强度解调型光纤光栅温度传感实验

光纤光栅在工程上应用广泛,但由于存在解调系统复杂、成本高,尤其是需要使用光纤光谱仪等波长解调仪器,使得光纤光栅很难走入本科教学的实验课堂.本文提出了一种基于窄线宽DFB激光器的强度解调方案,极大地简化了光纤光栅传感器的解调系统,完全满足较高分辨率和实时检测的系统要求.合理安排的温度传感实验可以非常直观地展示光纤光栅的线性传感性能,使得工程化的光纤光栅传感技术轻松走进本科教学课堂,具有极高的推广和应用潜力.     

一种基于图像融合和卷积神经网络的相位恢复方法

相位恢复法利用光波传输中某一(或某些)截面上的光强分布来传感系统波前,其结构简单,不易受震动及环境干扰,被广泛应用于光学遥感和像差检测等领域.传统相位恢复法采用迭代计算,很难满足实时性要求,且在一定程度上依赖于迭代转换或迭代优化初值.为克服上述问题,本文提出了一种基于卷积神经网络的相位恢复方法,该方法采用基于小波变换的图像融合技术对焦面和离焦面图像进行融合处理,可在不损失图像信息的同时简化卷积神经网络的输入.网络模型训练完成后可依据输入的融合图像直接输出表征波前相位的4-9阶Zernike系数,且波前传感精度均方根(root-mean-square,RMS)可达0.015λ,λ=632.8 nm.研究了噪声、离焦量误差和图像采样分辨率等因素对波前传感精度的影响,验证了该方法对噪声具有一定鲁棒性,相对离焦量误差在7.5%内时,波前传感精度RMS仍可达0.05λ,且随着图像采样分辨率的提升,波前传感精度有所改善,但训练时间成本随之增加.此外,分析了实际应用中,当系统像差阶数与网络训练阶数略有差异时,本方法所能实现的传感精度,并给出了解决方案.     

Enhancing optomechanical force sensing via precooling and quantum noise cancellation

We theoretically investigate optomechanical force sensing via precooling and quantum noise cancellation in two coupled cavity optomechanical systems.We show that force sensing based on the reduction of noise can be used to dramatically enhance the force sensing and that the precooling process can eifectively improve the quantum noise cancellation.Specifically,we examine the effect of optomechanical cooling and noise reduction on the spectral density of the noise of the force measurement;these processes can significantly enhance the performance of optomechanical force sensing,and setting up the system in the resolved sideband regime can lead to an optimization of the cooling processes in a hybrid system.Such a scheme serves as a promising platform for quantum back-action-evading measurements of the motion and a framework for an optomechanical force sensor.     

On the well‐posedness of a mathematical model of quorum‐sensing in patchy biofilm communities

We analyze a system of reaction–diffusion equations that models quorum‐sensing in a growing biofilm. The model comprises two nonlinear diffusion effects: a porous medium‐type degeneracy and super diffusion. We prove the well‐posedness of the model. In particular, we present for the first time a uniqueness result for this type of problem. Moreover, we illustrate the behavior of model solutions in numerical simulations. Copyright © 2011 John Wiley & Sons, Ltd.     

三维面形测量技术的新进展

三维面形测量是获取物质形态特征的一种重要手段，也是记录，比较，复制物体形态特征 的基础，三维面形测量技术在机器视觉，自动加工，工业检测，产品质量控制，实物仿形，生物和医学等领域具有重要意义和广阔的应用前景。     

Interaction of different types of nanocages (Al12N12, Al12P12, B12N12, Be12O12, Mg12O12, Si12C12 and C24) with HCN and ClCN: DFT,TD-DFT,QTAIM, and NBO calculations

In this work, the ability of different types of nanocages including Al₁₂N₁₂, Al₁₂P₁₂, Be₁₂O₁₂, B₁₂N₁₂, Si₁₂C₁₂, Mg₁₂O₁₂ and C₂₄ for the adsorption and detection of poisonous gases HCN and ClCN has been investigated, theoretically using the D3 dispersion corrected density functional theory (DFT-D3). The absorption spectra of HCN–nanocage and ClCN–nanocage complexes were calculated by the time-dependent density functional theory (TD-DFT) and compared with the calculated absorption spectrum of isolated nanocage to investigate the ability of nanocage for sensing of HCN and ClCN gases. It was found that the strongest interaction between HCN (ClCN) molecule and nanocage takes place when the molecule is adsorbed via its N atom on the surface of nanocage except for C₂₄. Also, it was shown that the Al₁₂N₁₂ is the best adsorbent for HCN and ClCN gases among the selected nanocages and Si₁₂C₁₂ is the best sensor for the detection of these gases using the electroconductivity and absorption spectroscopy techniques.     

B-, N-doped and BN codoped C60 heterofullerenes for environmental monitoring of NO and NO2: a DFT study

ABSTRACT

Using density functional theory calculations, we investigate the gas sensing performance of B-, N-doped and BN-codoped C₆₀ fullerenes towards NO and NO₂ molecules. The calculated adsorption energies and net charge-transfer values indicate that NO and NO₂ molecules have a stronger interaction with the BN-codoped fullerenes compared to the B- or N-doped ones. It is also found that the electronic properties of the BN-codoped C₆₀ exhibit a larger sensitivity towards NO and NO₂ molecules. An increase in the concentration of doped/co-doped B and N atoms tends to weaken the gas sensing ability of these systems.     

空间高层大气遥感远紫外成像光谱仪的光学系统

空间高层大气遥感远紫外成像光谱仪主要用于观测高层大气中的远紫外辐射和实现对其内部中尺度现象成像的功能。目前我国该类的相关仪器研究基础还比较薄弱,针对这种情况,在光学系统设计的角度上给出了一种适用于130～180nm波段探测的光学系统方案。该成像光谱仪光学系统以离轴抛物镜为物镜,串联Wadsworth结构为成像光谱系统;这种串联Wadsworth成像光谱系统采用离轴抛物镜做准直镜,分光器件为平面光栅和球面光栅串联,实现二次色散,同时球面光栅起到聚焦成像作用;在像差理论的基础上,对该结构的光程函数和各像差进行了分析,获得了改进结构的宽波段完善成像条件。针对低轨空间探测应用要求设计了相关改进型Wadsworth结构成像光谱仪光学系统,设计结果证明系统像差得到了充分校正,在奈奎斯特频率(20lp/mm)下全视场全波段调制传递函数值在0.6以上。该优化结构同时具备高空间分辨率和高光谱分辨率,性能优越。     

Learnable three-dimensional Gabor convolutional network with global affinity attention for hyperspectral image classification

Benefiting from the development of hyperspectral imaging technology, hyperspectral image (HSI) classification has become a valuable direction in remote sensing image processing. Recently, researchers have found a connection between convolutional neural networks (CNNs) and Gabor filters. Therefore, some Gabor-based CNN methods have been proposed for HSI classification. However, most Gabor-based CNN methods still manually generate Gabor filters whose parameters are empirically set and remain unchanged during the CNN learning process. Moreover, these methods require patch cubes as network inputs. Such patch cubes may contain interference pixels, which will negatively affect the classification results. To address these problems, in this paper, we propose a learnable three-dimensional (3D) Gabor convolutional network with global affinity attention for HSI classification. More precisely, the learnable 3D Gabor convolution kernel is constructed by the 3D Gabor filter, which can be learned and updated during the training process. Furthermore, spatial and spectral global affinity attention modules are introduced to capture more discriminative features between spatial locations and spectral bands in the patch cube, thus alleviating the interfering pixels problem. Experimental results on three well-known HSI datasets (including two natural crop scenarios and one urban scenario) have demonstrated that the proposed network can achieve powerful classification performance and outperforms widely used machine-learning-based and deep-learning-based methods.     

Iterative filtered ghost imaging

It is generally believed that, in ghost imaging, there has to be a compromise between resolution and visibility. Here we propose and demonstrate an iterative filtered ghost imaging scheme whereby a super-resolution image of a grayscale object is achieved, while at the same time the signal-to-noise ratio (SNR) and visibility are greatly improved, without adding complexity. The dependence of the SNR, visibility, and resolution on the number of iterations is also investigated and discussed. Moreover, with the use of compressed sensing the sampling number can be reduced to less than 1% of the Nyquist limit, while maintaining image quality with a resolution that can exceed the Rayleigh diffraction bound by more than a factor of 10.