波动增强感知下的二值指纹
Binary Fingerprints at Fluctuation-Enhanced Sensing
摘要：
我们开发了一种简单的方法来生成二进制模式的基础上，在不同的频率范围内的频谱斜率波动增强感测。这种模式可以被认为是气味的二进制“指纹”。该方法已被实验证明与商业半导体金属氧化物（田口）传感器暴露于细菌气味（大肠杆菌和炭疽替代枯草芽孢杆菌）和处理其随机信号。用一个田口传感器，空室，胰蛋白酶大豆琼脂（TSA）培养基，或TSA与细菌的情况下，可以区分与100%的再现性。细菌数量在2.5万至100万之间。为了说明超低功耗的相关性，我们证明了这种新型的信号处理和模式识别任务可以通过简单的模拟电路和几个逻辑门来实现，总功耗在微瓦范围内。
英文摘要：
We developed a simple way to generate binary patterns based on spectral slopes in different frequency ranges at fluctuation-enhanced sensing. Such patterns can be considered as binary "fingerprints" of odors. The method has experimentally been demonstrated with a commercial semiconducting metal oxide (Taguchi) sensor exposed to bacterial odors (Escherichia coli and Anthrax-surrogate Bacillus subtilis) and processing their stochastic signals. With a single Taguchi sensor, the situations of empty chamber, tryptic soy agar (TSA) medium, or TSA with bacteria could be distinguished with 100% reproducibility. The bacterium numbers were in the range of 25 thousands to 1 million. To illustrate the relevance for ultra-low power consumption, we show that this new type of signal processing and pattern recognition task can be implemented by a simple analog circuitry and a few logic gates with total power consumption in the microWatts range.
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