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A Real-Time Imaging Processing Method Based on Modified RMA with Sub-Aperture Images Fusion for Spaceborne Spotlight SAR

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A Real-Time Imaging Processing Method Based on Modified RMA ...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fang Zhou Jun Yang Guangcai Sun Jiajia Zhang School of Computer and Information Hefei University of Technology Hefei China National Key Lab of Radar Signal Processing Xidian University Xi'an China Key Laboratory of Aperture Array and Space Application East China Research Institute of Electronic Engineering Hefei China

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

The small satellite SAR has received increasing attention due to its flexibility and low cost. But limited by the data transmission technology, real-time transmission of a large amount of raw data generated by the spaceborne spotlight SAR can hardly be achieved. Meanwhile, the azimuth bandwidth of the spotlight mode is larger than the PRF, resulting in aliasing of the azimuth spectrum. Based on these, this paper proposes a real-time scheme for small satellite SAR with spotlight mode. The method can solve the problem of data transmission and eliminate spectrum overlap in Doppler domain by means of sub-aperture processing. The modified range migration algorithm (RMA) is used to perform range compression and range cell migration compensation (RCMC) on sub-aperture data. Then dechirp in the azimuth time domain is applied to obtain the low-resolution complex image focused in the range time-azimuth frequency domain. Finally, all theected onto a grid image with azimuth interval matching the azimuth full-resolution to complete image fusion.

关键词： Azimuth Spaceborne radar Real-time systems Synthetic aperture radar Doppler effect Imaging Bandwidth

Preflare very long-periodic pulsations observed in Hα emission before the onset of a solar flare

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arXiv 2020年

作者： Li, Dong Feng, Song Su, Wei Huang, Yu Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory CAS Nanjing210033 China State Key Laboratory of Space Weather Chinese Academy of Sciences Beijing100190 China CAS Key Laboratory of Solar Activity National Astronomical Observatories Beijing100101 China Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China MOE Key Laboratory of Fundamental Physical Quantities Measurements School of Physics Huazhong University of Science and Technology Wuhan430074 China

Context. Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-Ray (SXR) flux (see Tan et al. 2016). They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. Aims. In this letter, we report a preflare-VLP event prior to the onset of an M1.1 circular-ribbon flare on 2015 October 16. It was simultaneously observed in Hα, SXR, and extreme ultraviolet (EUV) wavelengths. Methods. The SXR fluxes in 1−8 Å and 1−70 Å were recorded by the Geostationary Operational Environmental Satellite (GOES) and Extreme Ultraviolet Variability Experiment (EVE), respectively;the light curves in Hα and EUV 211 Å were integrated over a small local region, which were measured by the 1m New Vacuum Solar Telescope (NVST) and the Atmospheric Imaging Assembly (AIA), respectively. The preflare-VLP is identified as the repeat and quasi-periodic pulses in light curves during preflare phase. The quasi-periodicity can be determined from the Fourier power spectrum with Markov chain Monte Carlo (MCMC)-based Bayesian (e.g., Liang et al. 2020). Results. Seven well-developed pulses are found before the onset of an M1.1 circular-ribbon flare. They are firstly seen in the local light curve in Hα emission and then discovered in full-disk SXR fluxes in GOES 1−8 Å and ESP 1−70 Å, as well as the local light curve in AIA 211 Å. These well-developed pulses can be regarded as the preflare-VLP, which might be modulated by LRC-circuit oscillation in the current-carrying plasma loop. The quasi-period is estimated to be ∼9.3 minutes. Conclusions. We present the first report of a preflare-VLP event in the local Hα line and EUV wavelength, which could be considered a precursor of a solar flare. This finding should therefore prove useful for the prediction of solar flares, especially for powerful flares. Copyright © 2020, The Authors. All rights reserved.

关键词： Gamma rays

Constructing accurate and efficient deep spiking neural networks with double-threshold and augmented schemes

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arXiv 2020年

作者： Yu, Qiang Ma, Chenxiang Song, Shiming Zhang, Gaoyan Dang, Jianwu Tan, Kay Chen Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advenced Institute of Science and Technology Japan The Department of Computer Science City University of Hong Kong Hong Kong

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges such as the high-power consumption encountered by artificial neural networks (ANNs), however there is still a gap between them with respect to the recognition accuracy on practical tasks. A conversion strategy was thus introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this paper, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets. The results show that the proposed double-threshold scheme can effectively improve accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast and efficient deep SNNs as compared to other state-of-the-art approaches. Our study therefore provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Transient Fault Diagnosis Algorithm based on Feature Classification and Its application in High-Power Laser facility

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Transient Fault Diagnosis Algorithm based on Feature Classif...

第32届中国控制与决策会议

作者： Qiang Ren Hang Gao Hailei Ren Baoran An Tianyou Yun Xiaoli Wang Institute of Computer Application China Academy of Engineering Physics State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System College of Electronic Science and TechnologyNational University of Defense Technology Institute of Fluid Physics China Academy of Engineering Physis

High-power laser facility plays an import role both in scientific research and military application,and the occurred faults are usually *** this paper,a transient fault diagnosis algorithm based on feature classification is proposed,and the algorithm has been applied in the high-power laser *** method extracts the features of input data with an autoencoder *** based on the extracted features,a multi-layer full connection neural network is adopted for fault *** comparing the results with those obtained by traditional fault diagnosis methods,the performance of the algorithm has been verified.

关键词： High-power laser facility transient fault diagnosis feature-extraction

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

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arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao Xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, Luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang Xu, Yan Xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading

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arXiv 2022年

作者： Wang, Yeqi Li, Longfei Li, Cheng Xi, Yan Zheng, Hairong Lin, Yusong Wang, Shanshan The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China The Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The School of Software Zhengzhou University Zhengzhou450002 China The Collaborative Innovation Center for Internet Healthcare The Hanwei IoT Institute Zhengzhou University Zhengzhou450002 China The Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing100864 China The Peng Cheng Laboratory Shenzhen518066 China The Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China

Radiomics and deep learning have shown high popularity in automatic glioma grading. Radiomics can extract hand-crafted features that quantitatively describe the expert knowledge of glioma grades, and deep learning is powerful in extracting a large number of high-throughput features that facilitate the final classification. However, the performance of existing methods can still be improved as their complementary strengths haven't been sufficiently investigated and integrated. Furthermore, lesion maps are usually needed for the final prediction at the testing phase, which is very troublesome. In this paper, we propose an expert knowledge-guided geometric representation learning (ENROL) framework. Geometric manifolds of hand-crafted features and learned features are constructed to mine the implicit relationship between deep learning and radiomics, and therefore to dig mutual consent and essential representation for the glioma grades. With a specially designed manifold discrepancy measurement, the grading model can exploit the input image data and expert knowledge more effectively in the training phase and get rid of the requirement of lesion segmentation maps at the testing phase. The proposed framework is flexible regarding deep learning architectures to be utilized. Three different architectures have been evaluated and five models have been compared, which show that our framework can always generate promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Grading

Outlier detection based on multiple information extraction

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Information Sciences 2025年 718卷

作者： Dayong Deng Tong Chen Zhixuan Deng Tianrui Li Pengfei Zhang Xingzhi College Zhejiang Normal University Lanxi 321100 China School of Computer Science and Technology (School of Artificial Intelligence) Zhejiang Normal University Jinhua 321004 China Zhejiang Province Intelligent Education Technology and Application Key Laboratory Zhejiang Normal University Jinhua 321004 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu 611137 China

Outlier detection is an important data analysis technique. Most existing outlier detection methods are based on the relationship between instances and neighborhoods, but neglect the connection between neighborhoods and the entire dataset, insufficiently extracting neighborhood information. To address these issues, multiple information is extracted from the connection between the whole dataset and neighborhoods, and then four algorithms relevant to outlier detection are proposed. Firstly, a novel kind of fuzzy neighborhood is proposed with rough set theory. Fuzzy similarity matrices on features are fused into a weighted fuzzy similarity matrix with fuzzy information entropy. An algorithm (WFS) is designed to calculate the weighted fuzzy similarity matrix. Secondly, neighborhood information and significance information are extracted to construct neighborhood-weighted entropy. An outlier detection algorithm (FS-NWOD) is proposed with the neighborhood-weighted entropy. Thirdly, neighborhood difference and ratio information are extracted to structure weighted fuzzy difference-ratio entropy. An outlier detection algorithm (FS-DROD) is proposed with the weighted fuzzy difference-ratio entropy. Fourthly, a novel outlier detection algorithm (FSOD) based on multiple information extraction is designed with the algorithms FS-NWOD and FS-DROD. Finally, experiments on 18 heterogeneous datasets show that the FSOD algorithm outperforms 12 state-of-the-art algorithms.

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A Convex and Global Solution for the PnP Problem in 2D Forward-Looking Sonar

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arXiv 2025年

作者： Su, Jiayi Qian, Jingyu Yang, Liuqing Yuan, Yufan Fu, Yanbing Wu, Jie Wei, Yan Qu, Fengzhong Guangzhou511458 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Ocean College Zhejiang University Zhoushan316021 China Provincial Key Laboratory of Cutting-edge Scientific Instruments R&D and Application Ocean College Zhejiang University Zhoushan316021 China Hainan Institute of Zhejiang University Sanya572025 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong

The perspective-n-point (PnP) problem is important for robotic pose estimation. It is well studied for optical cameras, but research is lacking for 2D forward-looking sonar (FLS) in underwater scenarios due to the vastly different imaging principles. In this paper, we demonstrate that, despite the nonlinearity inherent in sonar image formation, the PnP problem for 2D FLS can still be effectively addressed within a point-to-line (PtL) 3D registration paradigm through orthographic approximation. The registration is then resolved by a duality-based optimal solver, ensuring the global optimality. For coplanar cases, a null space analysis is conducted to retrieve the solutions from the dual formulation, enabling the methods to be applied to more general cases. Extensive simulations have been conducted to systematically evaluate the performance under different settings. Compared to non-reprojection-optimized state-of-the-art (SOTA) methods, the proposed approach achieves significantly higher precision. When both methods are optimized, ours demonstrates comparable or slightly superior precision. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

DCache: A Distributed Cache Mechanism for HDFS based on RDMA

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DCache: A Distributed Cache Mechanism for HDFS based on RDMA

IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xiao Zhang Binbin Liu Zian Gou Jia Shi Xiaonan Zhao School of Computer Science Northwestern Polytechnical University 127 Youyi xi Road Xi’an Shaanxi China Key Laboratory of Big Data Storage and Management of MIIT in NWPU National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology in NWPU

ISBN: (纸本)9781728176505

Hadoop Distributed File System (HDFS) is an important component of Hadoop, which provides data storage service. The performance of the IO subsystem has a great influence on data processing efficiency. In the Hadoop system, computing jobs are scheduled to the nodes where the data is located to reduce IO time. The job scheduler and data distribution have a great impact on the efficiency of data processing. HDFS provides a mechanism for users to specify files or directories to be cached. But the cached data is only accessible for the jobs running in the same data nodes. In this paper, we present a distributed cache mechanism for HDFS with RDMA-capable network. We separate the caching function from the original data nodes. We design and implement a new kind of component for the caching function. The cached data in these nodes can be accessed directly by any nodes in the cluster through Remote Direct Memory Access (RDMA). Compared with the existing caching mechanism, our approach improves the performance and stability of the IO process. We also accelerate the data writing process using our caching mechanism. The experiments show that compared with the existing caching mechanism of HDFS, our scheme improves the reading latency up to 3 times, improves the throughput by 30%~95% for large files, and the writing performance by 64%. When the task runs on different nodes, the IO fluctuation caused by different data distribution is dropped from 20.71 to 0.50.

关键词： Fluctuations File systems High performance computing Conferences Memory Writing Throughput