In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-�...
In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-�...
In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-�...
Various researchers in literature have proposed their model for gas recognition using this own developed DNN model. The author claims to achieve 98.75%�...
The sensitivity and selectivity profiles of gas sensors are always changed by sensor drifting, sensor aging, and the surroundings (e.g., temperature and�...
Sep 4, 2024 � A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition http://mdpi.com/1424-8220/23/5/2433…
In this paper, we develop a bio-inspired spiking neural network (SNN) to recognize nine types of flammable and toxic gases, which supports few-shot class-�...
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The proposed multisensory SNN boasts a recognition accuracy of 95.21% for fire/smoke detection, while remaining highly hardware friendly and, enabling on-chip�...
A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition. Download. Open Access. Profile Image. Dexuan Huo.
A Bio-Inspired Spiking Neural Network with Few-Shot Class-Incremental Learning for Gas Recognition � Engineering, Computer Science. Sensors � 2023.