Microlens array snapshot hyperspectral microscopy system for the biomedical domain

2021 ◽  
Vol 60 (7) ◽  
pp. 1896
Author(s):  
Changben Yu ◽  
Jin Yang ◽  
Nan Song ◽  
Ci Sun ◽  
Mingjia Wang ◽  
...  
Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Wei Yuan ◽  
Cheng Xu ◽  
Li Xue ◽  
Hui Pang ◽  
Axiu Cao ◽  
...  

Double microlens arrays (MLAs) in series can be used to divide and superpose laser beam so as to achieve a homogenized spot. However, for laser beam homogenization with high coherence, the periodic lattice distribution in the homogenized spot will be generated due to the periodicity of the traditional MLA, which greatly reduces the uniformity of the homogenized spot. To solve this problem, a monolithic and highly integrated double-sided random microlens array (D-rMLA) is proposed for the purpose of achieving laser beam homogenization. The periodicity of the MLA is disturbed by the closely arranged microlens structures with random apertures. And the random speckle field is achieved to improve the uniformity of the homogenized spot by the superposition of the divided sub-beams. In addition, the double-sided exposure technique is proposed to prepare the rMLA on both sides of the same substrate with high precision alignment to form an integrated D-rMLA structure, which avoids the strict alignment problem in the installation process of traditional discrete MLAs. Then the laser beam homogenization experiments have been carried out by using the prepared D-rMLA structure. The laser beam homogenized spots of different wavelengths have been tested, including the wavelengths of 650 nm (R), 532 nm (G), and 405 nm (B). The experimental results show that the uniformity of the RGB homogenized spots is about 91%, 89%, and 90%. And the energy utilization rate is about 89%, 87%, 86%, respectively. Hence, the prepared structure has high laser beam homogenization ability and energy utilization rate, which is suitable for wide wavelength regime.


2021 ◽  
pp. 1-1
Author(s):  
Yuetian Huang ◽  
Shijie Li ◽  
Jin Zhang ◽  
Chen Yang ◽  
Yingxiu Kong ◽  
...  

2020 ◽  
pp. 1-21 ◽  
Author(s):  
Clément Dalloux ◽  
Vincent Claveau ◽  
Natalia Grabar ◽  
Lucas Emanuel Silva Oliveira ◽  
Claudia Maria Cabral Moro ◽  
...  

Abstract Automatic detection of negated content is often a prerequisite in information extraction systems in various domains. In the biomedical domain especially, this task is important because negation plays an important role. In this work, two main contributions are proposed. First, we work with languages which have been poorly addressed up to now: Brazilian Portuguese and French. Thus, we developed new corpora for these two languages which have been manually annotated for marking up the negation cues and their scope. Second, we propose automatic methods based on supervised machine learning approaches for the automatic detection of negation marks and of their scopes. The methods show to be robust in both languages (Brazilian Portuguese and French) and in cross-domain (general and biomedical languages) contexts. The approach is also validated on English data from the state of the art: it yields very good results and outperforms other existing approaches. Besides, the application is accessible and usable online. We assume that, through these issues (new annotated corpora, application accessible online, and cross-domain robustness), the reproducibility of the results and the robustness of the NLP applications will be augmented.


2019 ◽  
Vol 26 (4) ◽  
pp. 1159-1166
Author(s):  
Xiaojun Zhou ◽  
Aiguo Song ◽  
Shuai Wang ◽  
Mengjia Wang ◽  
Weixing Yu
Keyword(s):  

2012 ◽  
Vol 100 (13) ◽  
pp. 133701 ◽  
Author(s):  
Hewei Liu ◽  
Feng Chen ◽  
Qing Yang ◽  
Pubo Qu ◽  
Shengguan He ◽  
...  

1994 ◽  
Vol 106 (1-3) ◽  
pp. 39-44 ◽  
Author(s):  
E. Bonet ◽  
P. Andrés ◽  
J.C. Barreiro ◽  
A. Pons

2000 ◽  
Author(s):  
Sihai Chen ◽  
Xinjian Yi ◽  
Yi Li ◽  
Miao He ◽  
Sixiang Chen ◽  
...  

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