神光Ⅲ原型装置自适应光学系统应用技术研究

重点研究了高功率固体激光装置自适应光学系统的两个主要应用问题,包括波前传感器参考波前在线标定和预补偿方式下激光过滤波器小孔堵孔问题。文中对两种不同的在线标定方法进行了研究,分析各自的优缺点,结合自适应光学系统的校正对象确定合适的标定方法和标定结果。针对小口径注入位置预补偿的自适应光学布局方案,对影响过滤波小孔的主要因素进行了研究,并从自适应光学系统控制和激光器运行流程两个方面提出了解决方案,获得较好的实验结果。     

适用于神经元状态非等概率分布的神经网络模型及其光学实现

针对Hopfield模型在存储模式的神经元状态不具备理想的等概率分布时性能下降，以及光学难以实现多灰度阶互连的弱点，提出了一种非对称截值点的截值模型，在易于光学或光电子技术实现的同时，与其他模型相比，存储容量和容噪声能力都有较大提高.同时，提出了光束方向编码方法，并用该方法实现了上述模型，给出了实验结果. 关键词：     

基于能量最小的统计三级截值模型

从能量函数出发，用统计学方法建立了统计三级截值模型，克服了用光学技术实现Ｈｏｐｆｉｅｌｄ模型时难以表示其高动态范围互连权重的弱点。与光学中通常采用的模型相比，统计三级截值模型提高了网络的存储容量和寻址能力。     

高阶局域互联神经网络的关联存储

给出了高阶局域互联神经网络的数学模型。在计算机上对其关联存储能力进行了模拟计算。结果表明，这种局域互联神经网络的互联权重数大大减少，同时依然具有良好的关联存储能力；另一方面，如果限定了互联权重矩阵的大小，利用本文给出的高阶局域互联神经网络模型可构造较大的人工神经网络。     

分区适应截值模型及其在交通标志识别中的应用

针对Ｈｏｐｆｉｅｌｄ网络模型在存储模式不满足０和１状态的均匀分布及数目对等的条件下存储容量及寻址能力下降的缺点，提出并用光束方向编码光学实现了三值（１，０，－１）互连的分区适应截值模型，并把这一模型应用到交通标志的识别中，结果表明该模型及光学系统有很好的稳定性。     

利用电子俘获材料实现光学IPA神经网络模型

报道用自行研制的ＣａＳ（Ｅｕ，Ｓｍ）电子俘获材料表示互联机重矩阵以实现光学ＩＰＡ神经网络模型，由于ＣａＳ（Ｅｕ，Ｓｍ）的红外激励发光强度与俘获的电子密度及红外读出光强度之积成线性关系，故可用来表示互联权重矩阵以实现光学神经网络，这种互联权重矩阵具有很宽的数值范围，并可以用光学的办法进行快速擦除，重写。     

Towards an ideal photon correlator with a SAW-device

The advantages and disadvantages of the clipped correlator for photoelectric correlation measurements are analysed. A scheme is suggested by which the problem of inversion (an inherent disadvantage of clipping) is obviated. With the availability of surface acustic wave (SAW) delay lines, we would like to point out that one can make an almost ideal photon correlator.     

Signal restoration in intensity-modulated optical OFDM access systems

It is well known that deliberate signal clipping in an intensity-modulated (IM) laser transmitter helps to overcome the optical orthogonal frequency division multiplexing (OFDM) system performance limitation that is related to the signal high peak-to-average power ratio. The amplitude of a clipped OFDM signal has to be optimized in order to minimize the optical power that is required to achieve a specified system performance. However, the signal clipping introduces nonlinear distortion (so-called clipping noise) and leads to a system performance penalty. In this Letter, the performance of the IM optical OFDM system with digital baseband clipping distortion in the transmitter and clipping noise compensation by means of signal restoration in the digital signal processing unit of the system receiver is analytically evaluated. It is demonstrated that the system bit-error ratio can be reduced by more than an order of magnitude, from 10(-3) to 3.5×10(-5), by applying only the first iteration of the signal restoration algorithm proposed in this Letter. The results of the analytical analysis are verified with brute-force numerical simulations based on direct error counting.     

MASS-RAB: Robust adaptive beamforming for general-rank signal models via matched spatial spectrum processing

The wavefront of acoustic signal suffers from fast fluctuation after a long distance propagation in a random and inhomogeneous ocean channel, which makes the rank of the covariance matrix for the desired signal (signal of interest) remarkably higher than one. Consequently, the assumption of rank-one point signal model for existing adaptive beamforming algorithms is no longer suitable. In this paper, a matched spatial spectrum processing based robust adaptive beamforming (MASS-RAB) algorithm is presented for general-rank signal models. First, the interference-plus-noise covariance matrix and the desired signal covariance matrix are reconstructed using the matched spatial spectrum processing method. Second, the weight vector is directly calculated using these reconstructed covariance matrices for the minimum variance distortionless response (MVDR) algorithm, which is developed for the general-rank signal models. Due to covariance matrix reconstruction, the MASS-RAB algorithm is more robust than those methods relying on the sample covariance matrix. The cases of the rank-one point signal model and the full-rank non-point signal model are considered by several numerical examples. Experimental results have demonstrated the superiority of the proposed MASS-RAB method.     

局域互联神经网络的关联存储

基于全局互联的Hopfield模型,本文提出了局域互联关联存储的新概念.与全局互联相比,局域互联具有较小的关联矩阵,因而,有利于用现有的空间光调制器加以实现.同时,计算机模拟结果表明,它仍然具有全局关联存储的能力.     

一维局域互联关联存贮的光学实现

本文提出了一维局域互联关联存贮的光学实现方法，讨论了可用来实现局域互联网的三种光电混合系统。基于光电相关系统并用图象监示器编码互联权重矩阵，液晶显示器编码输入矢量，给出了一维光学实验结果。     

有源声呐中的改进加权自适应高分辨距离估计方法

为提高有源声呐对目标回波亮点的距离分辨能力,提出了一种改进加权自适应高分辨距离估计方法,所提方法功率谱中的权值矩阵可采用3种形式:第1个元素为残差平方和其余为0的对角阵、单位阵、残差平方和构成对角阵,采用不同参考子带高阶线性预测的调和平均构造功率谱表达式,避免了距离维自适应方法功率谱中的低阶线性预测。通过数值仿真和水池试验对所提方法与距离维自适应方法进行了比较研究,结果表明所提方法在接近的计算量下获得较优的距离分辨能力。      

Adaptive synchronization of neural networks with or without time-varying delay

In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.     

两维局域互联神经网络的关联存储

本文将局域互联神经网络的新概念推广到两维情形,并对两维局域互联关联存储进行了理论分析和大量的计算机模拟.结果表明,两维局域互联神经网络的优点是,在满足存储容量限制的前提下,它与全局互联神经网络具有相同的关联存储能力,而其互联权重矩阵要比全局互联网络小得多.因而,有利于使用现有的空间光调制器实现两维大规模的人工神经网络.     

洗牌型图样间联想光学神经网络模型

本文将洗牌型神经网络结构和图样间联想神经网络算法相结合,提出了一种洗牌型图样间联想神经网络(PS-IPA)模型。该模型具有极其简单、稀疏的互连权矩阵,十分适于大规模神经网络的光学实现。计算机模拟结果表明洗牌型图样间联想神经网络的稳定性和抑制噪音的能力均优于图样间联想网络IPA.本文还给出了洗牌互连的一般性原则,使网络结构得到优化,增强了洗牌型神经网络的灵活性和适应性。并采用3-洗牌和2-洗牌结合的PS-IPA对汽车牌照的字符进行识别,得到了较好的结果。     

神经网络模式识别系统互连权重二值化研究

在增量算法的基础上，利用截断方法和蒙塔卡罗算法，对以四类飞行目标旋转投影作为学习样本的级联神经网络互连权重进行了二值优化处理，并用非学习样本进行了容错性检验，计算机木匠虱到了满意的结果。     

A multilevel nano-scale interconnection RLC delay model

Based on the multilevel interconnections temperature distribution model and the RLC interconnection delay model of the integrate circuit,this paper proposes a multilevel nano-scale interconnection RLC delay model with the method of numerical analysis,the proposed analytical model has summed up the influence of the configuration of multilevel interconnections,the via heat transfer and self-heating effect on the interconnection delay,which is closer to the actual situation.Delay simulation results show that the proposed model has high precision within 5% errors for global interconnections based on the 65 nm CMOS interconnection and material parameter,which can be applied in nanometer CMOS system chip computer-aided design.     

光互联网的抗毁性设计研究

由于互联网层叠模型中多层都存在重复的保护与恢复机制，致使互联网的实现极其复杂，造成网络传输效率较低，而且拥塞。随着密集波分复用（DWDM）的广泛使用和网络底层性能的大幅度提高，已经不需要再对网络中多层进行重复保护与恢复。在此基础上，采用了未来光互联网3层 (应用层、IP层和光互联层) 协议模型，并对其中的光互联层的保护进行了深入的研究，提出了部分共享备用路径保护(P-SPP)方法。此模型能充分利用IP层电子的灵活恢复机制与光互联层的快速保护与恢复机制，提高光互联网的传输性能，以适应目前高速发展的互联网对传输带宽的要求。     

A hybrid neural network for image classification

Principles of the photorefractive perceptron learning algorithm are described. The influences of the finite response time and hologram erasure of the photorefractive gratings on the convergence property of the photorefractive perceptron learning are discussed. A novel neural network which could resolve these constraints is presented. It is a hybrid system which utilizes the photorefractive holographic gratings to implement the inner product between the input image and the interconnection matrix. A personal computer is used for storing the interconnection matrix and the updating procedure, and it also functions as a feedback means during the learning phase. After training the weight vectors are recorded in the volume hologram of an optical processor. This novel method combines the advantages of the massive parallelism of optical systems and the programmability of electronic computers. Experimental results of image classification are presented. It shows that the system could correctly classify the input patterns into one of the two groups after training on four examples in each group during successive iterations. The system has been extended to perform multi-category image classification.     

Adaptive Signal Processing in Antenna Arrays with Allowance for the Rank of the Impulse-Response Matrix of a Multipath Channel

We consider adaptive spatial signal processing in antenna arrays under conditions of multipath propagation. It is shown that estimation of the elements of the matrix of multichannel impulse response (MCIR) in the receiving channels of antenna arrays is not sufficient for performing such a processing, and we should also determine the MCIR-matrix rank, which is equal to the minimum number of adaptive spatial channels for reception of a multipath signal. We propose a threshold method for estimating the rank of the MCIR matrix on the basis of the statistical properties of maximum-likelihood estimation of the elements of this matrix and a priori information on the properties of internal noise in the receiving channels of the system. The weight vectors of the beamformer processor ensuring adaptive spatial signal processing are estimated.