In recent years, WiFi-based gesture recognition (WGR) has gained popularity due to its privacy-preserving nature and the wide availability of WiFi infrastructure. However, existing WGR systems suffer from scalability issues, i.e., requiring extensive data collection and re-training for each new gesture class. To address these limitations, we propose OneSense, a one-shot WiFi-based gesture recognition system that can efficiently and easily adapt to new gesture classes. Specifically, we first propose a data enrichment approach based on the law of signal propagation in physical world to generate virtual gestures, enhancing the diversity of the training set without extra overhead of real sample collection. Then, we devise an aug-meta learning (AML) framework to enable efficient and scalable few-short learning. This framework leverages two pre-training stages (i.e., aug-training and meta-training) to improve the model’s feature extraction and generalization abilities, and ultimately achieves accurate one-shot gesture recognition through fine-tuning. Experimental results demonstrate that OneSense achieves 93% one-shot gesture recognition accuracy, which outperforms the state-of-the-art approaches. Moreover, it maintains high recognition accuracy when facing new environments, user locations, and user orientations. Furthermore, the proposed AML framework reduces 86%+ pre-training latency compared to conventional meta-learning method.

Optical camera communication (OCC) holds potential for location-aware data transfer, facilitating applications such as localization and overlaying digital content for mixed reality experiences. However, existing OCC designs commonly require a clean background for reliable demodulation, rendering its use disruptive and impractical. To this end, we propose WinkLink, a novel OCC system capable of robust transmission behind complex backgrounds, even under low signal-to-noise ratio (SNR) conditions. We address the key challenge of extracting subtle signals in the lossy OCC channel by designing a two-stage deep neural network and a context-aware demodulation protocol. The proposed system is trained solely on a synthesized dataset yet generalizes effectively to unseen real-world backgrounds. Through experiments in 12 diverse environments, we demonstrate that WinkLink successfully transmits OCC signals under a low SNR of -20 dB, achieving a substantial 5.8 dB SNR gain. This low SNR translates to an extended distance to 5.5× of baseline (11m with a 10W LED transmitter) and negligible interference on concurrent vision applications. Finally, WinkLink proves its efficacy even when the device is moving, i.e., dynamic backgrounds, making it ready for deployment on mobile devices.

Wireless charging is becoming an essential power supply pattern for electronic devices. Currently, mainstream smartphones are almost compatible with wireless charging. However, when the charging efficiency is continuously improved, its security challenge still remains open yet overlooked. In this paper, we reveal that severe security flaws exist in the wireless charging procedure of off-the-shelf commodity smartphones. Specifically, we find that an attacker can utilize the electromagnetic induction effect between the wireless charger and the smartphone to detect the activities and operations performed on the smartphone. We term such attack asEM-Surfingside-channel attack and build a theoretical model to show its feasibility. To explore the hazard ofEM-Surfing, we propose a three-module attack method, with which we conduct real-world experiments over three mainstream models of smartphones. The results show that the attacker can achieve over 99%, 96%, 94%, and 97% accuracy when inferring the passcode, keystroke, App information, and speech content, respectively. We also design an App namedSecChargingto prevent smartphones fromEM-Surfingattacks. The defense experiment results demonstrate thatSecChargingcan mitigate the threats posed byEM-Surfingeffectively.