Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer novel query triples for few-shot relations from limited references. Despite promising progress, existing methods face two key challenges: (1) They often overlook rich higher-order neighbors, while traditional high-order aggregation methods are prone to introducing noise and lack effective alignment across multi-view neighborhood information. (2) Meta-learning methods over-rely on embeddings, making them susceptible to spurious relational patterns. Meanwhile, LLM-based methods, despite their potential, suffer from hallucinations and input constraints. To this end, we propose a novel framework that combines meta-learning, enhanced via a Local-Global Contrastive network, with LLM-guided Contextual Refinement (LGC-CR). At the data level, we design a local-global contrastive network to jointly aggregate relevant local features and capture stable global representations while filtering high-order noise, then align these two views through a dual contrast module to ensure consistency. At the model level, we employ an LLM refinement module, which retrieves relevant contexts to construct prompts and applies a knowledge selector to identify high-quality facts based on diversity and centrality, enabling efficient fine-tuning of LLMs to refine the preliminary predictions of meta-learning. The experimental results demonstrate that LGC-CR delivers better and more robust performance than state-of-the-art baselines, with Hit@1 improvements of 8.1%, 21.7%, and 20.6% on NELL, Wiki, and FB15K, respectively.

Knowledge graph question answering (KGQA) aims to answer natural language questions using knowledge graphs.Recent research leverages large language models (LLMs) to enhance KGQA reasoning, but faces limitations: retrieval-based methods are constrained by the quality of retrieved information, while agentbased methods rely heavily on proprietary LLMs.To address these limitations, we propose Retrieval-Judgment-Exploration (RJE), a framework that retrieves refined reasoning paths, evaluates their sufficiency, and conditionally explores additional evidence.Moreover, RJE introduces specialized auxiliary modules enabling small-sized LLMs to perform effectively: Reasoning Path Ranking, Question Decomposition, and Retriever-assisted Exploration.Experiments show that our approach with proprietary LLMs (such as GPT-4o-mini) outperforms existing baselines while enabling small open-source LLMs (such as 3B and 8B parameters) to achieve competitive results without fine-tuning LLMs.Additionally, RJE substantially reduces the number of LLM calls and token usage compared to agent-based methods, yielding significant efficiency improvements.1

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.

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems have shown significant promise in efficiently charging multiple devices simultaneously through beamforming technology. The existing works propose various mechanisms for achieving better charging performance, but they still lack exploration of transmitter (TX) coil orientation adjustment and rarely consider the dynamic deployment of TXs. In this work, we propose the D istributed A daptive E xploration and E xploitation ( DAEE ) algorithm for orientation adjustment in MRC-WPT systems, which includes both hardware and software innovations. The hardware component features a servo motor-based mechanical device that adjusts the TX coil orientation. In the software aspect, we decompose the charging performance optimization problem and devise a distributed orientation control algorithm combining exploration and exploitation mechanisms. We develop a system prototype for the DAEE algorithm and conduct extensive experiments to validate its performance. Specifically, the TX orientation adjustment significantly enhances performance, achieving an average 103% improvement in power-delivered-to-load (PDL) compared to state-of-the-art frequency adjustment-based solutions that do not adjust orientation. Additionally, the combination of exploration and exploitation strategies in the DAEE algorithm proves effective, delivering a 24% performance improvement over the random beamforming (RB)-based exploration method.

Hand gesture recognition (HGR) is essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., demanding multi-task ability to support newly arrived HGR tasks. In this paper, we propose the first IMU-vision based system hosted on wrist-worn devices to support multi-task HGR, denoted as MultiHGR. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gesture through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism. Since only the second task-related stage should be executed for each new task, MultiHGR could accommodate multiple tasks with significant reduced training cost and storage requirement. The evaluation results on three HGR tasks demonstrates that MultiHGR reduces 64.92% training time, and 24.04% storage as compared with traditional multimodal single-task models, and MultiHGR outperforms unimodal single-task models with 14.37%, 19.28%, and 31% improvements in these three tasks, respectively. As compared with state-of-the-art multimodal single-task model, MultiHGR achieves average 6.35% accuracy improvement, along with 65.74% training time reduction.

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.

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn’t be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems can charge multiple devices concurrently and efficiently via beamforming technology. The existing work proposes various mechanisms for achieving better charging performance, but still lacks the exploration about transmitter (TX) coil direction adjustment, and rarely considers the dynamic deployment of TXs. Thus, in this paper, we propose a Distributed Probability-adaptive Direction Adjustment algorithm for MRC-WPT systems (called DPDA). On the one hand, we design and implement a servo motor-based mechanical device to realize the ability of TX coil direction adjustment. On the other hand, we decompose the charging performance optimization problem, and then devise a distributed direction control algorithm based on random beamforming. We implement the system prototype for DPDA, and conduct extensive experiments on it. The experimental results demonstrate the effectiveness of the proposed algorithm, e.g., in case of large TX-RX horizontal misalignment, DPDA can achieve an average 1.98X improvement of power-delivered-to-load (PDL) as compared with the state-of the-art frequency adjustment-based solution.

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX, TX-TX, or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching–enhanced PDL optimization in MIMO MRC-WPT systems (called IMeP ), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX/TX-TX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping. We then solve them through alternating direction method of multipliers–based, randomized beamforming–based, and graph clique cover–based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency maximization solution, the proposed algorithm IMeP achieves a 74.7× performance improvement of PDL on average.

Wireless power transfer (e.g., based on RF or magnetic) enables convenient device charging, and triggers innovative applications that typically call for faster, smarter, economic, and even simultaneous adaptive charging for multiple smart devices. Designing such a wireless charging system meeting these multi-requirements faces critical challenges, mainly including the better understanding of real-time energy receivers’ status and the power-transferring channels, the limited capability and the smart coordination of the transmitters and receivers. In this work, we devise Camel , a context-aware MIMO MRC-WPT (magnetic resonant coupling based wireless power transfer) system, which enables adaptive charging of multiple devices simultaneously with a novel context sensing scheme. In Camel , we craft an innovative MIMO MRC-WPT channels’ state estimation and collision-aware parallel in-band communication among multiple transmitters and receivers. We design and implement the Camel prototype and conduct extensive experimental studies. The results validate our design and demonstrate that Camel can support simultaneous charging of as many as 10 devices, high-speed context sensing within 50 ms, and efficient parallel communication among transceivers within proximity of ∼0.5 m.

Magnetic resonant coupling (MRC) wireless power transfer (WPT) is a convenient and potential power supply solution for smart devices. The scheduling problem in the multiple-input multiple-output (MIMO) scenarios is essential to concentrate energy at the receiver (RX) side. Meanwhile, strong TX-RX coupling could ensure better power transfer efficiency (PTE), but may cause lower power delivered to load (PDL) when transmitter voltages are bounded. In this paper, we propose the frequency adjustment based PDL maximization scheme for MIMO MRC-WPT systems. We formulate such joint optimization problem and decouple it into two sub-problems, i.e., high-level frequency adjustment and low-level voltage adaptation. We solve these two sub-problems with gradient descent based and alternating direction method of multipliers (ADMM) based algorithms, respectively. We further design an energy-voltage transform matrix algebra based estimation mechanism to reduce context measurement overhead. We prototype the proposed system, and conduct extensive experiments to evaluate its performance. As compared with the PTE maximization solutions, our system trades smaller efficiency for larger energy, i.e., 361% PDL improvement with respect to 26% PTE losses when TX-RX distance is 10cm.

The great popularity of smartwatches leads to a growing demand for smarter interactive systems. Hand gesture is suitable for interaction due to its unique features. However, the existing single-modal gesture interactive systems have different biases in diverse scenarios, which makes it intractable to be applied in real life. In this paper, we propose a multi-modal smartwatch interactive system named Mudra, which fuses vision and Inertial Measurement Unit (IMU) signals to recognize and identify hand gestures for convenient and robust interaction. We carefully design a parallel attention multi-task model for different modals, and fuse classification results at the decision level with an adaptive weight adjustment algorithm. We implement a prototype of Mudra and collect data from 25 volunteers to evaluate its effectiveness. Extensive experiments demonstrate that Mudra can achieve 95.4% and 92.3% F1-scores on recognition and identification tasks, respectively. Meanwhile, Mudra can maintain stability and robustness under different experimental settings.

Recently, the developed techniques such as magnetic resonant coupling (MRC) and multiple-input multiple-output (MIMO) transmission have significantly improved the charging efficiency and distance for wireless power transfer (WPT) systems. However, the electromagnetic radiation (EMR) safety of wireless charging is critical in practice while mostly ignored. In this work, we take the EMR safety into account in MIMO MRC-WPT systems. We propose a safety ensured high-efficient scheduling algorithm for magnetic MIMO wireless power transfer system (called Shield). Technically, we firstly devise a simple but accurate Z-axis rotational symmetrical EMR model along with a magnetic-field-line-based meshing scheme. Further, we express the EMR safety requirement in the continuous physical space with a limited number of constraints via random sampling and rule-based filtering. Finally, we build up a system prototype for Shield and conduct extensive experiments. With the given power budget and resonant frequency, the results reveal that the EMR safety requirement only influences the charging performance of an MRC-WPT system within a certain range. Furthermore, Shield can dramatically improve the payload power transfer efficiency (PTE) by up to 66.60% compared with state-of-the-art baselines while guaranteeing the EMR safety.

In recent years, multiple-input multiple-output (MIMO) technology has been imported into magnetic resonance coupled (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. Besides the traditional performance optimization methods (e.g., TX current scheduling, system frequency adjustment, etc.), receiver (RX) grouping will also severely influence the achieved power- delivered-to-load (PDL). In this paper, we investigate the optimal RX grouping issue to maximize the proportional fairness of RX achieved PDL, which is a joint optimization problem involving RX grouping and time-slice allocation among groups. By decoupling the problem, we solve the group generation sub-problem with a impedance-matching based greedy algorithm to generate potential RX group candidates, and we further solve the time slice allocation sub-problem with a genetic algorithm to distribute resources among group candidates. We prototype the proposed system, denoted as IMRG, and conduct extensive experiments to evaluate the performance. The experimental results validate the effectiveness of the proposed algorithm, e.g., IMRG achieves average 59.6% PDL improvement through RX grouping compared to the simultaneous charging scheme.

Magnetic wireless power transfer (WPT) has received widespread attention from both academia and industry, and magnetic resonance coupling (MRC) based WPT systems have a longer charging distance to support the scenarios with multiple transmitters (TXs) and multiple receivers (RXs). In such systems, an in-band reliable TX-RX communication protocol is essential to guarantee to charge performance. In this paper, we devise Onion, a dependency-aware in-band communication protocol for MIMO MRC-WPT systems. Technically, we extend the well-known EPCglobal C1G2 protocol in Radio Frequency Identification (RFID) fielded and make it suitable for mutual inductance based communication links in MRC-WPT systems. Furthermore, we craft an innovative onion-style layer-dependency based communication mechanism to utilize the positive impact of the relay phenomenon. We design and implement the Onion prototype and conduct extensive experiments to evaluate it. The experiment results demonstrate the effectiveness of the proposed protocol, which increases the communication success ratio by an average of 40% as compared to the dependency-unaware scheme.

Wireless power transfer (e.g., based on RF or magnetic) enables convenient device-charging, and triggers innovative applications that typically call for faster, smarter, economic, and even simultaneous adaptive charging for multiple smart-devices. Designing such a wireless charging system meeting these multi-requirements faces critical challenges, mainly including the better understanding of real-time energy receivers' status and the power-transferring channels, the limited capability and the smart coordination of the transmitters and receivers. In this work, we devise Camel, a context-aware MIMO MRC-WPT (magnetic resonant coupling-based wireless power transfer) system, which enables adaptive charging of multiple devices simultaneously with a novel context sensing scheme. In Camel, we craft an innovative MIMO WPT channels' state estimation and collision-aware in-band parallel communication among multiple transmitters and receivers. We design and implement the Camel prototype and conduct extensive experimental studies. The results validate our design and demonstrate that Camel can support simultaneous charging of as many as 10 devices, high-speed context sensing within 50 milliseconds, and efficient parallel communication among transceivers within proximity of ~0.5m.

Fine-grained energy consumption analysis has great potential value in applications of Smart Grids, renewable energy, and Artificial Intelligence of Things. Non-Intrusive Load Monitoring (NILM) is a single-sensor alternative to the conventional one-sensor-for-one-appliance solution due to its ability to deduce individual appliances states from mixed measurements from the main power interface. Despite its advantages of low cost and easy maintenance, a few drawbacks hinders its widespread adoption. To enhance the Quality of Service (QoS) of NILM, four objectives should be achieved by careful designing: high accuracy, user transparency, low response delay, and low data redundancy.Inspired by observations of discriminative yet redundant current waveform and model sparsity, we propose LCL, a lightweight, contactless, plug-and-play solution for real-time load monitoring. The filtering module skips over unchanged input and compresses the measurements of interest using Compressed Sensing. The reconstruction-free inference module runs an attentional multi-label classification and returns all functioning appliance states directly from the compressed input. The compression module leverages model sparsity for real-time processing on edge devices. Evaluations based on our prototype deployed in real-life scenarios attest to the high QoS of LCL with a subset accuracy of 94.2% and a delay reduction of 52.2%. Our solution further filters out 96.8% of the redundant input and attains a Measurement Rate of 0.1 without noticeable impact on the performance.

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching enhanced PDL optimization in MIMO MRC-WPT systems (called IMP), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping, and solve them through alternating direction method of multipliers (ADMM) based, tabu search (TS) based, and graph clique cover based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency (PTE) maximization solution, the proposed algorithm IMP achieves a 74.7X performance improvement of PDL on average.

WiFi-based gait recognition is an appealing device-free user identification method, but the environment-sensitive WiFi signal hinders it from easy deployment for a new environment. On the other hand, the Inertial Measurement Unit (IMU) based method could obtain environment-independent gait features, however, it suffers from uncomfortable experiences due to device wearing. In this paper, we propose IMFi, a novel cross-modal gait recognition system to achieve device-free and easy deployment at the same time. We carefully choose the torso and foot speed curves as common features for cross-modal matching. In the enrollment phase, we extract and store the environment-independent IMU-based gait features with two IMU devices attached to the waist and ankle, respectively. In the recognition phase, we retrieve environment-related CSI-based gait features for user identification, along with the environment adaptive Principal Component Analysis (PCA) selection method for better noise reduction. We perform cross-modal matching between IMU and CSI-based features through a simple Convolution Neural Network (CNN) with a limited number of trained environments. The effectiveness of the proposed system is verified via extensive experiments. The results demonstrate that IMFi could be easily deployed to the new environment without the need for retraining. Specifically, our proposed system achieves 85% binary classification accuracy and 96% top-3 multi-class classification accuracy in the new environment.

Backscatter communication integrated with RF energy harvesting provides a promising solution to prolong the lifetime of wireless sensor networks (WSNs). However, the existing centralized or flooding-based routing protocols can not be applied directly to large-scale backscatter-enabled WSN due to fussy implementation or excessive messages. In this paper, we investigate the routing protocol for such networks to maximize the throughput by arranging the uploading path of each sensor. We first propose a centralized solution by converting the original problem into a maximum flow problem. Then, after inspecting the characteristics of the backscatter-enabled sensors, we propose a flow balancing-based push-relabel algorithm. We conduct extensive experiments to evaluate the proposed algorithms. The results demonstrate the effectiveness of our distributed protocol, which outperforms the other baseline solutions and keeps close approximation to the centralized solution.

The rapid proliferation of Smart Grids calls for a more in-depth understanding of user energy consumption behaviors, based on large data volumes collected by various sources of sensors such as voltmeter and ammeter. Non-Intrusive Load Monitoring (NILM) is a single sensor solution, which can effectively disaggregate individual appliance states from measurements only at the interface to the power source, albeit at the cost of requiring circuit modifications thus introducing suspension of services and potential safety hazards. To overcome the undesirable attribute of NILM and achieve a safe yet highly accurate solution, we devise a contactless sensing system based on inductive current measurements that can conduct load disaggregation without tampering with the power system. Despite using single modality, i.e., the inductive current, our scheme attains state-of-the-art accuracy in existing multi-modality datasets by leveraging modality distillation technique to handle arbitrary input structure. Our main contributions enlist: (1) devising and deploying the first, to the best of our knowledge, purely contactless non-intrusive load disaggregation system; (2) the design of an oracle-apprentice network structure to leverage multi-modality input for training, while operating with single modality; (3) a high estimation accuracy of 95.44% and 96.21%, respectively, is attested on two public datasets, which proves the efficiency of our method.