Haishan Tian

Optical bistability (OB) is a fundamental nonlinear effect enabling all-optical switching, logic, memory, and sensing, yet its practical implementation is often hindered by high power thresholds and limited tunability. Here, we theoretically demonstrate low-threshold and widely tunable OB in a Fabry-Pérot (FP) cavity integrated with a Weyl semimetal (WSM) layer. By combining the strong local-field enhancement of the FP cavity with the large third-order nonlinear conductivity of the WSM, we achieve pronounced bistable hysteresis loops in both the transmitted field amplitude and transmittance at terahertz frequencies, with incident field switching thresholds on the order of 10⁶ V/m. The bistability thresholds and hysteresis width can be flexibly controlled by tuning the WSM Fermi energy, the position of the WSM layer inside the cavity, and the mirror transmittance. Moreover, exploiting the strong dependence of the down-switching threshold on small changes in the refractive index of the cavity medium, we propose a terahertz refractive-index sensor with a maximum sensitivity of 296.8 GW·m⁻²·RIU⁻¹. These results provide a theoretical basis for tunable nonlinear photonic devices and threshold-based terahertz refractive-index sensors using topological semimetals.

Optical bistability (OB) with low threshold and high tunability is crucial for advanced photonic devices. This work theoretically investigates low-threshold and tunable OB in a one-dimensional photonic crystal Fabry-Pérot (FP) cavity embedded with a Weyl semimetal (WSM) layer. By combining the local field enhancement of the cavity and the large third-order nonlinear refractive index of the WSM, we achieve OB in the terahertz regime with a low threshold of ~10⁵ V/m. Furthermore, the OB threshold and hysteresis width can be flexibly manipulated via the Fermi energy of WSM, incident angle, photonic crystal period, the refractive index of the dielectric inside the cavity, and the position of the WSM inside the FP cavity. Moreover, based on the high sensitivity of switching thresholds to refractive index and displacement variations, we also propose a dual-parameter sensing scheme. This exhibits excellent sensing performance for gas sensing (sensitivity up to 96.48 × 10⁵ V/m·RIU) and high sensitivity for displacement sensing (0.91 × 10⁶ V/m·μm). We believe that the above results can provide reference schemes for nonlinear photonic devices.

Unmanned aerial vehicles (UAVs) frequently operate under complex illumination conditions during urban and industrial security patrols, including low light, fog, and strong backlighting. These conditions severely degrade the performance of existing dual-modal object detection methods. To overcome this limitation, we introduce a lightweight dual-modal object detection algorithm called M²CA-FFM, built upon multi-directional Mamba cross-attention feature fusion. First, a multi-directional Mamba enhancement module is developed to model spatial feature sequences via four-directional scanning. Second, a dedicated spectral enhancement module is designed to process spectral components in a differentiated manner. Together, these two modules enhance single-modal features from both the spatial and frequency domains, leading to improved feature robustness. For dual-modal object detection, we propose a Mixed Scanning Fusion Module that dynamically generates scanning paths focused on the target regions. This dynamic mechanism complements the fixed four-direction scanning and strengthens global modeling, enabling efficient fusion and deep complementarity between visible light and infrared features. Extensive experiments are conducted on a self-built dataset (OPVM-VIRD) and three public benchmarks (FLIR, M3FD, and VEDAI). Our method consistently outperforms current mainstream dual-modal detection algorithms across all datasets. On the OPVM-VIRD dataset, it achieves an mAP50 of 95.7%, which is 2.2% higher than the best result obtained with other fusion methods. Preliminary verification on the edge computing platform Atlas 200I DK A2 shows that the algorithm has significant lightweight advantages, with a parameter volume of only 72.07 M and a computational complexity of 134.41 GFLOPs, demonstrating the reasoning potential to meet real-time processing requirements. Overall, the proposed M²CA-FFM method offers a real-time detection strategy with strong generalization capabilities, holding significant practical value for around-the-clock security patrols in urban and industrial parks.

Target detection systems based on UAV platforms have the advantages of speed, flexibility, and agility in safety monitoring and rescue operations on hilly roads. However, due to the high altitude of aerial imaging, terrain occlusions, and interference from complex backgrounds, trapped individuals often appear as visually negligible small targets, leading to high miss rates and delayed responses in traditional detection methods. To address this urgent need, this paper proposes a lightweight small-target detector called FCML-YOLO. First, a frequency-domain feature enhancement (FDFE) module is designed, which extracts frequency-domain features using the discrete cosine transform and enhances global context perception through adaptive global pooling and multi-branch fully connected layers. Then, a content-aware reassembly of features (CARAFE) module is incorporated to preserve fine-grained image details during upsampling. Additionally, a multi-scale feature reconstruction (MSFR) module is developed, which integrates features from multiple scales and reduces redundant information using an adaptive weighting mechanism. Building on this, we construct a lightweight multi-level feature fusion (LMFF) network by removing redundant structures and fully exploiting deep and shallow features. The experimental results on multiple datasets demonstrate that, compared to YOLO11s, FCML-YOLO achieves 4.4% improvement in mAP50 on the self-built OPVM-VIRD dataset. Additionally, the model demonstrates a significant advantage over mainstream detection models on public datasets such as VisDrone, USOD, DOTA, and TinyPerson. Furthermore, experiments are extended to the search-and-rescue-oriented SARD dataset to verify the applicability of FCML-YOLO in UAV-based rescue scenarios. The model is deployed on a self-developed UAV-mounted detection pod system, with the number of parameters reduced by 62.6% compared to the baseline model, achieving real-time performance at 64 frames per second (FPS).

UAVs are widely used for all-weather, round-the-clock security inspections in urban and industrial areas. However, pure visible-light systems fail at night or in adverse weather conditions, while pure infrared methods are limited by thermal noise, low spatial resolutions, and high false alarm rates. Multispectral images render the task of object detection highly reliable and robust by providing complementary target feature information. This study suggests a frequency-based cross-attention transformer (FCAT) for multispectral object detection as a solution to this issue. This approach collects cross-modal complementary characteristics, effectively learns and integrates global contextual information via the cross-attention mechanism, and greatly increases multispectral object detection accuracy. At the same time, spatial-domain features are mapped to the frequency domain via the Fourier transform, and the scaled dot product attention is estimated via element-wise product operations, which break through the limitation of traditional spatial-domain matrix multiplication and effectively reduce the computational cost of the model. Additionally, this study independently builds a multi-scene multi-time climate visible–infrared dataset (OPVM-VIRD), which contains 20,025 target instances, to address the issue of the lack of all-weather cross-spectral data in object detection tasks from the perspective of UAVs. Experimental findings from the OPVM-VIRD, M3FD, and FLIR datasets demonstrate that our proposed approach outperforms prevailing state-of-the-art multispectral object detection algorithms on public benchmarks, while the FCAT model achieves an mAP50 score of 94.7% on our custom-built dataset—10.8% higher than ICAF. At the same time, the number of FCAT parameters is 85.26 M, which is significantly lower than that of mainstream models, such as ICAF. Therefore, the FCAT is a change detection strategy with strong model generalization abilities, and it has important application value in the all-day and all-weather security patrol of cities and industrial parks carried out by UAVs.

This preliminary study examined the effects of wearing an elevation training mask^® 2.0 (ETM) during high-intensity interval training (HIIT) on the aerobic capacity of young men. Twenty-six young men were recruited and randomly divided into an experimental group (EG, n = 13) and a control group (CG, n = 13). The former performed HIIT on a running platform while wearing an ETM, and the latter performed HIIT only. Before and after the intervention, the following parameters were measured: exercise performance indicator is a 3000-m running test; aerobic capacity indicators are maximal oxygen uptake (VO_2max), ventilatory anaerobic threshold (VT), and running economy (RE); the physiological indicators are blood, pulmonary function, and cardiac function. After 6 weeks of intervention, there were significant differences (p < 0.05) between the time and time × group interaction for the 3000-m running time. There were no significant differences (p > 0.05) in VO_2max between the time and time × group interaction. The VT and RE demonstrated significant differences (p < 0.05) between the time and time × group interaction. Among the physiological indicators, pulmonary and cardiac function differed significantly (p < 0.05) be-tween the time and time × group interaction. Blood parameters showed no significant differences (p > 0.05) between the time points and time × group interaction. These preliminary results suggest that using ETM in conjunction with HIIT for 6 weeks increased young men’s aerobic performance much more than conventional HIIT. This effect may be attributed to the significantly improved respiratory function and cardiac function caused by the combination of ETM with HIIT.

In order to solve the problem that the air-to-ground data transfer rate is much lower than the radar data rate, the onboard system is commonly used for storing the airborne radar data. However, there are two main problems in the data storage using the traditional file management method. The first is that the frequent data updating of the file allocation table (FAT) and the file directory table (FDT) cause a high frequency of address jumps among the discontinuous areas, which leads to a long response time. The second is that the updating frequencies of the FAT, the FDT and the data region are seriously inconsistent, which results in uneven wear of the three areas. To solve these two problems, a file management method, which optimizes the data writing in the three areas of the FAT, the FDT and the data region, is proposed in this study. An actual measurement is carried out on a data storage system of the airborne radar using the proposed file management method. The result shows that the proposed method significantly reduces the updating frequency of FAT and FDT, and achieves the wear levelling of file area and data region.

This paper presents crucial techniques for the realization of frequency modulated continuous wave synthetic aperture radar (FMCW SAR). To begin with, the principle of FMCW SAR is reviewed in detail, then digital unit is focused to present. The digital unit includes real-time signal processor, mass data storage. Real-time imaging algorithm and its mapping to digital signal processor (DSP) as well as the SAR video formation are also proposed. Finally, beneficial results of the flight trial are presented to show the performance of the FMCW SAR system.