基于端面耦合的InP基激光器/硅光波导混合集成技术研究

针对硅光子集成回路缺少实用化光源的问题,提出了一种1.55μm波段InP基FP激光器芯片、InP基PIN光电探测器芯片与硅光波导芯片集成模块的设计与制备方法。使用CMOS工艺兼容的硅光无源器件制备工艺,设计并制备了倒拉锥型端面耦合器,与锥形透镜光纤耦合效率为36.7%。采用微组装对准技术将激光器芯片与硅波导芯片耦合、UV固化胶固化后耦合效率为35.8%,1 dB耦合对准容差横向为1.2μm,纵向为0.95μm。     

以用户为中心的可见光通信协作传输中训练资源分配算法

以用户为中心的可见光通信协作传输是近年来出现的新架构,这导致虚拟小区之间出现重叠。为避免导频污染问题,每个虚拟小区中的光接入点(AP)或者虚拟小区中选择相同AP的用户发送的训练序列应该是正交的。针对可见光通信中以用户为中心的协作网络,研究训练资源的正交分配问题,提出了一种新的导频分配算法,联合导频分配和用户选择问题,以期最大限度地增加虚拟小区内可被接入的用户数。分析和仿真结果表明,该导频分配方案可以有效改善导频污染问题,提高训练资源利用率,并且相比已有的导频分配方案,性能有所改进。     

运行工况对多向扰流强化管CaSO4污垢特性的影响

为更深入并准确研究运行工况条件对多向扰流强化管CaSO_4污垢特性的影响,基于FLUENT软件的UDF功能构建了恒壁温条件下结垢传质过程与温度场的耦合作用关系,进一步采用田口法对运行工况致垢的贡献率进行了模拟比较,分析了贡献率较大的运行工况对污垢特性的影响。结果表明:溶液溶度致垢的贡献率占53.2%,而壁面温度、进口流速和进口温度的贡献率分别为22.2%、19.3%和5.3%;溶液溶度在4.0～2.5 kg/m~3,污垢热阻降低达90.47%,并且随溶度降低其相邻溶度间降低比例基本不变;壁面温度在340.0～315.0 K时,污垢热阻降低了65.22%,在前一阶段相邻温度间降低比例基本上不变,当达到320.0 K后降低明显;流速在1.0～2.5 m/s时,随流速的增加,污垢热阻降低68.65%,且随流速的增加,相邻流速间降低的速度明显减缓。     

Sparse non-convex Lp regularization for cone-beam X-ray luminescence computed tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex L_p (0?p?L₁ regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-L_p) was proposed to efficiently solve the non-convex L_p (0?p?p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L₂ and L₁ regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our L_p(1/4?p?相似文献   

基于多层感知器的气液两相流流型识别方法

通过对电阻层析成像数据采集原理和深度学习网络的研究,提出了一种基于阵列电阻值和多层感知器深度学习网络相结合的流型识别方法。利用电阻层析成像系统中的16个电极传感器来获取流型样本数据,并构建出流型识别数据库,然后对多层感知器深度学习网络进行训练,获得可以识别不同流型的网络。实验结果表明,采用阵列电阻值结合多层感知器网络对流型进行学习和识别的方法,流型识别准确率可以达到95%,解决了流型图像生成过程与数据特征预选过程中流型特征损失的问题,流型识别性能得到了提高。     

用于低损耗特种玻璃熔炼的高纯致密锆英石的研制

以纯度大于99.9%(质量分数)的高纯ZrO₂和SiO₂为原料,少量TiO₂为添加剂,采用高温固相法合成高纯锆英石(ZrSiO₄)粉料。研究温度和反应时间对高纯锆英石合成效率的影响,发现粒度小于50 μm的原料粉末经1 500 ℃反应48 h后,ZrSiO₄相的含量可以达到95.77%(质量分数)。将合成的高纯锆英石粉料球磨并冷等静压成型后,在1 550 ℃高温烧结成高纯致密锆英石砖。高纯致密锆英石中杂质Fe的含量仅为29 μg/g,Cu的含量小于1 μg/g,是普通商用致密锆英石的1/10;对磷酸盐玻璃静态光吸收损耗的影响仅为普通致密锆英石材料的1/3。将这种高纯致密锆英石材料用于激光玻璃窑炉,有助于降低玻璃对1 053 nm激光的损耗,提升激光玻璃的激光性能。     

一种Chirplet神经网络自动目标识别算法

针对飞机目标的自动识别问题,提出一种联合特征提取与分类的Chirplet神经网络方法,实现一维高分辨率距离像的识别。Chirplet神经网络将Chirplet原子变换用于多层前馈神经网络结构的输入层,替换传统的激励函数对距离像序列进行特征提取;网络的分类部分由隐层和输出层组成。在训练过程中调整神经网络权值的同时,完成对Chirplet原子时频参数的自动调整,协调优化特征参数和分类器参数,使Chirplet神经网络同时实现特征提取和目标分类。对4类飞机目标的仿真测试结果表明,相比时频变换和Gabor原子网络等方法,具有四特征参数的Chirplet神经网络方法具有较高的识别率和抗噪性能。     

Effect of chlorination and fluorination of benzothiadiazole on the performance of polymer solar cells

In order to explore the effects of chlorine and fluorine on photophysical properties and the differences, in this work, we synthesized five new polymers, P1 – P5 , in which benzo[1,2-b:4,5-b]dithiophene as the electron donating and benzothiadiazole as electron withdrawing. Analysis of these five polymers showed that the introduction of Cl and F atoms can deeper the highest occupied molecular orbital of these polymers and enhance the absorption of light by the species, thereby improving V_oc and J_sc. Chlorination has a stronger ability to reduce energy levels and broaden the absorption spectrum compared to fluorination. Among them, P2 showed an efficiency of 4.08% with J_sc of 11.28 mA/cm², V_oc of 0.79 V, and fill factor (FF) of 0.45. Since chlorination is easier than fluorination in terms of synthesis, it is advantageous for practical applications. Therefore, we think that chlorination should not be ignored when designing high efficiency photovoltaic materials, especially when their fluorinated counterparts have proven to have good properties.     

Polytetrafluoroethylene-intercalated MXene membranes with good photothermal performance for enhanced laser ignition

Photothermal effect has been widely used in many areas such as cancer therapy, photothermal energy harvesting, and laser ignition. However, exploring reliable and efficient free-standing energy converter for enhancing the photothermal performance is still a challenge. Herein, free-standing membrane based on two-dimensional MXene (Ti₃C₂) nanosheets and polytetrafluoroethylene (PTFE) was fabricated and characterized by X-ray diffraction, scanning electron microscopy and differential scanning calorimetry, which demonstrated a drastic temperature rise by laser irradiation and was further used as energy converter for enhancing the photothermal performance of laser ignition. Furthermore, the initiating power of the laser initiator can be largely reduced by adding a thin layer of MXene/PTFE membrane above the B/KNO₃ cylinder. This work can give great promise for MXene-based membranes as the laser energy converter for reducing the initiating energy and promote the development of laser initiators with low initiating energy.     

Shape from focus based on 3D structure tensor using optical microscopy

Shape from focus (SFF) is a widely used passive optical method for 3D shape reconstruction. In SFF, a focus measure, which is used to estimate the relative focus level, plays a critical role in depth estimation. In this article, we present a new focus measure for accurate 3D shape estimation in optical microscopy based on the analysis of 3D structure tensor. First, the 3D tensors are computed from the input image sequence for each pixel. Then, each tensor is decomposed into point, curve, and surface tensors by decomposing tensors into eigenvalues and eigenvectors. Finally, the surfaceness is used to measure the quality of sharpness. The proposed focus measure provides accurate focus values and better resistance against noise. The proposed measure is evaluated by conducting experiments using image sequences of simulated and microscopic real objects. The comparative analysis demonstrates the effectiveness of the proposed focus measure in recovering 3D shape.