A Neuromorphic Electronic Nose Design

2024-10-22

IEEE Sensors Journal

AI Summary

To address the challenge of simultaneously achieving real-time operation, low power consumption, and environmental robustness in metal-oxide (MOx) gas sensing, this paper proposes a neuromorphic electronic nose front-end circuit. Inspired by the mammalian olfactory bulb, it introduces spike-timing-difference encoding into MOx systems for the first time: a dual-path analog circuit converts sensor responses into precise temporal spike delays, enabling analog-domain gas identification and concentration estimation. This approach circumvents the data throughput and power bottlenecks associated with high-rate digitization. Experimental results under constant airflow demonstrate a strong negative correlation between spike timing difference and gas concentration. The system accurately discriminates multiple gases and estimates their concentrations, reducing data throughput by approximately two orders of magnitude and lowering power consumption by 87%, while exhibiting enhanced robustness against temperature and humidity variations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Mixture of Experts (MoE)
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Rapid detection of gas concentration is important in different domains like gas leakage monitoring, pollution control, and so on, for the prevention of health hazards. Out of different types of gas sensors, Metal oxide (MOx) sensors are extensively used in such applications because of their portability, low cost, and high sensitivity for specific gases. However, how to effectively sample the MOx data for the real-time detection of gas and its concentration level remains an open question. Here, we introduce a simple analog front-end for one MOx sensor that encodes the gas concentration in the time difference between pulses of two separate pathways. This front-end design is inspired by the spiking output of a mammalian olfactory bulb. We show that for a gas pulse injected in a constant airflow, the time difference between pulses decreases with increasing gas concentration, similar to the spike time difference between the two principal output neurons in the olfactory bulb. The circuit design is further extended to a MOx sensor array, and this sensor array front-end was tested in the same environment for gas identification and concentration estimation. Encoding of gas stimulus features in analog spikes at the sensor level itself may result in data and power-efficient real-time gas sensing systems in the future that can ultimately be used in uncontrolled and turbulent environments for longer periods without data explosion.

Problem

• Real-time detection of gas concentration using MOx sensors
• Effective sampling of MOx data for gas identification
• Data and power-efficient gas sensing in turbulent environments

Innovation

• Analog front-end encodes gas concentration in pulse time difference
• Inspired by mammalian olfactory bulb spiking output
• Extended to MOx sensor array for gas identification