Smoke triggered corona discharge sensor

This paper presents an ionic smoke sensor working without a radioactive ionization source. The presence of smoke particles reduces significantly the effective corona discharge threshold of air by a factor greater than 5. The smoke sensor consists of a wire under an intermediate continuous voltage which generates a current only in presence of smoke. The sensor electric consumption is therefore very low and can operate for a long time. Results of a prototype operating under 600 V with a 25-μm-diameter wire are shown.     

Sommerfeld Radiation Condition at Threshold

We prove Besov space bounds of the resolvent at low energies in any dimension for a class of potentials that are negative and obey a virial condition with these conditions imposed at infinity only. We do not require spherical symmetry. The class of potentials includes in dimension ≥3 the attractive Coulomb potential. There are two boundary values of the resolvent at zero energy which we characterize by radiation conditions. These radiation conditions are zero energy versions of the well-known Sommerfeld radiation condition.     

Bayesian analysis of loss reserving using dynamic models with generalized beta distribution

A Bayesian approach is presented in order to model long tail loss reserving data using the generalized beta distribution of the second kind (GB2) with dynamic mean functions and mixture model representation. The proposed GB2 distribution provides a flexible probability density function, which nests various distributions with light and heavy tails, to facilitate accurate loss reserving in insurance applications. Extending the mean functions to include the state space and threshold models provides a dynamic approach to allow for irregular claims behaviors and legislative change which may occur during the claims settlement period. The mixture of GB2 distributions is proposed as a mean of modeling the unobserved heterogeneity which arises from the incidence of very large claims in the loss reserving data. It is shown through both simulation study and forecasting that model parameters are estimated with high accuracy.     

Passive synthesis rules of coupled-cavity quantum cascade lasers

A new approach to passive electromagnetic modelling of coupled–cavity quantum cascade lasers is presented in this paper. One of challenges in the rigorous analysis of such eigenvalue problem is its large size as compared to wavelength and a high quality factor, which prompts for substantial computational efforts. For those reasons, it is proposed in this paper to consider such a coupled-cavity Fabry-Perot resonant structure with partially transparent mirrors as a two-port network, which can be considered as a deterministic problem. Thanks to such a novel approach, passive analysis of an electrically long laser can be split into a cascade of relatively short sections having low quality factor, thus, substantially speeding up rigorous electromagnetic analysis of the whole quantum cascade laser. The proposed method allows to determine unequivocally resonant frequencies of the structure and the corresponding spectrum of a threshold gain. Eventually, the proposed method is used to elaborate basic synthesis rules of coupled–cavity quantum cascade lasers.     

基于机器视觉的量子点STM形貌图像识别研究

为减轻量子点表面形貌分析过程中的人工工作,使量子点的STM图像分析更加自动化,基于机器视觉对衬底的斜切角及量子点的形貌特性展开研究.利用腐蚀和边缘检测提取台阶形状,并通过反三角变换计算斜切角.利用二值化和阈值下降对量子点的数量与空间坐标进行提取,在此基础上,通过邻域密度计算分析其均匀性,并在解决图像中的粘连问题后找出量子点的尺寸.实验结果显示,与人工统计相比,斜切角、量子点计数及尺寸的平均误差分别为5.02%, 0.7788%及1.12%;并实现量子点均匀性的自动化统计与分析.基于机器视觉算法的自动识别过程,对协助研究者分析量子点表面形貌有实际意义.     

有序抖动半调图像的无损压缩算法

在分析不同有序抖动模板对所生成抖动半调图像的影响因素基础上,设计了尺度变化的分块及块间异或预处理策略,大大提高了二值游程长度.进而使用游程和哈夫曼相结合的编码思想提出了一种新的有序抖动半调图像无损压缩算法.仿真实验表明,该算法在编码效率上具有较大优势.     

Bootstrap of the offspring mean in the critical process with a non-stationary immigration

In applications of branching processes, usually it is hard to obtain samples of a large size. Therefore, a bootstrap procedure allowing inference based on a small sample size is very useful. Unfortunately, in the critical branching process with stationary immigration the standard parametric bootstrap is invalid. In this paper, we consider a process with non-stationary immigration, whose mean and variance vary regularly with nonnegative exponents α and β, respectively. We prove that 1+2α is the threshold for the validity of the bootstrap in this model. If β<1+2α, the standard bootstrap is valid and if β>1+2α it is invalid. In the case β=1+2α, the validity of the bootstrap depends on the slowly varying parts of the immigration mean and variance. These results allow us to develop statistical inferences about the parameters of the process in its early stages.