Ramesh Kumar, R. K. Ratnesh, Jay Singh, Ashok Kumar, Ramesh Chandra
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引用次数: 0
摘要
本文重点关注当前对最新工业革命的重视,尤其是人工智能与物联网(IoT)的创新融合。研究探讨了电阻抗层析成像(EIT)与物联网的无缝集成,提出了一个开创性的框架,其中基于阻抗的传感在增强物联网生态系统的动态和适应性方面发挥了重要作用。这一贡献有助于智能决策和实时监控。该研究调查了无创 EIT 在快速识别人体或模拟物体电阻抗微小变化方面的应用。通过应用高频、低电流信号,安装在人体模型圆柱体两端的电极可测量阻抗变化。图像重建采用正向和反向解决方案,利用三角有限元法网格确定基于推荐模型的传导性分布。物联网的集成实现了数据采集,通过远程监控提高了可访问性。事实证明,新颖的物联网系统有利于各种工程研究应用,可在商业和临床环境中提供易于监测的参数。
IoT-Driven Experimental Framework for Advancing Electrical Impedance Tomography
This paper focuses on the current emphasis on the latest industrial revolution, particularly the innovative integration of artificial intelligence and the Internet of Things (IoT). The study explores the seamless integration of electrical impedance tomography (EIT) with IoT, presenting a groundbreaking framework where impedance-based sensing plays a vital role in enhancing the dynamic and adaptable qualities of IoT ecosystems. This contribution facilitates intelligent decision-making and real-time monitoring. The research investigates the application of non-invasive EIT for the rapid identification of minor changes in the electrical impedance of the body or a simulated object. Electrodes positioned at the ends of the phantom's cylinder measure impedance changes through the application of a high-frequency, low-current signal. Image reconstruction employs both forward and inverse solutions, utilizing a triangular finite element method mesh to determine conductivity distribution based on recommended phantom models. The integration of IoT enables data capture, enhancing accessibility through remote monitoring. The novel IoT system proves advantageous for various engineering research applications, providing easily monitored parameters in both commercial and clinical contexts.