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Wireless microphones are essential tools in business, education, entertainment, and other domains. However, most existing designs rely on batteries, leading to the inconvenience of frequent recharging and the risk of unexpected power failure during use. In this paper, we present TagMic, a battery-free wireless microphone enabled by a novel radio frequency (RF) backscatter technology. TagMic is built on two key innovations.(i) Parametric backscatter tag design:This design enables the RF tag to operate at separate excitation and reflection frequencies, fundamentally mitigating the self-interference problem inherent in conventional RFID systems. Unlike harmonic backscatter approaches, it also requires a significantly lower activation voltage, resulting in a longer communication range.(ii) Voice modulation via RF coupling:A passive piezoelectric sensor is integrated with the RF tag through RF coupling to enable analog-domain frequency modulation (FM), directly encoding voice signals onto the backscattered signal. This eliminates the need for digital signal processing, allowing for truly continuous voice streaming. We have built a prototype of TagMic and evaluated it under realistic conditions. Extensive experiments demonstrate its effectiveness in achieving battery-free, continuous, and seamless wireless voice streaming in realistic applications. 

Eye motion tracking plays a vital role in many applications such as human-computer interaction (HCI), virtual reality, and disease detection. Camera-based eye tracking, albeit accurate and easy to use, may raise privacy concerns and appear to be unreliable in poor lighting conditions. In this paper, we present RadEye, a radar system capable of detecting fine-grained human eye motions from a distance. RadEye is realized through an integrated hardware and software design. It customizes a sub-6GHz FMCW radar so as to detect millimeter-level eye movement while extending its detection range using low frequency. It further employs a deep neural network (DNN) to refine the detection accuracy through camera-guided supervisory training. We have built a prototype of RadEye. Extensive experimental results show that it achieves 90% accuracy when detecting human eye rotation directions (up, down, left, and right) in various scenarios. 

This paper aims to design and implement a radio device capable of detecting a person’s handwriting through a wall. Although there is extensive research on radio frequency (RF) based human activity recognition, this task is particularly challenging due to the through-wall requirement and the tiny-scale handwriting movements. To address these challenges, we present RadSee—a 6 GHz frequency modulated continuous wave (FMCW) radar system designed for detecting handwriting content behind a wall. RadSee is realized through a joint hardware and software design. On the hardware side, RadSee features a 6 GHz FMCW radar device equipped with two custom-designed, high-gain patch antennas. These two antennas provide a sufficient link power budget, allowing RadSee to “see” through most walls with a small transmission power. On the software side, RadSee extracts effective phase features corresponding to the writer’s hand movements and employs a bidirectional LSTM (BiLSTM) model with an attention mechanism to classify handwriting letters. As a result, RadSee can detect millimeter-level handwriting movements and recognize most letters based on their unique phase patterns. Additionally, it is resilient to interference from other moving objects and in-band radio devices. We have built a prototype of RadSee and evaluated its performance in various scenarios. Extensive experimental results demonstrate that RadSee achieves 75% letter recognition accuracy when victims write 62 random letters, and 87% word recognition accuracy when they write articles. 

UHF RFID tags have been widely used for contactless inventory and tracking applications. One fundamental problem with RFID readers is their limited tag reading rate. Existing RFID readers (e.g., Impinj Speedway) can read about 35 tags per second in a read zone, which is far from enough for many applications. In this paper, we present the first-of-its-kind RFID reader (mReader), which borrows the idea of multi-user MIMO (MU-MIMO) from cellular networks to enable concurrent multi-tag reading in passive RFID systems. mReader is equipped with multiple antennas for implicit beamforming in downlink transmissions. It is enabled by three key techniques: uplink collision recovery, transition-based channel estimation, and zero-overhead channel calibration. In addition, mReader employs a Q-value adaptation algorithm for medium access control to maximize its tag reading rate. We have built a prototype of mReader on USRP X310 and demonstrated for the first time that a two-antenna reader can read two commercial off-the-shelf (COTS) tags simultaneously. Numerical results further show that mReader can improve the tag reading rate by 45% compared to existing RFID readers.