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2023 Annual IEEE International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2023

作者： Bai, Yifan Xiao, Bo Liu, Xuelian Wang, Chunyang Liang, Shuning Huang, Siling Zhao, Shijie Xi'an Technological University School of Optoelectronic Engineering Xi'an China Photoelectric Imaging Detection Technology Xi'an Key Laboratory of Active Xi'an China Xi'an Technological University Xi'an China Changchun University of Science and Technology School of Optoelectronic Engineering Changchun China Inner Mongolia Institute of Metal Materials Optoelectronic Laboratory Ningbo China Fine Optical Engineering Research Center Optoelectronic Laboratory Chengdu China

ISBN: (纸本)9798350344486

For non-confirmed processing for the ring pendulum double-sided polishing (RPDP), this article constructs a machining prediction model based on the motion principle and pressure distribution principle of the RPDP equipment, and conducts simulation experiments using the machining prediction model as a platform It was found that the instantaneous material reduction is mainly distributed within the radius range of the pendulum grinding disc at the center of the component. The distribution of the large instantaneous material reduction band does not exceed the radius range of the pendulum grinding disc at the center of the component, and is independent of changes in the size and swing mode of the pendulum grinding disc. The large instantaneous material reduction band contracts inward as the swing interval of the pendulum grinding disc moves outward. Based on this law, this article guides practical processing by cupper the surface profile of optical components from 2.27 λ(λ=632.8 ~nm) reduced to 1.85 λ, and the obtained processing rules can effectively guide actual processing. © 2023 IEEE.

关键词： Pendulums

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SWIR Fluorescenceimaging In Vivo Monitoring and Evaluating Implanted M2 Macrophages in Skeletal Muscle Regeneration

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Engineering 2024年第2期33卷 283-294页

作者： Mo Chen Yuzhou Chen Sijia Feng Shixian Dong Luyi Sun Huizhu Li Fuchun Chen Nguyen Thi Kim Thanh Yunxia Li Shiyi Chen You Wang Jun Chen Sports Medicine Institute of Fudan University Department of Sports MedicineHuashan HospitalFudan UniversityShanghai 200040China Department of Bone and Joint Surgery Department of OrthopedicsRenji HospitalSchool of MedicineShanghai Jiaotong UniversityShanghai 200127China Department of Anatomy and Physiology School of MedicineShanghai Jiao Tong UniversityShanghai 200025China Key Laboratory of Infrared System Detection&Imaging Technology Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Biophysics Group Department of Physics and AstronomyUniversity College LondonLondon WC1E 6BTUK Healthcare Biomagnetic and Nanomaterials Laboratories University College LondonLondon W1S 4BSUK

Skeletal muscle has a robust regeneration ability that is impaired by severe injury,disease,and *** in a decline in skeletal muscle ***,improving skeletal muscle regeneration is a key challenge in treating skeletal muscle-related *** to their significant role in tissue regeneration,implantation of M2 macrophages(M2MФ)has great potential for improving skeletal muscle ***,we present a short-wave infrared(SWIR)fluorescence imaging technique to obtain more in vivo information for an in-depth evaluation of the skeletal muscle regeneration effect after M2MФ*** fluorescence imaging was employed to track implanted M2MФin the injured skeletal muscle of mouse *** is found that the implanted M2MФaccumulated at the injury site for two ***,SWIR fluorescence imaging of blood vessels showed that M2MФimplantation could improve the relative perfusion ratio on day 5(1.09±0.09 vs 0.85±0.05;p=0.01)and day 9(1.38±0.16 vs 0.95±0.03;p=0.01)post-injury,as well as augment the degree of skeletal muscle regencration on day 13 ***,multiple linear regression analyses determined that post-injury time and relative perfusion ratio could be used as predictive indicators to evaluate skeletal muscle *** results provide more in vivo details about M2MФin skeletal muscle regeneration and confirm that M2MФcould promote angiogenesis and improve the degree of skeletal muscle repair,which will guide the research and development of M2MФimplantation to improve skeletal muscle regeneration.

关键词： In vivo Short-wave infrared Skeletal muscle Macrophage Regeneration

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A spatially correlated fractional integral-based method for denoising geiger-mode avalanche photodiode light detection and ranging depth images

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Optik 2023年第1期288卷

作者： Wang, Xinjian Wang, Chunyang Xie, Da Wei, Xuyang Huang, Tingsheng Liu, Xuelian School of Electronic and Information Engineering Changchun University of Science and Technology Changchun130022 China Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology Xi'an Technological University Xi'an710021 China

Geiger-mode avalanche photodiode (GM-APD) light detection and ranging (LiDAR) target echo signals are susceptible to interference from atmospheric backscatter, daylight, and other noise, and extracted target depth images contain a large amount of anomalous noise. Accordingly, this paper devises a method based on a spatially correlated fractional integral for denoising GM-APD LiDAR depth images. First, a multi-scale superpixel fusion algorithm is employed to perform null-pixel complementation on a target depth image extracted using histogram statistics. Next, a pixel neighborhood spatial correlation kernel function is used to optimize the Grünwald–Letnikov fractional integral operator to correct the large amount of anomalous noise and thus achieve GM-APD depth image denoising. Simulation and experimental results demonstrate that the denoising performance of the algorithm on GM-APD LiDAR depth images is significantly better than that of median filtering and bilateral filtering, with at least 22.8 % and 4.8 % improvements in the target reduction degree (K) and peak signal-to-noise ratio (PSNR), respectively. In addition, the K and PSNR reach 91.36 % and 18.0241 dB, respectively, with 25 statistical frames in an outdoor imaging experiment. This demonstrates that the algorithm can effectively denoise GM-APD LiDAR depth images with few statistical frames and thus improve the frame rate of GM-APD LiDAR imaging. © 2023 Elsevier GmbH

关键词： Pixels

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Accurate vehicle detection and counting algorithm for traffic data collection

Accurate vehicle detection and counting algorithm for traffi...

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International Conference on Connected Vehicles and Expo (ICCVE)

作者： Ye Tian Yangli Wang Rui Song Huansheng Song State Key Laboratory of Integrated Service Networks Xidian University Xi'an P.R. China Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of Sciences Shanghai P.R. China Department of Electronic and Information Engineering Chang'an University Xi'an P.R. China

ISBN: (纸本)9781509002658

Number of vehicles on road is very important traffic data and is essential for transportation safety and management. In this paper, an approach for vehicle detection is presented. In this approach, virtual line based sensors which are just straight detection lines are first set on road lanes. Then two features, namely gradient and range feature, are proposed for vehicle detection. This is carried out by extracting and analyzing the two features on detection lines. Meanwhile, the solution for vehicle occlusion have also been proposed. Our proposed method has an outstanding advantage that it performs excellent in traffic jams as well as under various conditions, such as sunny, cloudy, and rainy days, or night time, or even tunnels with complex illumination. The high accuracy rate of our method is verified with the experiment results.

关键词： Vehicles Feature extraction Vehicle detection Roads Delays Video sequences Connected vehicles

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Curing reaction, Thermal and Mechanical Properties of Low-temperature Elastic Epoxy Resin

Curing reaction, Thermal and Mechanical Properties of Low-te...

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2014中国溶胶-凝胶学术研讨会暨国际论坛

作者： Weiwei Xu Ai Du Yanhong Zhong Qingjun Liao Jun Shen Bin Zhou Shanghai Key Laboratory of Special Microstructure Materials and Technology Tongji UniversityShanghai 200092 China Key Laboratory of infrared imaging materials and detectors Shanghai Institute of Technical PhysicsChinese Academy of Sciences

As the desire underfill material for the application on the infrared focal plane detector (IRFPA), the low-temperature epoxy resin with tetrahydrofuran (THF) copolyether modification was prepared by elastic epoxy and varying content of curing agent (C).The thermal and mechanical properties of the cured samples were studied using dynamic mechanical analysis (DMA) with different testing modes and temperature *** of elastic modulus were found through the liner fitting to strain-stress curves, which were measured by tensile loading fixture with a gradually increased static *** a dynamic test, temperature scanning mode was employed to characterize the glass transition temperature (Tg) of final samples, with which related data ranged from 30 to 46 ℃ was indirectly obtained from the value of loss modulus peak ***, DMA methods could be efficiently used to simulate the practically changed temperature environment and timely record the testing data, which is significant for studying the chip failure mechanism in IRFPA.

关键词： underfill material epoxy resin dynamic mechanical analysis (DMA) thermal and mechanical properties failure mechanism

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Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

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Quantitative Biology 2022年第3期10卷 253-263页

作者： Xiaokang Chai Yachao Di Zhao Feng Yue Guan Guoqing Zhang Anan Li Qingming Luo Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for OptoelectronicsMoE Key Laboratory for Biomedical PhotonicsHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical EngineeringHainan UniversityHaikou 570228China CAS Key Laboratory of Computational Biology Bio-Med Big Data CenterShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesShanghai 200031China Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging Chinese Academy of Medical SciencesHUST-Suzhou Institute for BrainsmaticsJITRISuzhou 215123China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience *** are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big *** knowledge acquired from image data generally echoes with the literature accumulated over the *** knowledge within the literature can provide a comprehensive context for a deeper understanding of the image ***,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale ***,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale ***:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText *** model takes advantage of BioBERT and CRF to predict entity labels while ***:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText *** BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,*** used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience ***:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity *** rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

关键词： brain region entity extraction literature mining WhiteText deep learning

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Radon inversion via deep learning

arXiv

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arXiv 2018年

作者： He, Ji Ma, Jianhua Department of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology Guangzhou510515 China

Radon transform is widely used in physical and life sciences and one of its major applications is the X-ray computed tomography (X-ray CT), which is significant in modern health examination. The Radon inversion or image reconstruction is challenging due to the potentially defective radon projections. Conventionally, the reconstruction process contains several ad hoc stages to approximate the corresponding Radon inversion. Each of the stages is highly dependent on the results of the previous stage. In this paper, we propose a novel unified framework for Radon inversion via deep learning (DL). The Radon inversion can be approximated by the proposed framework with an end-to-end fashion instead of processing step-by-step with multiple stages. For simplicity, the proposed framework is short as iRadonMap (inverse Radon transform approximation). Specifically, we implement the iRadonMap as an appropriative neural network, of which the architecture can be divided into two segments. In the first segment, a learnable fully-connected filtering layer is used to filter the radon projections along the view-angle direction, which is followed by a learnable sinusoidal back-projection layer to transfer the filtered radon projections into an image. The second segment is a common neural network architecture to further improve the reconstruction performance in the image domain. The iRadonMap is overall optimized by training a large number of generic images from ImageNet database. To evaluate the performance of the iRadonMap, clinical patient data is used. Qualitative results show promising reconstruction performance of the iRadonMap. Copyright © 2018, The Authors. All rights reserved.

关键词： Computerized tomography

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Time-lag Liquid Crystal Optical Phased Array Beam Deflection Control Method Based on Smith's Prediction Compensation

Time-lag Liquid Crystal Optical Phased Array Beam Deflection...

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第43届中国控制会议

作者： Jinyang Yu Chunyang Wang Mingkan Ta Jiliang Jin Qianjin Lin Xuelian Liu School of Electronic and Information Engineering Changchun University of Science and Technology Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology Xi'an Technological University Shanghai Aerospace Control Technology Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the response delay problem in the liquid crystal optical phased array(LCOPA) system,this paper proposes a time-lagged liquid crystal optical phased array beam deflection control method based on Smith prediction compensation to compensate for the hysteresis that exists in the system,improve the oscillations and overshoots brought about by data transmission and processing hysteresis,and eliminate the adverse effects of hysteresis,so as to realize the fast,accurate and stable deflection of the light beam by the liquid crystal optical phased array. According to the time lag fractional order model of liquid crystal optical phased array system,the Smith predictor compensator and fractional order control system are designed,the Smith predictor is used to compensate for the delay of the system,the fractional order PID controller is used to finely adjust the response of the system,and the sparrow search algorithm is proposed to be utilized for the tuning of the controller parameters. The time-lagged system was evaluated using step response,beam deflection tracking,and robustness tests. The simulation results indicate that the liquid crystal optical phased array beam deflection system,equipped with a Smith predictor and a fractional-order controller,demonstrates rapid responsiveness,precise control,effective tracking capabilities,and strong robustness.

关键词： LCOPA fractional-order PID Smith predictor Parameterization

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A Novel Adaptive Graph Transformer For Point Cloud Object detection

A Novel Adaptive Graph Transformer For Point Cloud Object De...

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IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Shaoyu Sun Chunhao Shi Chunyang Wang Xuelian Liu School of Electronic and Information Engineering Changchun University of Science and Technology Changchun China Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology Xi'an Technological University Xi'an China

With the advancement of autonomous driving technology, Lidar-driven 3D object detection has become a focal point of research. However, many existing point-driven approaches face challenges due to rigid local dependencies and entangled foreground-background points. The direct utilization of point clouds for 3D object detection is also complicated by the inherent sparsity and irregularities of Lidar data. To counter these challenges, we propose AGT-SSD a single-stage graph-based 3D detector to improve object performance especially for small objects. Which comprises two critical modules adaptive graph feature convolution module (AGF) and a point cloud transformer layer with self-attention mechanism (TSA). AGF module utilizes an adaptive graph kernel to process graph features, facilitating feature interactions within the feature domain to help detect small objects. Non-local transformer layer incorporates self-attention module, designed to capture long-range dependencies to encode global sematic and spatial feature to improve detection performance. This enhancement is attributed to the geometric structure refinement through graph convolution methods and the use of a self-attention mechanism to grasp long-range dependencies. Both modules employ non-local operations, adapt at discerning long-range correlations. Extensive experiments on the KITTI dataset demonstrated that our detector is not flexible and plug and play for other detectors but also highlight the superior accuracy of our method compared to other state-of-the-art detectors.

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Analysis of Internal Atmosphere of InGaAs Detectors with Different Degassing Conditions

Analysis of Internal Atmosphere of InGaAs Detectors with Dif...

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System Reliability and Safety Engineering (SRSE), International Conference on

作者： Canxiong Lai Wen Sun Shaohua Yang Bin Zhou Science and Technology on Reliability Physics and Application of Electronic Component Laboratory CEPREI Guangzhou China Key Laboratory of Infrared Imaging Materials and Detectors Shanghai Institute of Technical Physics Shanghai China

ISBN: (纸本)9781665401616

the internal atmosphere composition of the cavity package of InGaAs detector has an important impact on its reliability. In this paper, the internal atmosphere contents of the detectors prepared at different degassing conditions are analyzed. The results show that vacuum baking at a relatively low temperature such as 45°C can reach an acceptable internal atmosphere level. Higher baking temperature °fif ((db further reduce the moisture content to about 1000ppmv, which would make the detector more reliable for long-term operation and thus be an ideal choice for degassing. Besides vacuum baking, the degassing effectiveness of nitrogen gas washing is also confirmed to be acceptable, although the moisture and oxygen contents are higher than those of vacuum baking.

关键词： Vacuum systems Atmosphere Moisture Detectors Reliability engineering Indium gallium arsenide Temperature control

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