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2021

Record 85 of

Title:Optimal optical path difference of an asymmetric common-path coherent-dispersion spectrometer

Author(s):Chen, Shasha(1,2,3); Wei, Ruyi(1,3,4); Xie, Zhengmao(3); Wu, Yinhua(5); Di, Lamei(1,3); Wang, Feicheng(1,3); Zhai, Yang(6,7)
Source: Applied Optics Volume: 60 Issue: 16 DOI: 10.1364/AO.425491 Published: June 1, 2021
Abstract:Optical path difference (OPD) is a very significant parameter in the asymmetric common-path coherent-dispersion spectrometer (CODES), which directly determines the performance of the CODES. In order to improve the performance of the instrument as much as possible, a temperature-compensated optimal optical path difference (TOOPD) method is proposed. The method does not only consider the influence of temperature change on the OPD but also effectively solves the problem that the optimal OPD cannot be obtained simultaneously at different wavelengths. Taking the spectral line with a Gaussian-type power spectral density distribution as a representative, the relational expression between the OPD and the visibility of interference fringes formed by the CODES is derived for the stellar absorption/emission line. Further, the optimal OPD is deduced according to the efficiency function, and the relationship between the optimalOPDand wavelength is analyzed. Then, based on the materials' dispersion characteristics, different optical materials are combined and added to the interferometer's reflected and transmitted optical path to implement the optimalOPDat different wavelengths, thereby improving the detection precision. Meanwhile, the materials whose refractive index negatively changes with temperature are selected to reduce or even offset the temperature impact on OPD, and hence the system's stability is improved and further improves the detection precision. Under certain input conditions, the material combination that approximates the optimal OPD is performed within the range of 0.66-0.9 μm. The simulation results show that the maximal difference between the optimal OPD obtained by the efficiency function and the OPD produced by the material combination is 0.733 mm for the absorption line and 1.122 mm for the emission line, which is reduced by 1 time compared with only one material. The influence of temperature on the OPD can be reduced by 2-3 orders of magnitude by material combination, which greatly ameliorates the stability of the whole spectrometer. Hence, the TOOPD method provides a new idea for further improving the high-precision radial velocity detection of the asymmetric common-pathCODES. ?2021 Optical Society of America.
Accession Number: 20212210426952
Record 86 of

Title:Scalable wide neural network: A parallel, incremental learning model using splitting iterative least squares

Author(s):Xi, Jiangbo(1,2); Ersoy, Okan K.(3); Fang, Jianwu(4); Cong, Ming(1,2); Wei, Xin(5,6); Wu, Tianjun(7)
Source: IEEE Access Volume: 9 Issue: DOI: 10.1109/ACCESS.2021.3068880 Published: 2021
Abstract:With the rapid development of research on machine learning models, especially deep learning, more and more endeavors have been made on designing new learning models with properties such as fast training with good convergence, and incremental learning to overcome catastrophic forgetting. In this paper, we propose a scalable wide neural network (SWNN), composed of multiple multi-channel wide RBF neural networks (MWRBF). The MWRBF neural network focuses on different regions of data and nonlinear transformations can be performed with Gaussian kernels. The number of MWRBFs for proposed SWNN is decided by the scale and difficulty of learning tasks. The splitting and iterative least squares (SILS) training method is proposed to make the training process easy with large and high dimensional data. Because the least squares method can find pretty good weights during the first iteration, only a few succeeding iterations are needed to fine tune the SWNN. Experiments were performed on different datasets including gray and colored MNIST data, hyperspectral remote sensing data (KSC, Pavia Center, Pavia University, and Salinas), and compared with main stream learning models. The results show that the proposed SWNN is highly competitive with the other models. ? 2013 IEEE.
Accession Number: 20211310151075
Record 87 of

Title:Dark gap solitons in periodic nonlinear media with competing cubic-quintic nonlinearities

Author(s):Chen, Junbo(1); Zeng, Jianhua(1)
Source: Research Square Volume: Issue: DOI: 10.21203/rs.3.rs-292763/v1 Published: March 23, 2021
Abstract:Solitons are nonlinear self-sustained wave excitations and probably among the most interesting and exciting emergent nonlinear phenomenon in the corresponding theoretical settings. Bright solitons with sharp peak and dark solitons with central notch have been well known and observed in various nonlinear systems. The interplay of periodic potentials, like photonic crystals and lattices in optics and optical lattices in ultracold atoms, with the dispersion has brought about gap solitons within the finite band gaps of the underlying linear Bloch-wave spectrum and, particularly, the bright gap solitons have been experimentally observed in these nonlinear periodic systems, while little is known about the underlying physics of dark gap solitons. Here, we theoretically and numerically investigate the existence, property and stability of one-dimensional gap solitons and soliton clusters in periodic nonlinear media with competing cubic-quintic nonlinearity, the higher-order of which is self-defocusing and the lower-order (cubic) one is chosen as self-defocusing or focusing nonlinearities. By means of the conventional linear-stability analysis and direct numerical calculations with initial perturbations, we identify the stability and instability areas of the corresponding dark gap solitons and clusters ones. ? 2021, CC BY.
Accession Number: 20220209769
Record 88 of

Title:Effects of secondary electron emission yield properties on gain and timing performance of ALD-coated MCP

Author(s):Guo, Lehui(1,2,3); Xin, Liwei(1,3); Li, Lili(1,2,3); Gou, Yongsheng(1); Sai, Xiaofeng(1); Li, Shaohui(1); Liu, Hulin(1); Xu, Xiangyan(1); Liu, Baiyu(1); Gao, Guilong(1); He, Kai(1); Zhang, Mingrui(1); Qu, Youshan(1); Xue, Yanhua(1); Wang, Xing(1); Chen, Ping(1,3,4); Tian, Jinshou(1,3)
Source: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment Volume: 1005 Issue: DOI: 10.1016/j.nima.2021.165369 Published: July 21, 2021
Abstract:The technology of atomic layer deposition has been used to improve the lifetime of the microchannel plate-photomultiplier tube (MCP-PMT) effectively and makes MCP possible to choose to coat different potential emissive materials on the internal surface of the MCP channels in the future. However, it is still an open question to what extent the secondary electron emission (SEE) yield properties of the emissive materials influence the behavior of the ALD-coated MCP. In this work, the dependences of the gain and timing performance on the SEE yield properties were assessed by using the Monte Carlo and particle-in-cell methods. We established the three-dimensional MCP single channel model in Computer Simulation Technology (CST) Particle Studio. Three important secondary electron emissions, the backscattered, rediffused and true SEEs, were discussed numerically based on the probabilistic model. The secondary electron cascade processes in the MCP single channel were simulated. The simulation results indicate that the opportunities for improving the gain of the ALD-coated MCP by improving the SEE yields corresponding to the incident energies of 0 eV–100 eV. The backscattered and rediffused electrons are found to have strong effects on the gain and timing performance of the MCP. Although the higher the SEE yield the higher the MCP gain, the drawback is the extremely high SEE yield will make the MCP saturated prematurely and degrade the time resolution. The simulation results will be used to guide the design and selection of emissive material for ALD-coated MCP development. ? 2021 Elsevier B.V.
Accession Number: 20211910320664
Record 89 of

Title:Real-time study of coexisting states in laser cavity solitons

Author(s):Hanzard, Pierre Henry(1); Rowley, Maxwell(1); Cutrona, Antonio(1); Chu, Sai T.(2); Little, Brent E.(3); Morandotti, Roberto(4,5); Moss, David J.(6); Wetzel, Benjamin(7); Gongora, Juan Sebastian Totero(1); Peccianti, Marco(1); Pasquazi, Alessia(1)
Source: Optics InfoBase Conference Papers Volume: Issue: DOI: null Published: 2021
Abstract:We experimentally demonstrate the presence of two coexisting states in Laser Cavity Solitons (LCS) Microcombs. By using the Dispersive Fourier Transform technique, we show the simultaneous presence of both LCS and a background modulation. ? OSA 2021, ? 2021 The Author(s)
Accession Number: 20214711207854
Record 90 of

Title:A motor imagery EEG signal classification algorithm based on recurrence plot convolution neural network

Author(s):Meng, XianJia(1); Qiu, Shi(2); Wan, Shaohua(3); Cheng, Keyang(4); Cui, Lei(1)
Source: Pattern Recognition Letters Volume: 146 Issue: DOI: 10.1016/j.patrec.2021.03.023 Published: June 2021
Abstract:With the promotion of brain-computer interface technology, it is possible to study brain control system through EEG signals in recent years. In order to solve the problem of EEG signal classification effectively, a motor imagery classification algorithm based on recurrence plot convolution neural network is proposed. Firstly, EEG signals are preprocessed to enhance the signal intensity in the exercise interval. Secondly, time-domain and frequency-domain features are extracted respectively to construct the feature mode of recurrence plot. Finally, a new neural network is established to realize the accurate recognition of left and right movements. This research can also be transferred to other research fields. ? 2021 Elsevier B.V.
Accession Number: 20211410166776
Record 91 of

Title:Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air

Author(s):Niu, Chen(1); Cheng, Xuemei(1); Zhang, Tianlong(2); Wang, Xing(3); He, Bo(1); Zhang, Wending(1); Feng, Yaozhou(2); Bai, Jintao(1); Li, Hua(2,4)
Source: Analytical Chemistry Volume: 93 Issue: 4 DOI: 10.1021/acs.analchem.0c04155 Published: February 2, 2021
Abstract:Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantitative analysis of multiple constituents in a single aerosol particle with the noncontact in situ manner is still a challenging task. In this work, we explore the laser trapping-LIBS-machine learning to analyze four elements (Zn, Ni, Cu, and Cr) absorbed in a single micro-carbon black particle in air. By employing a hollow laser beam for trapping, the particle can be restricted in a range as small as ～1.72 μm, which is much smaller than the focal diameter of the flat-topped LIBS exciting laser (～20 μm). Therefore, the particle can be entirely and homogeneously radiated, and the LIBS spectrum with a high signal-to-noise ratio (SNR) is correspondingly achieved. Then, two types of calibration models, i.e., the univariate method (calibration curve) and the multivariate calibration method (random forests (RF) regression), are employed for data processing. The results indicate that the RF calibration model shows a better prediction performance. The mean relative error (MRE), relative standard deviation (RSD), and root-mean-squared error (RMSE) are reduced from 0.1854, 363.7, and 434.7 to 0.0866, 179.8, and 216.2 ppm, respectively. Finally, simultaneous and quantitative determination of the four metal contents with high accuracy is realized based on the RF model. The method proposed in this work has the potential for online single aerosol particle analysis and further provides a theoretical basis and technical support for the precise prevention and control of composite air pollution. ? 2021 The Authors. Published by American Chemical Society.
Accession Number: 20210509858682
Record 92 of

Title:High-throughput fast full-color digital pathology based on Fourier ptychographic microscopy via color transfer

Author(s):Gao, Yuting(1,2); Chen, Jiurun(1,2); Wang, Aiye(1,2); Pan, An(1); Ma, Caiwen(1); Yao, Baoli(1)
Source: arXiv Volume: Issue: DOI: null Published: January 19, 2021
Abstract:Full-color imaging is significant in digital pathology. Compared with a grayscale image or a pseudo-color image that only contains the contrast information, it can identify and detect the target object better with color texture information. Fourier ptychographic microscopy (FPM) is a high-throughput computational imaging technique that breaks the tradeoff between high resolution (HR) and large field-of-view (FOV), which eliminates the artifacts of scanning and stitching in digital pathology and improves its imaging efficiency. However, the conventional full-color digital pathology based on FPM is still time-consuming due to the repeated experiments with tri-wavelengths. A color transfer FPM approach, termed CFPM was reported. The color texture information of a low resolution (LR) full-color pathologic image is directly transferred to the HR grayscale FPM image captured by only a single wavelength. The color space of FPM based on the standard CIE-XYZ color model and display based on the standard RGB (sRGB) color space were established. Different FPM colorization schemes were analyzed and compared with thirty different biological samples. The average root-mean-square error (RMSE) of the conventional method and CFPM compared with the ground truth is 5.3% and 5.7%, respectively. Therefore, the acquisition time is significantly reduced by 2/3 with the sacrifice of precision of only 0.4%. And CFPM method is also compatible with advanced fast FPM approaches to reduce computation time further. Copyright ? 2021, The Authors. All rights reserved.
Accession Number: 20210045222
Record 93 of

Title:The ensemble deep learning model for novel COVID-19 on CT images

Author(s):Zhou, Tao(1,3); Lu, Huiling(2); Yang, Zaoli(4); Qiu, Shi(5); Huo, Bingqiang(1); Dong, Yali(1)
Source: Applied Soft Computing Volume: 98 Issue: DOI: 10.1016/j.asoc.2020.106885 Published: January 2021
Abstract:The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19. ? 2020 Elsevier B.V.
Accession Number: 20204709509999
Record 94 of

Title:Spectral Discrimination of Rabbit Liver VX2 Tumor and normal Tissue Based on Genetic Algorithm-Support Vector Machine

Author(s):Liu, Chen-Yang(1,2); Xu, Huang-Rong(2,3); Duan, Feng(4); Wang, Tai-Sheng(1); Lu, Zhen-Wu(1); Yu, Wei-Xing(3)
Source: Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis Volume: 41 Issue: 10 DOI: 10.3964/j.issn.1000-0593(2021)10-3123-06 Published: October 2021
Abstract:Rabbit liver VX2 tumor is a tumor model that can grow rapidly in various organs, such as liver, lung, rectum, etc., and is often used in tumor research. In this paper, using high-near-infrared spectrum technology to four rabbits VX2 liver tumor and normal tissue in vivo and in vitro reflection spectrum detection, then respectively the Two categories based on support vector machine (normal liver tissue and liver VX2 tumor tissue) and Four categories (not bleeding living normal liver tissue, not living liver VX2 tumor tissue bleeding, bleeding in vitro normal liver tissue and hemorrhage in vitro liver VX2 tumor tissue). According to its spectral reflection curve characteristics, the data in the range of 400~1 800 nm are selected as characteristic variables. In order to further improve the classification accuracy, the kernel parameter g and penalty factor c of the support vector machine was optimized by using a 50 fold cross-validation and genetic algorithm, respectively. The optimization parameters and classification results of the 50-fold cross-validation are as follows: penalty parameter c of the dichotomy optimization is 4, kernel parameter g is 0.125 0, and the accuracy of the correction set and prediction set reaches 100%. The optimized parameters c and g are 8 and 0.121 1, and the accuracy of the correction set and the prediction set are 99.242 4% and 93.33 3%, respectively. The optimized parameters and results of the genetic algorithm are as follows: the optimized parameters c and g in dichotomy are 0.845 6 and 0.062 5, respectively, and the accuracy of Two categories, the correction set and the prediction set, is agreed to reach 100%.The optimized parameter C in the Four categories was 5.530 7 and g was 0.068 5, and the accuracy of the correction set and the prediction set reached 99.242 4% and 100%, respectively. The results show that the two optimization methods have achieved good results, and the genetic algorithm is more accurate in the classification of the Four categories. In order to further improve the speed of the algorithm, the method of variable selection at intervals was adopted to reduce the characteristic variables continuously. Finally, a variable was selected for every 100 nm spectral segment, and a total of 14 spectral segments were selected as the characteristic variables. Parameters of support vector machine were optimized by using genetic algorithm for the classification was studied, the results show that the Two categories and Four categories of both results of the calibration set and prediction set were 99.242 4%, and the running time of 11.4 s and 20.0 s respectively, and choosing all band running time: 340.3 s and 491.0 s compared to how spectroscopy can be in the identification of hepatic VX2 tumor tissue and normal liver tissue. The classification accuracy rate can reach more than 99%, and the running time shorten a lot. Therefore, it also lays a foundation for realising rapid real-time online detection and classification of tumor tissues in the future clinical tumor diagnosis with multi-spectrum technology, showing great application potential. ? 2021, Peking University Press. All right reserved.
Accession Number: 20214111001467
Record 95 of

Title:Cross-model retrieval with deep learning for business application

Author(s):Wang, Yufei(1); Wang, Huanting(2,3); Yang, Jiating(2); Chen, Jianbo(3)
Source: IOP Conference Series: Earth and Environmental Science Volume: 1802 Issue: 3 DOI: 10.1088/1742-6596/1802/3/032035 Published: March 9, 2021
Abstract:Cross-modal retravel has been used in many fields, such as business and search engines. Most search engines for business are text-based, but text-based search engines are limited by equipment and the strict requirement for knowledge. Text-based search needs keyboards to finish the search process, which requires users to have the knowledge of using keyboards. Compared to the text-based search, audio-based search has advantages. First, it avoids the traditional ways of inputting information. And it gets rid of the gap in time between inputting information for searching and getting useful information. In this paper, we propose a way to use audio to search images for business applications. We use deep learning to implement cross-modal retrieval systems between images and audio. We first extract features from images and audio respectively. And then we implement a neural network with two identical networks to learn the correspondence between images and audio. The first network extracts the features from images and audio further for calculation, and the second network learns whether two features from different modalities are related. This research provides a new way for business applications to search for information more instantly. ? Published under licence by IOP Publishing Ltd.
Accession Number: 20211210123555
Record 96 of

Title:Real-Time Study of Coexisting States in Laser Cavity Solitons

Author(s):Hanzard, Pierre Henry(1); Rowley, Maxwell(1); Cutrona, Antonio(1); Chu, Sai T.(2); Little, Brent E.(3); Morandotti, Roberto(4,5); Moss, David J.(6); Wetzel, Benjamin(7); Gongora, Juan Sebastian Totero(1); Peccianti, Marco(1); Pasquazi, Alessia(1)
Source: 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings Volume: Issue: DOI: null Published: May 2021
Abstract:We experimentally demonstrate the presence of two coexisting states in Laser Cavity Solitons (LCS) Microcombs. By using the Dispersive Fourier Transform technique, we show the simultaneous presence of both LCS and a background modulation. ? 2021 OSA.
Accession Number: 20214911280709