并行多处理机系统中的光纤互连网络的设计与实现

在并行多处理机系统中应用光纤互连网络完成处理单元之间的数据交换,可以极大地提高系统中数据通信的速率、带宽、抗干扰性和准确可靠性.本文详细阐述了并行多处理机系统的体系结构、实现方法、性能分析和实验结果.     

利用语音学知识和神经网络识别非特定人的复合元音

本文主要研究：以音素为识别基元，运用语音学知识，对非特定人的普通话复合元音进行识别。其特点是音素识别由神经网络（ＮＮ）完成，为了便于利用语音知识ＮＮ输入层的刺激采用语音的功率谱，用单元音训练的ＮＮ识另非特定人的普通话复合元音，识别率是５４％。而运用语音学知识后，其识别率提高到９０％。     

一种基于并行化方法的自适应光学闭环预测控制器

自适应光学系统的性能受限于伺服系统的延迟误差和波前传感器的光电子噪声。提出了一种多模型单变量预测模型,该模型采用基于Levenberg-Marquardt学习算法的前馈型神经网络。利用计算机多核处理器,设计了一个具有并行处理能力的预测控制器,来实现对自适应光学闭环控制电压的预测,以消除延迟误差的影响。通过数值仿真实验,研究了预测控制器对控制电压和远场斯特雷尔比的影响,与未采用预测控制器的系统进行了比较,并对预测算法的并行性能进行了分析。实验结果表明,使用并行化方法的预测控制器可以有效缩短系统的预测时间,提高预测算法的加速比,与经典比例积分(PI)控制算法相比可以更有效地降低系统由于伺服延迟引起的误差,远场的斯特雷尔比有明显地提高。     

并行处理阵列中光电混合光纤Crossbar互连网络的研制

本文综述了用于三维光电混合并行处理器阵列系统中的双向可编程多样重构光纤Crossbar互连网络。重点介绍了基于光纤通信的Crossbar互连网络中光纤互连器的设计原理与制作方法,并给出一种具体的电路图,用自制的伪随机码发生器测试了器件的整体性能,而后进行了应用性联机测试。结果表明,该光纤互连器能很好地满足系统中处理器20Mbps传输速率的要求,电路简单,具有通用性和较高的性能价格比。     

ATMOS (ATM optical switching)—system perspective

The expected traffic growth from new broadband services will require network capacities that can be beneficially provided by optical WDM transport networks. Optical packet techniques are essential for large networks to provide flexibility and granularity. A layered network architecture with a transparent optical layer and optical packet layers has been elaborated. Four switching concept options for large optical ATM switch fabrics in the public network and small access nodes are presented. The technology for the related key functions of space and wavelength switching was developed in the RACE ATMOS project and the feasibility of concepts and technologies was verified in four system demonstrators.     

光寻址2×2光学开关及3－D集成光学蝶形互连网络的实现

在光学多级互连网络的实现中，光学２×２开关的构造是至关重要的。本文介绍一种构成光寻址２×２光学开关的方法，该方法是在方解石晶体的双折射性质的基础上，利用普克尔斯只读光调制器（ＰＲＯＭ）来控制入射光的偏振态，从而实现光寻址２×２光学开关，这种开关具有许多优点，并且可以在３－Ｄ方向堆栈集成。利用该开关可以很方便地实现３－Ｄ集成的蝶形互连网络     

二维光学神经网络自由空间互连结构设计

本文分析了以透镜阵列元件建立空间互连通道的光学神经网络中神经元数的理论极限.为了提高网络中的神经元数,我们提出了一种并行结构设计,该结构克服了小透镜成象中的离轴象散的影响,提高了系统的光能利用效率,同时降低了系统中的对位精度误差,提高了互连运算精度.     

An anti-attack model based on complex network theory in P2P networks

Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on a dynamic compensation process and a reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.     

Characterization of chaotic dynamic behavior in the gas–liquid slug flow using directed weighted complex network analysis

Understanding the nonlinear and complex dynamics underlying the gas–liquid slug flow is a significant but challenging problem. We systematically carried out gas–liquid two-phase flow experiments for measuring the time series of flow signals, which is studied in terms of the mapping from time series to complex networks. In particular, we construct directed weighted complex networks (DWCN) from time series and then associate different aspects of chaotic dynamics with the topological indices of the DWCN and further demonstrate that the DWCN can be exploited to detect unstable periodic orbits of low periods. Examples using time series from classical chaotic systems are provided to demonstrate the effectiveness of our approach. We construct and analyze numbers of DWCNs for different gas–liquid flow patterns and find that our approach can quantitatively distinguish different experimental gas–liquid flow patterns. Furthermore, the DWCN analysis indicates that slug flow shows obvious chaotic behavior and its unstable periodic orbits reflect the intermittent quasi-periodic oscillation behavior between liquid slug and large gas slug. These interesting and significant findings suggest that the directed weighted complex network can potentially be a powerful tool for uncovering the underlying dynamics leading to the formation of the gas–liquid slug flow.     

Building and analyzing the US airport network based on en-route location information

From a complex network perspective, this study sets out two aims around the US airport network (USAN) which is built from en-route location information of domestic flights in the US. First, we analyze the structural properties of the USAN with respect to its binary and weighted graphs, and second we explore the airport patterns, which have wide-ranging implications. Results from the two graphs indicate the following. (1) The USAN exhibits scale-free, small-world and disassortative mixing properties, which are consistent with the mainstream perspectives. Besides, we find (2) a remarkable power relationship between the structural measurements in the binary graph and the traffic measurements in the weighted counterpart, namely degree versus capacity and attraction versus volume. On the other hand, investigation of the airport patterns suggests (3) that all the airports can be classified into four categories based on multiple network metrics, which shows a complete typology of the airports. And it further indicates (4) that there is a subtle relationship between the airport traffic and the geographical constraints as well as the regional socioeconomic indicators.