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108 results on '"Lu, Xingjian"'

1. Ultra-conformable Liquid Metal Particle Monolayer on Air/water Interface for Substrate-free E-tattoo

Author

Li, Fali, Lei, Wenjuan, Wang, Yuwei, Lu, Xingjian, Li, Shengbin, Xu, Feng, He, Zidong, Liu, Jinyun, Yang, Huali, Wu, Yuanzhao, Shang, Jie, Liu, Yiwei, and Li, Run-Wei

Subjects
Physics - Applied Physics, Computer Science - Human-Computer Interaction
Abstract

Gallium-based liquid metal is getting increased attention in conformal flexible electronics for its high electrical conductivity, intrinsic deformability and biocompatibility. A series of flexible devices are developed based on the micro-particles of liquid metal. But it is still challenging to fabricate conformal liquid metal film with a large area and high uniformity. Interfacial self-assembly is a competitive candidate method. Traditional interfacial self-assembly methods have difficulties assembling liquid metal particles because the floating state of the high-density microparticles could be easily disturbed by gravity. Here, we realized the multi-size universal self-assembly (MUS) for liquid metal particles with various diameters (0~500{\mu}m). By introducing a simple z-axis undisturbed interfacial material releasing strategy, the interference of gravitational energy on the stability of floating particles is avoided. Benefits from this, the ultra-conformable monolayer film, with large area (>100 cm2) and high floating yield (50%~90%), can be fabricated by liquid metal particles. Furthermore, the monolayer can be conformally transferred to any interesting complex surface such as human skin and plant leaf, to fabricate substrate-free flexible devices. Without interference from the mechanical response of traditional substrate, the liquid metal e-tattoo is more user-friendly and can realize feel-less continuous monitoring.

Published
2023

8. Incorporating Network Structure with Node Information for Semi-supervised Anomaly Detection on Attributed Graphs

Author

Chen, Bofeng, Li, Jingdong, Lu, Xingjian, Sha, Chaofeng, Wang, Xiaoling, Zhang, Ji, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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11. AQapprox: Aggregation Queries Approximation with Distribution-Aware Online Sampling

Author

Wu, Han, Wang, Xiaoling, Lu, Xingjian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, Zhisheng, editor, Beek, Wouter, editor, Wang, Hua, editor, Zhou, Rui, editor, and Zhang, Yanchun, editor

Published
2020
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12. WFApprox: Approximate Window Functions Processing

Author

Lin, Chunbo, Li, Jingdong, Wang, Xiaoling, Lu, Xingjian, Zhang, Ji, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nah, Yunmook, editor, Cui, Bin, editor, Lee, Sang-Won, editor, Yu, Jeffrey Xu, editor, Moon, Yang-Sae, editor, and Whang, Steven Euijong, editor

Published
2020
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14. UHRP: Uncertainty-Based Pruning Method for Anonymized Data Linear Regression

Author

Liu, Kun, Liu, Wenyan, Cheng, Junhong, Lu, Xingjian, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Li, Guoliang, editor, Yang, Jun, editor, Gama, Joao, editor, Natwichai, Juggapong, editor, and Tong, Yongxin, editor

Published
2019
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22. ARDock: A Web-AR Based Real-Time Tangible Edugame for Molecular Docking

Author

He, Gaoqi, Sun, Fei, Hu, Dong, Lu, Xingjian, Guo, Yi, Lai, Shuhua, Pan, Zhigeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Josef, Kittler, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, El Rhalibi, Abdennour, editor, Tian, Feng, editor, Pan, Zhigeng, editor, and Liu, Baoquan, editor

Published
2016
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26. Multifunctional fluorescent carbon quantum dots for Zr4+ ion detection, pH response and cell imaging.

Author

Liao, Xiaokun, Zhou, Li, Lu, Xingjian, Zou, Hao, Cao, Jun, Pan, Jiaqi, Li, Chaorong, and Zheng, Yingying

Abstract

In this paper, novel multifunctional nitrogen and sulfur co-doped fluorescent carbon quantum dots (Y-CDs) were prepared by a solvothermal method using o-phenylenediamine (OPD) as the carbon and nitrogen sources, L-Cysteine (L-Cys) as the sulfur source, and ethanol as the solvent. The prepared Y-CDs had high selectivity for the detection of Zirconium (Zr4+) with a detection limit of 68 nM due to the doping of S elements which formed electrical interactions with Zr4+ and thus caused aggregation fluorescence quenching. In addition, Y-CDs had excellent pH reversible response properties. The fluorescence of Y-CDs was almost completely quenched in the pH range of 1–5, and was restored when the pH returned to neutral or basic. Y-CDs also had good biocompatibility and low toxicity with the application of clear yellow fluorescence imaging in MCF-7 and EC cells. Yellow fluorescence Y-CDs focused mainly on the nucleus in MCF-7 cancer cells, while focused mainly on the cell membrane region in EC normal cells due to the different pH conditions of these two cells. The prepared Y-CDs would have promising applications in the fields of Zr4+ ion detection, pH response and cell imaging and cell identification. [ABSTRACT FROM AUTHOR]

Published
2023
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27. Measurement of detection distance of laser pointer based on CCD detection technology

Author

WANG Nan, WU Liangsheng, YANG Yang, and LU Xingjian

Subjects
laser pointer, detection distance, ccd sensor, optical power, gray scale, Mining engineering. Metallurgy, TN1-997
Abstract

In view of the problem of poor stability, low efficiency existed in distance detection method of low power semiconductor laser pointer, a measurement method of detection distance of laser pointer based on CCD detection technology was put forward. The detection distance equation of laser pointer with human eye as the receiver was deduced and modeling by using the CCD sensor and image processing technology. The function mathematical model between the two parameters of optical power and gray scale was also established. The least square method was used to solve correlation coefficient of the equation，the detection distance can be solved by correlation coefficient. The method realizes quantitative detection of detection distance of low power semiconductor laser pointer. The feasibility of the method is verified by experiment.

Published
2016
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28. An Approach for Bursty and Self-similar Workload Generation

Author

Lu, Xingjian, Yin, Jianwei, Chen, Hanwei, Zhao, Xinkui, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Lin, Xuemin, editor, Manolopoulos, Yannis, editor, Srivastava, Divesh, editor, and Huang, Guangyan, editor

Published
2013
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30. An Efficient Data Dissemination Approach for Cloud Monitoring

Author

Lu, Xingjian, Yin, Jianwei, Li, Ying, Deng, Shuiguang, Zhu, Mingfa, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Liu, Chengfei, editor, Ludwig, Heiko, editor, Toumani, Farouk, editor, and Yu, Qi, editor

Published
2012
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33. Deep Neural Networks for Semantic Segmentation of Lung Nodules

Author

Lu Xingjian, Jingqi Ma, Liangsheng Wu, Huan Leiv, and Li Chentong

Subjects
Training set, Lung, medicine.diagnostic_test, Computer science, business.industry, Deep learning, Computed tomography, Nodule (medicine), Pattern recognition, Image segmentation, respiratory system, respiratory tract diseases, medicine.anatomical_structure, Sørensen–Dice coefficient, medicine, Segmentation, Artificial intelligence, medicine.symptom, business
Abstract

Segmentation of lung nodule from computed tomography (CT) images is of great importance for lung nodule analysis. In order to improve the segmentation accuracy of lung nodules, a lot of research work has been done. The two main challenges in segmentation of lung nodules are lung nodules have the similar characteristics as surrounding tissues and lung nodules are extremely unbalanced with the background. In this study, we investigated the performance of U-Net to segment the lung nodules in CT images. Two imaging pre-process methods, image resizing and intensity normalization, are applied to alleviate the problem of small nodules in lung and improve the quality of training images. In addition, to increase the diversity and variation of training data, we propose a fusion over-sampling method to augment the data. The dice coefficient loss function was also introduced to solve the problem of imbalance distribution of positive and negative samples. Experiments on the LUNA16 dataset demonstrate that this method can improve the performance of lung nodules segmentation network, which is better than the state-of -the-art methods in the aspect of lung nodules segmentation.

Published
2021

36. Target tracking method based on the fusion of structured SVM and KCF algorithm

Author

Lu Xingjian, Lei Huan, and Ma Jingqi

Subjects
Support vector machine, Fusion, Statistical classification, Structured support vector machine, Computer science, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, 020206 networking & telecommunications, 020201 artificial intelligence & image processing, 02 engineering and technology, Filter (signal processing), Algorithm, Classifier (UML)
Abstract

Aiming at the drift problem of traditional KCF (kernelized correlation filter) target tracking algorithm caused by scale change and fast movement, a tracking method based on the fusion of structured SVM (support vector machine) and KCF algorithm is proposed. Combined with the location and scale information of the tracking target, a structured SVM classification and prediction model is established to replace the ridge regression filtering method in KCF, train the samples collected by cyclic matrix, and solve the problem of not detailed classification. In addition, a search strategy optimization method based on tracking target motion features is proposed to reduce unnecessary search time in dense sampling, further improve the search efficiency and classifier accuracy of traditional KCF algorithm in dense sampling, improve its calculation efficiency and target tracking performance in complex environment. This can solve the problem that the target cannot be accurately tracked due to the drift caused by the change of target scale and fast movement. Experiments show that the algorithm can locate the target area more accurately when the target scale changes and fast movement. Compared with the traditional KCF tracking algorithm, it has better performance and stronger robustness.

Published
2020

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