With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to res...With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.展开更多
In recent years,neural networks have become an increasingly powerful tool in scientific computing.The universal approximation theorem asserts that a neural network may be constructed to approximate any given continuou...In recent years,neural networks have become an increasingly powerful tool in scientific computing.The universal approximation theorem asserts that a neural network may be constructed to approximate any given continuous function at desired accuracy.The backpropagation algorithm further allows efficient optimization of the parameters in training a neural network.Powered by GPU’s,effective computations for scientific and engineering problems are thereby enabled.In addition,we show that finite element shape functions may also be approximated by neural networks.展开更多
Universal service obligation of network industries, such as telecommunication, electric power, post, railway, and aviation, has greatly hampered the progress of their marketization reform. Establishing universal servi...Universal service obligation of network industries, such as telecommunication, electric power, post, railway, and aviation, has greatly hampered the progress of their marketization reform. Establishing universal service fund could be an efficient solution. This paper provides a method to calculate the charging rate, imposing and allowancing range of universal service fund for network industries, it also proves the feasibility of accomplishing universal service through universal service fund in network industries.展开更多
Most of the content of the course Meteorology and Climatology in common colleges and universities is what students are interested in,and most students have been exposed to or understood many contents.When studying thi...Most of the content of the course Meteorology and Climatology in common colleges and universities is what students are interested in,and most students have been exposed to or understood many contents.When studying this part of the content,students often show varying degrees of interest.Of course,each student s own situation is different.In classroom teaching,teachers should comprehensively consider various factors.Especially in the network classroom teaching situation,teachers should combine the learning content to use multi platform teaching in the teaching,students performance to flexibly carry out interaction between teachers and students,and students own situation to pay attention to individualized cultivation.展开更多
Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction...Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.展开更多
DeConverter is core software in a Universal Networking Language(UNL) system.A UNL system has EnConverter and DeConverter as its two major components.EnConverter is used to convert a natural language sentence into an e...DeConverter is core software in a Universal Networking Language(UNL) system.A UNL system has EnConverter and DeConverter as its two major components.EnConverter is used to convert a natural language sentence into an equivalent UNL expression,and DeConverter is used to generate a natural language sentence from an input UNL expression.This paper presents design and development of a Punjabi DeConverter.It describes five phases of the proposed Punjabi DeConverter,i.e.,UNL parser,lexeme selection,morphology generation,function word insertion,and syntactic linearization.This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter.Syntactic linearization is the process of defining arrangements of words in generated output.The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph,a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper.Special cases of syntactic linearization with respect to Punjabi language for UNL relations like 'and','or','fmt','cnt',and 'seq' have also been presented in this paper.This paper also provides implementation results of the proposed Punjabi DeConverter.The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology(IIT),Bombay,India,as gold-standards.The proposed system generates 89.0% grammatically correct sentences,92.0% faithful sentences to the original sentences,and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale.The system is also able to achieve a bilingual evaluation understudy(BLEU) score of 0.72.展开更多
文摘With the arrival of the 4G and 5G,the telecommunications networks have experienced a large expansion of these networks.That enabled the integration of many services and adequate flow,thus enabling the operators to respond to the growing demand of users.This rapid evolution has given the operators to adapt,their methods to the new technologies that increase.This complexity becomes more important,when these networks include several technologies to access different from the heterogeneous network like in the 4G network.The dimensional new challenges tell the application and the considerable increase in demand for services and the compatibility with existing networks,the management of mobility intercellular of users and it offers a better quality of services.Thus,the proposed solution to meet these new requirements is the sizing of the EPC(Evolved Packet Core)core network to support the 5G access network.For the case of Orange Guinea,this involves setting up an architecture for interconnecting the core networks of Sonfonia and Camayenne.The objectives of our work are of two orders:(1)to propose these solutions and recommendations for the heart network EPC sizing and the deployment to be adopted;(2)supply and architectural interconnection in the heart network EPC and an existing heart network.In our work,the model of traffic in communication that we use to calculate the traffic generated with each technology has link in the network of the heart.
基金This work was supported in part by the National Natural Sci-ence Foundation of China(Grants 11521202,11832001,11890681 and 11988102).
文摘In recent years,neural networks have become an increasingly powerful tool in scientific computing.The universal approximation theorem asserts that a neural network may be constructed to approximate any given continuous function at desired accuracy.The backpropagation algorithm further allows efficient optimization of the parameters in training a neural network.Powered by GPU’s,effective computations for scientific and engineering problems are thereby enabled.In addition,we show that finite element shape functions may also be approximated by neural networks.
文摘Universal service obligation of network industries, such as telecommunication, electric power, post, railway, and aviation, has greatly hampered the progress of their marketization reform. Establishing universal service fund could be an efficient solution. This paper provides a method to calculate the charging rate, imposing and allowancing range of universal service fund for network industries, it also proves the feasibility of accomplishing universal service through universal service fund in network industries.
文摘Most of the content of the course Meteorology and Climatology in common colleges and universities is what students are interested in,and most students have been exposed to or understood many contents.When studying this part of the content,students often show varying degrees of interest.Of course,each student s own situation is different.In classroom teaching,teachers should comprehensively consider various factors.Especially in the network classroom teaching situation,teachers should combine the learning content to use multi platform teaching in the teaching,students performance to flexibly carry out interaction between teachers and students,and students own situation to pay attention to individualized cultivation.
基金supported by National Natural Science Foundation of China(12402336,82201637,U20A2070,and 12025202)National High-Level Talent Project(YQR23069)+6 种基金Natural Science Foundation of Jiangsu Province(BK20230876)the Young Elite Scientist Sponsorship Program by CAST(YESS20210238)Forwardlooking layout projects(1002-ILB24009)Zhejang Provincial Medical and Health Technology Project(Grant No.2024KY246,2025KY180)Scientific Research Foundation of Hangzhou City University(No.J-202402)Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence,Zhejiang University(Grant No.BMI2400025)Hangzhou Science and Technology Bureau.
文摘Light-field imaging has wide applications in various domains,including microscale life science imaging,mesoscale neuroimaging,and macroscale fluid dynamics imaging.The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing,however,current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale.Considering the multiscale imaging capacity of light-field technique,a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging.Unfortunately,to our knowledge,no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale,mesoscale,and macroscale.To fill this gap,we present a real-time and universal network(RTU-Net)to reconstruct high-resolution light-field images at any scale.RTU-Net,as the first network that works over multiscale light-field image reconstruction,employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability.We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images,including microscale tubulin and mitochondrion dataset,mesoscale synthetic mouse neuro dataset,and macroscale light-field particle imaging velocimetry dataset.The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300μm×300μm×12μm to 25 mm×25 mm×25 mm,and demonstrated higher resolution when compared with recently reported light-field reconstruction networks.The high-resolution,strong robustness,high efficiency,and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.
文摘DeConverter is core software in a Universal Networking Language(UNL) system.A UNL system has EnConverter and DeConverter as its two major components.EnConverter is used to convert a natural language sentence into an equivalent UNL expression,and DeConverter is used to generate a natural language sentence from an input UNL expression.This paper presents design and development of a Punjabi DeConverter.It describes five phases of the proposed Punjabi DeConverter,i.e.,UNL parser,lexeme selection,morphology generation,function word insertion,and syntactic linearization.This paper also illustrates all these phases of the Punjabi DeConverter with a special focus on syntactic linearization issues of the Punjabi DeConverter.Syntactic linearization is the process of defining arrangements of words in generated output.The algorithms and pseudocodes for implementation of syntactic linearization of a simple UNL graph,a UNL graph with scope nodes and a node having un-traversed parents or multiple parents in a UNL graph have been discussed in this paper.Special cases of syntactic linearization with respect to Punjabi language for UNL relations like 'and','or','fmt','cnt',and 'seq' have also been presented in this paper.This paper also provides implementation results of the proposed Punjabi DeConverter.The DeConverter has been tested on 1000 UNL expressions by considering a Spanish UNL language server and agricultural domain threads developed by Indian Institute of Technology(IIT),Bombay,India,as gold-standards.The proposed system generates 89.0% grammatically correct sentences,92.0% faithful sentences to the original sentences,and has a fluency score of 3.61 and an adequacy score of 3.70 on a 4-point scale.The system is also able to achieve a bilingual evaluation understudy(BLEU) score of 0.72.
