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Dynamic multi-sensor operation and read-out for highly selective gas sensor systems

机译:高选择性气体传感器系统的动态多传感器操作和读数

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摘要

We describe hardware and algorithms which enable highly selective and sensitive operation of the two gas sensor types used in the SENSIndoor project. The resistance of a metal-oxide semiconductor (MOS) type can rise above 1 G Omega in temperature cycled operation (TCO), which is measured using a logarithmic amplifier. A silicon-carbide based, gas-sensitive field-effect transistor (SiC-FET) driven with a combination of TCO and gate-bias cycled operation (GBCO) is used as second, complimentary sensor. The cyclic sensor signals exhibit distinct shape changes depending on the gas present which is captured by pattern recognition. In this study we use Linear Discriminant Analysis (LDA) for discrimination and Partial Least Squares Regression (PLSR) for quantification of ppb concentrations of target VOCs in changing ppm concentrations of interfering gases for indoor air quality assessment. (C) 2016 The Authors. Published by Elsevier Ltd.
机译:我们描述了硬件和算法,它们可以对SENSIndoor项目中使用的两种气体传感器进行高度选择性和灵敏的操作。在使用对数放大器测量的温度循环操作(TCO)中,金属氧化物半导体(MOS)类型的电阻可以升高到1 GΩ以上。基于碳化硅的气敏场效应晶体管(SiC-FET)由TCO和栅极偏置循环操作(GBCO)组合驱动,用作第二个互补传感器。循环传感器信号根据模式识别所捕获的存在的气体表现出明显的形状变化。在这项研究中,我们使用线性判别分析(LDA)进行判别,使用偏最小二乘回归(PLSR)进行目标VOC ppb浓度量化,以改变干扰气体的ppm浓度,以评估室内空气质量。 (C)2016作者。由Elsevier Ltd.发布

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