随着网络和多媒体技术的广泛应用,数字网络视频得到了飞速发展。尤其在监控领域,将传统的模拟视频监控信号转换为数字视频信号,并且利用现有 I P 网络设计一个不受距离限制的廉价通用网络视频监控设备成为了新的热点。本文根据这方面的...随着网络和多媒体技术的广泛应用,数字网络视频得到了飞速发展。尤其在监控领域,将传统的模拟视频监控信号转换为数字视频信号,并且利用现有 I P 网络设计一个不受距离限制的廉价通用网络视频监控设备成为了新的热点。本文根据这方面的应用需求提出一种设计方案。本方案的实现平台是基于 Intel PXA255 的嵌入式硬件平台以及基于嵌入式Linux 的软件平台,采用先进的M P E G - 4 编码标准。最终实现一个具有实时视频采集压缩及传输功能的可以直接接入以太网的网络摄像机。展开更多
The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralize...The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralized resourceintensive cloud platform for service implementation.Edge Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users.Additionally,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time requests.Artificial Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.Although extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite few.To address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV.Firstly,we establish the general condition and relative concepts about IoV,EC,and AI.Secondly,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.Finally,we discuss a number of open issues in optimizing edge services with AI.展开更多
目的构建基于特定Intel芯片组中random number generator(RNG)单元的真随机数生成器。方法在Intel 815E 芯片组的个人电脑上安装Intel Security Driver(ISD)后,使用Microsoft Visual C++ 6编程,通过寄存器读取的方式获取RNG中的随机数...目的构建基于特定Intel芯片组中random number generator(RNG)单元的真随机数生成器。方法在Intel 815E 芯片组的个人电脑上安装Intel Security Driver(ISD)后,使用Microsoft Visual C++ 6编程,通过寄存器读取的方式获取RNG中的随机数。结果生成的500个随机数通过的NIST FIPS 140-1和χ2拟合优度检验(α=0.05 ),表明本方法所生成的随机数满足独立性和分布均匀性的要求。生成7500个随机数经域值变换后与随机数表中的同等数目的随机数进行了统计学比较,结果显示前者的均值偏移、SD, SE和CV均小于后者。结论基于Intel RNG的真随机数生成器可以生成满足独立性和分布均匀性的真随机数,生成的随机数效果与随机数表中的随机数没有显著性区别。但是基于Intel RNG的真随机数生成器能解决使用随机数表获取随机数中可能存在的问题,具有较好的普遍性和实用性。展开更多
Currently,manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization.Hence,they have to extend their production mo...Currently,manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization.Hence,they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays,distributed manufacturing systems have been widely adopted in industrial production processes.In recent years,many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems.By summarizing and evaluating existing studies on distributed scheduling problems,we analyze the achievements and current research status in this field and discuss ongoing studies.Insights regarding prior works are discussed to uncover future research directions,particularly swarm intelligence and evolutionary algorithms,which are used for managing distributed scheduling problems in manufacturing systems.This work focuses on journal papers discovered using Google Scholar.After reviewing the papers,in this work,we discuss the research trends of distributed scheduling problems and point out some directions for future studies.展开更多
文摘随着网络和多媒体技术的广泛应用,数字网络视频得到了飞速发展。尤其在监控领域,将传统的模拟视频监控信号转换为数字视频信号,并且利用现有 I P 网络设计一个不受距离限制的廉价通用网络视频监控设备成为了新的热点。本文根据这方面的应用需求提出一种设计方案。本方案的实现平台是基于 Intel PXA255 的嵌入式硬件平台以及基于嵌入式Linux 的软件平台,采用先进的M P E G - 4 编码标准。最终实现一个具有实时视频采集压缩及传输功能的可以直接接入以太网的网络摄像机。
基金supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps(No.2020DB005)the National Key R&D Program of China(No.2019YFE0190500)+3 种基金the National Natural Science Foundation of China(Nos.61702442,61862065,and 61702277)the Application Basic Research Project in Yunnan Province(No.2018FB105)the Major Project of Science and Technology of Yunnan Province(Nos.202002AD080002 and 2019ZE005)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund。
文摘The Internet of Vehicles(IoV)plays a crucial role in providing diversified services because of its powerful capability of collecting real-time information.Generally,collected information is transmitted to a centralized resourceintensive cloud platform for service implementation.Edge Computing(EC)that deploys physical resources near road-side units is involved in IoV to support real-time services for vehicular users.Additionally,many measures are adopted to optimize the performance of EC-enabled IoV,but they hardly help make dynamic decisions according to real-time requests.Artificial Intelligence(AI)is capable of enhancing the learning capacity of edge devices and thus assists in allocating resources dynamically.Although extensive research has employed AI to optimize EC performance,summaries with relative concepts or prospects are quite few.To address this gap,we conduct an exhaustive survey about utilizing AI in edge service optimization in IoV.Firstly,we establish the general condition and relative concepts about IoV,EC,and AI.Secondly,we review the edge service frameworks for IoV and explore the use of AI in edge server placement and service offloading.Finally,we discuss a number of open issues in optimizing edge services with AI.
基金supported in part by the National Natural Science Foundation of China(Nos.61603169,61703220,and 61873328)China Postdoctoral Science Foundation Funded Project(No.2019T120569)+3 种基金Shandong Province Outstanding Youth Innovation Team Project of Colleges and Universities of China(No.2020RWG011)Shandong Province Colleges and Universities Youth Innovation Talent Introduction and Education Programthe Faculty Research Grants(FRG)from Macao University of Science and TechnologyShandong Provincial Key Laboratory for Novel Distributed Computer Software Technology。
文摘Currently,manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization.Hence,they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations.Nowadays,distributed manufacturing systems have been widely adopted in industrial production processes.In recent years,many studies have been done on the modeling and optimization of distributed scheduling problems.This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems.By summarizing and evaluating existing studies on distributed scheduling problems,we analyze the achievements and current research status in this field and discuss ongoing studies.Insights regarding prior works are discussed to uncover future research directions,particularly swarm intelligence and evolutionary algorithms,which are used for managing distributed scheduling problems in manufacturing systems.This work focuses on journal papers discovered using Google Scholar.After reviewing the papers,in this work,we discuss the research trends of distributed scheduling problems and point out some directions for future studies.
