Ensuring the safety and extended operational life of fighter aircraft necessitates frequent and exhaustive inspections. While surface defect detection is feasible for human inspectors, manual methods face critical limitations in scalability, efficiency, and consistency due to the vast surface area, structural complexity, and operational demands of aircraft maintenance. We propose a smart surface damage detection and localization system for fighter aircraft, termed J-DDL. J-DDL integrates 2D images and 3D point clouds of the entire aircraft surface, captured using a combined system of laser scanners and cameras, to achieve precise damage detection and localization. Central to our system is a novel damage detection network built on the YOLO architecture, specifically optimized for identifying surface defects in 2D aircraft images. After detecting damage in 2D images, the system maps the identified anomalies onto corresponding 3D point clouds, enabling accurate 3D localization of defects across the aircraft surface. To facilitate further advancements in this domain, we have developed the first publicly available dataset specifically focused on aircraft damage. Experimental evaluations validate the effectiveness of our framework, underscoring its potential to significantly advance automated aircraft inspection technologies.

Synthetic Aperture Radar (SAR) offers all-weather, high-resolution imaging capabilities, but its complex imaging mechanism often poses challenges for interpretation. In response to these limitations, this paper introduces an innovative generative model designed to transform SAR images into more intelligible optical images, thereby enhancing the interpretability of SAR images. Specifically, our model backbone is based on the recent diffusion models, which have powerful generative capabilities. We employ SAR images as conditional guides in the sampling process and integrate color supervision to counteract color shift issues effectively. We conducted experiments on the SEN12 dataset and employed quantitative evaluations using peak signal-to-noise ratio, structural similarity, and fréchet inception distance. The results demonstrate that our model not only surpasses previous methods in quantitative assessments but also significantly enhances the visual quality of the generated images.

Synthetic aperture radar (SAR) offers all-weather and all-day high-resolution imaging, yet its unique imaging mechanism often necessitates expert interpretation, limiting its broader applicability. Addressing this challenge, this letter proposes a generative model that bridges SAR and optical imaging, facilitating the conversion of SAR images into more human-recognizable optical aerial images. This assists in the interpretation of SAR data, making it easier to recognize. Specifically, our model backbone is based on the recent diffusion models, which have powerful generative capabilities. We have innovatively tailored the diffusion model framework, incorporating SAR images as conditional constraints in the sampling process. This adaptation enables the effective translation from SAR to optical images. We conduct experiments on the satellite GF3 and SEN12 datasets and use structural similarity (SSIM) and Fréchet inception distance (FID) for quantitative evaluation. The results show that our model not only surpasses previous methods in quantitative evaluation but also significantly improves the visual quality of the generated images. This advancement underscores the model’s potential to enhance SAR image interpretation.

This paper presents a strong data-mining method based on a rough set, which can simultaneously realize feature selection, classification, and knowledge representation. Although a rough set, a popular method for feature selection, has good interpretability, it is not sufficiently efficient and accurate to deal with large-scale datasets with high dimensions, which prevents it from being immediately applied to real-world scenarios. To address the efficiency issue of a rough set, we discover the stability of the local redundancy (SLR) of attributes and propose a theorem to prove it rigorously. Based on SLR, only the parts of objects in the boundary region are partitioned when calculating outer significance, which further improves the efficiency of the rough set. With regard to the accuracy issue, we show that overfitting may lead to ineffectiveness of the rough set, especially when processing noise attributes. We then propose relative importance, a robust measurement for an attribute, to alleviate such overfitting issues. In this paper, we propose a novel rough-set framework that significantly improves the efficiency and accuracy of existing rough-set methods. We further develop our rough set framework by proposing a “rough concept tree” for knowledge representation and classification. Experimental results on public benchmark datasets show that our proposed framework achieves higher accuracy than seven state-of-the-art feature-selection methods. All the codes are available athttps://github.com/syxiaa/powerroughset.

Current deployed emergency communications systems are only available to the rescuing workers. In this paper, a framework of wireless emergency communications is proposed for common communications in the disasters based on relaying and cognitive radio. In this framework, relaying provides small coverage expansion and high capacity for common communications. On the other hand, cognitive radio based frequency lowering provides large coverage expansion and low system capacity for special number communications. The coverage performance is evaluated by the calculation and simulation. To balance the tradeoff between coverage and capacity, both two-hop relaying and cognitive radio are adopted appropriately to satisfy the requirements of emergency communications according to their characteristics. The performance of the proposed framework is also investigated in this paper.