Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design
Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design
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DOI：
10.1007/s10470-011-9638-1
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发表时间：
2012-01-01
影响因子：
1.4
通讯作者：
Bakkaloglu, Bertan
中科院分区：
文献类型：
--
作者：
Kim, Hyuntae;Konnanath, Bharatan;Bakkaloglu, Bertan
环境监测依赖于能够实时检测污染物的紧凑型便携式传感器系统。研制了一种集成化的化学传感器阵列系统，用于检测和识别柴油和汽油尾气中的环境污染物。该系统由低噪声基底模拟前端（AFE）和信号处理级组成。在本文中，我们提出的技术来检测，抑制，去噪和分类的一组特定的分析物。建议AFE读出8个电导传感器和8个电流型电化学传感器的输出，并达到91 dB的SNR在23.4 mW的静态功耗为所有通道。我们演示了信号去噪使用离散小波变换为基础的技术。从传感器数据中提取适当的特征，并使用模式分类方法来识别分析物。几个现有的模式分类算法用于分析物检测和比较结果。
Environmental monitoring relies on compact, portable sensor systems capable of detecting pollutants in real-time. An integrated chemical sensor array system is developed for detection and identification of environmental pollutants in diesel and gasoline exhaust fumes. The system consists of a low noise floor analog front-end (AFE) followed by a signal processing stage. In this paper, we present techniques to detect, digitize, denoise and classify a certain set of analytes. The proposed AFE reads out the output of eight conductometric sensors and eight amperometric electrochemical sensors and achieves 91 dB SNR at 23.4 mW quiescent power consumption for all channels. We demonstrate signal denoising using a discrete wavelet transform based technique. Appropriate features are extracted from sensor data, and pattern classification methods are used to identify the analytes. Several existing pattern classification algorithms are used for analyte detection and the comparative results are presented.