多域光网络互联互通技术

首先介绍了光网络设备的发展状况,然后分析了现有光网络域间互联互通中的主要问题,接着介绍了现有的几种关于多域光网络域间互联互通的技术,包括静态互联技术、GNMS技术、E-NNI技术、PCE技术和SDON技术.通过分析各种技术的优缺点,介绍了两种技术结合使用的方案.     

MEMS光开关

采用MEMS体硅工艺,制作了三种结构的微机械光开关:水平驱动2D(二维)光开关、垂直驱动2D光开关和扭摆驱动2D、3D(三维)光开关.水平驱动光开关采用单层体硅结构,另外两种光开关都采用了硅-玻璃的键合结构.它们的工作原理都基于硅数字微镜技术.这三种光开关均采用了静电力驱动,具有较低的驱动电压,其中扭摆式光开关的驱动电压小于15V.对于2D开关阵列,在硅基上制作了光纤自对准耦合槽.对后两种光开关的开关特性进行了计算机模拟与分析,结果表明这两种光开关具有小于1ms的开关时间.     

MEMS光开关

采用 MEMS体硅工艺 ,制作了三种结构的微机械光开关 :水平驱动 2 D(二维 )光开关、垂直驱动 2 D光开关和扭摆驱动 2 D、3D(三维 )光开关 .水平驱动光开关采用单层体硅结构 ,另外两种光开关都采用了硅 -玻璃的键合结构 .它们的工作原理都基于硅数字微镜技术 .这三种光开关均采用了静电力驱动 ,具有较低的驱动电压 ,其中扭摆式光开关的驱动电压小于 15 V.对于 2 D开关阵列 ,在硅基上制作了光纤自对准耦合槽 .对后两种光开关的开关特性进行了计算机模拟与分析 ,结果表明这两种光开关具有小于 1ms的开关时间     

承载多媒体业务的光接入系统设计

文章设计完成了DVB、IPTV两种多媒体业务的光接入解决方案,根据不同的接入环境可以灵活选择最合适的光接入技术.系统支持广播和点播两种收看方式,并且实现有线和无线接入的融合.同时系统最大限度地利用了已有的入户线资源,降低建设成本.     

光开关主流技术

光开关是光网络中完成全光交换的核心器件,它的研究日益成为全光通信领域关注的焦点.文章重点介绍了光开关在全光网络中的应用、MEMS光开关和热光开关的基本工作原理及两种光开关技术的进展,并就其他光开关作了简要介绍.     

ROF系统中毫米波产生法的研究

本文对ROF系统中毫米波副载波的几种主要的产生方法进行了综述,着重介绍了利用直接调制,外部调制以及光外差技术产生毫米波的方案,并创新性地提出了一种基于光纤布拉格光栅的新型光外差技术.该技术利用光纤布拉格光栅滤波的方法,产生两束来自于同一光源的光波,从而消除了传统光外差技术中的相位噪声,提高了系统性能.     

光多输入多输出技术研究进展

介绍了光多输入多输出(MIMO)系统模型及其特点,分析讨论了光纤MIMO和自由空间光MIMO两种模型的信道矩阵及相关的编码技术和检测技术,指出当前光MIMO技术存在的一些问题,并对以后的研究作了进一步的探讨.     

WDM全光网中的包交换技术

WDM技术为通信网络提供了巨大的带宽资源,包交换技术提高了带宽的利用率,二者的结合即在WDM网中进行包交换必然是下一代光网络的发展趋势.本文讨论了光包交换的分层网络模型、目前正在研究的两种包交换技术以及实现包交墓丶际?最后就光包交换技术对未来光通信网络的影响作了论述.     

机载综合模块化处理平台光互联方法研究

目前航电系统中应用的机载综合模块化处理平台存在两种构型,分别是集中式机箱配置LRM模块和托架安装的ARINC600标准LRU单元.针对这两种构型及机载光互联技术现状,分别描述了各自的特点及光互联方法采用的光电转换、高密度接口传输等工程化实现方式.最后对当前光互联方法的验证结果进行了描述,对未来改进方向进行了探讨.     

一种融合EPON和GPON的接入方案

无源光网络(PON)突破了传统的点到点解决方法的局限,一直被认为是光接入网中最具发展前景的技术.由于光接入网应用环境复杂多样,PON应用技术也呈现出多样化的特点.以太网无源光网络(EPON)和千兆无源光网络(GPON)是其中两种具有代表性的接入技术.文章根据光纤到户实际工程的需要,论述了一种融合EPON和GPON技术的接入方案.     

模糊强化学习型的图像矢量量化算法

本文给出了一种新的图像矢量量化码书的优化设计方法.传统矢量量化方法只考虑了码字与训练矢量之间的吸引影响,所以约束了最优解的寻解空间.本文提出了一种新的学习机理--模糊强化学习机制,该机制在传统的吸引因子基础上,引入新的排斥因子,极大地释放了吸引因子对最优解的寻解空间的约束.新的模糊强化学习机制没有采用引入随机扰动的方法来避免陷入局部最优码书,而是通过吸引因子和排斥因子的合力作用,较准确地确定了每个码字的最佳移动方向,从而使整体码书向全局最优解靠近.实验结果表明,基于模糊强化学习机制的矢量量化算法始终稳定地取得显著优于模糊K-means算法的性能,较好地解决了矢量量化中的码书设计容易陷入局部极小和初始码书影响优化结果的问题.     

基于语音存在概率的语音活动检测方法

首先总结了几种常见的语音活动检测（VAD）方法，然后从计算每帧每个频率点的语音存在概率出发．提出了一种新的VAD方法，并就其中的一些参数选择问题进行了讨论。最后给出新方法与传统方法实验结果对比。     

On adaptive HMM state estimation

New online adaptive hidden Markov model (HMM) state estimation schemes are developed, based on extended least squares (ELS) concepts and recursive prediction error (RPE) methods. The best of the new schemes exploit the idempotent nature of Markov chains and work with a least squares prediction error index, using a posterior estimates, more suited to Markov models than traditionally used in identification of linear systems. These new schemes learn the set of N Markov chain states, and the a posteriori probability of being in each of the states at each time instant. They are designed to achieve the strengths, in terms of computational effort and convergence rates, of each of the two classes of earlier proposed adaptive HMM schemes without the weaknesses of each in these areas. The computational effort is of order N. Implementation aspects of the proposed algorithms are discussed, and simulation studies are presented to illustrate convergence rates in comparison to earlier proposed online schemes     

An improved method for estimating epicardial potentials from thebody surface

The authors present a new method for regularizing the ill-posed problem of computing epicardial potentials from body surface potentials. The method simultaneously regularizes the equations associated with all time points, and relies on a new theorem which states that a solution based on optimal regularization of each integral equation associated with each principal component of the data will be more accurate than a solution based on optimal regularization of each integral equation associated with each time point. The theorem is illustrated with simulations mimicking the complexity of the inverse electrocardiography problem. As must be expected from a method which imposes no additional a priori constraints, the new approach addresses uncorrelated noise only, and in the presence of dominating correlated noise it is only successful in producing a “cleaner” version of a necessarily compromised solution. Nevertheless, in principle, the new method is always preferred to the standard approach, since it (without penalty) eliminates pure noise that would otherwise be present in the solution estimate     

Scalable on-demand media streaming with packet loss recovery

Previous scalable on-demand streaming protocols do not allow clients to recover from packet loss. This paper develops new protocols that: (1) have a tunably short latency for the client to begin playing the media; (2) allow heterogeneous clients to recover lost packets without jitter as long as each client's cumulative loss rate is within a tunable threshold; and (3) assume a tunable upper bound on the transmission rate to each client that can be as small as a fraction (e.g., 25%) greater than the media play rate. Models are developed to compute the minimum required server bandwidth for a given loss rate and playback latency. The results of the models are used to develop the new protocols and assess their performance. The new protocols, Reliable Periodic Broadcast and Reliable Bandwidth Skimming, are simple to implement and achieve nearly the best possible scalability and efficiency for a given set of client characteristics and desirable/feasible media quality. Furthermore, the results show that the new reliable protocols that transmit to each client at only twice the media play rate have similar performance to previous protocols that require clients to receive at many times the play rate.     

Maximum likelihood reconstruction for emission tomography

Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density lambda = lambda(x, y, z) generates, and is to be reconstructed from, the number of counts n(*)(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate lambdainsertion mark of lambda which maximizes the probability p(n(*)|lambda) of observing the actual detector count data n(*) over all possible densities lambda. Let independent Poisson variables n(b) with unknown means lambda(b), b = 1, ..., B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ..., D with p(b,d) a one-step transition matrix, assumed known. We observe the total number n(*) = n(*)(d) of emissions in each detector unit d and want to estimate the unknown lambda = lambda(b), b = 1, ..., B. For each lambda, the observed data n(*) has probability or likelihood p(n(*)|lambda). The EM algorithm of mathematical statistics starts with an initial estimate lambda(0) and gives the following simple iterative procedure for obtaining a new estimate lambdainsertion mark(new), from an old estimate lambdainsertion mark(old), to obtain lambdainsertion mark(k), k = 1, 2, ..., lambdainsertion mark(new)(b)= lambdainsertion mark(old)(b)Sum of (n(*)p(b,d) from d=1 to D/Sum of lambdainsertion mark()old(b('))p(b('),d) from b(')=1 to B), b=1,...B.     

Mixing tone mapping operators on the GPU by differential zone mapping based on psychophysical experiments

In this paper, we present a new technique for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) displays in an efficient way on the GPU. The described process has three stages. First, the input image is segmented into luminance zones. Second, the tone mapping operator (TMO) that performs better in each zone is automatically selected. Finally, the resulting tone mapping (TM) outputs for each zone are merged, generating the final LDR output image. To establish the TMO that performs better in each luminance zone we conducted a preliminary psychophysical experiment using a set of HDR images and six different TMOs. We validated our composite technique on several (new) HDR images and conducted a further psychophysical experiment, using an HDR display as the reference that establishes the advantages of our hybrid three-stage approach over a traditional individual TMO. Finally, we present a GPU version, which is perceptually equal to the standard version but with much improved computational performance.     

Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN

Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.     

基于置信传播的优化译码算法研究

该文在对LDPC码的译码算法分析的基础上,针对校验矩阵中含有的环对译码算法的影响,提出了一种在置信传播算法基础之上的译码算法。该算法通过及时切断消息在环上的重传回路,可消除因校验矩阵中的环回传原始信息对译码造成的影响,保证优质的原始信息能尽可能地传播到其能传播的节点,从而提升了LDPC码的译码性能。仿真实验表明,在低信噪比的信道中,该算法具有相当于传统算法的性能和更低的计算复杂度;在良好的信道条件下可以取得比传统算法更优异的性能。     

串行网络中ABR业务的流量控制和缓存设计

本文提出一种ABR业务速率在优化率和请求率之间的切换控制方法。在上述控制算法基础上,研究了保证信元在交换节点处无丢弃,同时输出带宽得到充分利用的交换节点的缓存和阈值设计问题。新的流量控制机制实现了源节点发送率和交换节点缓存的小抖动控制。