波前分割DOE阵列半导体激光器光束的品字形整形

半导体激光器（LD）输出光束受工作电流、个体差异的影响,发散角、输出光强出现波动。常规衍射光学元件（DOE）的激光光束整形设计只针对特定的输入输出光场,使用宽容度比较小。文中在LD 的整形中利用DOE 阵列化的处理,输入光场被分割成许多小单元,不同强度的光重新在成像平面内预定的区域内叠加,实现对光束的整形。用均匀平面波设计阵列DOE 每个单元,把高斯分布的球面波整形成品字形光强分布,衍射效率为90.53%,均匀性大约为96%;发散角在快、慢轴方向上2~16的范围内变化,均匀性为95.8%以上、衍射效率为90%以上;在离焦量16 m 内,光束质量变化不大。阵列DOE 提高了LD 光束整形系统的稳定性。     

波前分割DOE阵列半导体激光器光束的品字形整形

半导体激光器(LD)输出光束受工作电流、个体差异的影响,发散角、输出光强受此影响而出现波动。常规衍射光学元件(DOE)的激光光束整形设计只针对特定的输入输出光场,使用宽容度比较小。在LD的整形中利用DOE阵列化的处理,输入光场被分割成许多小单元,不同强度的光重新在成像平面内预定的区域内叠加,实现对光束的整形。用均匀平面波设计阵列DOE每个单元,把高斯分布的球面波整形成品字形光强分布,衍射效率90.53%,均匀性在96%附近;发散角在快、慢轴方向上2°~16°变化,均匀性95.8%以上、衍射效率90%以上;在离焦量±16μm内,光束质量变化不大。阵列DOE提高了LD光束整形系统的稳定性。     

双曲余弦高斯光束的模相关和模结构分析

基于二阶矩方法和正交模系展开法 ,推导出双曲余弦高斯光束的光束传输M2 因子和模相干系数的解析表达式 ,从而可对双曲余弦高斯光束的模相关和模结构进行分析。提出了一种在实验室中产生双曲余弦高斯光束的简单方法。     

双曲余弦高斯光束的模相关和模结构分析

基于二阶矩方法和正交模系展开法,推导出双曲余弦高斯光束的光束传输M2因子和模相干系数的解析表达式,从而可对双曲余弦高斯光束的模相关和模结构进行分析。提出了一种在实验室中产生双曲余弦高斯光束的简单方法。     

激光束整形为矩形光束的DOE的设计及实验

本文以几何理论为基础来设计把激光束整形为矩形光束的衍射光学元件 (DOE) ,推导出了衍射光学元件的相位表达式 ;并以高斯光束整形为例 ,把其整形为所需要的光强分布 ;进而进行了计算机模拟 ,得到了令人满意的结果 ;同时也进行了实验验证 ,实验结果证明了设计方法、理论推导以及程序设计的正确性     

光纤激光及其相干合成光场的准确分析

为了准确地研究光纤激光及其相干合成光束的远场传输特性,采用将LP01模分解为许多不同拉盖尔-高斯模叠加的方法,分析了LP01模以及多个光纤激光相干合成光束的传输光场分布和光束质量,并与用高斯函数表示的光纤激光的光场进行了比较.分析中发现,基模高斯光束与LP01模的初始场分布基本相同,但传输后的光场分布会产生较大误差,在光纤激光相干合成光场的分析中两者间的差别也非常明显.结果表明,为更准确地分析光纤激光及其相干合成光束的传输特性,光纤激光的模场表达式应采用拉盖尔-高斯模的叠加式.     

激光束整形为正方框形光束的DOE的设计及实验

以几何理论为基础来设计将激光束整形为正方框形光束的衍射光学元件(DOE),推导出了衍射光学元件表面上的相位表达式;以高斯光束整形为例,将其整形为所需要的光强分布;进行了计算机模拟,得到了比较好的结果;同时也进行了实验验证,实验结果证明了设计方法、理论推导以及程序设计的正确性。     

部分相干平顶光束的M2因子、模分解及合成

推导出以模式间相互独立的厄米高斯光束为基底的部分相干平顶光束的模系数和M2因子的解析式,对部分相干平顶光束的模分解及合成进行了研究.研究结果表明,随着光束阶数M及表征光束相干性的参数w0/ν0的增加,基模的份额减小,M2因子增大.采用模式间相互独立的厄米高斯光束叠加的方法合成部分相干平顶光束的光强矩形分布与光束M2因子二者无法同时兼顾,即若要求空间分布非常接近矩形,则光束的M2因子较大,若要求光束的M2因子较小,则只能降低对光强矩形分布的要求.在实际工作中,可以通过选择恰当的基底基模高斯光束的束腰尺寸以及控制各模式的光功率,由模式间相互独立的厄米高斯光束合成部分相干平顶光束.     

双曲余弦-高斯光束的相干模分解

利用傅里叶变换和正交模系展开法,推导出了双曲余弦—晶斯光束的M2因子和归一化模系数的解析表达式,从而建立了双曲余弦-高斯光束的相干模分解理论。提出了一种在实验室中产生双曲余弦-高斯光束的简单方法。     

光波导耦合的光束传播方法

本文利用光束传播方法和简正模技术来处理两高斯光束之间的耦合,简便而直观地求得光束间的能量耦合和差拍等特性,从而为渐变折射率光波导耦合系统的分析提供了一种新的方法。     

Gaussian sum particle filtering

We use the Gaussian particle filter to build several types of Gaussian sum particle filters. These filters approximate the filtering and predictive distributions by weighted Gaussian mixtures and are basically banks of Gaussian particle filters. Then, we extend the use of Gaussian particle filters and Gaussian sum particle filters to dynamic state space (DSS) models with non-Gaussian noise. With non-Gaussian noise approximated by Gaussian mixtures, the non-Gaussian noise models are approximated by banks of Gaussian noise models, and Gaussian mixture filters are developed using algorithms developed for Gaussian noise DSS models. As a result, problems involving heavy-tailed densities can be conveniently addressed. Simulations are presented to exhibit the application of the framework developed herein, and the performance of the algorithms is examined.     

A Numerical-Integration Perspective on Gaussian Filters

This paper proposes a numerical-integration perspective on the Gaussian filters. A Gaussian filter is approximation of the Bayesian inference with the Gaussian posterior probability density assumption being valid. There exists a variation of Gaussian filters in the literature that derived themselves from very different backgrounds. From the numerical-integration viewpoint, various versions of Gaussian filters are only distinctive from each other in their specific treatments of approximating the multiple statistical integrations. A common base is provided for the first time to analyze and compare Gaussian filters with respect to accuracy, efficiency and stability factor. This study is expected to facilitate the selection of appropriate Gaussian filters in practice and to help design more efficient filters by employing better numerical integration methods.     

KCS-new kernel family with compact support in scale space:formulation and impact

Multiscale representation is a methodology that is being used more and more when describing real-world structures. Scale-space representation is one formulation of multiscale representation that has received considerable interest in the literature because of its efficiency in several practical applications and the distinct properties of the Gaussian kernel that generate the scale space. Together, some of these properties make the Gaussian unique. Unfortunately, the Gaussian kernel has two practical limitations: information loss caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. We propose a new kernel family derived from the Gaussian with compact supports that are able to recover the information loss while drastically reducing processing time. This family preserves a great part of the useful Gaussian properties without contradicting the uniqueness of the Gaussian kernel. The construction and analysis of the properties of the proposed kernels are presented in this paper. To assess the developed theory, an application of extracting handwritten data from noisy document images is presented, including a qualitative comparison between the results obtained by the Gaussian and the proposed kernels.     

基于差族构造高斯整数周期互补序列

该文给出了基于差族的高斯整数互补序列构造方法。利用差族与互补序列之间的联系,首先推导出高斯整数互补序列存在的充分条件,进而直接构造了阶数为2的高斯整数互补序列。为进一步增加高斯整数互补序列数目,又利用映射方法构造了阶数为4的高斯整数互补序列。同传统的2元互补序列相比,高斯整数互补序列的存在数目很多,因此该文方法可以为通信系统提供大量的互补序列。     

基于改进GPR模型的发酵过程软测量建模

针对生物反应过程中许多关键参量难以在线检测的难题,提出了一种改进的高斯过程回归建模方法。由于传统高斯过程的均值函数不易确定,从而简单预置为零,导致模型对数据的解释性不够完全的缺点。改进的方法是利用一种组合基函数来确定高斯过程回归模型的一个非零均值函数,基函数的选取是通过高斯过程建立多项式回归噪声模型的方式推导而出,最后进行软测量模型的预测输出。基于氨基酸类典型菌种L-赖氨酸反应过程关键生物量参数预测的试验研究表明：与传统的高斯过程回归模型和支持向量机相比,改进的高斯过程回归模型具有更好的预测精度和泛化能力,能很好的跟踪预测值的变化趋势。     

Group codes for the Gaussian broadcast channel with two receivers

The two-receiver Gaussian broadcast channel is a model of a communication system where a single codeword is transmitted over two distinct Gaussian channels that have different signal-to, noise ratios. The receiver with the better signal-to-noise ratio decodes all of the information carried by the codeword, while the other receiver decodes only some of the information. The concept of group codes for the Gaussian broadcast channel is developed and applied to permutation codes for the Gaussian broadcast channel. A group code for the Gaussian broadcast channel is a group code for the single-user Gaussian channel in which a subgroup of the generating matrix group is used to partition the codewords into disjoint subsets called clouds. Distinct clouds represent distinct messages for the noisier channel while the individual codewords represent distinct messages for the other channel. The clouds share some of the distance properties of group codes. Necessary and sufficient conditions are given for finding good group codes for the Gaussian broadcast channel (in terms of minimum distance).     

高斯光束质量与波像差之间的关系

为了考察光阑截断时高斯光束质量与波像差之间的关系,采用高斯光束值作为截断高斯光束质量的评价参量,通过数值仿真的方法分析光学系统波像差对截断高斯光束质量的影响,给出了高斯光束值与波像差间的拟合关系。讨论了高斯光束质量与Kolmogoroff大气湍流强度间的关系,并给出了二者间的拟合公式。结果表明,该拟合公式的计算结果在相当广的湍流强度范围内与数值仿真结果较好吻合,这也进一步验证了高斯光束值与波像差拟合关系的正确性。     

Application of the Volterra Functional Expansion in the Detection of Nonlinear Functions of Gaussian Processes

Detection of a memoryless nonlinear functional of a Gaussian process in additive Gaussian white noise is considered. The Volterra functional expansion for the likelihood ratio, and two examples of calculating the kernels are presented. It is shown that kernels up to third order can be obtained for a hard-limited Gaussian process and for the absolute value of a Gaussian process. For the case of hard limiting, the kernels are nonlinear functions of the autocorrelation of the Gaussian process. For the absolute value case, the kernels are nonlinear functions of the kernel derived for the linear problem. A Monte Carlo simulation of receiver performance is presented for the case of detection of the absolute value of a first-order Butterworth process in additive Gaussian white noise. The suboptimal detector is obtained by truncating the log likelihood ratio to second order.     

相干和非相干平顶高斯光束的比较研究

对完全相干和非相干平顶高斯光束的传输特性作了详细研究。提出了用有限个拉盖尔-高斯模或厄米-高斯模的部分相干迭加来产生部分相干平顶高斯光束,以满足某些实际应用要求的新方法。     

基于粒子群算法的超宽带脉冲波形设计

首先分析了在超宽带系统中高斯脉冲成形因子和微分阶数对其能量谱密度的影响,并在此基础上,为了提高高斯脉冲波形的频谱利用率,采用高斯导函数线性组合来产生脉冲波形,提出并分析了一种基于粒子群（PSO）优化算法的超宽带无线脉冲波形优化设计方法,仿真表明该方法产生的高斯导函数线性组合脉冲能够很好地满足FCC的频谱掩模限制。