A Comparison of Multi-Transducer Arrays and Single-Transducer Arrays for the Determination of Multi-Vapor Mixtures
This study provides a comprehensive analysis of multi-transducer (MT) array performance, illustrating quantitatively the advantages of such arrays over their single-transducer (ST) counterparts. Calibrations were performed for 11 vapors with five cantilevers, five capacitive, and five calorimetric s...
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creator | Chunguang Jin Zellers, E.T. Kurzawski, P. Hierlemann, A. |
description | This study provides a comprehensive analysis of multi-transducer (MT) array performance, illustrating quantitatively the advantages of such arrays over their single-transducer (ST) counterparts. Calibrations were performed for 11 vapors with five cantilevers, five capacitive, and five calorimetric sensors coated with five different polymers. Using these data in Monte Carlo simulations coupled with a disjoint principal component regression pattern recognition algorithm we examine the predicted rates of recognition achievable for analyses of individual vapors and their binary and ternary mixtures. Optimal MT arrays consistently outperform optimal ST arrays of similar dimension, and a number of ternary mixtures could be reliably analyzed with MT arrays that could not be analyzed with any ST arrays. Recognition rates did not increase significantly by including > 5 or 6 sensors in the array. |
doi_str_mv | 10.1109/ICSENS.2007.4388628 |
format | Conference Proceeding |
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Calibrations were performed for 11 vapors with five cantilevers, five capacitive, and five calorimetric sensors coated with five different polymers. Using these data in Monte Carlo simulations coupled with a disjoint principal component regression pattern recognition algorithm we examine the predicted rates of recognition achievable for analyses of individual vapors and their binary and ternary mixtures. Optimal MT arrays consistently outperform optimal ST arrays of similar dimension, and a number of ternary mixtures could be reliably analyzed with MT arrays that could not be analyzed with any ST arrays. 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Calibrations were performed for 11 vapors with five cantilevers, five capacitive, and five calorimetric sensors coated with five different polymers. Using these data in Monte Carlo simulations coupled with a disjoint principal component regression pattern recognition algorithm we examine the predicted rates of recognition achievable for analyses of individual vapors and their binary and ternary mixtures. Optimal MT arrays consistently outperform optimal ST arrays of similar dimension, and a number of ternary mixtures could be reliably analyzed with MT arrays that could not be analyzed with any ST arrays. Recognition rates did not increase significantly by including > 5 or 6 sensors in the array.</description><subject>Calibration</subject><subject>Capacitive sensors</subject><subject>Capacitors</subject><subject>Coatings</subject><subject>Microsensors</subject><subject>Pattern recognition</subject><subject>Performance analysis</subject><subject>Resonance</subject><subject>Sensor arrays</subject><subject>Transducers</subject><issn>1930-0395</issn><issn>2168-9229</issn><isbn>1424412617</isbn><isbn>9781424412617</isbn><isbn>9781424412624</isbn><isbn>1424412625</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNptkE1OwzAUhM2fRFp6gm58gRT72YntZRUKVGphkcK2MskzGDVJZacSvT2RKBILVjPSjEb6hpApZzPOmbldFuXiqZwBY2omhdY56DMyMUpzCVJyyEGekwR4rlMDYC7I6Dfg6pIk3AiWMmGyazKK8ZMxYBnohIQ5Lbpmb4OPXUs7R9eHXe_TTbBtrA8VBjoPwR4jtW1NS9--7_Cf0HWB9h9I77DH0PjW9v7v2qvdD4W1_-oPAeMNuXJ2F3Fy0jF5uV9sisd09fywLOar1HOV9WkFToEVWHGEWrmBSRlVS64rY9mAllkncpdxZXOoEdmbMjVnUqnBYea0GJPpz65HxO0--MaG4_b0nfgGckFgQw</recordid><startdate>200710</startdate><enddate>200710</enddate><creator>Chunguang Jin</creator><creator>Zellers, E.T.</creator><creator>Kurzawski, P.</creator><creator>Hierlemann, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200710</creationdate><title>A Comparison of Multi-Transducer Arrays and Single-Transducer Arrays for the Determination of Multi-Vapor Mixtures</title><author>Chunguang Jin ; Zellers, E.T. ; Kurzawski, P. ; Hierlemann, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-c2f72a3ec1e2d7f229797d418c9a04245af36f517a62dee0b79d104770b7e5f83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Calibration</topic><topic>Capacitive sensors</topic><topic>Capacitors</topic><topic>Coatings</topic><topic>Microsensors</topic><topic>Pattern recognition</topic><topic>Performance analysis</topic><topic>Resonance</topic><topic>Sensor arrays</topic><topic>Transducers</topic><toplevel>online_resources</toplevel><creatorcontrib>Chunguang Jin</creatorcontrib><creatorcontrib>Zellers, E.T.</creatorcontrib><creatorcontrib>Kurzawski, P.</creatorcontrib><creatorcontrib>Hierlemann, A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chunguang Jin</au><au>Zellers, E.T.</au><au>Kurzawski, P.</au><au>Hierlemann, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Comparison of Multi-Transducer Arrays and Single-Transducer Arrays for the Determination of Multi-Vapor Mixtures</atitle><btitle>2007 IEEE Sensors</btitle><stitle>ICSENS</stitle><date>2007-10</date><risdate>2007</risdate><spage>1217</spage><epage>1220</epage><pages>1217-1220</pages><issn>1930-0395</issn><eissn>2168-9229</eissn><isbn>1424412617</isbn><isbn>9781424412617</isbn><eisbn>9781424412624</eisbn><eisbn>1424412625</eisbn><abstract>This study provides a comprehensive analysis of multi-transducer (MT) array performance, illustrating quantitatively the advantages of such arrays over their single-transducer (ST) counterparts. Calibrations were performed for 11 vapors with five cantilevers, five capacitive, and five calorimetric sensors coated with five different polymers. Using these data in Monte Carlo simulations coupled with a disjoint principal component regression pattern recognition algorithm we examine the predicted rates of recognition achievable for analyses of individual vapors and their binary and ternary mixtures. Optimal MT arrays consistently outperform optimal ST arrays of similar dimension, and a number of ternary mixtures could be reliably analyzed with MT arrays that could not be analyzed with any ST arrays. Recognition rates did not increase significantly by including > 5 or 6 sensors in the array.</abstract><pub>IEEE</pub><doi>10.1109/ICSENS.2007.4388628</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Calibration Capacitive sensors Capacitors Coatings Microsensors Pattern recognition Performance analysis Resonance Sensor arrays Transducers |
title | A Comparison of Multi-Transducer Arrays and Single-Transducer Arrays for the Determination of Multi-Vapor Mixtures |
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