As a named data-based clean-slate future Internet architecture,Content-Centric Networking(CCN)uses entirely different protocols and communication patterns from the host-to-host IP network.In CCN,communication is wholl...As a named data-based clean-slate future Internet architecture,Content-Centric Networking(CCN)uses entirely different protocols and communication patterns from the host-to-host IP network.In CCN,communication is wholly driven by the data consumer.Consumers must send Interest packets with the content name and not by the host’s network address.Its nature of in-network caching,Interest packets aggregation and hop-byhop communication poses unique challenges to provision of Internet applications,where traditional IP network no long works well.This paper presents a comprehensive survey of state-of-the-art application research activities related to CCN architecture.Our main aims in this survey are(a)to identify the advantages and drawbacks of CCN architectures for application provisioning;(b)to discuss the challenges and opportunities regarding service provisioning in CCN architectures;and(c)to further encourage deeper thinking about design principles for future Internet architectures from the perspective of upper-layer applications.展开更多
The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are anal...The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are analyzed,and NGN's features in aspects of network architecture,service provision and network management are summarized.展开更多
This paper presents the networking observation capabilities of Chinese ocean satellites and their diverse applications in ocean disaster prevention,ecological monitoring,and resource development.Since the inaugural la...This paper presents the networking observation capabilities of Chinese ocean satellites and their diverse applications in ocean disaster prevention,ecological monitoring,and resource development.Since the inaugural launch in 2002,China has achieved substantial advancements in ocean satellite technology,forming an observation system composed of the HY-1,HY-2,and HY-3 series satellites.These satellites are integral to global ocean environmental monitoring due to their high resolution,extensive coverage,and frequent observations.Looking forward,China aims to further enhance and expand its ocean satellite capabilities through ongoing projects to support global environmental protection and sustainable development.展开更多
通过分析政企业务的特点,结合人工智能(AI)和量子加密等新兴技术,探讨了政企OTN(Optical Transport Network)省内骨干网三种组网模式:智能独立光层模式、动态共享波分光层模式和波道透传模式。研究表明,智能独立光层模式适用于业务需求...通过分析政企业务的特点,结合人工智能(AI)和量子加密等新兴技术,探讨了政企OTN(Optical Transport Network)省内骨干网三种组网模式:智能独立光层模式、动态共享波分光层模式和波道透传模式。研究表明,智能独立光层模式适用于业务需求量大、对网络安全性要求极高且配套资源充足的地区;动态共享波分光层模式适用于现网波分系统覆盖完善但光缆纤芯等基础资源紧张的地区;波道透传模式适用于初期业务不大、现网波道资源丰富的地区。此外,根据未来技术发展趋势,提出了政企OTN网络建设的演进方向,为运营商在政企业务领域的持续发展提供了参考。展开更多
新型电力系统面向未来算力接入、算力互联等场景的新业务发展需求,对电力通信网络的承载能力、接入灵活性、多业务适应性等提出诸多挑战。针对现网大量存在的1 Gbit/s以下客户信号承载效率较低等问题,分析了提供小颗粒业务承载的细颗粒...新型电力系统面向未来算力接入、算力互联等场景的新业务发展需求,对电力通信网络的承载能力、接入灵活性、多业务适应性等提出诸多挑战。针对现网大量存在的1 Gbit/s以下客户信号承载效率较低等问题,分析了提供小颗粒业务承载的细颗粒光传送网(fine grain optical transport network,fgOTN)技术体系,并结合实验数据,验证fgOTN的技术优势和可行性。结果表明,fgOTN相较于当前虚容器(virtual container,VC)/分组传输(packet transmission,PKT)/光通道数据单元(optical channel data unit,ODU)多平面光网络终端(optical transport network,OTN)承载技术在多个方面具有明显优势,fgOTN在新型电力系统中的应用部署及架构演进将是未来的研究重点。展开更多
随着新型电力系统建设的稳步推进和智能电网的发展,电力通信网作为电力系统的重要组成部分,其技术选型和网络架构设计对电力系统的可靠性至关重要。针对细颗粒光传送网(fine grain optical transport network,fgOTN)技术在电力通信网中...随着新型电力系统建设的稳步推进和智能电网的发展,电力通信网作为电力系统的重要组成部分,其技术选型和网络架构设计对电力系统的可靠性至关重要。针对细颗粒光传送网(fine grain optical transport network,fgOTN)技术在电力通信网中的应用,深入分析了fgOTN技术演进及其特点,并与同步数字体系(synchronous digital hierarchy,SDH)和光传送网(optical transport network,OTN)进行对比,发现fgOTN具备强大的管控能力、灵活的组网与业务承载能力、可靠的保护机制,以及良好的兼容性和演进能力,非常适合电力等行业的通信网络应用。展开更多
随着人类太空探索和航天工程的不断发展,空间任务呈现业务种类日益繁多、数据总量持续增长、空间组网愈加复杂的趋势,OTN(Optical Transport Network)技术凭借超高带宽传输能力、强抗误码能力以及成熟的产业链优势,成为未来空天地一体...随着人类太空探索和航天工程的不断发展,空间任务呈现业务种类日益繁多、数据总量持续增长、空间组网愈加复杂的趋势,OTN(Optical Transport Network)技术凭借超高带宽传输能力、强抗误码能力以及成熟的产业链优势,成为未来空天地一体化组网的可行星间激光承载方案之一.在高速运动场景下,非同轨LEO(Low-Earth-Orbit)卫星之间以及LEO卫星和GEO(Geosynchronous Orbit)卫星之间相对运动引发的多普勒频移随时间发生周期性变化,在接收侧引发链路速率波动效应,导致接收侧比特速率失调,严重影响空天地一体化网络的通信效率.围绕基于OTN技术的激光链路引发的链路速率波动劣化效应,提出一种面向链路速率波动的通用机制框架.基于该框架,提出一种基于开销信道调整的速率波动抑制机制,并给出帧结构修改的具体细节.针对框架中的关键参数,基于Walker星座链路仿真结果以及连续时间的数学模型进行求解,证明抑制机制能实现61 ppm(Parts per Million)的速率调整,同时在LEO-LEO和LEOGEO两种场景下均足以实现调节目标.展开更多
面向光传送网络(Optical Transport Network,OTN)中多业务动态调度的复杂需求,设计基于人工智能(Artificial Intelligence,AI)的调度技术路径,构建以链路资源状态和业务请求特征为核心的状态表达方式。此路径引入自适应策略生成机制来...面向光传送网络(Optical Transport Network,OTN)中多业务动态调度的复杂需求,设计基于人工智能(Artificial Intelligence,AI)的调度技术路径,构建以链路资源状态和业务请求特征为核心的状态表达方式。此路径引入自适应策略生成机制来实现资源分配的协调优化,提出可感知资源动态变化的调度流程,以及精细化建模调度约束与目标函数。在仿真拓扑环境下进行性能评估,验证了所提策略在调度成功率、资源利用率及响应稳定性等指标上的技术优势,结果表明其具备良好的推广与算法移植能力。展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vi...The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vision,Federated Learning(FL)has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data,thereby preserving privacy and reducing the need for centralized storage.This capability is particularly attractive for vision-based applications,where image and video data are both sensitive and bandwidth-intensive.However,the integration of FL with 6G networks presents unique challenges,including communication bottlenecks,device heterogeneity,and trade-offs between model accuracy,latency,and energy consumption.In this paper,we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments.We formalize the system model,incorporating both the federated averaging(FedAvg)training process and a simplified communication costmodel that captures bandwidth constraints,packet loss,and variable latency across edge devices.Using standard image datasets(e.g.,MNIST,CIFAR-10)as benchmarks,we analyze how factors such as the number of participating clients,degree of data heterogeneity,and communication frequency influence convergence speed and model accuracy.Additionally,we evaluate the effectiveness of lightweight communication-efficient strategies,including local update tuning and gradient compression,in mitigating network overhead.The experimental results reveal several key insights:(i)communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed;(ii)judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy;and(iii)communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G.These findings highlight the synergistic role of AI and nextgeneration networks in enabling privacy-preserving,real-time vision applications,and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G.展开更多
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
针对计算机网络通信对高带宽、低时延与高安全性的需求,构建基于光传送网(Optical Transport Network,OTN)物理层硬加密与全光调度的安全传输架构。该架构通过节点配置、映射规程与波长隔离优化,实现密文数据的透明低损耗承载。测试结...针对计算机网络通信对高带宽、低时延与高安全性的需求,构建基于光传送网(Optical Transport Network,OTN)物理层硬加密与全光调度的安全传输架构。该架构通过节点配置、映射规程与波长隔离优化,实现密文数据的透明低损耗承载。测试结果显示,该架构在性能与稳定性方面具有优越性。展开更多
光传送网(Optical Transport Network,OTN)技术凭借其独特的优势,在智能电网通信领域得到了广泛应用。深入探讨OTN技术在智能电网通信中的应用,详细阐述OTN技术的基本原理、对智能电网通信需求的适配情况、关键应用,分析其面临的挑战并...光传送网(Optical Transport Network,OTN)技术凭借其独特的优势,在智能电网通信领域得到了广泛应用。深入探讨OTN技术在智能电网通信中的应用,详细阐述OTN技术的基本原理、对智能电网通信需求的适配情况、关键应用,分析其面临的挑战并提出相应解决方案,为进一步提高智能电网通信水平提供理论依据和实践指导。展开更多
雅砻江流域基于同步数字体系(synchronous digital hierarchy,SDH)的现有通信组织方式无法满足一体化能源基地建设需求,在分析现有通信网面临问题和流域集控调度通信网后续需求的基础上,针对场站投运数量与带宽需求的关系给出带宽分配...雅砻江流域基于同步数字体系(synchronous digital hierarchy,SDH)的现有通信组织方式无法满足一体化能源基地建设需求,在分析现有通信网面临问题和流域集控调度通信网后续需求的基础上,针对场站投运数量与带宽需求的关系给出带宽分配组网策略及估算方法。提出一种基于光传送网(optical transport network,OTN)源网服务平台的流域集控调度通信网建设方案,从技术体制选择、业务落地方式、长站距接入关键技术、自愈保护机制建立等方面进行具体技术探讨和工程建设构想。文章探讨的通信组网需求方案结合工程实际建立理论估算模型,方案充分利用厂网已有资源有效降低补充建设的生产投入,对于后续系统工程的分阶段实施具有现实参考意义。展开更多
基金supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61671081in part by the Funds for International Cooperation and Exchange of NSFC under Grant 61720106007+2 种基金in part by the 111 Project under Grant B18008in part by the Beijing Natural Science Foundation under Grant 4172042in part by the Fundamental Research Funds for the Central Universities under Grant 2018XKJC01
文摘As a named data-based clean-slate future Internet architecture,Content-Centric Networking(CCN)uses entirely different protocols and communication patterns from the host-to-host IP network.In CCN,communication is wholly driven by the data consumer.Consumers must send Interest packets with the content name and not by the host’s network address.Its nature of in-network caching,Interest packets aggregation and hop-byhop communication poses unique challenges to provision of Internet applications,where traditional IP network no long works well.This paper presents a comprehensive survey of state-of-the-art application research activities related to CCN architecture.Our main aims in this survey are(a)to identify the advantages and drawbacks of CCN architectures for application provisioning;(b)to discuss the challenges and opportunities regarding service provisioning in CCN architectures;and(c)to further encourage deeper thinking about design principles for future Internet architectures from the perspective of upper-layer applications.
文摘The article introduces ZTE's Softswitch-based NGN solutions. such as the long distance VolP service solution over data back- bone,and local voice and data service solution over MAN.Three application cases are analyzed,and NGN's features in aspects of network architecture,service provision and network management are summarized.
基金Supported by Remote Sensing Support for Offshore Ocean Environment and Polar Sea Ice Early Warning Services(102121201550000009004)。
文摘This paper presents the networking observation capabilities of Chinese ocean satellites and their diverse applications in ocean disaster prevention,ecological monitoring,and resource development.Since the inaugural launch in 2002,China has achieved substantial advancements in ocean satellite technology,forming an observation system composed of the HY-1,HY-2,and HY-3 series satellites.These satellites are integral to global ocean environmental monitoring due to their high resolution,extensive coverage,and frequent observations.Looking forward,China aims to further enhance and expand its ocean satellite capabilities through ongoing projects to support global environmental protection and sustainable development.
文摘通过分析政企业务的特点,结合人工智能(AI)和量子加密等新兴技术,探讨了政企OTN(Optical Transport Network)省内骨干网三种组网模式:智能独立光层模式、动态共享波分光层模式和波道透传模式。研究表明,智能独立光层模式适用于业务需求量大、对网络安全性要求极高且配套资源充足的地区;动态共享波分光层模式适用于现网波分系统覆盖完善但光缆纤芯等基础资源紧张的地区;波道透传模式适用于初期业务不大、现网波道资源丰富的地区。此外,根据未来技术发展趋势,提出了政企OTN网络建设的演进方向,为运营商在政企业务领域的持续发展提供了参考。
文摘新型电力系统面向未来算力接入、算力互联等场景的新业务发展需求,对电力通信网络的承载能力、接入灵活性、多业务适应性等提出诸多挑战。针对现网大量存在的1 Gbit/s以下客户信号承载效率较低等问题,分析了提供小颗粒业务承载的细颗粒光传送网(fine grain optical transport network,fgOTN)技术体系,并结合实验数据,验证fgOTN的技术优势和可行性。结果表明,fgOTN相较于当前虚容器(virtual container,VC)/分组传输(packet transmission,PKT)/光通道数据单元(optical channel data unit,ODU)多平面光网络终端(optical transport network,OTN)承载技术在多个方面具有明显优势,fgOTN在新型电力系统中的应用部署及架构演进将是未来的研究重点。
文摘随着新型电力系统建设的稳步推进和智能电网的发展,电力通信网作为电力系统的重要组成部分,其技术选型和网络架构设计对电力系统的可靠性至关重要。针对细颗粒光传送网(fine grain optical transport network,fgOTN)技术在电力通信网中的应用,深入分析了fgOTN技术演进及其特点,并与同步数字体系(synchronous digital hierarchy,SDH)和光传送网(optical transport network,OTN)进行对比,发现fgOTN具备强大的管控能力、灵活的组网与业务承载能力、可靠的保护机制,以及良好的兼容性和演进能力,非常适合电力等行业的通信网络应用。
文摘随着人类太空探索和航天工程的不断发展,空间任务呈现业务种类日益繁多、数据总量持续增长、空间组网愈加复杂的趋势,OTN(Optical Transport Network)技术凭借超高带宽传输能力、强抗误码能力以及成熟的产业链优势,成为未来空天地一体化组网的可行星间激光承载方案之一.在高速运动场景下,非同轨LEO(Low-Earth-Orbit)卫星之间以及LEO卫星和GEO(Geosynchronous Orbit)卫星之间相对运动引发的多普勒频移随时间发生周期性变化,在接收侧引发链路速率波动效应,导致接收侧比特速率失调,严重影响空天地一体化网络的通信效率.围绕基于OTN技术的激光链路引发的链路速率波动劣化效应,提出一种面向链路速率波动的通用机制框架.基于该框架,提出一种基于开销信道调整的速率波动抑制机制,并给出帧结构修改的具体细节.针对框架中的关键参数,基于Walker星座链路仿真结果以及连续时间的数学模型进行求解,证明抑制机制能实现61 ppm(Parts per Million)的速率调整,同时在LEO-LEO和LEOGEO两种场景下均足以实现调节目标.
文摘面向光传送网络(Optical Transport Network,OTN)中多业务动态调度的复杂需求,设计基于人工智能(Artificial Intelligence,AI)的调度技术路径,构建以链路资源状态和业务请求特征为核心的状态表达方式。此路径引入自适应策略生成机制来实现资源分配的协调优化,提出可感知资源动态变化的调度流程,以及精细化建模调度约束与目标函数。在仿真拓扑环境下进行性能评估,验证了所提策略在调度成功率、资源利用率及响应稳定性等指标上的技术优势,结果表明其具备良好的推广与算法移植能力。
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
文摘The forthcoming sixth generation(6G)of mobile communication networks is envisioned to be AInative,supporting intelligent services and pervasive computing at unprecedented scale.Among the key paradigms enabling this vision,Federated Learning(FL)has gained prominence as a distributed machine learning framework that allows multiple devices to collaboratively train models without sharing raw data,thereby preserving privacy and reducing the need for centralized storage.This capability is particularly attractive for vision-based applications,where image and video data are both sensitive and bandwidth-intensive.However,the integration of FL with 6G networks presents unique challenges,including communication bottlenecks,device heterogeneity,and trade-offs between model accuracy,latency,and energy consumption.In this paper,we developed a simulation-based framework to investigate the performance of FL in representative vision tasks under 6G-like environments.We formalize the system model,incorporating both the federated averaging(FedAvg)training process and a simplified communication costmodel that captures bandwidth constraints,packet loss,and variable latency across edge devices.Using standard image datasets(e.g.,MNIST,CIFAR-10)as benchmarks,we analyze how factors such as the number of participating clients,degree of data heterogeneity,and communication frequency influence convergence speed and model accuracy.Additionally,we evaluate the effectiveness of lightweight communication-efficient strategies,including local update tuning and gradient compression,in mitigating network overhead.The experimental results reveal several key insights:(i)communication limitations can significantly degrade FL convergence in vision tasks if not properly addressed;(ii)judicious tuning of local training epochs and client participation levels enables notable improvements in both efficiency and accuracy;and(iii)communication-efficient FL strategies provide a promising pathway to balance performance with the stringent latency and reliability requirements expected in 6G.These findings highlight the synergistic role of AI and nextgeneration networks in enabling privacy-preserving,real-time vision applications,and they provide concrete design guidelines for researchers and practitioners working at the intersection of FL and 6G.
文摘Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
文摘光传送网(Optical Transport Network,OTN)技术凭借其独特的优势,在智能电网通信领域得到了广泛应用。深入探讨OTN技术在智能电网通信中的应用,详细阐述OTN技术的基本原理、对智能电网通信需求的适配情况、关键应用,分析其面临的挑战并提出相应解决方案,为进一步提高智能电网通信水平提供理论依据和实践指导。
文摘雅砻江流域基于同步数字体系(synchronous digital hierarchy,SDH)的现有通信组织方式无法满足一体化能源基地建设需求,在分析现有通信网面临问题和流域集控调度通信网后续需求的基础上,针对场站投运数量与带宽需求的关系给出带宽分配组网策略及估算方法。提出一种基于光传送网(optical transport network,OTN)源网服务平台的流域集控调度通信网建设方案,从技术体制选择、业务落地方式、长站距接入关键技术、自愈保护机制建立等方面进行具体技术探讨和工程建设构想。文章探讨的通信组网需求方案结合工程实际建立理论估算模型,方案充分利用厂网已有资源有效降低补充建设的生产投入,对于后续系统工程的分阶段实施具有现实参考意义。
