Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks

Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217 , MRC award no. MR/T046759/1 ), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539 ). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R , Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigación Carlos III, Share4Rare Project (Grant agreement 780262 ), and ACCIÓ (Innotec A CE014/20/000018 ). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra Húnter Program . B2SLab is certified as 2017 SGR 952. Funding Information: We thank A. J. Lilienthal, M. Psarrou and S. Sutton for fruitful discussions on multiple occasions, which led to valuable insights. MS was funded by the NSF/CIHR/DFG/FRQ/UKRI-MRC Next Generation Networks for Neuroscience Program (NSF award no. 2014217, MRC award no. MR/T046759/1), and the EU Flagship Human Brain Project SGA3 (H2020 award no. 945539). JF acknowledges the Spanish Ministry of Economy and Competitiveness DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine, initiatives of Instituto de Investigaci?n Carlos III, Share4Rare Project (Grant agreement 780262), and ACCI? (Innotec ACE014/20/000018). JF also acknowledges the CERCA Programme/Generalitat de Catalunya and the Serra H?nter Program. B2SLab is certified as 2017 SGR 952. Publisher Copyright: © 2022Metal oxide (MOx) gas sensors are a popular choice for many applications, due to their tunable sensitivity, space efficiency and low cost. Publicly available sensor datasets are particularly valuable for the research community as they accelerate the development and evaluation of novel algorithms for gas sensor data analysis. A dataset published in 2013 by Vergara and colleagues contains recordings from MOx gas sensor arrays in a wind tunnel. It has since become a standard benchmark in the field. Here we report a latent property of this dataset that limits its suitability for gas classification studies. Measurement timestamps show that gases were recorded in separate, temporally clustered batches. Sensor baseline response before gas exposure were strongly correlated with the recording batch, to the extent that baseline response was largely sufficient to infer the gas used in a given trial. Zero-offset baseline compensation did not resolve the issue, since residual short-term drift still contained enough information for gas/trial identification using a machine learning classifier. A subset of the data recorded within a short period of time was minimally affected by drift and suitable for gas classification benchmarking after offset-compensation, but with much reduced classification performance compared to the full dataset. We found 18 publications where this dataset was used without precautions against the circumstances we describe, thus potentially overestimating the accuracy of gas classification algorithms. These observations highlight potential pitfalls in using previously recorded gas sensor data, which may have distorted widely reported results.Peer reviewe

Rapid Inference of Geographical Location with an Event-based Electronic Nose

© 2022 The Author(s). This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1145/3517343.3517381Sensory information determines successful interaction of an agent with its environment. Animals have been shown to be able to encode rapid fluctuations in odour plumes, but this has yet received little attention in electronic gas sensing. State-of-the-art gas sensors actively modify the sensing site using temperature modulation, which decreases the integration time and increases the discriminability. In this work, we propose a novel approach for asynchronous event sampling for temperature-modulated gas sensor data and investigate the effectiveness of different event encoding schemes for solving an inference problem. A multichannel heater-modulated electronic nose was used to record field data at 1kHz. The data consisted of an approx. 90-minute walk in Lisbon covering multiple chemical environments and olfactory sceneries. Single temperature-cycle sensor conductance windows of 140ms were normalised and a model curve was subtracted from each sensor response. Using send-on-delta sampling, on- and off-events were generated and further compressed by considering either their rate, time-to-first-spike, firing-order, or a reconstructed signal. The different representations were assessed by their performance in inferring geographical location on unseen single 140 ms cycles using a linear SVM (75%/25% training/test split). We found that with a small spiking threshold the event-reconstructed signal achieved 82.5±1.0% accuracy, very close to the raw data (84.2±1.2%), and gracefully degraded when reducing the event count by increasing the spike threshold. The rate, latency and rank-order codes could not match that of the reconstructed signal, suggesting that temporal dynamics of the intra-cycle signal contain essential information. We conclude that heater-modulated gas sensors lend themselves to event-based processing, allowing for rapid inference in the sub-second regime. Our work could pave the way from distinguishing broad olfactory scenes to recognising individual odorants in turbulent plumes, with the potential to break new ground in traditional and neuromorphic gas sensing

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Machine learning approaches to classify anatomical regions in rodent brain from high density recordings

Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 % and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps

Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world applications. Recent deep learning approaches have reached outstanding accuracy in such tasks, but their implementation on conventional embedded solutions is still very computationally and energy expensive. Tactile sensing in robotic applications is a representative example where real-time processing and energy-efficiency are required. Following a brain-inspired computing approach, we propose a new benchmark for spatio-temporal tactile pattern recognition at the edge through braille letters reading. We recorded a new braille letters dataset based on the capacitive tactile sensors/fingertip of the iCub robot, then we investigated the importance of temporal information and the impact of event-based encoding for spike-based/event-based computation. Afterwards, we trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihi neuromorphic chip for fast and efficient inference. We confronted our approach to standard classifiers, in particular to a Long Short-Term Memory (LSTM) deployed on the embedded Nvidia Jetson GPU in terms of classification accuracy, power/energy consumption and computational delay. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy-efficient than the LSTM on Jetson, requiring an average power of only 31mW. This work proposes a new benchmark for tactile sensing and highlights the challenges and opportunities of event-based encoding, neuromorphic hardware and spike-based computing for spatio-temporal pattern recognition at the edge.Comment: 20 pages, submitted to Frontiers in Neuroscience - Neuromorphic Engineerin