Correcting saturation effects of the arterial input function in dynamic susceptibility contrast-enhanced MRI: a Monte Carlo simulation

To prevent systematic errors in quantitative brain perfusion studies using dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI), a reliable determination of the arterial input function (AIF) is essential. We propose a novel algorithm for correcting distortions of the AIF caused by saturation of the peak amplitude and discuss its relevance for longitudinal studies. The algorithm is based on the assumption that the AIF can be separated into a reliable part at low contrast agent concentrations and an unreliable part at high concentrations. This unreliable part is reconstructed, applying a theoretical framework based on a transport-diffusion theory and using the bolus-shape in the tissue. A validation of the correction scheme is tested by a Monte Carlo simulation. The input of the simulation was a wide range of perfusion, and the main aim was to compare this input to the determined perfusion parameters. Another input of the simulation was an AIF template derived from in vivo measurements. The distortions of this template was modeled via a Rician distribution for image intensities. As for a real DSC-MRI experiment, the simulation returned the AIF and the tracer concentration-dependent signal in the tissue. The novel correction scheme was tested by deriving perfusion parameters from the simulated data for the corrected and the uncorrected case. For this analysis, a common truncated singular value decomposition approach was applied. We find that the saturation effect caused by Rician-distributed noise leads to an overestimation of regional cerebral blood flow and regional cerebral blood volume, as compared to the input parameter. The aberration can be amplified by a decreasing signal-to-noise ratio (SNR) or an increasing tracer concentration. We also find that the overestimation can be successfully eliminated by the proposed saturation-correction scheme. In summary, the correction scheme will allow DSC-MRI to be expanded towards higher tracer concentrations and lower SNR and will help to increase the measurement to measurement reproducibility for longitudinal studies.     

氩原子基态波函数和能量的解析计算

以第一性原理和变分原理为基础,给出了氩原子基态波函数的一种解析表达式,计算了基态氩原子(含类氩离子)的能量,导出了所涉及的所有积分的解析表达式.对氩原子,所得到的能量理论值与实验值的相对误差为0.22%.     

基于瑞利熵的多无源传感器数据关联算法

针对当前多无源传感器数据关联算法构造关联代价时,未考虑位置估计不确定性所引入的误差,提出一种基于位置估计不确定性的被动传感器数据关联算法。首先通过量测与伪量测概率密度函数之间的瑞利熵构建关联代价函数,以准确描述两个相似的概率密度函数之间差异,然后通过具体实验测试本文算法的有效性和优越性。实验结果表明,相对于当前经典的数据关联算法,本文算法提高了数据关联的正确率和速度,具有更高的实际应用价值。     

多值测试诊断策略优化设计

针对电子系统中多值测试条件下的测试排序问题,提出了一种诊断策略优化方法。首先,以相关性矩阵模型为基础,将已有的二值测试的优化算法同多值测试问题相结合,提出了适用于多值测试的基于霍夫曼编码的启发式函数;其次,将平均测试代价最小和平均测试步骤最少作为优化目标,采用与或树启发式搜索算法生成诊断树,得到多值测试的诊断策略,并给出了诊断策略优化方法的具体实现步骤;最后,将其应用到航空设备的实例中。结果表明提出的基于霍夫曼编码的与或树启发式搜索算法是可行的,其生成的诊断树是最优的,比基于信息熵的与或树启发式搜索算法具有更小的平均测试代价和更少的平均测试步骤。     

基于云环境的分布式软件接口自动化测试

软件测试是保证软件质量,提供可靠服务的重要技术手段。目前基于Web Service的分布式软件越来越多,其测试技术手段也越来越受到关注。Web Service的分布性和多样性使手工测试变得非常低效,因而需要不断提高Web Service测试的自动化程度。另外,云计算因其计算成本低、可伸缩性强的特点为自动化测试提供了新的支持环境。本文结合Web Service的测试需求,首次提出了基于云环境的Web Service接口自动化测试的技术框架,分析了框架内原子Web Service、组合Web Service测试的关键技术,并研发了基于CloudStack云平台的自动化测试的原型系统。实验结果表明,本文所提出的基于云平台的WebService自动化测试方案可行且提高了测试效率。     

取向比对椭球气溶胶粒子散射特性的影响

利用T矩阵和离散坐标法研究了取向比对椭球粒子散射特性的影响, 计算了小尺度范围内椭球粒子的散射特征参量, 包括消光效率因子、不对称因子、单次散射反照率、散射相矩阵及双向反射函数(BRDF). 结果表明, 椭球粒子的散射特性与取向比密切相关, 粒子取向比会影响散射参量的振荡频率和振幅, 与球形粒子散射参量的相对差异也呈周期振荡趋势. 研究还发现, 某些特殊粒子尺寸的散射参量与粒子取向比基本无关. 在多次散射条件下, 分析不同取向比粒子群的BRDF随反射角和光学厚度的变化特性. 结果显示: 不同取向比粒子群的BRDF随反射角的变化趋势基本一致, 球形粒子群比非球形粒子群的BRDF曲线波动振幅更大; 球形-非球形粒子的BRDF相对差异随光学厚度和取向比的增大而减小, 随入射角的增大而增大.     

Painlevé analysis and exact solutions of the Korteweg–de Vries equation with a source

We consider the Korteweg–de Vries equation with a source. The source depends on the solution as polynomials with constant coefficients. Using the Painlevé test we show that the generalized Korteweg–de Vries equation is not integrable by the inverse scattering transform. However there are some exact solutions of the generalized Korteweg–de Vries equation for two forms of the source. We present these exact solutions.     

Dynamics and optimal control in a spatially structured economic growth model with pollution diffusion and environmental taxation

In the first part of this paper we present a spatially structured dynamic economic growth model which takes into account the level of pollution and a possible taxation based on the amount of produced pollution. In the second part we analyze an optimal harvesting control problem with an objective function composed of three terms, namely the intertemporal utility of the decision maker, the space–time average of the level of pollution in the habitat, and the disutility due to the imposition of taxation.     

Joint distributions of random sets and their relation to copulas

Random sets are set-valued random variables. They have been applied in various fields like stochastic geometry, statistics, economics, engineering or computer science, and are often used for modeling uncertainty. In an earlier paper the author has defined joint capacity and joint containment functionals which are multivariate set functions describing the joint distribution of random sets. This paper is concerned with the question how copulas can be used to describe or model the dependence of random sets. It is demonstrated that a joint containment functional can be related to its margins by a family of copulas. Furthermore, the paper provides a first insight how copulas can be used to define joint containment functionals.     

Accelerated Landweber iteration with convex penalty for linear inverse problems in Banach spaces

In recent years, Landweber iteration has been extended to solve linear inverse problems in Banach spaces by incorporating non-smooth convex penalty functionals to capture features of solutions. This method is known to be slowly convergent. However, because it is simple to implement, it still receives a lot of attention. By making use of the subspace optimization technique, we propose an accelerated version of Landweber iteration with non-smooth convex penalty which significantly speeds up the method. Numerical simulations are given to test the efficiency.