Nong Li 1,2Dongwei Jiang 1,2,3,*Guowei Wang 1,2,3Weiqiang Chen 1,2[ ... ]Zhichuan Niu 1,2,3,**
Author Affiliations
Abstract
1 State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
2 College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 101408, China
3 Center of Materials Science and Optoelectronics Engineering, College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
4 Key Laboratory of Semiconductor Materials Science, Beijing Key Laboratory of Low Dimensional Semiconductor Materials and Devices, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
For the measurement of responsivity of an infrared photodetector, the most-used radiation source is a blackbody. In such a measurement system, distance between the blackbody, the photodetector and the aperture diameter are two parameters that contribute most measurement errors. In this work, we describe the configuration of our responsivity measurement system in great detail and present a method to calibrate the distance and aperture diameter. The core of this calibration method is to transfer direct measurements of these two parameters into an extraction procedure by fitting the experiment data to the calculated results. The calibration method is proved experimentally with a commercially extended InGaAs detector at a wide range of blackbody temperature, aperture diameter and distance. Then proof procedures are further extended into a detector fabricated in our laboratory and consistent results were obtained.
infrared photodetectors responsivity calibration cavity blackbody Journal of Semiconductors
2023, 44(10): 102301
上海交通大学 轻合金精密成型国家工程研究中心,上海 200240
匹配层材料及结构对于提高阵列式换能器的带宽、灵敏度及轴向分辨率有重要作用。为了探究不同匹配层材料对阵列式换能器性能的影响，该文通过有限元法模拟了包括高分子材料、0-3复合材料及镁合金等多种匹配层材料的阵列式换能器，综合比较了各自的频域特性及时域特性。仿真结果表明，使用AZ31B镁合金作为第一匹配层、Epo-Tek 301环氧树脂作为第二匹配层的阵列式换能器模型具有最佳的综合性能，从而为高性能阵列式压电超声换能器的开发、研究提供了新的匹配层设计参考方案。
阵列式换能器 有限元法(FEM) 频域分析 时域分析 声阻抗匹配层 镁合金 array transducer finite element method(FEM) frequency domain analysis time domain analysis acoustic impedance matching layer magnesium alloy
钛酸钡(BaTiO3)陶瓷作为传统的介质电容器材料，其强铁电性会导致储能密度低下，但通过掺杂可以削弱铁电性来获得弛豫铁电体，提高储能性能。利用铋系化合物可增强弛豫特性，本文设计了BiScO3和(Sr0.7Bi0.2)TiO3取代改性的BaTiO3基三元陶瓷材料: (0.99-x)Ba(Zr0.1Ti0.9)O3-x(Sr0.7Bi0.2)TiO3-0.01BiScO3(缩写为BZT-xSBT-BS)。采用传统固相法制备的BZT-xSBT-BS陶瓷，相结构没有因为掺杂发生改变，在室温下均为纯的三方相钙钛矿结构。介电和铁电的测试与分析表明，BZT-xSBT-BS陶瓷具有典型的弛豫铁电特性。由于不等价离子Sr2+、Bi3+的掺杂导致界面松弛极化，可以增大BZT-xSBT-BS陶瓷的介电常数，但是受制于其慢的响应速度，陶瓷的介电损耗也显著增加。适量(Sr0.7Bi0.2)TiO3可以提升BZT-xSBT-BS陶瓷的介电、铁电、应变和储能性能，x=0.015时的BZT-xSBT-BS陶瓷的综合性能较优: εr~10 372,tanδ~0.019,Pmax=16.42 μC/cm2，Ec=1.41 kV/cm，S+max=0.12%(@40 kV/cm),WD=0.181 J/cm3，η=80.4%(@60 kV/cm)。
BaTiO3基陶瓷 固相法 弛豫铁电体 介质电容器 介电性能 铁电性能 储能 BaTiO3-based dielectric ceramic (Sr0.7Bi0.2)TiO3 (Sr0.7Bi0.2)TiO3 solid-state method relaxor ferroelectric dielectric capacitor dielectric property ferroelectric property energy storage
Author Affiliations
Abstract
1 Shanghai Institute of Technology Shanghai, P. R. China
2 Shanghai Jiaotong University Shanghai, P. R. China
Determination of the precise location and the degree of the Choroidal neovascularization (CNV) lesion is essential for diagnosation Neovascular age-related macular degeneration (AMD) and evaluation the efficacy of treatment. Noninvasive imaging techniques with specific contrast for CNV evaluation are demanded. In this paper, two noninvasive imaging techniques, namely Optical coherence tomography (OCT) and Photoacoustic microscopy (PAM), are combined to provide specific detection of CNV for their complimentary contrast mechanisms. In vivo timeserial evaluation of Laser-induced CNV in rats is present at days 1, 3, 5, 7, 14, 21 after laser photocoagulation is applied to the rat fundus. Both OCT and PAM show that the CNV increases to its maximum at day 7 and decreases at day 14. Quantification of CNV area and CNV thickness is given. The dual-modal information of CNV is consistent with the histologic evaluation by hematoxylin and eosin (H&E) staining.
Optical coherence tomography photoacoustic microscopy medical and biological imaging Journal of Innovative Optical Health Sciences
2021, 14(3): 2140012
利用传统固相法制备了0.6［(1-x)(Bi0.5Na0.5)TiO3-xNaSbO3］-0.4(Sr0.7Bi0.2)TiO3陶瓷材料(缩写为BNT-NSO-SBT-x),其中x=0.04 mol,0.06 mol,0.08 mol和0.1 mol。研究了引入第三组元NaSbO3后对BNT-NSO-SBT-x陶瓷相结构、介电和铁电性能以及储能特性的影响。Sb5+对相结构并未造成显著影响,BNT-NSO-SBT-x陶瓷在室温具有三方和四方共存相。BNT-NSO-SBT-x陶瓷为呈现典型弥散特征的弛豫铁电体(γ~2),这源于A、B位离子复合占位而引起的成分不均一,以及三方和四方PNRs共存造成的结构不均一。Sb5+取代量增大时,Tp和Tm逐渐靠近; 由于Tp(<80 ℃)靠近室温,引起弱极性四方相PNRs增多,使得陶瓷的铁电性能恶化,但却有利于提高储能特性。x=0.06时BNT-NSO-SBT-x陶瓷的储能较优,W1=0.227 J/cm3,η=76.2%(E=40 kV/cm, f=10 Hz)。
BNT基无铅陶瓷 Sb5+离子 弛豫铁电体 介电性能 铁电性能 BNT based lead-free ceramics Sb5+ ion relaxor ferroelectric dielectric property ferroelectric property
为了实现沙棘汁品牌的快速无损鉴别,提出了采用可见近红外光谱分析技术(NIR)鉴别沙棘汁品牌的方法。采用FieldSpec3光谱仪对三种沙棘汁进行光谱分析,各获取40个样本数据。采用平均平滑法和多元散射校正(MSC)方法对样本数据进行预处理,再用主成分法(PCA)对光谱数据进行聚类分析并获得各主成分数据。将120个沙棘汁样本随机分成90个建模样本和30个预测样本,把基于累计可信度选择的建模样本的8个主成分(PCs)数据作为BP网络的输入变量,沙棘汁品牌作为输出变量,建立三层反向传播(BP)神经网络鉴别模型,并对30个预测样本进行预测。结果表明,在阈值设定为±0.1的情况下,该模型对预测集样本品牌鉴别准确率达到了100%。所以应用近红外光谱技术结合主成分分析和BP神经网络算法识别沙棘汁品牌是一种有效的方法。
可见-近红外光谱 主成分分析 人工神经网络 品牌 沙棘汁
Author Affiliations
Abstract
School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710049
Fusion of multiple instances within a modality for biometric verification performance improvement has received considerable attention. In this letter, we present an iris recognition method based on multi-instance fusion, which combines the left and right irises of an individual at the matching score level. When fusing, a novel fusion strategy using minimax probability machine (MPM) is applied to generate a fused score for the final decision. The experimental results on CASIA and UBIRIS databases show that the proposed method can bring obvious performance improvement compared with the single-instance method. The comparison among different fusion strategies demonstrates the superiority of the fusion strategy based on MPM.
生物特征认证 虹膜识别 多实例融合 最小最大概率机 100.5010 Pattern recognition 100.2000 Digital image processing 100.2960 Image analysis Chinese Optics Letters
2008, 6(11): 824
