Cross-modal remote sensing object detection holds significant potential for around-the-clock applications. However, the modality differences between cross-modal data and the degradation of feature quality under adverse weather conditions limit detection performance. To address these challenges, this paper presents a novel cross-modal remote sensing object detection framework designed to overcome two critical challenges in around-the-clock applications: (1) significant modality disparities between visible light, infrared, and synthetic aperture radar data, and (2) severe feature degradation under adverse weather conditions including fog, and nighttime scenarios. Our primary contributions are as follows: First, we develop a multi-scale feature extraction module that employs a hierarchical convolutional architecture to capture both fine-grained details and contextual information, effectively compensating for missing or blurred features in degraded visible-light images. Second, we introduce an innovative feature interaction module that utilizes cross-attention mechanisms to establish long-range dependencies across modalities while dynamically suppressing noise interference through adaptive feature selection. Third, we propose a feature correction fusion module that performs spatial alignment of object boundaries and channel-wise optimization of global feature consistency, enabling robust fusion of complementary information from different modalities. The proposed framework is validated on visible light, infrared, and SAR modalities. Extensive experiments on three challenging datasets (LLVIP, OGSOD, and Drone Vehicle) demonstrate our framework’s superior performance, achieving state-of-the-art mean average precision scores of 66.3%, 58.6%, and 71.7%, respectively, representing significant improvements over existing methods in scenarios with modality differences or extreme weather conditions. The proposed solution not only advances the technical frontier of cross-modal object detection but also provides practical value for mission-critical applications such as 24/7 surveillance systems, military reconnaissance, and emergency response operations where reliable around-the-clock detection is essential.

The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically, HERL-ViT integrates a multi-level pyramid structure to capture multi-scale features, a regional-to-local attention mechanism to reduce computational complexity, an Optimized Position Encoding Generator for dynamic relative position encoding, and enhanced MLP and downsampling modules to balance performance and efficiency. Key contributions include: (1) A unified framework integrating visualization, adversarial training, and hybrid attention for malware detection; (2) Regional-local attention to achieve both global awareness and local detail capture with lower complexity; (3) Optimized PEG to enhance spatial perception and reduce overfitting; (4) Lightweight network design (5.8M parameters) ensuring high efficiency. Experimental results show HERL-ViT achieves 99.2% accuracy (Loss = 0.066) on malware classification and 98.9% accuracy (Loss = 0.081) on adversarial samples, demonstrating superior performance and robustness compared to state-of-the-art methods.

The Finite Difference Time-Domain (FDTD) method is a powerful tool for electromagnetic field analysis. In this work, we develop a variation of the algorithm to accurately calculate antenna, microwave circuit, and target scattering problems. To improve efficiency, a single-field (SF) FDTD method is proposed as a numerical solution to the time-domain Helmholtz equations. New formulas incorporating resistors and voltage sources are derived for the SF-FDTD algorithm, including hybrid implicit–explicit and weakly conditionally stable SF-FDTD methods. The correctness of these formulas is verified through numerical simulations of a newly designed dual-band wearable antenna with an artificial magnetic conductor (AMC) structure. A novel antenna fed by a coplanar waveguide with a compact size of 15.6 × 20 mm² has been obtained after being optimized through an artificial intelligent method. A double-layer, dual-frequency AMC structure is designed to improve the isolation between the antenna and the human body. The simulation and experiment results with different bending degrees show that the antenna with the AMC structure can cover two frequency bands, 2.4 GHz–2.48 GHz and 5.725 GHz–5.875 GHz. The gain at 2.45 GHz and 5.8 GHz reaches 5.3 dBi and 8.9 dBi, respectively. The specific absorption rate has been reduced to the international standard range. In particular, this proposed SF-FDTD method can be extended to analyze other electromagnetic problems with fine details in one or two directions.

To achieve different compression rates for images while preserving important regions, a semantic network-based deep residual variational auto-encoder is introduced in this paper. The network is divided into two components, a semantic analysis network and an image compression network. The former evaluates the importance of image pixels and accurately locates important semantic regions and key information in the image. According to the semantic importance of different regions, the encoding strategy is dynamically adjusted. The latter utilizes a deep residual variational autoencoder to efficiently encode and decode images, while combining Lagrange multipliers to adjust the model flexibly and the weights of the bitrate. With this model, multiple compression rates have been implemented. Quality of reconstruction achieves better performance with compression at different rates. Finally, a semantic loss function is proposed to replace traditional compression loss functions. Extensive experiments conducts on several datasets, including the Kodak, CLIC and Tecnick TESTIMAGES, which were public datasets. Results demonstrate that our method can effectively improve the quality of image reconstruction at various compression rates. Compared with traditional methods, the peak signal-to-noise ratio is increased by an average of 2dB at the same bitrate, and the structural similarity index is the most close to 0.998. The subjective visual quality is better, especially when processing complex scene images, which can better preserve the details and textures of key objects. This approach can effectively avoid common distortions such as block effects and blurring in traditional methods.

The Internet of Things is the most heated topic in the current electronic information technology industry. It is generally believed that it will become a new driving force for the development of human industry, and its comprehensive application will promote the human society to step forward to the “smart earth”. In this paper, based on artificial intelligence and Internet of Things technology, experimental design of fuzzy control and PID control algorithm anti-interference performance test simulation box. Experimental data show that due to the Internet of Things control system in the process of application, the controlled object in different degrees of non-linear, large lag, parameter time-varying and model uncertainty characteristics. It can be seen from the principle and simulation results that fuzzy control does not require the precise model of the controlled object and has strong adaptability. The experimental results show that if the pulses with a height of 0.1, a period of 10s, a width of 2S and a delay of 4s are set, interference is added to the output of a sampling time controller. Among them, $\mathrm{T}1=2, \mathrm{T}2=1,\, =0.5$ , then the initial PID $\text{Kp}0=7.6,\ \text{Ki}0=1.12,\ \text{Kd}0=4.88$ in the fuzzy PID control system. The Internet of Things control system server has the advantages of comprehensive functions, good expansibility, easy operation and maintenance, etc., and the adoption of automated testing technology improves the efficiency and standardization of the server design process.