Spike latency coding in a biologically inspired micro-electronic nose
Identifieur interne : 002836 ( Main/Exploration ); précédent : 002835; suivant : 002837Spike latency coding in a biologically inspired micro-electronic nose
Auteurs : Hung Tat Chen [Hong Kong] ; Kwan Ting Ng [Hong Kong] ; Amine Bermak [Hong Kong] ; Man Kay Law [Hong Kong] ; Dominique Martinez [France]Source :
English descriptors
Abstract
Recent theoretical and experimental findings suggest that biological olfactory systems utilize relative latencies or time-to-first spikes for fast odor recognition. These time domain encoding methods have been demonstrated to exhibit reduced computational requirements and improved classification robustness [9], [12]. In this paper, we introduce a microcontroller (MCU) based electronic nose system using time domain encoding schemes to achieve a power efficient, compact and robust gas identification system. A compact (4.5cm×5cm×2.2cm) electronic nose, which is integrated with a tin oxide gas sensor array and capable of wireless communication with computers or mobile phones through Bluetooth, was implemented and characterized using three different gases (ethanol, carbon monoxide and hydrogen). During operation, the readout circuit digitizes the gas sensor resistances into a concentration independent spike timing pattern, which is unique for each individual gas. Both sensing and recognition operations have been successfully demonstrated in hardware. Two classification algorithms (rank-order and spikedistance) have been implemented. Both algorithms require no explicit knowledge of the gas concentration to achieve simplified training procedures, and exhibit comparable performances with conventional pattern recognition algorithms while enabling hardware friendly implementation.
Url:
Affiliations:
- France, Hong Kong
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Electronic nose</term><term>Gas Sensors</term><term>Neuromorphic engineering</term><term>Spiking neurons</term><term>olfactory system</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Recent theoretical and experimental findings suggest that biological olfactory systems utilize relative latencies or time-to-first spikes for fast odor recognition. These time domain encoding methods have been demonstrated to exhibit reduced computational requirements and improved classification robustness [9], [12]. In this paper, we introduce a microcontroller (MCU) based electronic nose system using time domain encoding schemes to achieve a power efficient, compact and robust gas identification system. A compact (4.5cm×5cm×2.2cm) electronic nose, which is integrated with a tin oxide gas sensor array and capable of wireless communication with computers or mobile phones through Bluetooth, was implemented and characterized using three different gases (ethanol, carbon monoxide and hydrogen). During operation, the readout circuit digitizes the gas sensor resistances into a concentration independent spike timing pattern, which is unique for each individual gas. Both sensing and recognition operations have been successfully demonstrated in hardware. Two classification algorithms (rank-order and spikedistance) have been implemented. Both algorithms require no explicit knowledge of the gas concentration to achieve simplified training procedures, and exhibit comparable performances with conventional pattern recognition algorithms while enabling hardware friendly implementation.</div></front></TEI><affiliations><list><country><li>France</li><li>Hong Kong</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><country name="Hong Kong"><noRegion><name sortKey="Chen, Hung Tat" sort="Chen, Hung Tat" uniqKey="Chen H" first="Hung Tat" last="Chen">Hung Tat Chen</name></noRegion><name sortKey="Bermak, Amine" sort="Bermak, Amine" uniqKey="Bermak A" first="Amine" last="Bermak">Amine Bermak</name><name sortKey="Law, Man Kay" sort="Law, Man Kay" uniqKey="Law M" first="Man Kay" last="Law">Man Kay Law</name><name sortKey="Ng, Kwan Ting" sort="Ng, Kwan Ting" uniqKey="Ng K" first="Kwan Ting" last="Ng">Kwan Ting Ng</name></country><country name="France"><region name="Grand Est"><name sortKey="Martinez, Dominique" sort="Martinez, Dominique" uniqKey="Martinez D" first="Dominique" last="Martinez">Dominique Martinez</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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