文献类型：期刊文献

英文题名：Low-power adaptive sampling electronic nose system with a Radon transform-based convolutional neural network for optimized gas recognition

作者：Li, Zhuoheng[1,2];Wang, Tao[3];Yang, Jianhua[1,2];Zhu, Yudi[1,2];Ni, Wangze[1,2];Li, Xiuwei[1,2];Fang, Hongyi[4,5];Zeng, Min[1];Hu, Nantao[1,2];Yang, Zhi[1]

机构：[1]Shanghai Jiao Tong Univ, Natl Key Lab Adv Micro & Nano Manufacture Technol, Shanghai 200240, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Dept Micro Nano Elect, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect Tech, Shanghai 200237, Peoples R China;[4]AECC Sichuan Gas Turbine Estab, Mianyang 621000, Peoples R China;[5]Northwestern Polytech Univ, Natl Elite Inst Engn, Xian 710072, Peoples R China

年份：2025

卷号：423

外文期刊名：SENSORS AND ACTUATORS B-CHEMICAL

收录：;EI(收录号：20244117182171);WOS:【SCI-EXPANDED(收录号:WOS:001333961700001)】；

基金：This work was supported by the National Key Research and Development Program of China (2022YFB3205500) , the National Natural Science Foundation of China (62371299, 62301314, and 62101329) , the China Postdoctoral Science Foundation (2023M732198) , and the Natural Science Foundation of Shanghai (23ZR1430100) . We also acknowledge analysis support from the Instrumental Analysis Center of Shanghai Jiao Tong University and the Center for Advanced Electronic Materials and Devices of Shanghai Jiao Tong University. The computations in this paper were run on the pi 2.0 cluster supported by the Center for High Performance Computing at Shanghai Jiao Tong University.

语种：英文

外文关键词：Adaptive sampling; Electronic nose; Gas sensing; Low power consumption; Radon transform

摘要：The electronic nose (E-nose) systems provide great promise in capturing information about the identity of gaseous chemical analytes as well as their temporal variation. However, the regular and dense sampling events of the E-nose often lead to a substantial amount of redundancy in the temporal structure, which in turn limits their application in efficiency-sensitive scenarios such as advanced robotics and the Internet of Things. Herein, we introduce a novel operating mechanism for E-noses, which dynamically adjusts the time interval of operation to reduce power consumption and information content through an adaptive sampling mechanism. Three strategic schemes with different operating principles are presented to implement this mechanism: the accurate scheme targets the optimized discrimination accuracy, the vague scheme is oriented toward power-sensitive scenarios, and the balanced scheme aims to balance cost and benefit. In order to ensure that the adaptively sampled inhomogeneous data can be compatible with each other, we further propose an algorithm based on the Radon transform that can convert non-uniform time series with timestamp information into the same size tensor. Finally, we designed a convolutional neural network-based classification model for gaseous chemical analytes, providing guidance on scheme and parameter selection with an accuracy of up to 99 % and power savings of up to 96 %. Overall, this work provides a novel solution to optimize temporal redundancy in E-nose systems and a generalized approach to the corresponding deep-learning data processing.