Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification
<|reference_start|>Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing: Detection and Classification: Olfaction sensing in autonomous robotics faces challenges in dynamic operations, energy efficiency, and edge processing. It necessitates a machine learning algorithm capable of managing real-world odor interference, ensuring resource efficiency for mobile robotics, and accurately estimating gas features for critical tasks such as odor mapping, localization, and alarm generation. This paper introduces a hybrid approach that exploits neuromorphic computing in combination with probabilistic inference to address these demanding requirements. Our approach implements a combination of a convolutional spiking neural network for feature extraction and a Bayesian spiking neural network for odor detection and identification. The developed algorithm is rigorously tested on a dataset for sensor drift compensation for robustness evaluation. Additionally, for efficiency evaluation, we compare the energy consumption of our model with a non-spiking machine learning algorithm under identical dataset and operating conditions. Our approach demonstrates superior efficiency alongside comparable accuracy outcomes.<|reference_end|>
@article{kausar2024efficient,
    title={Efficient Hybrid Neuromorphic-Bayesian Model for Olfaction Sensing:
  Detection and Classification},
    author={Rizwana Kausar, Fakhreddine Zayer, Jaime Viegas, and Jorge Dias},
    journal={arXiv preprint arXiv:2407.04714},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.04714},
    primaryClass={eess.SP cs.RO}
}