Radiation-induced acoustic computed tomography(RACT)is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues.Traditional backprojection(BP)reconstructions carry no...Radiation-induced acoustic computed tomography(RACT)is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues.Traditional backprojection(BP)reconstructions carry noisy and limited-view artifacts.Model-based algorithms have been demonstrated to overcome the drawbacks of BPs.However,model-based algorithms are relatively more complex to develop and computationally demanding.Furthermore,while a plethora of novel algorithms has been developed over the past decade,most of these algorithms are either not accessible,readily available,or hard to implement for researchers who are not well versed in programming.We developed a user-friendly MATLAB-based graphical user interface(GUI;RACT2D)that facilitates back-projection and model-based image reconstructions for twodimensional RACT problems.We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI.The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer,thus further accelerating the reconstruction speed.We also share the MATLAB-based codes for evaluating RACT reconstructions,which users with MATLAB programming expertise can further modify to suit their needs.The shared GUI and codes can be of interest to researchers across the globe and assist them in e±cient evaluation of improved RACT reconstructions.展开更多
移动应用是近10年来兴起的新型计算模式,深刻地影响人民的生活方式.移动应用主要以图形用户界面(graphical user interface,GUI)方式交互,而对其进行人工测试需要消耗大量人力和物力.为此,研究者提出针对移动应用GUI的测试自动生成技术...移动应用是近10年来兴起的新型计算模式,深刻地影响人民的生活方式.移动应用主要以图形用户界面(graphical user interface,GUI)方式交互,而对其进行人工测试需要消耗大量人力和物力.为此,研究者提出针对移动应用GUI的测试自动生成技术以提升测试效率并检测潜在缺陷.收集了145篇相关论文,系统地梳理、分析和总结现有工作.提出了“测试生成器-测试环境”研究框架,将该领域的研究按照所属模块进行分类.特别地,依据测试生成器所基于的方法,将现有方法大致分为基于随机、基于启发式搜索、基于模型、基于机器学习和基于测试迁移这5个类别.此外,还从缺陷类别和测试动作等其他分类维度梳理现有方法.收集了该领域中较有影响力的数据集和开源工具.最后,总结当前面临的挑战并展望未来的研究方向.展开更多
The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intens...The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% accuracy in damage level classification. To enhance model interpretability, visual explanation heatmaps are incorporated, highlighting semantically relevant regions that the model uses for decision-making. These heatmaps closely align with real-world structural and damage features, confirming that STPEIC learns meaningful representations rather than relying on spurious correlations. Additionally, a graphical user interface (GUI) has been developed to streamline image input, classification, and interpretability visualization, improving the practical usability of the system. Overall, STPEIC provides a reliable, interpretable, and user-friendly solution for rapid post-earthquake structural evaluation.展开更多
基金supported by the National Institute of Health (R37CA240806)and American Cancer Society (133697-RSG-19-110-01-CCE)support from UCI Chao Family Comprehensive Cancer Center (P30CA062203).
文摘Radiation-induced acoustic computed tomography(RACT)is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues.Traditional backprojection(BP)reconstructions carry noisy and limited-view artifacts.Model-based algorithms have been demonstrated to overcome the drawbacks of BPs.However,model-based algorithms are relatively more complex to develop and computationally demanding.Furthermore,while a plethora of novel algorithms has been developed over the past decade,most of these algorithms are either not accessible,readily available,or hard to implement for researchers who are not well versed in programming.We developed a user-friendly MATLAB-based graphical user interface(GUI;RACT2D)that facilitates back-projection and model-based image reconstructions for twodimensional RACT problems.We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI.The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer,thus further accelerating the reconstruction speed.We also share the MATLAB-based codes for evaluating RACT reconstructions,which users with MATLAB programming expertise can further modify to suit their needs.The shared GUI and codes can be of interest to researchers across the globe and assist them in e±cient evaluation of improved RACT reconstructions.
文摘移动应用是近10年来兴起的新型计算模式,深刻地影响人民的生活方式.移动应用主要以图形用户界面(graphical user interface,GUI)方式交互,而对其进行人工测试需要消耗大量人力和物力.为此,研究者提出针对移动应用GUI的测试自动生成技术以提升测试效率并检测潜在缺陷.收集了145篇相关论文,系统地梳理、分析和总结现有工作.提出了“测试生成器-测试环境”研究框架,将该领域的研究按照所属模块进行分类.特别地,依据测试生成器所基于的方法,将现有方法大致分为基于随机、基于启发式搜索、基于模型、基于机器学习和基于测试迁移这5个类别.此外,还从缺陷类别和测试动作等其他分类维度梳理现有方法.收集了该领域中较有影响力的数据集和开源工具.最后,总结当前面临的挑战并展望未来的研究方向.
基金support from General Project Supported by Natural Science Basic Research Plan in Shaanxi Province of China(2025JC-YBMS-443)Fundamental Research Funds for the Central Universities,CHU(300102213209)+1 种基金Research Funds for the Interdisciplinary Projects,CHU(300104240915)National Natural Science Foundation of China(Grant No.52361135806).
文摘The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% accuracy in damage level classification. To enhance model interpretability, visual explanation heatmaps are incorporated, highlighting semantically relevant regions that the model uses for decision-making. These heatmaps closely align with real-world structural and damage features, confirming that STPEIC learns meaningful representations rather than relying on spurious correlations. Additionally, a graphical user interface (GUI) has been developed to streamline image input, classification, and interpretability visualization, improving the practical usability of the system. Overall, STPEIC provides a reliable, interpretable, and user-friendly solution for rapid post-earthquake structural evaluation.
文摘嵌入式图形用户界面(Embedded GUI)是嵌入式计算机系统的核心技术之一。本文首先阐述Embedded GUI的应用现状,指出Microwindows(最新版本0.9)的优势所在;然后,分别从体系结构和应用编程接口的角度,结合源代码,深入分析了Microwindows;最后,在Red Hat Linux8.0的仿真环境下演示了Microwindows的应用。
