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Updated: Jul 1, 2026

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A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.

Anup Vanarse1, Adam Osseiran1, Alexander Rassau1

  • 1School of Engineering, Edith Cowan University, 6027 Perth, Australia.

Sensors (Basel, Switzerland)
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Olfaction01:25

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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Application of a Brain-Inspired Spiking Neural Network Architecture to Odor Data Classification.

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Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks.

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An Investigation into Spike-Based Neuromorphic Approaches for Artificial Olfactory Systems.

Sensors (Basel, Switzerland)·2017

This study introduces a novel spiking neural network (SNN) classifier for electronic noses, achieving over 90% accuracy with low latency. This bio-inspired approach minimizes power and computational costs for real-time sensor data classification.

Area of Science:

  • Bio-inspired computing
  • Artificial olfaction
  • Neuromorphic engineering

Background:

  • Conventional electronic nose systems face challenges with high power consumption, computational cost, latency, and classification accuracy.
  • Spike-based bio-inspired approaches offer improved accuracy and reduced resource requirements for sensor data processing.

Purpose of the Study:

  • To develop and evaluate a spiking neural network (SNN)-based classifier for electronic noses.
  • To address limitations in processing latency and hardware deployment for spike-based systems.
  • To enable real-time classification of multivariate sensor data with high accuracy and low power.

Main Methods:

  • Implementation of a spiking neural network (SNN) classifier within a chip-emulation environment.
  • Design and integration of a novel encoder for artificial olfactory systems.
Keywords:
SNN-based classificationbio-inspired electronic nose systemsneuromorphic olfaction

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  • Utilizing unsupervised spike-timing-dependent plasticity (STDP) for learning.
  • Deployment on a neuromorphic system-on-a-chip (NSoC) development platform.

    • Main Results:

    • The SNN classifier achieved over 90% accuracy in classifying real-valued sensor data across three complexity levels.
    • Maximum processing latency was recorded at 3 seconds on the software-based platform.
    • Demonstrated capability for early classification, highlighting potential for real-time applications.

    Conclusions:

    • The proposed SNN-based classifier offers a viable, efficient, and accurate solution for electronic nose systems.
    • The approach minimizes power and computational demands, suitable for neuromorphic hardware deployment.
    • This work lays the foundation for real-time, low-latency sensor data analysis using bio-inspired computing.