The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the ...The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.展开更多
Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convol...Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.展开更多
基金supportedin part by the National Science Foundation of China(NSFC)under Grant 61631005,Grant 61771065,Grant 61901048in part by the Zhijiang Laboratory Open Project Fund 2020LCOAB01in part by the Beijing Municipal Science and Technology Commission Research under Project Z181100003218015。
文摘The efficient integration of satellite and terrestrial networks has become an important component for 6 G wireless architectures to provide highly reliable and secure connectivity over a wide geographical area.As the satellite and cellular networks are developed separately these years,the integrated network should synergize the communication,storage,computation capabilities of both sides towards an intelligent system more than mere consideration of coexistence.This has motivated us to develop double-edge intelligent integrated satellite and terrestrial networks(DILIGENT).Leveraging the boost development of multi-access edge computing(MEC)technology and artificial intelligence(AI),the framework is entitled with the systematic learning and adaptive network management of satellite and cellular networks.In this article,we provide a brief review of the state-of-art contributions from the perspective of academic research and standardization.Then we present the overall design of the proposed DILIGENT architecture,where the advantages are discussed and summarized.Strategies of task offloading,content caching and distribution are presented.Numerical results show that the proposed network architecture outperforms the existing integrated networks.
文摘Artificial intelligence plays an essential role in the medical and health industries.Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis.However,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected region.Therefore,an optimized deep convolution network is utilized for analyzing disease-affected regions in this work.Different disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning network.This method identifies affected pixels by the gray wolf hunting process.The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis.The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule.The pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation process.This process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.
