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arXiv 2024年

作者： Zhao, Haotian Chen, Ruifeng Yan, Jing Feng, Juan Xiang, Jun Chen, Yang Liang, Dong Li, Yinsheng Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Laboratory of Image Science and Technology Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China X-Ray Department United Imaging Healthcare Limited Company Shanghai China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China Research Center for Medical Artificial Intelligence Key Laboratory of Biomedical Imaging Science and System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

—To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm conebeam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of typical C-arm CBCT are much poorer than those of multi-detector-row CT in the diagnostic imaging suite. The current quantitative perfusion imaging includes two cascaded steps, time-resolved image reconstruction and perfusion parametric estimation. For time-resolved image reconstruction, technical challenge imposed by poor temporal resolution and poor sampling density causes inaccurate quantification of the temporal variation of cerebral artery and tissue attenuation values. For perfusion parametric estimation, it remains a technical challenge to appropriately design the handcrafted regularization for better solving the associated deconvolution problem. These two challenges together prevent from obtaining quantitatively accurate perfusion images using C-arm CBCT. The purpose of this work is to simultaneously address these two challenges via combining the two cascaded steps into a single joint optimization problem and reconstructing quantitative perfusion images directly from the measured sinogram data. In the developed direct cerebral perfusion parametric image reconstruction technique, TRAINER in short, the quantitative perfusion images have been represented as a subject-specific conditional generative model trained under the constraint of the time-resolved CT forward model, perfusion convolutional model, and the subject’s own measured sinogram data. Results shown in this paper demonstrated that using TRAINER, quantitative cerebral perfusion images can be accurately obtained using C-arm CBCT in the interventional suite. Copyright © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Fast Construction Algorithm of Cooperative Relay Network for Unmanned Aerial Vehicle

Fast Construction Algorithm of Cooperative Relay Network for...

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第33届中国控制与决策会议

作者： Guoqiang Wang Pengfei Cheng He Luo School of Management Hefei University of Technology Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Engineering Research Center for Intelligent Decision-making & Information Systems Technologies Ministry of Education Key Laboratory of Urban ITS Technology Optimization and Integration Ministry of Public Security of China

In view of the situation that the relay network needs to be constructed between the combat nodes with communication interruption in battlefield environment,a fast construction algorithm of Unmanned Aerial Vehicle(UAV) cooperative relay network is proposed,which is based on the theory of multiple shortest paths and strategy of giving priority to the relay placement node that is the most difficult to reach for *** with the existing algorithms,this algorithm can further shorten the construction time of UAV cooperative relay network on the premise of minimizing the number of UAVs used for ***,the simulation experiment results demonstrate the effectiveness of the proposed algorithm.

关键词： UAV Relay network Node Placement Multiple shortest paths

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Film Mulching Mapping Based on Very High Resolution Satellite Imagery

Film Mulching Mapping Based on Very High Resolution Satellit...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhihao Wei Yaokui Cui Zhaoyuan Yao Shangjin Wang Sien Li Xuhui Wang Wenjie Fan Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing China Beijing Key Laboratory of Spatial Information Integration and Its Applications Peking University Beijing China College of Water Conservancy and Civil Engineering China Agricultural University Beijing China Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University Beijing China

Plastic mulch has been widely used in agricultural cultivation for its significantly increasement toward crop yield since last century, and has recently draw lots of attention from the government side, due to the environmental concerns caused by the agricultural plastic mulch. In this study, a few-shot learning based deep learning model is designed for film mulching mapping using very high resolution satellite imagery. Firstly, the image restoration model is pre-trained by massive unlabeled very resolution satellite imagery samples. Then, the film mulching mapping model is set by the pre-training model weights and trained by few labeled film mulching imagery samples. Results show the proposed method could achieve well film mulching mapping performance, and the F1-score reaches 0.9, which is useful in the agricultural irrigation management and yield prediction.

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Room Impulse Response Reshaping-Based Blind Source Separation for an Underdetermined Reverberant Environment

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SSRN 2024年

作者： Xie, Yuan Zou, Tao Yang, Junjie Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation under an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation maximization algorithm. However, it is limited to high reverberation environments, resulting in bad or even invalid separation performance. To eliminate this restriction, a room impulse response reshaping-based expectation maximization method is designed to solve the problem of blind signal separation in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo on the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation maximization method is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than popular expectation maximization methods. © 2024, The Authors. All rights reserved.

关键词： Reverberation

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Cone-Beam Computed Tomography Limited-Angle Image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

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SSRN 2023年

作者： Xie, Yuan Zou, Tao Chen, Ci Sun, Weijun Xie, Shengli School of Mechanical and Electrical Engineering Guangzhou University Guangdong-Hong Kong-Macao Key Laboratory of Multi-scale Information Fusion and Collaborative Optimization Control of Complex Manufacturing Process Guangzhou510006 China School of Automation Guangdong University of Technology Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education of the P.R.C. Guangzhou510006 China School of Automation Guangdong University of Technology Guangdong Key Laboratory of IoT Information Processing Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China School of Automation Guangdong University of Technology Key Laboratory of Intelligent Detection and the IoT in Manufacturing Ministry of Education 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Room reverberation and echoes have an important influence on acoustics quality, causing great difficulty in independent source separation. In the paper, an adaptive time-frequency blind source separation algorithm (ATF-BSS) is designed to deal with high reverberation and echoes, which can adaptively attenuate audible echoes and enhance the harmonic structure of real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components that have less energy than the dominant signal. To the best of our knowledge, this is the first attempt to incorporate the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking to solve blind separation with high reverberation and echoes. Experimental results on public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

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Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

SSRN

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SSRN 2023年

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Because the room reverberation and echoes have an important influence on acoustics quality, bringing great difficulty to the independent source separation. In the paper, an adaptive time-frequency blind source separation (ATF-BSS) algorithm is designed to deal with the high reverberation and echoes, which can adaptively attenuate the audible echoes and enhance the harmonic structure of the real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under the high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components having less energy than the dominant signal. To the best of our knowledge, this is the first attempt to incorporate the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking to solve blind separation with high reverberation and echoes. Experimental results on the public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

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Atf-Bss: Adaptive Time-Frequency Blind Source Separation for High Reverberation and Echo Environments

SSRN

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SSRN 2023年

Blind source separation with high reverberation and echoes is an open and extremely challenging issue. Room reverberation and echoes have an important influence on acoustics quality, causing great difficulty in independent source separation. In the paper, an adaptive time-frequency blind source separation algorithm (ATF-BSS) is designed to deal with high reverberation and echoes, which can adaptively attenuate audible echoes and enhance the harmonic structure of real-world audio signals, achieving excellent separation performance. Firstly, an equalizer network is designed for the room impulse response to weakening audible echoes under high reverberation and echo environments without changing the perceived timbre. Furthermore, an adaptive time-frequency Wiener-like masking is presented to enhance the dominant signal and attenuate the other components that have less energy than the dominant signal. ATF-BSS algorithm incorporates the advantages of an equalizer network for removing or weakening audible echoes and adaptive time-frequency masking for high reverberation and echoes scene, improving the performance of blind separation. Experimental results on public audio datasets demonstrate that the proposed ATF-BSS algorithm improves signal intelligibility and achieves much better separation performance than state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Reverberation

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Room Impulse Response Reshaping-Based Blind Source Separation in an Underdetermined Reverberant Environment

SSRN

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SSRN 2023年

Blind source separation in an underdetermined reverberation environment is a very challenging issue. The classical method is based on the expectation-maximization algorithm. However, it is limited in a high reverberation environment, resulting in bad or even invalid separation performance. To get rid of this restriction, a room impulse response reshaping-based blind source separation algorithm is designed to solve the signal separation problem in an underdetermined reverberant environment. Firstly, a room impulse response reshaping technology is designed to eliminate the influence of audible echo for the reverberant environment, improving the quality of the received signals. Then, a new mathematical model of time-frequency mixing signals is established to reduce the approximation error of model transformation caused by high reverberation. Furthermore, an improved expectation-maximization algorithm is proposed for real-time update learning rules of model parameters, and then the sources are separated using the estimators provided by the improved expectation maximization method. Experimental results based on source separation of speech and music mixtures demonstrate that the proposed algorithm achieves better separation performance while maintaining much better robustness than the popular expectation-maximization methods. © 2023, The Authors. All rights reserved.

关键词： Blind source separation

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Exploring the Causality of Accident Severity on Mountainous Freeways With a Two-Stage Approach

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Journal of Advanced Transportation 2025年第1期2025卷

作者： Lingzhi Kong Changan Xiong Wenchen Yang Weiliang Zeng National Engineering Research Center of Geological Disaster Prevention in Land Transportation Broadvision Engineering Consultants Co. Ltd. Kunming China Yunnan Key Laboratory of Digital Communications Kunming China School of Automation Guangdong University of Technology Guangzhou China *** Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China meb.gov.tr Guangdong Provincial Key Laboratory of Intelligent Transportation System Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou China

Studies on accident severity on mountainous freeways have predominantly centered on the personal injury level, rather than the aggregation level. However, for quantifying the accident causality, clustering the accident severity from multidimensional perspectives based on data-driven approach is seldom investigated in existing studies. To address this research gap, we propose a two-stage methodology that integrates accident clustering with Bayesian inference. Initially, a Gaussian mixture clustering algorithm is developed to categorize accident severity. Subsequently, a Bayesian network is constructed to explore the risk factors associated with accident severity. The proposed model is calibrated and validated using accident data collected from mountainous freeways in Yunnan Province, China, spanning the period from 2016 to 2021. The findings suggest that our proposed accident clustering method exhibits superior robustness compared to alternative clustering techniques. Bayesian inference analysis further elucidates that accident severity is significantly influenced by factors such as driving behavior, weather conditions, and road surface conditions.

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