各种光网络解决方案 相关技术的研究和探索Word下载.docx

1、 GPeterNet, a graph theoretic framework, and FraNtiC, a fractal geometric architecture, for arbitrary access network deployments. The performance of these topologies is analyzed in terms of different system metrics topological robustness and reliability, system costs and network exposure due to fail

2、ure conditions. Our analysis shows that a combination of different mesh-based multi-hop access topologies, coupled with emerging wireless backhaul technologies, can cater carrier-class services for next generation radio access networks, providing significant advantages over existing access technolog

3、ies.Article Outline1. Introduction 1.1. Motivation and previous work1.2. Our contributions2. Optical wireless technology3. The PeterNet and GPeterNet architectures 3.1. The generalized PeterNet4. The FraNtiC architecture 4.1. Flexibility and scalability5. Robustness, reliability and network exposure

4、 5.1. Robustness 5.1.1. Centrality and its role in access topology5.2. Reliability analysis 5.2.1. Reliability analysis of FraNtiC5.2.2. Reliability analysis of GPeterNet5.3. Network exposure6. Performance evaluation framework 6.1. System parameters6.2. Evaluation platform7. ConclusionAcknowledgemen

5、tsAppendix A. Appendix B. ReferencesVitae硬件人工智能系统 二十年的发展历程及启示和经验Artificial neural networks in hardware: A survey of two decades of progressNeurocomputing神经网络计算学报This article presents a comprehensive overview of the hardware realizations of artificial neural network （ANN） models, known as hardware ne

6、ural networks （HNN）, appearing in academic studies as prototypes as well as in commercial use. HNN research has witnessed a steady progress for more than last two decades, though commercial adoption of the technology has been relatively slower. We study the overall progress in the field across all m

7、ajor ANN models, hardware design approaches, and applications. We outline underlying design approaches for mapping an ANN model onto a compact, reliable, and energy efficient hardware entailing computation and communication and survey a wide range of illustrative examples. Chip design approaches （di

8、gital, analog, hybrid, and FPGA based） at neuronal level and as neurochips realizing complete ANN models are studied. We specifically discuss, in detail, neuromorphic designs including spiking neural network hardware, cellular neural network implementations, reconfigurable FPGA based implementations

9、, in particular, for stochastic ANN models, and optical implementations. Parallel digital implementations employing bit-slice, systolic, and SIMD architectures, implementations for associative neural memories, and RAM based implementations are also outlined. We trace the recent trends and explore po

10、tential future research directions.1. Introduction2. Evaluation parameters and classification 2.1. Hardware neural network classification3. Hardware approaches to neuronal design 3.1. Digital neuron3.2. Analog neuron3.3. Silicon implementation of spiking neuron and its synaptic dynamics4. Hardware n

11、eural network chips 4.1. Digital neurochips4.2. Analog neurochips4.3. Hybrid neurochips4.4. FPGA based implementations4.5. Other implementations 4.5.1. Associative neural memories4.5.2. RAM based implementations5. CNN implementations6. Neuromorphic HNNs 6.1. Spiking neural network hardware7. Optical

12、 neural networks8. Conclusions and discussionDatabase architectures: Current trends and their relationships to environmental data management数据结构：当前发展趋势 及其与数据管理环境的关系Environmental Modelling & SoftwareAn MPLS-based architecture for scalable QoS and traffic engineering in converged multiservice mobile I

13、P networksImpact of network structure on the capacity of wireless multihop ad hoc communicationPhysica A: Statistical Mechanics and its Application网络结构设置与网络通信能力的关系及相互影响 无线网络通讯 多路由连接与布局Reconfigurable turbo decoding for 3G applicationsSignal Processing3G应用软件中，本蓝C语言代码调试与程序编写Software radio and reconfigu

14、rable systems represent reconfigurable functionalities of the radio interface. Considering turbo decoding function in battery-powered devices like 3GPP mobile terminals, it would be desirable to choose the optimum decoding algorithm: SOVA in terms of latency, and log-MAP in terms of performance. In

15、this paper it is shown that the two algorithms share common operations, making feasible a reconfigurable SOVA/log-MAP turbo decoder with increased efficiency. Moreover, an improvement in the performance of the reconfigurable architecture is also possible at minimum cost, by scaling the extrinsic inf

16、ormation with a common factor. The implementation of the improved reconfigurable decoder within the 3GPP standard is also discussed, considering different scenarios. In each scenario various frame lengths are evaluated, while the four possible service classes are applied. In the case of AWGN channel

17、s, the optimum algorithm is proposed according to the desired quality of service of each class, which is determined from latency and performance constraints. Our analysis shows the potential utility of the reconfigurable decoder, since there is an optimum algorithm for most scenarios.2. Why reconfig

18、uration only between SOVA and log-MAP?3. Mathematical analysis of the algorithms 3.1. SOVA analysis3.2. Log-MAP analysis3.3. Reconfigurable operations between SOVA and log-MAP 3.3.1. BMC block3.3.2. FSMC and RSMC blocks 3.3.2.1. FPMC, RPMC sub-blocks3.3.2.2. FAC, RAC sub-blocks3.3.2.3. SPC, FAC sub-

19、blocks4. Data transfer in 3GPP 4.1. Quality of service architecture5. Simulation model6. Improving the SOVA/log-MAP reconfigurable decoder7. Latency calculation8. Simulation results and implementation scenarios in 3GPP 8.1. Scenario 1: 28.8kbps radio bearer service8.2. Scenario 2: 57.6kbps radio bearer service 8.2.1. Streaming service class8.3. Scenario 3: 648.3.1. Streaming service class8.4. Scenario 4: 1288.4.1. Streaming service class8.5. Scenario 5: 1448.