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.

Pesticide overuse poses significant risks to human health and environmental integrity. Addressing the limitations of existing approaches, which struggle with the diversity of pesticide compounds, portability issues, and environmental sensitivity, this paper introduces Hornbill. A wireless and battery-free electrochemical bio-tag that integrates the advantages of NFC technology with electrochemical biosensors for portable, precise, and touchless multi-pesticide detection. The basic idea of Hornbill is comparing the distinct electrochemical responses between a pair of biological receptors and different pesticides to construct a unique set of feature fingerprints to make multi-pesticide sensing feasible. To incorporate this idea within small NFC tags, we reengineer the electrochemical sensor, spanning the antenna to the voltage regulator. Additionally, to improve the system's sensitivity and environmental robustness, we carefully design the electrodes by combining microelectrode technology and materials science. Experiments with 9 different pesticides show that Hornbill achieves a mean accuracy of 93% in different concentration environments and its sensitivity and robustness surpass that of commercial electrochemical sensors.

This paper presents the design and implementation of Fusang, a low-barrier system that brings accurate and robust 3D object recognition to Commercial-Off-The-Shelf mmWave devices. The basic idea of Fusang is leveraging the large bandwidth of mmWave Radars to capture a unique set of fine-grained reflected responses generated by object shapes. Moreover, Fusang constructs two novel graph-structured features to robustly represent the reflected responses of the signal in the frequency domain and IQ domain, and carefully designs a neural network to accurately recognize objects even in different multipath scenarios. We have implemented a prototype of Fusang on a commodity mmWave Radar device. Our experiments with 24 different objects show that Fusang achieves a mean accuracy of 97% in different multipath environments. The code, dataset, and trained models of Fusang can be obtained at https://github.com/OpenNISLab/Pro-Fusang.

Recent advances in resistive synaptic devices have enabled the emergence of brain-inspired smart chips. These chips can execute complex cognitive tasks in digital signal processing precisely and efficiently using an efficient neuromorphic system. The neuromorphic synapses used in such chips, however, are very sensitive to the external environment, thereby weakening their resistance to malicious modifications such as hardware Trojans and backdoors. Accordingly, in this paper, we propose HTcatcher, a security verification technique for hardware threat detection in neuromorphic computing systems, incorporating finite state machine and feature verification simultaneously, which has never been considered in prior work. Furthermore, we propose a pseudo-random matrix verifying technique for memory optimization, which can reduce the memory overhead of the multi-dimensional features in the system significantly. Experimental results confirm that the proposed method can identify the malicious modifications in the system accurately, while reducing the memory usage by 25%-50%.