E-Nose: Multichannel Analog Signal Conditioning Circuit With Pattern Recognition for Explosive Sensing
| Autor: | Gaurav Gupta, Soumyo Mukherji, Shambhulingayya N. Doddapujar, Maryam Shojaei Baghini, Saurabh A. Chandorkar, V. Ramgopal Rao, Vijay Palaparthy, Pallabi Das |
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| Rok vydání: | 2020 |
| Předmět: |
Physics
business.industry 010401 analytical chemistry Pattern recognition 01 natural sciences Noise (electronics) 0104 chemical sciences law.invention Analog signal law Demodulation Sensitivity (control systems) Artificial intelligence Electrical and Electronic Engineering Resistor business Instrumentation Signal conditioning DC bias Electronic circuit |
| Zdroj: | IEEE Sensors Journal. 20:1373-1382 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2019.2946253 |
| Popis: | This paper presents E-Nose, a novel cost-effective, field-deployable portable system that constitutes a 4-channel signal conditioning circuit and multi-coated piezo-resistive micro-cantilever sensors for explosive sensing. E-Nose also features an embedded PCA and K-means based pattern recognition (PR) algorithm for the classification of explosives from non-explosives. The 4-channel configuration is a stack of two 2-channel circuits that are capable of measuring the change in the sensor resistance or capacitance in four optional modes of $\Delta \text{R}$ - $\Delta $ R, $\Delta \text{R}$ - $\Delta \text{C}$ , $\Delta $ C- $\Delta \text{R}$ , and $\Delta \text{C}$ - $\Delta \text{C}$ by using time multiplexing. The circuit uses a bidirectional AC current excitation method to drive the sensor bridge for significant reduction of DC offset errors, 1/f noise, line noise, and DC drifts. The proposed signal conditioning circuit uses the phase-sensitive synchronous rectification (PSSR) method for AC-to-DC conversion by using balanced demodulation. The circuit can measure a wide range of resistors that range from $100~\Omega $ to 4 $\text{M}\Omega $ , with a sensitivity of 0.4mV/ppm and the worst relative error of 2.6%. The capacitive measurement range is from 100pF to $100~\mu \text{F}$ with the worst relative error of 3.3%. The entire data processing and the PR algorithms run on Raspberry Pi (R-Pi), which is integrated into the E-Nose system. The system performance is tested with MEMS cantilevers for the detection of explosive compounds, such as TNT and its derivatives, RDX and PETN in a controlled environment at a concentration that was as low as 16ppb TNT, 56ppb RDX and 134ppb of PETN. Measurements show that the E-Nose can detect explosives with 77% as true positive results without considering the environmental and mixed vapor effects. |
| Databáze: | OpenAIRE |
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