from a brief mention of typical wind tunnel flow speeds or Reynolds numbers to contextualize the turbulent regime.
  2. [Dataset description] Explicit comparison of the temporal resolutions and sampling rates of the PLIF and e-nose systems would clarify the dataset's suitability for kilohertz-scale sensor dynamics studies.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and constructive review. We are pleased that the significance of the open benchmark dataset is recognized, particularly its value for studying MOx sensor dynamics in turbulent plumes and supporting related research in robotics and environmental monitoring. The recommendation for acceptance is appreciated.

Circularity Check

0 steps flagged

No significant circularity; experimental dataset release with no derivations or predictions

full rationale

The manuscript is a dataset paper whose central claim is the release of synchronized PLIF concentration fields and high-rate MOx sensor recordings from a wind-tunnel experiment. No equations, models, predictions, or parameter fits are presented. The abstract and description emphasize co-located deployment, timestamp alignment, open data formats, calibration files, and example scripts, all of which are direct experimental outputs rather than derived quantities. No self-citations, ansatzes, or uniqueness claims appear as load-bearing steps. The contribution is self-contained against external benchmarks (standard PLIF processing for acetone) and contains no internal reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical derivations or new physical models are introduced; the work rests on established experimental techniques whose standard assumptions are not enumerated in the abstract.

pith-pipeline@v0.9.0 · 5578 in / 1197 out tokens · 50837 ms · 2026-05-10T01:38:51.497525+00:00 · methodology

discussion (0)

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Works this paper leans on

58 extracted references · 56 canonical work pages

  1. [1]

    & Matsukura, H

    Ishida, H., Wada, Y . & Matsukura, H. Chemical Sensing in Robo tic Applications: A Review. IEEE Sensors J. 12, 3163–3173, 10.1109/JSEN.2012.2208740 (2012)

    work page doi:10.1109/jsen.2012.2208740 2012

  2. [2]

    & Miura, N

    Y amazoe, N. & Miura, N. Development of gas sensors for enviro nmental protection. IEEE Transactions on Components, Packag. Manuf. T echnol. Part A18, 252–256, 10.1109/95.390298 (1995)

    work page doi:10.1109/95.390298 1995

  3. [3]

    & V ergassola, M

    Celani, A., Villermaux, E. & V ergassola, M. Odor Landscapes in Turbulent Environments. Phys. Rev. X 4, 041015, 10.1103/PhysRevX.4.041015 (2014)

    work page doi:10.1103/physrevx.4.041015 2014

  4. [4]

    & Nowotny, T

    Pannunzi, M. & Nowotny, T. Odor Stimuli: Not Just Chemical Id entity. Front. Physiol. 10, 1428, 10.3389/fphys.2019.01428 (2019)

    work page doi:10.3389/fphys.2019.01428 2019

  5. [5]

    Crimaldi, J. et al. Active sensing in a dynamic olfactory world. J. Comput. Neurosci. 50, 1–6, 10.1007/s10827-021-00798-1 (2022)

    work page doi:10.1007/s10827-021-00798-1 2022

  6. [6]

    C., Crimaldi, J

    Ouyang, B., True, A. C., Crimaldi, J. P . & Ermentrout, B. Simp le olfactory navigation in air and water. J. Theor. Biol. 595, 111941, 10.1016/j.jtbi.2024.111941 (2024)

    work page doi:10.1016/j.jtbi.2024.111941 2024

  7. [7]

    Fackrell, J. E. & Robins, A. G. The effects of source size on co ncentration fluctuations in plumes. Boundary-Layer Meteorol. 22, 335–350, 10.1007/BF00120014 (1982). 9/12

    work page doi:10.1007/bf00120014 1982

  8. [8]

    & Cardé, R

    Mafra-Neto, A. & Cardé, R. T. Fine-scale structure of pherom one plumes modulates upwind orientation of flying moths. Nature 369, 142–144, 10.1038/369142a0 (1994)

    work page doi:10.1038/369142a0 1994

  9. [9]

    Riffell, J. A. et al. Flower discrimination by pollinators in a dynamic chemical environment. Science 344, 1515–1518, 10.1126/science.1251041 (2014)

    work page doi:10.1126/science.1251041 2014

  10. [10]

    & Huerta, R

    Schmuker, M., Bahr, V . & Huerta, R. Exploiting plume structu re to decode gas source distance using metal-oxide gas sensors. Sensors Actuators, B: Chem. 235, 636–646, 10.1016/j.snb.2016.05.098 (2016)

    work page doi:10.1016/j.snb.2016.05.098 2016

  11. [11]

    Hopfield, J. J. Olfactory computation and object perception . Proc. Natl. Acad. Sci. 88, 6462–6466, 10.1073/pnas.88.15.6462 (1991)

    work page doi:10.1073/pnas.88.15.6462 1991

  12. [12]

    Ackels, T. et al. Fast odour dynamics are encoded in the olfactory system and g uide behaviour. Nature 593, 558–563, 10.1038/s41586-021-03514-2 (2021)

    work page doi:10.1038/s41586-021-03514-2 2021

  13. [13]

    C., Stark, E., Crimaldi, J

    Tootoonian, S., True, A. C., Stark, E., Crimaldi, J. P . & Scha efer, A. T. Quantifying spectral information about source separation in multisource odour plumes. PLOS ONE 20, e0297754, 10.1371/journal.pone.0297754 (2025)

    work page doi:10.1371/journal.pone.0297754 2025

  14. [14]

    The role of morphology and crystallographi c structure of metal oxides in response of conductometric- type gas sensors

    Korotcenkov, G. The role of morphology and crystallographi c structure of metal oxides in response of conductometric- type gas sensors. Mater. Sci. Eng. R: Reports 61, 1–39, 10.1016/j.mser.2008.02.001 (2008)

    work page doi:10.1016/j.mser.2008.02.001 2008

  15. [15]

    Gardner, E. L. W ., Gardner, J. W . & Udrea, F. Micromachined Th ermal Gas Sensors—A Review. Sensors 23, 681, 10.3390/s23020681 (2023)

    work page doi:10.3390/s23020681 2023

  16. [16]

    Dennler, N. et al. High-speed odor sensing using miniaturized electronic nos e. Sci. Adv. 10, eadp1764, 10.1126/sciadv.adp1764 (2024)

    work page doi:10.1126/sciadv.adp1764 2024

  17. [17]

    K., Peddi, R., Dennler, N

    France, K. K., Peddi, R., Dennler, N. & Daescu, O. Position: O lfaction standardization is essential for the advancement of embodied artificial intelligence. arXiv preprint arXiv:2506.00398 (2025)

    work page arXiv 2025

  18. [18]

    V ergara, A. et al. On the performance of gas sensor arrays in open sampling syst ems using Inhibitory Support V ector Machines. Sensors Actuators, B: Chem. 185, 462–477, 10.1016/j.snb.2013.05.027 (2013)

    work page doi:10.1016/j.snb.2013.05.027 2013

  19. [19]

    & Huerta, R

    Fonollosa, J., Rodríguez-Luján, I., Trincavelli, M., V erg ara, A. & Huerta, R. Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors V alidated by Gas Chromatograp hy-Mass Spectrometry. Sensors 14, 19336–19353, 10.3390/s141019336 (2014)

    work page doi:10.3390/s141019336 2014

  20. [20]

    Deep Learning Based Calibration Time Reduction for MOS Gas S ensors with Transfer Learning

    Robin, Y .et al. Deep Learning Based Calibration Time Reduction for MOS Gas S ensors with Transfer Learning. Atmo- sphere 13, 1614, 10.3390/atmos13101614 (2022)

    work page doi:10.3390/atmos13101614 2022

  21. [21]

    Chemical gas sensor drift compensation using classifier ens embles

    V ergara, A.et al. Chemical gas sensor drift compensation using classifier ens embles. Sensors Actuators B: Chem. 166- 167, 320–329, 10.1016/j.snb.2012.01.074 (2012)

    work page doi:10.1016/j.snb.2012.01.074 2012

  22. [22]

    Kumar, J. R. R. & Chouksey, P . Gas Sensor Array Drift in an E-No se System: A Dataset for Machine Learning Applica- tions. Int. J. on Recent Innov. Trends Comput. Commun. 11, 167–171, 10.17762/ijritcc.v11i6.7343 (2023)

    work page doi:10.17762/ijritcc.v11i6.7343 2023

  23. [23]

    & Pein-Hackelbusch, M

    Wörner, J., Eimler, J. & Pein-Hackelbusch, M. Long-term dri ft behavior in metal oxide gas sensor arrays: a one-year dataset from an electronic nose. Sci. Data 12, 1628, 10.1038/s41597-025-05993-8 (2025)

    work page doi:10.1038/s41597-025-05993-8 2025

  24. [24]

    & Sch muker, M

    Dennler, N., Rastogi, S., Fonollosa, J., van Schaik, A. & Sch muker, M. Drift in a popular metal oxide sensor dataset re- veals limitations for gas classification benchmarks. Sensors Actuators B: Chem. 361, 131668, 10.1016/j.snb.2022.131668 (2022)

    work page doi:10.1016/j.snb.2022.131668 2022

  25. [25]

    & Schmuker, M

    Dennler, N., van Schaik, A. & Schmuker, M. Limitations in odo ur recognition and generalization in a neuromorphic olfactory circuit. Nat. Mach. Intell. 6, 1451–1453, https://doi.org/10.1038/s42256-024-00952-1 (2024)

    work page doi:10.1038/s42256-024-00952-1 2024

  26. [26]

    & Schmuker, M

    Dennler, N., True, A., V an Schaik, A. & Schmuker, M. Neuromor phic principles for machine olfaction. Neuromorphic Comput. Eng. 5, 023001, 10.1088/2634-4386/add0dc (2025)

    work page doi:10.1088/2634-4386/add0dc 2025

  27. [27]

    A., Murlis, J

    Justus, K. A., Murlis, J. & Jones, C. Measurement of Odor-Plu me Structure in a Wind Tunnel Using a Photoionization Detector and a Tracer Gas. Environ. Fluid Mech. 115–142, 10.1023/A:1016227601019 (2002)

    work page doi:10.1023/a:1016227601019 2002

  28. [28]

    Drewnick, F. et al. Design of a mobile aerosol research laboratory and data proc essing tools for effective stationary and mobile field measurements. Atmospheric Meas. T ech.5, 1443–1457, 10.5194/amt-5-1443-2012 (2012)

    work page doi:10.5194/amt-5-1443-2012 2012

  29. [29]

    Hargather, M. J. & Settles, G. S. Natural-background-orien ted schlieren imaging. Exp. fluids 48, 59–68, 10.1007/s00348-009-0709-3 (2010)

    work page doi:10.1007/s00348-009-0709-3 2010

  30. [30]

    G., McHugh, M

    Connor, E. G., McHugh, M. K. & Crimaldi, J. P . Quantification o f airborne odor plumes using planar laser-induced fluorescence. Exp. Fluids 59, 137, 10.1007/s00348-018-2591-3 (2018). 10/12

    work page doi:10.1007/s00348-018-2591-3 2018

  31. [31]

    Peng, J. B. et al. Visualization of Flow Field: Application of PLIF Technique . J. Spectrosc. 2018, 1–6, 10.1155/2018/8759898 (2018)

    work page doi:10.1155/2018/8759898 2018

  32. [32]

    Álvarez Salvado, E. et al. Elementary sensory-motor transformations underlying olf actory navigation in walking fruit- flies. eLife 7, e37815, 10.7554/eLife.37815 (2018)

    work page doi:10.7554/elife.37815 2018

  33. [33]

    True, A. C. & Crimaldi, J. P . Distortion of passive scalar str ucture during suction-based plume sampling. Sensors Actuators B: Chem. 367, 132018, 10.1016/j.snb.2022.132018 (2022)

    work page doi:10.1016/j.snb.2022.132018 2022

  34. [34]

    A., Murlis, J., Long, X., Li, W

    Farrell, J. A., Murlis, J., Long, X., Li, W . & Carde, R. Filame nt-Based Atmospheric Dispersion Model to Achieve Short Time-Scale Structure of Odor Plumes:. Tech. Rep., Defense T echnical Information Center, Fort Belvoir, V A (2002). 10.21236/ADA399832

    work page doi:10.21236/ada399832 2002

  35. [35]

    & Gonzalez-Jimenez, J

    Monroy, J., Hernandez-Bennetts, V ., Fan, H., Lilienthal, A . & Gonzalez-Jimenez, J. GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environm ents. Sensors 17, 1479, 10.3390/s17071479 (2017)

    work page doi:10.3390/s17071479 2017

  36. [36]

    H., V an Breugel, F., Rao, R

    Singh, S. H., V an Breugel, F., Rao, R. P . N. & Brunton, B. W . Emergent behaviour and neural dynamics in artificial agents tracking odour plumes. Nat. Mach. Intell. 5, 58–70, 10.1038/s42256-022-00599-w (2023)

    work page doi:10.1038/s42256-022-00599-w 2023

  37. [37]

    Burton, G. C. The nonlinear large-eddy simulation method ap plied to sc ≈ 1 and sc ≫ 1 passive-scalar mixing. Phys. Fluids 20, https://doi.org/10.1063/1.2840199 (2008)

    work page doi:10.1063/1.2840199 2008

  38. [38]

    & Y eung, P

    Donzis, D. & Y eung, P . Resolution effects and scaling in nume rical simulations of passive scalar mixing in turbulence. Phys. D: Nonlinear Phenom. 239, 1278–1287, https://doi.org/10.1016/j.physd.2009.09.024 (2010)

    work page doi:10.1016/j.physd.2009.09.024 2009

  39. [39]

    G., González-Jiménez, J

    Monroy, J. G., González-Jiménez, J. & Blanco, J. L. Overcomi ng the Slow Recovery of MOX Gas Sensors through a System Modeling Approach. Sensors 12, 13664–13680, 10.3390/s121013664 (2012)

    work page doi:10.3390/s121013664 2012

  40. [40]

    Fast Measurements with MOX Senso rs: A Least-Squares Approach to Blind Deconvolution

    Martinez, Burgués & Marco. Fast Measurements with MOX Senso rs: A Least-Squares Approach to Blind Deconvolution. Sensors 19, 4029, 10.3390/s19184029 (2019)

    work page doi:10.3390/s19184029 2019

  41. [41]

    & Schmuker, M

    Drix, D. & Schmuker, M. Resolving fast gas transients with me tal oxide sensors. ACS Sensors 10.1021/acssensors.0c02006 (2021)

    work page doi:10.1021/acssensors.0c02006 2021

  42. [42]

    & Marques, L

    Marjovi, A. & Marques, L. Multi-robot olfactory search in st ructured environments. Robotics Auton. Syst. 59, 867–881, 10.1016/j.robot.2011.07.010 (2011)

    work page doi:10.1016/j.robot.2011.07.010 2011

  43. [43]

    A., Xing, Y ., Cole, M

    Vincent, T. A., Xing, Y ., Cole, M. & Gardner, J. W . Investigat ion of the response of high-bandwidth MOX sensors to gas plumes for application on a mobile robot in hazardous env ironments. Sensors Actuators B: Chem. 279, 351–360, 10.1016/j.snb.2018.08.125 (2019)

    work page doi:10.1016/j.snb.2018.08.125 2018

  44. [44]

    France, K. K. & Daescu, O. Olfactory Inertial Odometry: Meth odology for Effective Robot Navigation by Scent. In IEEE 2025 22nd International Conference on Ubiquitous Robots (U R), 51–58, 10.1109/UR65550.2025.11078037 (2025)

    work page doi:10.1109/ur65550.2025.11078037 2025

  45. [45]

    M., Thorson, J., Halliday, H., Milfor d, J

    Collier-Oxandale, A. M., Thorson, J., Halliday, H., Milfor d, J. & Hannigan, M. Understanding the ability of low-cost MOx sensors to quantify ambient VOCs. Atmospheric Meas. T ech.12, 1441–1460, 10.5194/amt-12-1441-2019 (2019)

    work page doi:10.5194/amt-12-1441-2019 2019

  46. [46]

    Khorramifar, A. et al. Environmental engineering applications of electronic nos e systems based on mox gas sensors. sensors 23, 5716, https://doi.org/10.3390/s23125716 (2023)

    work page doi:10.3390/s23125716 2023

  47. [47]

    Z., Trowell, S

    Diamond, A., Schmuker, M., Berna, A. Z., Trowell, S. & Nowotn y, T. Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the in sect olfactory system. Bioinspiration & Biomimetics 11, 026002, 10.1088/1748-3190/11/2/026002 (2016)

    work page doi:10.1088/1748-3190/11/2/026002 2016

  48. [48]

    & Na wrot, M

    Jürgensen, A.-M., Khalili, A., Chicca, E., Indiveri, G. & Na wrot, M. P . A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain. Neuromorphic Comput. Eng. 1, 024008, 10.1088/2634-4386/ac3ba6 (2021)

    work page doi:10.1088/2634-4386/ac3ba6 2021

  49. [49]

    Han, J.-K. et al. Artificial olfactory neuron for an in-sensor neuromorphic n ose. Adv. Sci. 9, 2106017, https://doi.org/10.1002/advs.202106017 (2022)

    work page doi:10.1002/advs.202106017 2022

  50. [50]

    Shraiman, B. I. & Siggia, E. D. Scalar turbulence. Nature 405, 639–646, 10.1038/35015000 (2000)

    work page doi:10.1038/35015000 2000

  51. [51]

    V ogel, C. R. Computational Methods for Inverse Problems , vol. 23 of Frontiers in Applied Mathematics (SIAM, 2002)

    2002

  52. [52]

    System Identification

    Ljung, L. System Identification. In Procházka, A., Uhlí ˇr, J., Rayner, P . W . J. & Kingsbury, N. G. (eds.) Signal Analysis and Prediction, 163–173, 10.1007/978-1-4612-1768-8_11 (1998)

    work page doi:10.1007/978-1-4612-1768-8_11 1998

  53. [53]

    Karpatne, A. et al. Theory-Guided Data Science: A New Paradigm for Scientific Di scovery from Data. IEEE Transactions on Knowl. Data Eng. 29, 2318–2331, 10.1109/TKDE.2017.2720168 (2017). 11/12

    work page doi:10.1109/tkde.2017.2720168 2017

  54. [54]

    & V assilicos, J

    Hurst, D. & V assilicos, J. C. Scalings and decay of fractal-g enerated turbulence. Phys. Fluids 19, 035103, 10.1063/1.2676448 (2007)

    work page doi:10.1063/1.2676448 2007

  55. [55]

    & Hanson, R

    Lozano, A., Yip, B. & Hanson, R. K. Acetone: a tracer for conce ntration measurements in gaseous flows by planar laser-induced fluorescence. Exp. Fluids 13, 369–376, 10.1007/bf00223244 (1992)

    work page doi:10.1007/bf00223244 1992

  56. [56]

    Fritsch, F. N. & Carlson, R. E. Monotone piecewise cubic inte rpolation. SIAM J. on Numer. Analysis 17, 238–246, https://doi.org/10.1137/0717021 (1980)

    work page doi:10.1137/0717021 1980

  57. [57]

    & and, A

    Dennler, N., Stark, E., Güntner, A. & and, A. T. Dataset: Odou r sensing in turbulent plumes with high-speed electronic nose and non-invasive ground truth. ETH Zurich Research Col lection (2026). http://hdl.handle.net/20.500.11850/782611

    2026

  58. [58]

    Husnain, A. U. et al. Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning ag ents. PLOS ONE 19, e0296969, 10.1371/journal.pone.0296969 (2024). 12/12

    work page doi:10.1371/journal.pone.0296969 2024