广义de Bruijn多计算机互连网络

（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ多计算机系统互连网络限制网络的规模必须为ｄ＾ｍ。本文把（ｄ，ｍ）ｄｅ Ｂｒｕｉｊｎ网络推广到了任意顶点情形，提出了广义ｄｅ Ｂｒｕｉｊｎ网络，研究了广义ｄｅ Ｂｒｕｉｊｎ网络的自路由性质，设计了路由算法，研究了广义ｄｅ Ｂｒｕｉｊｎ网络顶点度的性质和容错特性。     

“健康云”如何实现医疗信息化综述

对云计算技术实现医疗信息化的背景、研究基础、核心技术进行探讨,具体阐述了“健康云”在居民电子健康档案中的应用以及在医院信息管理系统中的应用,包括系统特点、整体架构等方面,并对其未来发展予以展望。     

云计算的可信性

云计算是一种新兴的商业计算模型,同时又是一种能够动态伸缩的虚拟化资源。云计算的安全问题其实就是一种对可信的挑战。     

浅析可信计算技术及其发展

首先介绍了可信计算技术的发展背景,然后对可信计算技术的体系结构进行了研究,最后列举了一些可信计算技术在实际中一些的应用。     

如何化解云计算的潜在风险

随着云计算技术的快速发展和更广泛应用,云计算的潜在风险越来越受到关注。文章介绍了云计算的应用现状及重要特点,分析了云计算存在的风险,从技术的角度对云计算的潜在风险进行分析。     

城市建筑点云的自适应分割方法

自动地提取城市建筑点云数据中的高层次结构是城市数字化等应用的重要基础步骤.通过提出"自适应分割"的概念来自动地获取三维城市建筑点云的一种灵活、层次化的结构表示.由于城市建筑的单元分布大都呈一种级联或者交错的形式,与之前采用全局规则格点进行分析的方法不同,文中采用一种自适应的方法对建筑点云进行分析;也可将自适应分割的概念用于建筑图像的分析上.最后给出了在不同类型的建筑点云和图像进行分析的结果以及一些相关的应用.     

解剖学的数字化实验教学平台的构建探索

随着数字医学及数字医疗技术的迅速发展,针对高等医学院校解剖实验教学中存在的问题,如何提高解剖学教学质量,文章介绍了数字化解剖,并分析了数字化解剖实验室的特点,将云计算技术应用到管理数字化实验教学中。给出了基于云计算技术的数字化解剖实验教学平台解决方案,改革数字化解剖实验室,提出一种新的数字化解剖教学模式。     

云计算时代高校IT人才培养模式的创新

阐述了云计算的应用优势;基于对云计算技术特点和知名招聘网站IT职位描述信息进行综合分析,总结未来IT人才需求的趋势,并针对高校相关专业IT人才培养模式提出了改革措施。     

Film and television industry cloud exhibition design based on 3D imaging and virtual reality

The three-dimensional virtual scene can provide users with a visual three-dimensional virtual environment, with various multimedia channels such as sound, video, force feedback equipment, etc., to bring users a completely immersive interactive experience. This paper introduces 3D imaging and virtual reality technology in the film and television industry cloud exhibition, and develops a virtual display platform. First of all, this paper divides the registration into two processes: camera calibration and joint calibration of the camera and laser scanner based on the calibration results. Camera calibration can determine the plane model of the calibration board in the camera coordinate system, and the joint calibration uses the RANSAC algorithm to extract the point cloud of the plane model of the calibration board, and then optimizes the distance between the points in the plane model point cloud and the corresponding plane in the camera coordinate system Find the optimal transformation between the two sets of data, and then calculate the registration relationship between the point cloud and the image. Secondly, this article conducts a demand analysis of the film and television industry cloud exhibition platform based on virtual reality technology, including the business goals set by the platform, platform system analysis, overall design, and system operating environment and configuration requirements. This model provides a feasible solution for the visual interaction of the cloud exhibition design of the film and television industry.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.