{"title":"基于CMOS的气体传感器的开发","authors":"R. Kumar, S. A. Imam, M. R. Khan","doi":"10.1109/MSPCT.2009.5164196","DOIUrl":null,"url":null,"abstract":"In this paper we review the development of CMOS compatible sensors for detection of injurious chemical compounds present in gas. Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. We reviewed gas identification approach based on a microelectronic gas sensors technology and GMM. And found that performance is achieved using GMM with a success rate of 94% obtained for different principal components which is much better response with respect to other well known traditional as well as advanced pattern recognition algorithms such as KNN, MLP, SVM, and PPCA. This points out to an important result, which suggests that higher generalization performance can be obtained by using feature reduction and selection techniques as preprocessing techniques. Using the operating temperature as a parameter to tune the selectivity of the sensor chip to different target gases was also proven to be an effective way to improve performance of the overall system. This approach is able to overcome drift problem.","PeriodicalId":179541,"journal":{"name":"2009 International Multimedia, Signal Processing and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development of CMOS based gas sensors\",\"authors\":\"R. Kumar, S. A. Imam, M. R. Khan\",\"doi\":\"10.1109/MSPCT.2009.5164196\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we review the development of CMOS compatible sensors for detection of injurious chemical compounds present in gas. Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. We reviewed gas identification approach based on a microelectronic gas sensors technology and GMM. And found that performance is achieved using GMM with a success rate of 94% obtained for different principal components which is much better response with respect to other well known traditional as well as advanced pattern recognition algorithms such as KNN, MLP, SVM, and PPCA. This points out to an important result, which suggests that higher generalization performance can be obtained by using feature reduction and selection techniques as preprocessing techniques. Using the operating temperature as a parameter to tune the selectivity of the sensor chip to different target gases was also proven to be an effective way to improve performance of the overall system. This approach is able to overcome drift problem.\",\"PeriodicalId\":179541,\"journal\":{\"name\":\"2009 International Multimedia, Signal Processing and Communication Technologies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Multimedia, Signal Processing and Communication Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSPCT.2009.5164196\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Multimedia, Signal Processing and Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSPCT.2009.5164196","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we review the development of CMOS compatible sensors for detection of injurious chemical compounds present in gas. Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. We reviewed gas identification approach based on a microelectronic gas sensors technology and GMM. And found that performance is achieved using GMM with a success rate of 94% obtained for different principal components which is much better response with respect to other well known traditional as well as advanced pattern recognition algorithms such as KNN, MLP, SVM, and PPCA. This points out to an important result, which suggests that higher generalization performance can be obtained by using feature reduction and selection techniques as preprocessing techniques. Using the operating temperature as a parameter to tune the selectivity of the sensor chip to different target gases was also proven to be an effective way to improve performance of the overall system. This approach is able to overcome drift problem.