Quantum Information Entropies on Hyperbolic Single Potential Wells

In this work, we study the quantum information entropies for two different types of hyperbolic single potential wells. We first study the behaviors of the moving particle subject to two different hyperbolic potential wells through focusing on their wave functions. The shapes of these hyperbolic potentials are similar, but we notice that their momentum entropy densities change along with the width of each potential and the magnitude of position entropy density decreases when the momentum entropy magnitude increases. On the other hand, we illustrate the behaviors of their position and momentum entropy densities. Finally, we show the variation of position and momentum entropies Sx and Sp with the change of the potential well depth u and verify that their sum still satisfies the BBM inequality relation.     

Quantitative Depth Profiling Using Online-Laser Ablation of Solid Samples in Liquid (LASIL) to Investigate the Oxidation Behavior of Transition Metal Borides

The increased demand for sustainability requires, among others, the development of new materials with enhanced corrosion resistance. Transition metal diborides are exceptional candidates, as they exhibit fascinating mechanical and thermal properties. However, at elevated temperatures and oxidizing atmospheres, their use is limited due to the fact of their inadequate oxidation resistance. Recently, it was found that chromium diboride doped with silicon can overcome this limitation. Further improvement of this protective coating requires detailed knowledge regarding the composition of the forming oxide layer and the change in the composition of the remaining thin film. In this work, an analytical method for the quantitative measurement of depth profiles without using matrix-matched reference materials was developed. Using this approach, based on the recently introduced online-LASIL technique, it was possible to achieve a depth resolution of 240 nm. A further decrease in the ablation rate is possible but demands a more sensitive detection of silicon. Two chromium diboride samples with different Si contents suffering an oxidation treatment were used to demonstrate the capabilities of this technique. The concentration profiles resembled the pathway of the formed oxidation layers as monitored with transmission electron microscopy. The stoichiometry of the oxidation layers differed strongly between the samples, suggesting different processes were taking place. The validity of the LASIL results was cross-checked with several other analytical techniques.     

Solitonic Information Transmission in General Relativity

An exact solution of the vacuum Einstein＇s field equations is presented, in which there exists a congruence of null geodesics whose shear behaves like a travelling wave of the KdV equation. On the basis of this exact solution, the feasibility of solitonic information transmission by exploiting the nonlinearity intrinsic to the Einstein field equations is discussed.     

Multi-Classifier Fusion Based on MI–SFFS for Cross-Subject Emotion Recognition

With the widespread use of emotion recognition, cross-subject emotion recognition based on EEG signals has become a hot topic in affective computing. Electroencephalography (EEG) can be used to detect the brain’s electrical activity associated with different emotions. The aim of this research is to improve the accuracy by enhancing the generalization of features. A Multi-Classifier Fusion method based on mutual information with sequential forward floating selection (MI_SFFS) is proposed. The dataset used in this paper is DEAP, which is a multi-modal open dataset containing 32 EEG channels and multiple other physiological signals. First, high-dimensional features are extracted from 15 EEG channels of DEAP after using a 10 s time window for data slicing. Second, MI and SFFS are integrated as a novel feature-selection method. Then, support vector machine (SVM), k-nearest neighbor (KNN) and random forest (RF) are employed to classify positive and negative emotions to obtain the output probabilities of classifiers as weighted features for further classification. To evaluate the model performance, leave-one-out cross-validation is adopted. Finally, cross-subject classification accuracies of 0.7089, 0.7106 and 0.7361 are achieved by the SVM, KNN and RF classifiers, respectively. The results demonstrate the feasibility of the model by splicing different classifiers’ output probabilities as a portion of the weighted features.     

任意信源随机和的一类随机Shannon-McMillan定理

采用构造相容分布与非负上鞅的方法研究任意信源随机和相对熵密度的强极限定理,并由此得出若干任意信源,m阶马氏信源,无记忆信源的随机Shannon-Mcmillan定理.将已有的关于离散信源的结果加以推广.     

基于标准互相关函数的图像分辨率自动标定方法

理想规则图像特征经标准互相关函数匹配后,相似函数C*(x)可用确定的解析式Z(x)表达.但是对于含噪声图像,相似函数C(x)较之C*(x)发生了变化,但存在"零相似不变性".将此原理应与于显微视觉中图像物面分辨率的在线标定,推导了矩形图像特征的一维相似函数解析式Z(x);求解C(x)=0作为Z(x)的近似,并给出了具体标定算法;通过仿真图像实验,给出标定算法的正确度在0.1～0.2 pixel;最后,将标定算法应用于可连续变焦的微对准装配系统,实测算法的精密度可达为0.08 pixel.实验结果表明,基于"零相似不变性"的标定算法是具有实用性的亚像素标定方法.～290nm波长激发下,310～390nm范围内产生荧光光谱,峰位波长在350nm,最佳激励波长为250nm;采用垂直偏振片(起偏角为0°)起偏照射样品,发现偏振荧光峰位不变,荧光峰强度随检偏角的增大而呈明显的线性递减关系.由实验数据计算得到荧光偏振度为0.783,表明分子具有一定确定取向;另外,分析认为酸性橙Ⅱ产生荧光,是由于分子中含有苯环和萘环结构吸收紫外光能量,以及氮键在光子作用下形成顺式异构体的激发单线态后,两者发射的光子所致.整个结果对酸性橙Ⅱ在食品中的违禁使用检测、特性表征、以及分子规律的更深入研究,有一定的参考价值.     

基于炉口辐射和改进神经网络的转炉终点预测模型

针对国内外转炉炼钢终点控制的现状,建立了一种用于终点预测的神经网络模型.以炉口辐射信息获取系统为实验平台,运用光纤谱分复用和颜色空间模型转换技术.分析发现了光谱与图像信息特征量在吹炼过程中晕现出中前期类似、末期相反的规律.从得到的特征规律曲线中选用一些关键特征量,在改进的修正系数算法基础上,进行了模型的训练和预测分析.实验结果表明:响应时间在2 s以内,满足快速判定的时间要求;改进算法的模型预测精度高于常规算法,该系统可以止常工作在转炉炼钢的恶劣环境下,达到了预期效果.     

基于双混沌置乱和扩频调制的彩色图像盲水印算法

提出了一种新的适应彩色图像的盲水印算法,先对宿主图像的绿色分量以8×8像素分块进行离散余弦变换(DCT)变换,用logistic映射生成两个混沌序列,然后用混沌序列置乱加密二值水印图像,并用两个互不相关的伪随机序列扩频调制水印,最后将调制好的水印嵌入到DCT变换域的中频子带系数上,进行分块DCT反变换得到水印化图像.提取水印时,通过比较两个伪随机序列和水印化图像的相关性大小来提取水印,不需要原始图像的参与,为盲提取水印算法.实验结果证明本文算法能有效地抵抗JPEG压缩、加噪、剪切等常见攻击,绿色分量嵌入水印比红色和蓝色分量嵌入水印能更好地抵抗JPEG压缩的攻击.     

基于Arnold变换的图像逆置乱算法

针对Arnold变换的周期依赖于图像的阶数这一特性,提出了一种反变换算法.该算法通过分析加密图像任一点处两坐标分量间关系,得到原图像相应点的坐标,从而实现图像的解密.该反变换也可作为图像置乱的正变换,相应的反变换就是Arnold变换.在此基础上,把二维反变换算法推广到m维的情形.实验结果表明,对于已应用Arnold变换进行预处理的置乱图像,在无须计算原图像变换周期的前提下可快速实现图像的逆置乱,该过程具有确定性,其迭代次数与预处理置乱次数相等.     

Formalising the Pathways to Life Using Assembly Spaces

Assembly theory (referred to in prior works as pathway assembly) has been developed to explore the extrinsic information required to distinguish a given object from a random ensemble. In prior work, we explored the key concepts relating to deconstructing an object into its irreducible parts and then evaluating the minimum number of steps required to rebuild it, allowing for the reuse of constructed sub-objects. We have also explored the application of this approach to molecules, as molecular assembly, and how molecular assembly can be inferred experimentally and used for life detection. In this article, we formalise the core assembly concepts mathematically in terms of assembly spaces and related concepts and determine bounds on the assembly index. We explore examples of constructing assembly spaces for mathematical and physical objects and propose that objects with a high assembly index can be uniquely identified as those that must have been produced using directed biological or technological processes rather than purely random processes, thereby defining a new scale of aliveness. We think this approach is needed to help identify the new physical and chemical laws needed to understand what life is, by quantifying what life does.