报告主题：工业和农业应用中的无线传感

报 告 人：金海明

报告时间：2023年3月24日 14:00-16:00

报告地点：计D305

报告摘要： 

  Wireless sensing, which applies wireless signals to sense objects, events, or phenomenon without physical contact, has been widely applied for many applications (e.g., gesture recognition, vital sign monitoring, localization). Though promising, existing wireless sensing methods mostly work in controlled environments. Enabling wireless sensing for complicated environments in industrial and agricultural applications still faces many challenges. In this talk, two of our wireless sensing systems designed respectively for industrial and agricultural applications will be presented. The first part of this talk focuses on mRotate, a novel and practical mmWave radar-based rotation speed sensing system that enables accurate rotation speed sensing from a safe distance, and is robust to the illumination conditions and the target object’s light reflectivity. We implement mRotate on a commercial mmWave radar and evaluate it in both lab environments and in a machining workshop for field tests. Specifically, mRotate achieves 38% lower error than that produced by a popular commercial laser tachometer. Besides, mRotate can also measure a spindle whose diameter is only 5mm, maintain a high accuracy with a sensing distance as far as 2.5m, and simultaneously measure the rotation speeds of multiple objects. The second part of this talk focuses on SoilId, a novel soil moisture sensing system with UAV-Mounted IR-UWB radar and deep learning. Specifically, we design a series of novel methods to help SoilId extract soil moisture related features from the received radar signals, and automatically detect and discard the data contaminated by the UAV’s uncontrollable motion and the multipath interference. We further leverage the powerful representation ability of deep neural networks and carefully design a neural network model to accurately map the extracted radar signal features to soil moisture estimations. The experimental results carried out by our UAV-based system validate that SoilId can push the accuracy limits of RF-based soil moisture sensing techniques to a 50% quantile MAE of 0.23%.

报告人简介：

  金海明，上海交通大学计算机科学与工程系长聘教轨副教授，上海交通大学约翰·霍普克罗夫特计算机科学中心副主任，博士生导师。金海明长期从事群智感知、无线感知、城市计算、强化学习等方面的研究，在IEEE/ACM TON、IEEE TMC等国际期刊和IEEE INFOCOM、ACM UbiComp、ACM KDD、ACM MobiHoc等国际会议上发表和录用学术论文50余篇，任领域内知名国际会议IEEE INFOCOM、ACM MobiHoc、IEEE ICDCS的程序委员，于2021年入围CCF A类会议IEEE INFOCOM的最佳论文提名。