Luminescence-based sensors for measuring oxygen concentration are widely used both in industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5 percent air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing of sensors.
Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring
Nanoengineered Systems Laboratory, Mechanical Engineering Department, University College London（大学学院伦敦纳米工程系统实验室，机械工程系，伦敦大学学院）；UCL Hawkes Institute, University College London（UCL霍克斯研究所，伦敦大学学院）；Department of Electrical and Computer Engineering, University of British Colombia（电气与计算机工程系，不列颠哥伦比亚大学）
Accelerating Edge Intelligence via Integrated Sensing and Communication
Southern University of Science and Technology（南方科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
Toward Integrated Sensing, Communications, and Edge Intelligence Networks
CEA-Leti, Université Grenoble Alpes（法国原子能与替代能源委员会列蒂实验室，格勒诺布尔阿尔卑斯大学）；WINGS ICT Solutions（WINGS ICT 解决方案公司）；CNIT（意大利电信与信息技术研究中心）；Sapienza University of Rome（罗马智慧大学）；National and Kapodistrian University of Athens（雅典国立卡波迪斯特里亚斯大学）
Holographic Intelligence Surface Assisted Integrated Sensing and Communication
Fudan University（复旦大学）；University of Electronic Science and Technology of China（电子科技大学）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Weizmann Institute of Science（魏茨曼科学研究所）
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
University of Calgary（卡尔加里大学）；Wuhan University（武汉大学）；China University of Geosciences (Beijing)（中国地质大学（北京））；Beijing University of Posts and Telecommunications（北京邮电大学）
Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC Networks
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Cyber Science and Engineering, Xi’an Jiaotong University（西安交通大学网络空间安全学院）
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
University of Tasmania, Australia（塔斯马尼亚大学）；Macquarie University, Australia（麦考瑞大学）；University of Electronic Science and Technology of China, China（电子科技大学）；Huazhong University of Science and Technology, China（华中科技大学）；University of California, San Diego, USA（加利福尼亚大学圣地亚哥分校）；University of Technology Sydney, Australia（悉尼科技大学）
Curriculum-Guided Heterogeneous Multi-Agent Intelligence for Multi-UAV Cooperative ISAC
University of Electronic Science and Technology of China（电子科技大学）；Nanjing University（南京大学）；Southeast University（东南大学）；China Mobile Zijin Innovation Institute（中国移动紫金创新研究院）
Towards a Collective Agenda on AI for Earth Science Data Analysis
Wageningen University（瓦赫宁根大学）；Ecole Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Bonn（波恩大学）；ETH Zurich（苏黎世联邦理工学院）；University of Kentucky（肯塔基大学）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Universitat de València（瓦伦西亚大学）
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（北京邮电大学计算机学院（国家示范性软件学院））；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）
