Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection

Millimeter-wave (MMW) imaging is emerging as a promising technique for safe security inspection. It achieves a delicate balance between imaging resolution, penetrability and human safety, resulting in higher resolution compared to low-frequency microwave, stronger penetrability compared to visible light, and stronger safety compared to X ray. Despite of recent advance in the last decades, the high cost of requisite large-scale antenna array hinders widespread adoption of MMW imaging in practice. To tackle this challenge, we report a large-scale single-shot MMW imaging framework using sparse antenna array, achieving low-cost but high-fidelity security inspection under an interpretable learning scheme. We first collected extensive full-sampled MMW echoes to study the statistical ranking of each element in the large-scale array. These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude. Additionally, we derived an untrained interpretable learning scheme, which realizes robust and accurate image reconstruction from sparsely sampled echoes. Last, we developed a neural network for automatic object detection, and experimentally demonstrated successful detection of concealed centimeter-sized targets using 10% sparse array, whereas all the other contemporary approaches failed at the same sample sampling ratio. The performance of the reported technique presents higher than 50% superiority over the existing MMW imaging schemes on various metrics including precision, recall, and mAP50. With such strong detection ability and order-of-magnitude cost reduction, we anticipate that this technique provides a practical way for large-scale single-shot MMW imaging, and could advocate its further practical applications

Preparation and Wave-absorbing Performance of Retinyl Schiff Base Salts Coordinated with Rare Earth La³⁺ and Ce³⁺ Ions

With vitamin A acetate as raw material, ethylenediamine retinyl Schiff base salts coordinated with rare earth ion La3+, Ce3+ were synthesized, and the structure of the products was characterized by FT-IR and Raman spectra. The electromagnetic parameters in the frequency range of 2-18 GHz were measured by microwave vector network analyzer, its reflectivity was calculated, and the main factors and mechanism that affect the wave-absorbing performance of rare retinyl Schiff base salts were discussed. The results show that the coordination bond is formed by the rare earth ions and Schiff base. The reflectivity of Schiff base coordinated with La3+ ion is –16 dB at 12.9 GHz (bandwidth with reflectivity better than –10 dB is 3.1 GHz), the reflectivity of Schiff base coordinated with Ce3+ ion is –18.8 dB at 12.7 GHz (bandwidth with reflectivity better than –10 dB is 3.4 GHz). Microwave absorption performances of the both are superior to the wave-absorbing performance of Schiff base coordinated with non-rare earth ions

Millimeter-Wave Image Deblurring via Cycle-Consistent Adversarial Network

Millimeter-wave (MMW) imaging has a tangible prospect in concealed weapon detection for security checks. Typically, a one-dimensional (1D) linear antenna array with mechanical scanning along a perpendicular direction is employed for MMW imaging. To achieve high-resolution imaging, the target under test needs to keep steady enough during the mechanical scanning process since slight movement can induce large phase variation for MMW systems, which will result in a blurred image. However, in the scenario of imaging of a human body, sometimes it is difficult to meet this requirement, especially for the elderly. Such blurred MMW images would reduce the detection accuracy of the concealed weapons. In this paper, we propose a deblurring method based on cycle-consistent adversarial network (Cycle GAN). Specifically, the Cycle GAN can learn the mapping between the blurred MMW images and the focused ones. To minimize the effect of the shaking blur, we introduce an identity loss. Moreover, a mean squared error loss (MSE loss) is utilized to stabilize the training, so as to obtain more refined deblurred results. The experimental results demonstrate that the proposed method can efficiently suppress the blurring effect in the MMW image