Deep Learning Design Framework: High-Precision Front-End RFEH for Food Monitoring Applications

Abstract

This article introduces an innovative deep learning design framework for front-end radio frequency energy harvesting (RFEH), using stacking ensemble learning (SEL). The framework integrates multioutput regression (MOR), multilabel classification (MLC), and multivariate time series (MTS) in sequence to achieve accurate prediction (R2 > 0.90) and classification (>0.95) at each stage. By transforming frequency to datetime and consolidating output parameters into consecutive time intervals, our approach intelligently combines receiving antennas, impedance matching networks (IMNs), and voltage multipliers (VMs) through MOR and MLC, while MTS guides the determination of geometrical parameters and values. To validate our method, we developed an efficient food sensor tag embedded with an RFEH circuit operating at 915 MHz. The tag, featuring antennas designed by MTS [including an inset-fed rectangular microstrip patch antenna (RMPA), a folded collinear antenna (FCA), and a quasi-Yagi antenna (QYA)], achieves a power conversion efficiency (PCE) of 55.17% with an input power of 6.25 dBm, demonstrating stable sensing capabilities. This integrated deep learning framework holds promise for tailored front-end RFEH design in food quality monitoring application, with significant implications across industries.