Abstract
Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.The data presented here have been deposited to the web site of The University of California at Irvine (UCI) Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Gas+sensor+array+under+flow+modulation). The code repository for reproducible analysis applied to the data is hosted at the GutHub web site (https://github.com/variani/pulmon).The data and code can be used upon citation of [1].
Highlights
Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection
While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation
The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air
Summary
The array was composed of 16 metal-oxide gas sensors of 5 different TGS models from Figaro Inc. [2]. The array was composed of 16 metal-oxide gas sensors of 5 different TGS models from Figaro Inc. The circuit board with the gas sensor array was placed in a 70 ml inner volume chamber connected to the mechanical ventilator. The ventilator includes a cylinder of volume 63.44 cm, a mechanical pump and three outlets, namely, ‘Source’, ‘To Animal’ and ‘From Animal’. The pump takes air from the outlet ‘Source’ and pushes the air sample through the outlet ‘To Animal’. The chamber with the sensors is interconnected with both ‘To Animal’ and ‘From Animal’ channels. The cylinder of the ventilator was fixed to a frequency of 5 breaths per minute, approximately equivalent to 0.08 Hz. The acquisition of sensor signals was performed by a PC104-standard embedded computer, which was designed for real-time acquisition, processing and visualization of sensory data for an autonomous mobile robot [4]. The embedded computer ran a custom built GNU/Linux image designed for a refined control of the measurement process
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