Wireless charging eliminates cumbersome cables, revolutionizing how to charge mobile devices, yet reliably detecting metal foreign objects (e.g., keys, SIM ejectors, paper clips) poses a persistent challenge. This detection is critical, as such objects can inadvertently enter the charging zone, absorb energy, and trigger overheating, diminished efficiency, device damage, and even burns or fires. Existing approaches mainly monitor energy loss at the mobile device to infer intrusions, but this loss mixes inherent system dissipation with object-induced effects, both highly variable across devices and conditions, which often results in missed detections (as found in a range of commercial chargers). In this paper, we present Met-Sentry, a novel system design that takes a fundamentally different approach. Our key insight is that, beyond energy absorption, metal foreign objects also alter the electromagnetic field: they disproportionately attenuate the high-frequency harmonics in the in-band communication waveforms during charging, acting as a low-pass filter and yielding a distinctive, physics-grounded fingerprint of their presence. MetSentry captures and analyzes these fingerprints through a lightweight sensing circuit and a tailored software pipeline that extracts robust, discriminative features, which can be seamlessly integrated into wireless chargers, enabling reliable detection. Extensive experiments with various commercial wireless chargers, smartphones, and metal foreign objects demonstrate that MetSentry consistently outperforms both built-in charger detection and state-of-the-art methods. © 2026 Copyright held by the owner/author(s).

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution.

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

Device authentication plays a crucial role in preventing illegal access and ensuring smooth usage of magnetic wireless charging. However, current authentication techniques suffer from security vulnerabilities and are incompatible with low-cost receiver devices, thus severely limiting their applications. In this paper, we propose FreAuth, a novel Frequency feature-based device Authentication technology for magnetic wireless power transfer systems. Technically, we begin by conducting circuit measurements at the transmitter side to subtly retrieve impedance information related to the receiver without the need for its corporation. Then, we employ a dual-frequency interleaved-based subtraction technique to remove the ideal receiver impedance and capture the fairly weak frequency features. Furthermore, we normalize the captured frequency features to account for environment variations. These steps allow us to generate and store a hardware fingerprint for the receiver based on its frequency features. During device authentication, we use a discrete Frechet distance-based algorithm for fingerprint matching. We devise and implement a prototype of FreAuth and conduct extensive experiments to evaluate the proposed scheme. The experimental results validate the reliability (95.74% authentication accuracy among 60+ devices) and robustness (anti-interference with device location variations) of our FreAuth.

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

Feature extraction is important for non-intrusive appliance load monitoring (NILM), because it includes crucial steps which altogether transform raw signals into distinct features of appliances. Although numerous studies have achieved good results using transient harmonic features, their performances degrade in practical scenarios. In this paper, a time-frequency analysis-based framework for transient harmonic feature extraction is proposed, which takes all factors that would eventually influence NILM system into account. More specffically, the framework proposes a new qualitative transient harmonic feature, along with current retrieval and data augmentation methods. The proposed framework is evaluated on two well-known datasets (i.e., Controlled On/Off Loads Library and Home Equipment Laboratory Dataset). As compared with the other state-of-art methods, the proposed framework achieves similar accuracy (i.e., more than 96%) on single-load data, and more than 33% accuracy improvement over multi-load data (e.g., from 59.4% to 93% when six devices are active at the same time).