Transient Analysis of Energy Harvester for UHF Wireless Applications

Mondal, Saikat; Cuddihy, Mark; Ghannam, M.Y.; Chahal, Premjeet

doi:10.1109/lmwc.2021.3072665

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…A stable power supply such as a cell battery is usually contained in the system. The passive sensors utilize the existing energy distributed in the environment to perform the sensing operations, such as solar energy harvesting, RF energy harvesting, and thermal energy harvesting [ 16 , 17 , 18 , 19 , 20 ]. For the RFID tag-integrated sensor in manufacturing industry applications, RF energy harvesting is the most reliable approach.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing

Song,

2024

Sensors

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Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.

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