Analysis of particle methods for structured population models with nonlocal boundary term in the framework of bounded Lipschitz distance

Recently developed theoretical framework for analysis of structured population dynamics in the spaces of nonnegative Radon measures with a suitable metric provides a rigorous tool to study numerical schemes based on particle methods. The approach is based on the idea of tracing growth and transport of measures which approximate the solution of original partial differential equation. In this article, we present analytical and numerical study of two versions of Escalator Boxcar Train algorithm which has been widely applied in theoretical biology, and compare it to the recently developed split‐up algorithm. The novelty of this article is in showing well‐posedness and convergence rates of the schemes using the concept of semiflows on metric spaces. Theoretical results are validated by numerical simulations of test cases, in which distances between simulated and exact solutions are computed using flat metric. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 30: 1797–1820, 2014     

A spectral transfer procedure for application of a single class-model to spectra recorded by different near-infrared spectrometers for authentication of olives in brine

Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set.     

Study of the interdependency of the data sampling ratio with retention time alignment and principal component analysis for gas chromatography

An in-depth study is presented to better understand how data reduction via averaging impacts retention alignment and the subsequent chemometric analysis of data obtained using gas chromatography (GC). We specifically study the use of signal averaging to reduce GC data, retention time alignment to correct run-to-run retention shifting, and principal component analysis (PCA) to classify chromatographic separations of diesel samples by sample class. Diesel samples were selected because they provide sufficient complexity to study the impact of data reduction on the data analysis strategies. The data reduction process reduces the data sampling ratio, S(R), which is defined as the number of data points across a given chromatographic peak width (i.e., the four standard deviation peak width). Ultimately, sufficient data reduction causes the chromatographic resolution to decrease, however with minimal loss of chemical information via the PCA. Using PCA, the degree of class separation (DCS) is used as a quantitative metric. Three "Paths" of analysis (denoted A-C) are compared to each other in the context of a "benchmark" method to study the impact of the data sampling ratio on preserving chemical information, which is defined by the DCS quantitative metric. The benchmark method is simply aligning data and applying PCA, without data reduction. Path A applies data alignment to collected data, then data reduction, and finally PCA. Path B applies data reduction to collected data, and then data alignment, and finally PCA. The optimized path, namely Path C, is created from Paths A and B, whereby collected data are initially reduced to fewer data points (smaller S(R)), then aligned, and then further reduced to even fewer points and finally analyzed with PCA to provide the DCS metric. Overall, following Path C, one can successfully and efficiently classify chromatographic data by reducing to a S(R) of ～15 before alignment, and then reducing down to S(R) of ～2 before performing PCA. Indeed, following Path C, results from an average of 15 different column length-with-temperature ramp rate combinations spanning a broad range of separation conditions resulted in only a ～15% loss in classification capability (via PCA) when the loss in chromatographic resolution was ～36%.     

基于Boxcar滤波器的荧光检测二氧化硫信号的去噪研究

荧光法以物质发射的荧光强度与浓度之间的线性关系进行定量分析。当采用荧光法检测大气中的二氧化硫等有害气体时,由于光电探测器等光电元件在无荧光时,仍会产生暗电流噪声,使得本底噪声信号对测量结果有直接的影响。在分析Boxcar滤波算法的基础上,运用小波滤波、EMD滤波和Boxcar滤波三种算法对淹没在本底噪声中的荧光信号进行有效提取和恢复,较之前两种滤波方法,Boxcar滤波效果更佳,对本底噪声的抑制更强,并验证了取样次数影响着荧光信号的信噪比。     

用Boxcar研究晶体二极管的反向恢复过程

用Boxcar研究二极管的反向恢复过程,比用取样示波器结构简单,可以用计算机采集、存储、处理数据,并可进行多次平均.本文介绍了其原理、仪器配置和实验线路,并讨论了实验结果.     

飞秒时间分辨取样光谱仪的研制

研制的飞秒（１０＾１５ｓ）时间分辨取样光谱仪由三部分组成；（１）飞秒激光探针弥散补偿光学系统，（２）氩激光泵浦参钛兰宝石飞秒激光系统，（３）信号探测，数据采集分析处理掺钛兰宝石飞秒激光器在７Ｗ氩激光全线泵浦下，能输出平均功率４００ｍＷ、持续期５０ｆｓ到５００ｆｓ的脉冲，其光谱可在７５０ｎｍ到９２０ｎｍ范围内调经ＬＢＯ晶体倍频，能得到３７５ｎｍ到４６０ｎｍ的二次谐波，整体取样光谱仪的时间分辨为９０ｆ     

Boxcar积分器模型参数选择

利用计算机运算速度快、描述形象的特点设计一模型参数选择软件,可根据被恢复波形的要求灵活地选择、计算或修正参数值,给 Ｂｏｘｃａｒ 的使用带来了很大的方便     

红外水份测量仪的改进

对传统的红外水份测量仪进行了改进 .采用二级半导体制冷代替传统的一级半导体制冷加水冷的结构对红外探测器进行精密的温度控制 ,有效地抑制了探测器的热噪声 ,增强了测量信号 ,提高了信噪比 .系统的成本大为降低 ,效率和可靠性也得到提高 .同时采用Boxcar积分器对测量信号进行多次累加平均 ,进一步有效地提高了信噪比 .水份测量精度控制在 0 .3%以内 .