光在分层散射介质中传输行为的蒙特卡罗模拟研究

建立了光在发层散射介质中传输行为的蒙特卡罗模型,计算了皮肢层状结构的光传输特性,考虑联折射率不匹配带来的影响及不同光束特性（如均匀分布平面光场、高斯光束、光纤光束）对传输行为的影响,分析了分界面处光通量的传输特性,给出了不同特性光束的生物组织的表面漫射率曲线,比较了高斯光束和平面光场的卷积结果。     

采用最早到达光透过高散射介质成像

综述了最早到达光方法透过散射介质成像技术,详细地介绍了克尔快门时间空间选通成像技术共焦扫描条纹相机成像技术、非线性二次谐波产生－互相关选通成像技术及电子学全息选通成像技术,对这几种选通成像技术目前扫展状况,优缺点给出了评述。     

光散射法测量微粒粒径分布的一种反演遗传算法

本文提出一种基于改进遗传算法的随机及演方法,该方法具有更强的全局收敛性,同时也具有对粒径分布形状不敏感、抗噪声能力强等优点,适合于工业现场应用．     

多孔介质中的分形与输运

介绍了多孔介质里隙空间和孔隙界面的分形结构，描述了多孔介质中的输运特性及其实验与计算机模拟方法，最后介绍了自相似多介质中输运特性的一种递推计算方法。     

用蒙特卡罗方法计算光通过薄层随机分布粒子的散射

本文利用蒙特卡罗方法计算准直光束通过薄层随机分布粒子散射的透射和反射光强,并和输运理论的扩散近似结果做了比较。当散射接近各向同性时两者符合良好。当散射明显地成为各向异性时,蒙特卡罗方法的结果是合理的,而输运理论的扩散近似失效。     

光在可变厚度介质中后向散射传输的偏振特性分析

针对不同浓度下介质厚度改变对后向散射光偏振特性影响的问题,以典型蒙特卡罗偏振模型为基础,提出了一种基于后向散射的可变介质厚度蒙特卡罗偏振模型。根据本文提出的模型开展了仿真实验,得到了不同浓度下介质厚度与后向散射偏振特性的影响关系。仿真结果表明,介质厚度在不同浓度下均对后向散射光的偏振度有着直接影响;在低浓度的情况下,介质厚度增加主要起消偏作用,浓度达到一定阈值后,介质厚度增加起起偏作用;在低浓度的情况下,线偏振光的后向散射光偏振保持特性优于圆偏振光,浓度达到一定阈值后,圆偏振光的后向散射光偏振保持特性优于线偏振光;在浓度极高的情况下,线偏振光与圆偏振光的保持特性趋于一致。     

偏振光在多层散射介质中传输的蒙特卡罗模拟研究

基于米氏散射理论,运用斯托克斯-穆勒形式,结合子午面法和拒绝法,提出了偏振光在多层散射介质中传输的蒙特卡罗模型,并首次分析了当菲涅耳公式失效时,多层介质界面处的全反射行为.同时用该模型研究了微粒直径和介质折射率对漫反射和漫透射的影响.结果表明:无论是折射率匹配介质还是非匹配介质,当粒子浓度一致时,漫反射率都随微粒直径的增加而增加,随介质折射率的增加而减小,而漫透射率则相反.研究结果为生物组织的偏振光散射研究提供了理论验证模型.     

非定常粒子输运蒙特卡罗散射源分层抽样方法

定常粒子输运蒙特卡罗并行计算是成功的,因为粒子游动是独立的,可以把模拟的粒子数等分到每个处理器去.然而,对非定常问题,由于每个时间步涉及散射源和几何网格的通讯,它严重的制约了并行规模,导致并行不可扩展.研究了两种算法,采用自适应分配处理器,提高了加速比和处理器的利用率;采用蒙特卡罗分层抽样大大降低了处理器之间散射源的通讯量,并行可扩展性显著改善,取得了理想的加速比.     

浑浊介质中激光侧向散射实验与仿真研究

通过对M ie散射相位函数的累积概率密度插值获得散射角,建立了激光在浑浊介质中传输的蒙特卡罗仿真模型.借助模型研究了激光在均匀单分散聚苯乙烯浑浊介质中的多次散射现象,探究了不同光学厚度和散射相位函数的浑浊介质对激光多次散射的影响.在研究中控制5μm和10μm两种粒径聚苯乙烯颗粒的浓度,改变浑浊介质的光学厚度分别为2、5...     

偏振光在多层散射介质中传输的蒙特卡罗模拟研究*

基于米氏散射理论,运用斯托克斯-穆勒形式,结合子午面法和拒绝法,提出了偏振光在多层散射介质中传输的蒙特卡罗模型,并首次分析了当菲涅耳公式失效时,多层介质界面处的全反射行为.同时用该模型研究了微粒直径和介质折射率对漫反射和漫透射的影响.结果表明：无论是折射率匹配介质还是非匹配介质,当粒子浓度一致时,漫反射率都随微粒直径的增加而增加,随介质折射率的增加而减小,而漫透射率则相反.研究结果为生物组织的偏振光散射研究提供了理论验证模型.     

基于遗传算法的三元复合宽带波片设计

提出在三元复合宽带波片设计中,采用遗传算法在特定入射光频率范围内对复合波片系统的相位延迟误差和等效快轴方向夹角误差进行全局优化,并采用蒙特卡洛方法,通过程序仿真分析在晶体加工过程中尺寸公差对消色差性能的影响.结果表明,设计出的红外复合宽带波片在晶体厚度和方位角公差分别为1μm和0.5°时具有很好的消色差性能.     

超宽频带THz脉冲在随机散射介质中传播的理论研究

研究了超宽频带THz脉冲入射到散射介质中时其透射脉冲的时空特性.根据Mie理论计算出随机散射介质的散射系数和各向异性因子,用时间分辨Monte Carlo方法模拟了超宽频带的THz脉冲在随机散射介质中的传播,研究了在不同散射颗粒半径,不同频宽的THz入射脉冲对透射脉冲的影响和散射对成像分辨率的影响.结果表明:散射会降低THz脉冲在随机散射介质中的成像分辨率,散射颗粒越小,散射介质越厚,其成像分辨率越差.     

轫致辐射靶的发射率及角分布分析

计算了不同能量下，几种常用单靶(W,Cu,Ta,Au)的X射线前向发射率和角分布, 从理论分析和蒙特卡罗方法模拟计算两方面分析了产生的X射线发射率及其角分布与靶参数和束流参数的关系, 并与已发表的数据及实验结果比较, 结果一致, 可为设计产生X射线的电子加速器及靶提供参数依据.     

用于光子相关光谱颗粒粒度测量的实数编码混合遗传算法

提出一种自动缩小反演范围的实数编码混合遗传算法,并用于光子相关光谱颗粒粒度测量中.该算法利用混沌遍历性质,融入模拟退火算法,具有较强的局部搜索能力.通过数值模拟,讨论在无噪声和多种噪声水平影响下单分散颗粒系的反演结果;分析90 nm聚苯乙烯标准颗粒样品的实验测量数据.结果表明,反演的粒径分布在峰值位置、峰宽度、峰对称性等方面很接近颗粒的真实分布,具有较强的抗噪声干扰能力.     

生物组织光传播的时域特征分析

采用蒙特卡罗方法对脉冲光在组织中传播的时域特征作了研究，并把计算结果与漫射理论的解析解作了比较，文中详细分析了组织光学特性参数与组织表面时间分辨的漫反射光分布之间的关系。结果表明：散射系影响着漫反射光强到达峰值的时间，吸收系数影响着漫反射光强在峰值之后随时间下降的速度，而ｇ因子对响应曲线的前沿影响较大，在后沿阶段基本上没有影响。     

MCNP Dose Calculations in a CT Phantom for Therapeutic External Photon Beam

In this paper, we have addressed the problem of the radiation transport with the Monte Carlo N particle(MCNP) code. This is a general purpose Monte Carlo tool designed to transport neutron, photon and electron in three dimensional geometries. To examine the performance of MCNP5 code in the field of external radiotherapy, we performed the modeling of an Electron Density phantom (EDP) irradiated by photons from 60Co source. The model was used to calculate the Percent Depth Dose (PDD) at different depths in an EDP. One field size for PDD has been examined. A 60Co photons source placed at 80 cm source to surface distance (SSD). The results of calculations were compared to TPS data obtained at National Institute of Oncology of Rabat.     

A Super Fast Algorithm for Estimating Sample Entropy

Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexity of a time series, which is essentially defined as −log(B/A), where B denotes the number of matched template pairs with length m and A denotes the number of matched template pairs with m+1, for a predetermined positive integer m. It has been widely used to analyze physiological signals. As computing sample entropy is time consuming, the box-assisted, bucket-assisted, x-sort, assisted sliding box, and kd-tree-based algorithms were proposed to accelerate its computation. These algorithms require O(N2) or O(N2−1m+1) computational complexity, where N is the length of the time series analyzed. When N is big, the computational costs of these algorithms are large. We propose a super fast algorithm to estimate sample entropy based on Monte Carlo, with computational costs independent of N (the length of the time series) and the estimation converging to the exact sample entropy as the number of repeating experiments becomes large. The convergence rate of the algorithm is also established. Numerical experiments are performed for electrocardiogram time series, electroencephalogram time series, cardiac inter-beat time series, mechanical vibration signals (MVS), meteorological data (MD), and 1/f noise. Numerical results show that the proposed algorithm can gain 100–1000 times speedup compared to the kd-tree and assisted sliding box algorithms while providing satisfactory approximate accuracy.     

用改进的遗传算法和高斯牛顿法联合反演三维地下水流模型参数

遗传算法在处理非线性优化问题时具有较好的全局搜索性能,但在局部搜索时搜索效率不高,解的精度亦不高,高斯牛顿法在处理非线性优化问题时的性质正好和遗传算法相反,利用遗传算法和高斯牛顿法的优点,用改进的遗传算法和高斯牛顿法联合反演地下水数值模型参数.首先用遗传算法求出地下水模型参数的初值,然后利用这组初值用高斯牛顿法进行数值模型参数的反演,并以一非均质各向同性三维承压非稳定流理想模型为例,结合有限元法讨论了用遗传算法和高斯牛顿法联合反演地下水数值模型参数的过程.计算结果表明,联合参数反演方法,具有收敛速度快、解的精度高的特点,在地下水渗流和水资源评价等领域可广泛应用.     

Cut-and-Permute Algorithm for Self-Avoiding Walks in the Presence of Surfaces

We present a dynamic nonlocal hybrid Monte Carlo algorithm consisting of pivot and cut-and-permute moves. The algorithm is suitable for the study of polymers in semiconfined geometries at the ordinary transition, where the pivot algorithm exhibits quasi-ergodic problems. The dynamic properties of the proposed algorithm are studied in d=3. The hybrid dynamics is ergodic and exhibits the same optimal critical behavior as the pivot algorithm in the bulk.     

Fast Estimation of Scatter Components Using the Ordered Subset Expectation Maximization Algorithm for Scatter Compensation

In this work we propose a method for scatter compensation in single photon emission computed tomography imaging, by which we can estimate the scatter components in projections in high speed with good accuracy. The method is that we first estimate the scatter components in projections based on scatter response kernels by one time of ordered subsets expectation maximization iterative reconstruction, and then subtract the estimated scatter components from the projections and complete reconstruction by filtered back-projection method. The principle is that the image corresponding to the scatter components in projections consists largely of low-frequency components of an activity distribution; these low-frequency components will converge faster than the high ones in iterative reconstruction. Therefore, we can estimate the low-frequency component image before the image converges with the high-frequency ones, and obtain the scatter components by re-projecting the low-frequency component image with scatter response kernels. The effects of the proposed method were compared with the dual- and triple-energy window methods using experimental measurements. The results show that good accuracy in estimated scatter components, good uniformity of scatter compensation at the center and the side of an object, and good noise property can be acquired by this method.