IEEE Trans Biomed Circuits Syst. 2011 Apr;5(2):160-8. doi: 10.1109/TBCAS.2010.2075928.
Recent theoretical and experimental findings suggest that biological olfactory systems utilize relative latencies or time-to-first spikes for fast odor recognition. These time-domain encoding methods exhibit reduced computational requirements and improved classification robustness. In this paper, we introduce a microcontroller-based electronic nose system using time-domain encoding schemes to achieve a power-efficient, compact, and robust gas identification system. A compact (4.5 cm × 5 cm × 2.2 cm) electronic nose, which is integrated with a tin-oxide gas-sensor array and capable of wireless communication with computers or mobile phones through Bluetooth, was implemented and characterized by using three different gases (ethanol, carbon monoxide, and hydrogen). During operation, the readout circuit digitizes the gas-sensor resistances into a concentration-independent spike timing pattern, which is unique for each individual gas. Both sensing and recognition operations have been successfully demonstrated in hardware. Two classification algorithms (rank order and spike distance) have been implemented. Both algorithms do not require any explicit knowledge of the gas concentration to achieve simplified training procedures, and exhibit comparable performances with conventional pattern-recognition algorithms while enabling hardware-friendly implementation.
最近的理论和实验发现表明，生物嗅觉系统利用相对延迟或首次尖峰的时间进行快速气味识别。这些时域编码方法具有较低的计算要求和更高的分类鲁棒性。在本文中，我们介绍了一种基于微控制器的电子鼻系统，该系统使用时域编码方案实现了节能、紧凑、稳健的气体识别系统。我们实现并表征了一个集成有氧化锡气敏传感器阵列的紧凑（4.5cm×5cm×2.2cm）电子鼻，该电子鼻能够通过蓝牙与计算机或移动电话进行无线通信。我们使用三种不同的气体（乙醇、一氧化碳和氢气）进行了操作。在操作过程中，读取电路将气体传感器的电阻数字化为浓度无关的尖峰时间模式，每个气体都有独特的模式。我们已经在硬件中成功地演示了传感和识别操作。我们实现了两种分类算法（排序和尖峰距离）。这两种算法都不需要任何气体浓度的明确知识即可实现简化的训练过程，并且在实现硬件友好的同时，与传统的模式识别算法具有可比的性能。
