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Your search keyword '"Zhu, Xingjun"' showing total 194 results
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194 results on '"Zhu, Xingjun"'

2. High-resolution tomographic reconstruction of optical absorbance through scattering media using neural fields

Author

Ren, Wuwei, Shen, Siyuan, Li, Linlin, Gao, Shengyu, Wang, Yuehan, Gu, Liangtao, Li, Shiying, Zhu, Xingjun, Jiang, Jiahua, and Yu, Jingyi

Subjects
Physics - Optics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air. Diffuse optical tomography (DOT) tackles scattering by volumetrically recovering the optical absorbance and has shown significance in medical imaging, remote sensing and autonomous driving. A conventional DOT reconstruction paradigm necessitates discretizing the object volume into voxels at a pre-determined resolution for modelling diffuse light propagation and the resulting spatial resolution of the reconstruction is generally limited. We propose NeuDOT, a novel DOT scheme based on neural fields (NF) to continuously encode the optical absorbance within the volume and subsequently bridge the gap between model accuracy and high resolution. Comprehensive experiments demonstrate that NeuDOT achieves submillimetre lateral resolution and resolves complex 3D objects at 14 mm-depth, outperforming the state-of-the-art methods. NeuDOT is a non-invasive, high-resolution and computationally efficient tomographic method, and unlocks further applications of NF involving light scattering.

Published
2023

19. Adaptive Multiscale Convolution Manifold Embedding Networks for Intelligent Fault Diagnosis of Servo Motor-Cylindrical Rolling Bearing Under Variable Working Conditions

Author

Zhu, Xingjun, Zhao, Xiaoli, Yao, Jianyong, Deng, Wenxiang, Shao, Haidong, and Liu, Zheng

Abstract

The rolling bearing of the servo motor is widely used in precision-controlled mechanical systems. It usually works at variable speed and load, possibly resulting in partial bearing failure. Meanwhile, the varying conditions may cause the smearing of classable features, increasing the diagnostic difficulty. To this end, an intelligent fault diagnosis method of servo motor-cylindrical roller bearings based on adaptive multiscale convolution manifold embedding networks (AMCMENet) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to reprocess the feature extracted by designed multiscale convolutional neural networks (MSCNN). In this way, the distribution differences of samples could be improved. The training sample under variable conditions is first input to the designed MSCNN for initial feature extraction. Afterward, the constructed locality sensitive discriminant analysis algorithm module is used, which is adjusted to optimal parameters by the particle swarm optimization algorithm, to enlarge the heterogeneous distance and narrow the homogeneous distance of the extracted feature. Finally, the testing subset is provided to the trained AMCMENet algorithm for fault diagnosis. The experimental results of two datasets demonstrate that the proposed intelligent fault diagnosis method performs better under cross working conditions.

Published
2024
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20. Multifunctional Optical Tomography System With High-Fidelity Surface Extraction Based on a Single Programmable Scanner and Unified Pinhole Modeling

Author

Gao, Shengyu, Zhang, Jianru, Hu, Yexing, Wu, Yanan, Li, Linlin, Hu, Qian, Lou, Xin, Zhu, Xingjun, Jiang, Jiahua, and Ren, Wuwei

Abstract

Objective: Macroscopic optical tomography is a non-invasive method that can visualize the 3D distribution of intrinsic optical properties or exogenous fluorophores, making it highly attractive for small animal imaging. However, reconstructing the images requires prior knowledge of surface information. To address this, existing systems often use additional hardware components or integrate multimodal information, which is expensive and introduces new issues such as image registration. Our goal is to develop a multifunctional optical tomography system that can extract surface information using a concise hardware design. Methods: Our proposed system uses a single programmable scanner to implement both surface extraction and optical tomography functions. A unified pinhole model is used to describe both the illumination and detection procedures for capturing 3D point cloud. Line-shaped scanning is adopted to improve both spatial resolution and speed of surface extraction. Finally, we integrate the extracted surface information into the optical tomographic reconstruction to more accurately map the fluorescence distribution. Result: Comprehensive phantom experiments with different levels of complexity were designed to evaluate the performance of surface extraction and fluorescence tomography. We also imaged the axillary lymph nodes in living mice after injection of fluorophore, demonstrating the proposed system facilitates more reliable fluorescence tomography. Conclusion: We have successfully developed a versatile optical tomography system by leveraging concise hardware design and unified pinhole modeling. Phantom validation demonstrates that our system provides high-precision surface information with a maximum error of 0.1 mm, while the surface-guided FMT reconstruction is more reliable than the blind reconstruction using simplified surface geometry, elevating several quantitative metrics including RMSE, CNR, and Dice. Significance: Our work explores the feasibility of obtaining additional surface information using existing components of standalone optical tomography. This makes the optical tomographic technique more accurate and more accessible to biomedical researchers.

Published
2024
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21. An erythrocyte membrane-modified biomimetic synergistic nanosystem for cancer anti-vascular therapy and initial efficacy monitoring.

Author

Zhang, Jieying, Li, Fang, Su, Lili, Hu, Qian, Li, Jianfeng, and Zhu, Xingjun

Abstract

Tumor vascular disruption has become a promising strategy for cancer therapy in recent decades. Nanocomposites loaded with therapeutic materials and drugs are expected to improve the accuracy of anti-vascular therapy and minimize side effects. However, how to prolong blood circulation of therapeutic nanocomposites for enhanced accumulation in tumor vasculature and how to monitor the initial efficacy of anti-vascular therapy for early evaluation of prognosis remain unsolved. Herein, a biomimetic nanosystem consisting of erythrocyte membrane modified nanocomposites (CMNCs) is developed for cooperation to achieve anti-vascular cancer therapy and initial efficacy monitoring. By utilizing poly(lactic-co-glycolic acid) (PLGA) as the interface material, functional nanomaterials and drug molecules are successfully integrated into CMNCs. The long circulation and immune escape features of the erythrocyte membrane facilitate CMNCs loaded with photothermal agents and chemodrugs to be delivered to the tumor region for anti-vascular treatment. Furthermore, the vascular damage-induced haemorrhage and the following coagulation process is labelled by near infrared emissive CMNCs to indicate the initial therapeutic efficacy of the treatment. This work not only points to a biomimetic strategy for conquering the challenges in anti-vascular cancer therapy, but also provides insights into the biological responses of erythrocyte membrane modified nanocomposites to exploit their biomedical applications. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Intelligent Health Assessment of Aviation Bearing Based on Deep Transfer Graph Convolutional Networks under Large Speed Fluctuations.

Author

Zhao, Xiaoli, Zhu, Xingjun, Yao, Jianyong, Deng, Wenxiang, Cao, Yudong, Ding, Peng, Jia, Minping, and Shao, Haidong

Subjects
*DEEP learning, *SPEED, *SPECTRUM analysis
Abstract

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Dual-channel lanthanide-doped nanoprobe for reliable multi-signal ratiometric detection of H2S in whole blood.

Author

Wei, Zheng, Cao, Tianqi, Li, Luoyuan, Zhu, Xingjun, Zhou, Jing, and Liu, Yuxin

Subjects
SERODIAGNOSIS, RARE earth metals, FLUORESCENCE, LIGHT absorbance
Abstract

Wavelength-dependent absorbance of blood has impeded the development of fluorescence biodetection in whole blood. Here, by replacing the fluorescence working signal with a temperature signal, reliable H2S detection was performed in samples of whole blood. The developed system was based on a dual-channel lanthanide-doped nanoprobe, which further allowed precise serodiagnosis of acute pancreatitis. [ABSTRACT FROM AUTHOR]

Published
2022
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