Up-conversion photoluminescence characteristics of Yb3+:Er3+:Tm3+ co-doped borosilicate glasses

Yb3+:Er3+:Tm3+ co-doped borosilicate glasses are prepared.Their strong up-conversion photoluminescence spectra in a range from ultra-violet to near-infrared,which are excited by a 978-nm laser diode,are measured,and the mechanisms of energy transfer among Yb3+,Er3+ and Tm3+ ions are discussed.The results show that there is an unexpected wavelength at 900-nm emission from Yb3+ Stark splitting levels to pump Tm3+ ions and there exists an optimum pump power.The concentration of the Tm3+ dopant gives rise to a prominent effect on the intensity of visible and near-infrared emissions for the Yb3+:Er3+:Tm3+ co-doped borosilicate glasses.     

Determination of effective wavelengths for discrimination of fruit vinegars using near infrared spectroscopy and multivariate analysis

Near infrared (NIR) spectroscopy based on effective wavelengths (EWs) and chemometrics was proposed to discriminate the varieties of fruit vinegars including aloe, apple, lemon and peach vinegars. One hundred eighty samples (45 for each variety) were selected randomly for the calibration set, and 60 samples (15 for each variety) for the validation set, whereas 24 samples (6 for each variety) for the independent set. Partial least squares discriminant analysis (PLS-DA) and least squares-support vector machine (LS-SVM) were implemented for calibration models. Different input data matrices of LS-SVM were determined by latent variables (LVs) selected by explained variance, and EWs selected by x-loading weights, regression coefficients, modeling power and independent component analysis (ICA). Then the LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS-DA model, and the optimal LS-SVM model was achieved with EWs (4021, 4058, 4264, 4400, 4853, 5070 and 5273 cm⁻¹) selected by regression coefficients. The determination coefficient (R²), RMSEP and total recognition ratio with cutoff value ±0.1 in validation set were 1.000, 0.025 and 100%, respectively. The overall results indicted that the regression coefficients was an effective way for the selection of effective wavelengths. NIR spectroscopy combined with LS-SVM models had the capability to discriminate the varieties of fruit vinegars with high accuracy.     

Comprehensive analysis of photonic nanojets in 3D dielectric cuboids excited by surface plasmons

In this paper we study the excitation of photonic nanojets (PNJ) in 3D dielectric cuboids by surface plasmons at telecommunication wavelengths. The analysis is done using the effective refractive index approach. It is shown that the refractive index contrast between the regions with and without cuboid should be roughly less than 2 in order to generate jets at the output of the cuboid. The best performance at λ₀ = 1550 nm is obtained when the height of the cuboid is 160 nm producing a jet just at the output interface with a subwavelength resolution of 0.68λ₀ and a high intensity enhancement (×5) at the focus. The multi‐wavelength response is also studied demonstrating that it is possible to use the proposed structure at different wavelengths. Finally, the backscattering enhancement is numerically evaluated by inserting a metal particle within the PNJ region, demonstrating a maximum value of ～2.44 dB for a gold sphere of radius 0.1λ₀.

     

Electric field patterns in finite two-dimensional wire photonic lattices

Electric field patterns in finite two-dimensional wire photonics lattices are investigated by means of electromagnetic simulations. These studies reveal a strong analogy with the envelope wavefunction approach extensively used for semiconductor superlattices. Several propagating eigenmodes in the pass-band of the periodic structure as well as evanescent modes in the stop-bands are clearly identified. They are interpreted on the basis of the matching of the wavelength of electromagnetic field to the lattice dimensions with an envelope wavefunction resulting of a Fabry–Pérot effect in the cavity formed by the periodic structure. In terms of transmission coefficient, the calculated frequency dependence are in agreement with those measured in finite metallic rod arrays between 50 and 110 GHz.     

基于高光谱成像的小麦白粉病与条锈病识别

小麦白粉病和条锈病是我国两种最普遍、最具破坏性的小麦病害,且田间常常混合发生。由于病源和发病机理不同,有必要对这两种病害进行准确区分和识别,以采取不同的防治措施。基于ImSpector V10E高光谱成像系统采集的条锈菌侵染叶片、白粉菌侵染叶片和健康叶片(共计320个)在375～1 017 nm范围内的高光谱图像,利用高斯平滑等预处理方法得到三种小麦叶片的平均光谱曲线,发现小麦白粉病和条锈病的敏感波段均集中在550～680 nm的色素强吸收位置,且趋势基本一致。针对两种病害的响应波段交叉重叠的问题,通过主成分分析-载荷法(PCA)、连续投影算法(SPA)和竞争性自适应重加权算法(CARS)对小麦叶片的光谱信息进行有效降维,分别优选出3、6、30个敏感波段和特征波长;在此基础上,采用最小二乘-支持向量机和极限学习机两种分类算法分别基于全波段、PCA、SPA和CARS的优选波段,建立白粉病、条锈病和健康叶片的判别模型。结果表明,8种模型的准确识别率均在94.58%以上。其中,主成分分析-载荷法结合极限学习机模型最优,训练集与校正集的正确识别率分别为99.18%和100%,且结构简单,仅含有三个变量(占全波段的1.1%)。最后,通过对小麦白粉病、条锈病以及健康叶片的显微结构分析,发现病菌入侵叶片,破环细胞结构,导致叶绿素含量减少,光合作用效能降低,进而使得小麦在可见光波段光吸收程度减弱,反射率增大。可见,利用作物的高光谱图像信息能够准确地识别不同类型的小麦病害,为研发作物病害在线识别的多光谱系统提供重要的理论依据。     

高光谱图像和叶绿素含量的水稻纹枯病早期检测识别

基于高光谱成像技术和化学计量方法,实现了对水稻纹枯病病害的早期检测识别。以幼苗时期的水稻植株为研究对象,对其进行纹枯病病菌侵染,获得染病植株,采集358～1 021 nm波段范围的高光谱图像,三次实验共240个样本,包括染病植株120个样本和健康植株120个样本。根据高光谱图像的光谱维,对染病水稻叶片和健康水稻叶片提取感兴趣区域（ROI）,利用感兴趣区域的光谱数据,对其进行Savitzky-Golay（SG）平滑、Savitzky-Golay（SG）一阶求导、Savitzky-Golay（SG）二阶求导、变量标准化（SNV）和多元散射校正（MSC）预处理,建立线性判别分析（LDA）和支持向量机（SVM）分类模型,结果表明：采用SG二阶求导预处理后的线性判别分析（LDA）模型取得了较好的性能,正确识别率在建模集达98.3%,在预测集达95%;利用载荷系数法（x-loading weights, x-LW）对原始光谱和5种预处理的光谱数据进行特征波长提取,然后根据选取的特征波长建立线性判别分析（LDA）和支持向量机（SVM）分类模型,其中采用SG二阶求导预处理后提取的12个特征波长的线性判别分析（LDA）模型取得了较好的性能,其正确识别率在建模集达97.8%,在预测集达95%,而且基于载荷系数法建立的模型性能与全波段相当,可以通过载荷系数法减少数据量对水稻纹枯病病害进行识别;根据高光谱图像的图像维,研究了基于图像主成分分析、基于概率滤波和基于二阶概率滤波的图像特征提取方法,利用提取的特征变量建立反向传播神经网络(BPNN)和支持向量机（SVM）分类模型,其中基于图像主成分分析的反向传播神经网络（BPNN）模型取得了较好的性能,建模集准确识别率达90.6%,预测集的准确识别率达83.3%;根据高光谱图像光谱维和图像维的最优模型,特将叶绿素含量作为建模的另一个特征,分别与光谱特征、图像特征组合,建立反向传播神经网络（BPNN）和线性判别分析（LDA）模型,提出基于光谱特征加叶绿素含量、图像特征加叶绿素含量和光谱、图像特征加叶绿素含量三种组合方式,其中,光谱特征和图像特征分别与叶绿素组合的方式比之前单独的光谱和图像特征建模性能都有所提升,而且三种组合方式中光谱特征加叶绿素含量的反向传播神经网络（BPNN）建模方式取得本研究所有建模方式中较优的性能,其准确识别率在建模集达100%,在预测集达96.7%。以上研究表明,基于高光谱图像和叶绿素含量对水稻纹枯病病害进行早期识别是可行的,为水稻病害的早期识别提供了一种新方法。     

LS-SVM的梨可溶性固形物近红外光谱检测的特征波长筛选

为提高梨可溶性固形物含量(soluble solids content,SSC)的近红外光谱模型的精度和稳定性,以160个梨样品为实验对象,分别对原始光谱、多元散射校正(MSC)和标准正态变量变换(SNV)处理后的光谱,经无信息变量消除算法(UVE)挑选后,再结合遗传算法(GA)和连续投影算法(SPA),筛选梨可溶性固形物的近红外光谱特征波长。将筛选后的波长作为输入变量建立梨可溶性固形物的最小二乘支持向量机(LS-SVM)模型。结果表明经过SNV-UVE-GA-SPA从全波段3112个波长中筛选出的30个特征波长建立的梨可溶性固形物LS-SVM模型效果最好,该模型的预测集相关系数(R_p)和预测均方根误差(RMSEP)分别为0.956和0.271。该模型简单可靠,预测效果好,能满足梨的可溶性固形物含量的快速检测,为在线检测和便携式设备开发提供了理论基础。     

高准确度的钙离子光频标

近年来,冷原子技术和激光技术促进了高精度光频标的发展,有望在建立时间基准、推动基础研究和满足国家需求等方面发挥重要的作用.本文介绍了中国科学院武汉物理与数学研究所近年来在高准确度钙离子(~(40)Ca~+)光频标研究方面的进展:采用新的ULE腔系统,实现了729 nm钟跃迁激光器1—100 s的频率稳定度均优于2×10~(-15),通过对外场和环境效应的控制及克服,特别是囚禁离子运动效应的抑制,获得单个钙离子光频标的不确定度优于5.5×10~(-17);通过两台光频标的比对,测得20000 s的稳定度也进入10~(-17)量级;基于高精度钙离子光频标平台,进行了相关精密测量的工作,包括:基于全球定位系统的超高精度远程光频绝对值测量方案,第二次测量了钙离子的光频跃迁绝对值,该测量结果再次被国际时间频率咨询委员会采纳,更新了钙离子的频率推荐值;精确测量了钙离子的钟跃迁魔幻波长,由此提出新型的全光囚禁离子光频标的方法;精密测量了钙离子的亚稳态寿命等参数.以上工作推动了基于冷原子的精密测量工作.     

巡天光谱中拼接异常光谱的自动检测和异常分级方法

拼接异常是光谱在红蓝两端拼接区域表现出的光谱连续性差的一种现象。在LAMOST的光谱处理中,仪器的稳定性、观测条件以及获得的响应函数等问题都是造成拼接异常的原因。光谱拼接是否正常对于光谱发布等后续工作的质量有重要影响。提出一种拼接异常光谱的自动检测方法,有效地提高了工作效率。该研究可以为LAMOST数据提供一个自动的标记,来评价拼接质量,也可以为用户提供一个使用数据时的选择。该方法首先将待测光谱进行流量归一化、去除钠线等预处理,并将其分为红蓝两端;然后对红蓝两端分别进行拟合;最后对两条拟合曲线,选取一系列等波长间隔的点,计算在这些点处的流量差值,得到所有流量差值的均值,标准差,并且计算两条曲线积分面积的差值;基于上述统计量,提出了一个判断光谱是否异常及其异常程度的评价函数。大量的实验证明,该方法具有良好的拼接异常光谱检测效果。     

Investigation of Alien Wavelength Quality in Live Multi-Domain,Multi-Vendor Link Using Advanced Simulation Tool

Abstract

This article presents an advanced optical model for simulation of alien wavelengths in multi-domain and multi-vendor dense wavelength-division multiplexing networks. The model aids optical network planners with a better understanding of the non-linear effects present in dense wavelength-division multiplexing systems and better utilization of alien wavelengths in future applications. The limiting physical effects for alien wavelengths are investigated in relation to power levels, channel spacing, and other factors. The simulation results are verified through experimental setup in live multi-domain dense wavelength-division multiplexing systems between two national research networks: SURFnet in Holland and NORDUnet in Denmark.