most citedUltralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification

2 citations · 2 across the 1 of their papers we have counts for

collaborators

1 paper

physics.app-ph2024★ 2 cited

Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification

Taejung Kim, Yonggi Kim, Wootaek Cho +5

This study presents a novel, ultralow-power single-sensor-based electronic nose (e-nose) system for real-time gas identification, distinguishing itself from conventional sensor-arr…