Food and Liquid Sensing in Practical Environments using RFIDs
Food and Liquid Sensing in Practical Environments using RFIDs
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
U. Ha;Junshan Leng;Alaa Khaddaj;Fadel M. Adib
中科院分区：
文献类型：
--
作者：
U. Ha;Junshan Leng;Alaa Khaddaj;Fadel M. Adib
我们提出了RF-EATS的设计和实现，该系统可以在封闭的容器中感应食物和液体，而无需打开它们或与其内容物进行任何接触。RF-EATS使用无源反向散射标签（例如，RFID）放置在容器上，并利用标签天线和容器内容物之间的近场耦合来非侵入性地感测它们。与侵入式或需要严格测量条件的现有方案相比，RF-EATS是非侵入式的，不需要任何校准;它可以鲁棒地识别实际室内环境中的内容，并推广到不可见的环境。这些能力是可能的学习框架，适应最新进展的变分推理的RF感测问题。该框架引入了一个RF内核，并结合了一个传输模型，使其能够以样本高效的方式推广到新的内容，使用户能够使用少量的测量将其扩展到新的推理任务。我们构建了一个RF-EATS的原型，并在七个不同的应用中进行了测试，包括识别假药、掺假婴儿配方奶粉和假冒美容产品。我们的研究结果表明，RF-EATS可以实现超过90%的分类精度的情况下，最先进的RFID传感系统不能比随机猜测更好地执行。
We present the design and implementation of RF-EATS, a system that can sense food and liquids in closed containers without opening them or requiring any contact with their contents. RF-EATS uses passive backscatter tags (e.g., RFIDs) placed on a container, and leverages near-field coupling between a tag’s antenna and the container contents to sense them noninvasively. In contrast to prior proposals that are invasive or require strict measurement conditions, RF-EATS is noninvasive and does not require any calibration; it can robustly identify contents in practical indoor environments and generalize to unseen environments. These capabilities are made possible by a learning framework that adapts recent advances in variational inference to the RF sensing problem. The framework introduces an RF kernel and incorporates a transfer model that together allow it to generalize to new contents in a sample-efficient manner, enabling users to extend it to new inference tasks using a small number of measurements. We built a prototype of RF-EATS and tested it in seven different applications including identifying fake medicine, adulterated baby formula, and counterfeit beauty products. Our results demonstrate that RF-EATS can achieve over 90% classification accuracy in scenarios where state-of-the-art RFID sensing systems cannot perform better than a random guess.