Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
This paper details the comprehensive development and rigorous validation of a functional digital twin for a conventional airport security X-ray baggage screening system. The primary objective was to create a physically accurate software simulation capable of generating high-fidelity synthetic X-ray images to address the scarcity of large-scale, annotated datasets in the aviation security domain. The modeling pipeline began with the meticulous creation of core, reusable three-dimensional assets. Using Blender 3D, a diverse library of baggage items, common contents, and prohibited threat objects was designed with precise geometric and material properties. These models were subsequently imported into the Unity 3D real-time development platform, which acted as the central rendering engine for scene composition and image synthesis. The cornerstone of the system's realism is a custom-built, physics-based rendering shader, programmed directly in High-Level Shading Language (HLSL). This shader implements a simplified yet effective model of X-ray transmission, simulating the differential absorption characteristics of various materials. To validate the visual fidelity and practical utility of the digital twin, its output was subjected to a qualitative comparative analysis against ground-truth images captured from a physical Krystal Vision X-Ray Baggage Scanner. The results indicate a significant degree of visual congruence between the synthetic and real X-ray images, confirming that the proposed methodology can produce perceptually convincing simulations. The successful implementation of this digital twin demonstrates a viable pathway for the on-demand generation of limitless, perfectly annotated training data. This tool holds substantial promise for two critical applications: first, for the cost-effective and scalable training of security screening personnel via realistic virtual simulators, and second, for accelerating the development and robustness testing of automated threat detection algorithms based on deep learning, all while circumventing the significant logistical, financial, and security constraints associated with continuous access to operational screening equipment....
Reliable detection of X-ray tire defects is essential for safety and quality assurance in manufacturing. However, low contrast and high noise make traditional methods unreliable. This paper presents DyReCS-YOLO, a dynamic re-parameterized channel-shuffle network based on YOLOv8. The model introduces a C2f_DyRepFusion module combining dynamic convolution and a shuffle-and-routing mechanism, enabling adaptive kernel adjustment and efficient cross-channel interaction. Experiments on an industrial X-ray tire dataset containing 8326 images across 58 defect categories demonstrate that DyReCS-YOLO achieves an mAP@0.5 of 0.741 and mAP@0.5:0.95 of 0.505, representing improvements of 4.5 and 2.8 percentage points over YOLOv8-s, and 9.2 and 7.7 percentage points over YOLOv11-s, respectively. The precision increases from 0.698 (YOLOv8-s) and 0.668 (YOLOv11-s) to 0.739, while maintaining real-time inference at 189.5 FPS, meeting industrial online detection requirements. Ablation results confirm that the combination of dynamic convolution and channel shuffle improves small-defect perception and robustness. Moreover, DyReCSYOLO achieves an mAP@0.5 of 0.975 on the public MT defect dataset, verifying its strong cross-domain generalization....
Large animals pose unique challenges when collecting diagnostic radiographic images due to the size and power constraints of current human-focused devices. A slit-scan radiographic system mounted on a purpose-built robotic manipulator can provide a solution to these imaging challenges. Accurate robotic motion over large spatial volumes is required for the gamut of large animal imaging tasks. Thus, we calibrate the robot mechanism parameters using motion capture measures and demonstrate that this system is able to achieve diagnostic quality X-ray imaging capabilities....
Transition metal nitrides are robust alternatives to noble metals in plasmonics, offering strong NIR absorption, high melting points, and chemical stability. Hafnium nitride (HfN) nanoparticles are especially promising owing to the high atomic number of hafnium, which could provide X-ray-based theranostic functionalities in addition to the prominent plasmonic properties. However, their biomedical potential has remained unexplored due to difficulties in synthesis of pure water-dispersible nanostructures. Here, we use femtosecond laser ablation in acetone to produce HfN-based nanoparticles exhibiting a red-shifted plasmon resonance spanning both the NIR-I and NIR-II windows.We then present the first in vivo assessment of the biocompatibility, photothermal performance, and radiosensitizing capacity of HfN-based nanomaterials. We show that PEGylated HfN nanoparticles exhibit negligible cytotoxicity across three cancer cell lines in vitro and no significant long-term adverse effects in healthy mice following intravenous administration. NIR-I photothermal therapy of 4T1 tumor-bearing mice after systemic nanoparticle administration leads to a 2.4-fold suppression of tumor growth and a significant extension of survival. The multimodal therapeutic potential of HfN is demonstrated by the enhanced efficacy of X-ray radiotherapy after local administration of nanoparticles. Laser ablated engineering of hafnium nitrides establishes this nanomaterial as a promising candidate for multimodal optical and radiosensitizing theranostics....
Aiming at the shortcomings of uneven image color and prominent noise in the default image display effect of existing dual-energy X-ray security inspection equipment, the corresponding improvement method is studied. Firstly, the improved Non-Local Means filter (hereinafter referred to as improved NLMeans) is used to perform adaptive smoothing on the original high and lowenergy images to obtain the corresponding material characteristic images. Furthermore, the improved NL-Means filter is used again to smooth the material characteristic images, so as to improve the phenomena of uneven image color and prominent noise points. However, this filtering method has a serious problem of low real-time efficiency, making it difficult to apply in engineering. This paper studies three acceleration methods. The first one is to selectively implement the improved NL-Means filter from the perspective of the image itself; the second one is to reasonably reduce the number of structural operations based on the principle and structure of the algorithm; the third one is to convert the floating-point exponential operation into a look-up table operation from the perspective of engineering practice. The experimental results show that the proposed method effectively improves the default image display effect of the security inspection equipment, which plays a very positive role not only in the security inspectors’ interpretation of images but also in improving the performance indicators of the equipment....
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