The widespread and often excessive use of pesticides presents serious risks to human health and environmental safety, calling for IoT-enabled monitoring in real agricultural environments. Current detection methods face challenges in handling diverse pesticide compounds, operating portably, and extracting discriminative signal features. To overcome these limitations, we presentHornbill+, a portable and high-precision electrochemical sensing system. By combining NFC technology with electrochemical biosensing,Hornbill+supports accurate, contactless, and multi-pesticide classification in field-friendly settings. The principle ofHornbill+involves recording electron transfer behaviors of selected biological materials under varying electrode potentials, producing time-variant electrochemical fingerprints that reflect distinct reaction signatures for different pesticides. To implement this approach, we developed a dual-channel fully differential potentiostat integrated into a low-power NFC tag, using DPV as the electrochemical readout method to enhance detection sensitivity. To enhance accuracy in complex real-world scenarios, we integrated a pyramid attention mechanism into a deep learning model for interpreting electrochemical dynamics.Hornbill+achieves over 93% average accuracy across 18 pesticides, five concentrations, and nine mixtures, surpassing existing techniques in both precision and coverage.

Contact-free finger gesture recognition unlocks plenty of applications in smart Human-Computer Interaction (HCI). However, existing solutions either require users to wear sensors on their fingers or use continuously monitored cameras, raising concerns regarding user comfort and privacy. In this paper, we propose mmFinger, an accurate and robust mmWave-based finger gesture recognition system that can extend the range of available custom commands. The core idea is that mmFinger leverages the finger tapping pattern as a basic gesture and encodes different number combinations of the basic gesture like Morse code. To enable reliable recognition across different locations and for various users, we carefully design a robust feature Dop-profile to effectively characterize finger movements. Furthermore, by leveraging the multi-views provided by multiple antennas of radar, we develop an adaptive weighted feature fusion network to enhance the system's robustness. Finally, we devise a novel sequence prediction network to enable the system to recognize new gestures without retraining. Comprehensive experiments demonstrate that mmFinger can achieve an average recognition accuracy of 92% for 36 predefined gestures and 88% for 5 new user-defined commands, and is robust against finger location and user diversity.

Long-range backscatter communication has the potential to provide enough time and space for vehicles to detect traffic information, which is an attractive solution for Vehicle-to-Infrastructure (V2I) communication. However, existing backscatter studies either require the tag to be close to the carrier source (ambient backscatter) or have poor receiver sensitivity (RFID), making it challenging to satisfy the high range requirements of V2I communication. In this article, we develop AllSpark to investigate the feasibility of long-range communication enabling backscatter to be applied to V2I. Specifically, to increase RSS to compensate for the enormous dual-path loss in backscatter systems, we first redesign the tag’s radio frequency (RF) front-end to amplify the incident signals, and then we design high-gain directional antennas for the tag and reader. Second, we present EC-Net, an end-to-end demodulator that selectively enhances signal features (denoising) and is tuned to lower classification (demodulation) error to maximize demodulation accuracy. Furthermore, we adopt convolutional encoding for tag data and use the output probability of EC-Net to design an effective soft decoder to resist occasional interference and noise, improving communication robustness. Our prototype and experiments outdoors verify the effectiveness of AllSpark which can provide a communication range of 700 m. Even in a moving scene, it can achieve a communication range of 600 m, demonstrating that AllSpark has the potential to be applied for autonomous driving and low-flying drones to detect traffic conditions.

This paper introduces a wireless phase calibration algorithm that can be applied on cheap commercial off-the-shelf (COTS) radio frequency identification (RFID) system and auto acquire an accurate radio frequency (RF) phase information without any offline training. The key observation is that the raw phase measurements even measured form different RFID tags contain a same set of unknown phase errors. With enough tags' phase measurements, we can determine all the unknown phase errors, since the number of known phase measurements is much larger than the number of unknown phase errors. Real-world experimental results demonstrate the effectiveness of the proposed method.