检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Han, Baohui Hu, Zheng Huang, Tongyu Cui, Wei Liu, Hongyuan Yang, Jianxiong Liao, Ran Ma, Hui Shenzhen Key Laboratory of Marine IntelliSensing and Computation Institute for Ocean Engineering Shenzhen International Graduate School Tsinghua University Shenzhen China Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology Shenzhen International Graduate School Tsinghua University Shenzhen China

High throughput classification of marine particles facilitates the fast environment monitoring of harmful microalgae blooms and microplastics pollution. A conceptual setup of flow Stokes microscopy is based on a Stokes microscope whose imaging area is passed through by the suspension flow in a microchannel to measure a time series of Stokes images. The setup’s measurement speed is 4.25 milliliter per hour and the particulate concentration error is less than 6%. Standard microplastics and cultured microalgae are measured and classified with general accuracy larger than 99%. Also, some of them are accurately classified when mixing with the natural seawater. Both the imaging and polarization features’ contribution are analyzed. Improvements of the microchannel are discussed to achieve higher throughput, more robust classification and broader application. This work demonstrates the flow Stokes microscopy’s power for high throughput classification of marine particles, which provides a promising tool in future aquatic environmental monitoring. © 2023, The Authors. All rights reserved.

关键词： Microplastic

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Comprehensive,Continuous,and Vertical Measurements of Seawater Constituents with Triple-Field-of-View High-Spectral-Resolution Lidar

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Research 2024年第2期2023卷 191-201页

作者： Kai Zhang Yatong Chen Hongkai Zhao Zhongping Lee Emmanuel Boss Iwona Stachlewska Davide Dionisi Cedric Jamet Paolo D.Girolamo Aleksey Malinka Chengchong Jiang Hongda Wu Lingyun Wu Feitong Chen Xiaolei Zhu Nanchao Wang Chuxiao Chen Qun Liu Lan Wu Yudi Zhou Weibiao Chen Dong Liu Ningbo Research Institute State Key Laboratory of Modern Optical InstrumentationCollege of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China ZJU-Hangzhou Global Scientific and Technological lnnovation Center Zhejang UniversityHangzhou 311200China Donghai Laboratory.Zhoushan 316021 China State Key Lab of Marine Environmental Science College of Ocean and Earth SciencesXiamen UniversityXiamen 361102China School of Marine Sciences University of MaineOronoME 04469-5741USA Faculty of Physics University of WarsawWarsaw 02093Poland institute of Marine Sciences Italian National Research CouncilRome 00133Italy Universitédu Littoral Cote d'Opale CNRSUniv.LilleIRDUMR 8187-LOG-Laboratoire d'Oceanologie et de GéosciencesWimereux F-6293 Francen Institute Scuola di Ingegneria Universita della BasilicataPotenza 85100Italy Institute of Physics National Academy of Sciences of BelarusMinsk 220072Belarus Key Laboratory of Space Laser Communication and Detection Technology Shanghai lnstitute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Inteligent Optics&Photonics Research Center Jiaxing Research Institute Zhejiang UniversityJiaxing 314000China Jiaxing Key Laboratory of Photonic Sensing&Intelligent Imaging Jiaxing 314000China

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and ***,existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity,synchronism,temporal-spatial resolution,and depth-resolved capacity of their data ***,we demonstrate a novel oceanic triple-field-of-view(FOV)high-spectral-resolution lidar(HSRL)with an iterative retrieval *** technique provides,for the first time,comprehensive,continuous,and vertical measurements of seawater absorption coefficient,scattering coefficient,and slope of particle size distribution,which are validated by simulations and field ***,it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon *** triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

关键词： lidar Vertical seawater

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Super-resolution Reconstruction Algorithm for Depth Image Based on Fractional Calculus

Super-resolution Reconstruction Algorithm for Depth Image Ba...

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Chinese Control and Decision Conference, CCDC

作者： Tingsheng Huang Xinjian Wang Chunyang Wang Xuelian Liu Yanqing Yu Wenqian Qiu 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 College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China

This study presents a new method for super-resolution of depth images by combining fractional calculus and inverse distance interpolation. The method improves edge recognition through fractional differential and solves the problem of missing data in traditional super-resolution algorithms for depth images. Results show that the proposed method outperforms classical interpolation algorithms, with improved PSNR by 3-10dB. The method effectively solves the impact of depth image edge distance on interpolation results, and provides more texture information.

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Direct energy-resolving CT imaging via energy-integrating ct images using a unified generative adversarial network

arXiv

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arXiv 2019年

作者： Yao, Lisha Li, Sui Zhu, Manman Zeng, Dong Bian, Zhaoying Ma, Jianhua Department of Biomedical Engineering Southern Medical University Guangzhou Guangdong510515 China Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology Guangzhou510515 China College of Automation Science and Engineering South China University of Technology Guangzhou510641 China

Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrating CT (EiCT) images via deep neural network. Specifically, different from other networks that produce ErCT images at one specific energy, this model employs a unified generative adversarial network (uGAN) to concurrently train EiCT datasets and ErCT datasets with different energies and then performs image-to-image translation from existing EiCT images to multiple ErCT image outputs at various energy bins. In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image slices and show that the present uGAN model can produce promising ErCT estimation results compared with the ground truth qualitatively and quantitatively. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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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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A Low Complexity Lossless Compression Based on General Code Table for Infrared Images

SSRN

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SSRN 2024年

作者： Zhu, Yaohua Zhang, Yong Huang, Mingsheng Zhu, Yanghang Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of Sciences Shanghai200083 China Shanghai Institute of Technical Phvsics Chinese Academy of Sciences Shanghai200083 China University of Chinese Academy of Sciences Beijing100049 China

In order to improve the storage and transmission of 14-bit image data acquired by line scanning infrared detectors, this paper proposes a novel method to realize real-time and efficient compression for infrared images. The proposed method utilizes the features of infrared images to reduce image redundancy, then a novel and improved Huffman coding is employed to achieve compression. This paper proposes a method for obtaining a general Huffman code table to increase compression speed and a trimming Huffman tree algorithm to reduce complexity caused by long codes of 14-bit images with low-loss compression ratios. The compression based on general code table avoids counting pixels and uses a simple rule to lookup code table that significantly improves the compression speed. The longest code in the general code table is employed to replace all the long codes in the 14-bit image, enabling low-complexity lossless compression. The proposed method outperforms other commonly used algorithms, which is very suitable for the compression of line scanning panoramic infrared images. The algorithmic idea can be applied not only to 14-bit images, but also to more-bit images. © 2024, The Authors. All rights reserved.

关键词： Trees (mathematics)

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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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Task-driven Deep Learning Network for Dynamic Cerebral Perfusion Computed Tomography Protocol Determination

Task-driven Deep Learning Network for Dynamic Cerebral Perfu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Sui Li Manman Zhu Danyang Li Qi Gao Zhaoying Bian Dong Zeng Jianhua Ma School of Biomedical Engineering and the Guangzhou Key Laboratory of Medical Radiation Imaging and Detection Technology Southern Medical University Guangzhou China College of Automation Science and Engineering South China University of Technology Guangzhou China School of Biomedical Engineering Southern Medical University Guangzhou China

ISBN: (数字)9781728141640

ISBN: (纸本)9781728141657

Dynamic cerebral perfusion computed tomography (DCPCT) imaging has the ability to detect ischemic stroke via hemodynamic maps. However, due to multiple acquisitions protocol, DCPCT scanning imposes high radiation doses on patients and might increase their potential cancer risks. The DCPCT protocol that decreases DCPCT samples by increasing sampling intervals can greatly reduce radiation dose, but this may introduce bias in the hemodynamic maps estimation, affecting the diagnosis. To address this issue, in this study, we present a deep learning network to determine the DCPCT protocol to realize the dose-reduction task, i.e., decreasing DCPCT samples, and the diagnosis-quality task, i.e., improve hemodynamic maps accuracy. Specifically, one interpolation convolutional neural network is fully designed to estimate the DCPCT images at the sampling interval, termed as dynamic cerebral perfusion interpolation network (DCPIN). The present network treats the DCPCT measurements as a "video" to characterize the maximum temporal coherence of spatial structure among phases, and interpolates a frame at any arbitrary time step between any two frames. First, a flow computation network is used to estimate the bi-directional optical flow between two input DCPCT frames by linearly fusing to approximate the required intermediate optical flow. Second, another flow interpolation network is designed to refine the flow approximations and predict soft visibility maps. Finally, the estimated flow approximations and visibility maps are merged together to jointly predict the intermediate DCPCT frame. Experimental results on patient data clearly demonstrate that the present DCPIN can achieve promising reconstruction performance, i.e., high-quality DCPCT images and high-accuracy hemodynamic maps.

关键词： Computed tomography Interpolation Hemodynamics Protocols Optical flow Image reconstruction Biomedical imaging

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An improvement method of a lossless compression scheme for the datacube from Geostationary Interferometric Infrared Sounder (GIIRS) and its FPGA implementation

An improvement method of a lossless compression scheme for t...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Minzhu Yang Raúl Guerra Hernández Changpei Han José Francisco López Feliciano Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province College of Information Engineering China Jiliang University Hangzhou China Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of Science Shanghai China Instituto Universitario de Microelectrónica Aplicada Universidad de Las Palmas de Gran Ganaria Las Palmas Spain

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

This paper briefly described an improved method for the original proposed scheme based on Vector Quantization and Linear Prediction to further enhance the compression ratio while reduce the time cost. Given the fact that the interferograms belong to the same datacube are similar to each other, there are two obvious modifications in this paper: predict only once to get the general predicted coefficients for the same datacube; along with treat datacube rather than sequence as the encoded unit. The time consumption spent by Linear Prediction part will shrink about 35.55% by predicting just once. Moreover, the change of the encoded object makes the final compression ratio get a further rise range from 2.62% to 3.711%.

关键词： Vector quantization Conferences Information technology Artificial intelligence Field programmable gate arrays

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Specificity-Preserving Federated Learning for MR Image Reconstruction

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Wang, Shanshan Xu, Yong Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology CAS Shenzhen518055 China Terminus Group China

Federated learning (FL) can be used to improve data privacy and efficiency in magnetic resonance (MR) image reconstruction by enabling multiple institutions to collaborate without needing to aggregate local data. However, the domain shift caused by different MR imaging protocols can substantially degrade the performance of FL models. Recent FL techniques tend to solve this by enhancing the generalization of the global model, but they ignore the domain-specific features, which may contain important information about the device properties and be useful for local reconstruction. In this paper, we propose a specificity-preserving FL algorithm for MR image reconstruction (FedMRI). The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution. Such scheme is then executed in the frequency space and the image space respectively, allowing exploration of generalized representation and client-specific properties simultaneously in different spaces. Moreover, to further boost the convergence of the globally shared encoder when a domain shift is present, a weighted contrastive regularization is introduced to directly correct any deviation between the client and server during optimization. Extensive experiments demonstrate that our FedMRI’s reconstructed results are the closest to the ground-truth for multi-institutional data, and that it outperforms state-of-the-art FL methods. © 2021, CC BY.

关键词： Image reconstruction

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