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arXiv 2021年

作者： Ye, Dongjie Ni, Zhangkai Wang, Hanli Zhang, Jian Wang, Shiqi Kwong, Sam The Department of Computer Science City University of Hong Kong 999077 Hong Kong The Department of Computer Science & Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China The Peng Cheng Laboratory Shenzhen518052 China The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China

Convolution neural networks (CNNs) have succeeded in compressive image sensing. However, due to the inductive bias of locality and weight sharing, the convolution operations demonstrate the intrinsic limitations in modeling the long-range dependency. Transformer, designed initially as a sequence-to-sequence model, excels at capturing global contexts due to the self-attention-based architectures even though it may be equipped with limited localization abilities. This paper proposes CSformer, a hybrid framework that integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. In the sampling module, images are measured block-by-block by the learned sampling matrix. In the reconstruction stage, the measurement is projected into dual stems. One is the CNN stem for modeling the neighborhood relationships by convolution, and the other is the transformer stem for adopting global self-attention mechanism. The dual branches structure is concurrent, and the local features and global representations are fused under different resolutions to maximize the complementary of features. Furthermore, we explore a progressive strategy and window-based transformer block to reduce the parameter and computational complexity. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing, which achieves superior performance compared to state-of-the-art methods on different datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Compressed sensing

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A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

arXiv

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arXiv 2022年

作者： Cong, Runming Qin, Qi Zhang, Chen Jiang, Qiuping Wang, Shiqi Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Information Science and Engineering Ningbo University Ningbo315211 China City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms of model framework, we decouple the task into label refinement sub-task and salient object detection sub-task, which cooperate with each other and train alternately. Specifically, the R-Net is designed as a two-stream encoder-decoder model equipped with Blender with Guidance and Aggregation Mechanisms (BGA), aiming to rectify the coarse labels for more reliable pseudo-labels, while the S-Net is a replaceable SOD network supervised by the pseudo labels generated by the current R-Net. Note that, we only need to use the trained S-Net for testing. Moreover, in order to guarantee the effectiveness and efficiency of network training, we design three training strategies, including alternate iteration mechanism, group-wise incremental mechanism, and credibility verification mechanism. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods both qualitatively and quantitatively. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Benchmarking

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Industrial Scene Text Detection with Refined Feature-attentive network

arXiv

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arXiv 2021年

作者： Guan, Tongkun Gu, Chaochen Lu, Changsheng Tu, Jingzheng Feng, Qi Wu, Kaijie Guan, Xinping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China The College of Engineering and Computer Science The Australian National University CanberraACT2600 Australia

Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded surfaces, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods could not locate low-contrast text areas very well. In this paper, we propose a refined feature-attentive network (RFN) to solve the inaccurate localization problem. Specifically, we first design a parallel feature integration mechanism to construct an adaptive feature representation from multi-resolution features, which enhances the perception of multi-scale texts at each scale-specific level to generate a high-quality attention map. Then, an attentive proposal refinement module is developed by the attention map to rectify the location deviation of candidate boxes. Besides, a re-scoring mechanism is designed to select text boxes with the best rectified location. To promote the research towards industrial scene text detection, we contribute two industrial scene text datasets, including a total of 102156 images and 1948809 text instances with various character structures and metal parts. Extensive experiments on our dataset and four public datasets demonstrate that our proposed method achieves the state-of-the-art performance. Both code and dataset are available at: https://***/TongkunGuan/RFN. © 2021, CC BY.

关键词： Character recognition

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One-to-Many Semantic Communication systems: Design, Implementation, Performance Evaluation

arXiv

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arXiv 2022年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics 210000 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications 210003 China The Department of Electrical and Computer Engineering Utah State University LoganUT United States

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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Misalignment Tolerance of Inductive Power Transfer Coupler with Low Loss and High Magnetic Induction Ferromagnetic Materials

Misalignment Tolerance of Inductive Power Transfer Coupler w...

引用

Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Jinping Kang Shaoyu Cheng Yubo Wang Xueying Zhang Fuyao Yang Guorui Xu Hassan H. Eldeeb Haisen Zhao State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University (NCEPU) Beijing China State Key Laboratory of Advanced Power Transmission Technology State Grid Smart Grid research Institute Co. LTD. China Noblesville Technical Center of BorgWarner Inc. Noblesville IN USA Department of Electrical and Computer Engineering Florida International University Miami FL USA

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478168

In an inductive power transfer (IPT) system, various magnetic materials have difference characteristics in enhancing the degree of coupling and misalignment tolerance between the primary and secondary coils. However, the iron losses in ferromagnetic materials may decrease the transfer efficiency of the IPT system significantly. Therefore, in this study, performance and misalignment tolerance of IPT systems with four types of ferromagnetic materials are investigated. Firstly, an equivalent circuit model of the IPT system is established and the variations of system efficiency with different ferromagnetic materials are also revealed. Then, a 3-D finite element analysis (FEA) model of the magnetic coupler is established. In addition, loss test platform is established to obtain the accurate loss coefficients. Finally, the transfer efficiency and magnetic field distributions are both investigated. The results show that the high efficiency and magnetic flux density can be achieved when nanocrystalline core with vertical laminations is used.

关键词： Solid modeling Magnetic cores Lamination Couplers Ferromagnetic materials Iron Finite element analysis

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Adaptive Dynamic Surface Control of Constrained Nonlinear systems

Adaptive Dynamic Surface Control of Constrained Nonlinear Sy...

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第32届中国控制与决策会议

作者： Ailiang Xing Biao Song Yixin Yin School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Demonstration and Research Department of Network Information System Beijing Institute of Computer Application

This paper presents an adaptive dynamic surface control for uncertain nonlinear strict feedback systems with constraints on both input and *** technology is employed in the controller *** performance method with asymmetric barrier Lyapunov function(BLF) is used to deal with the output *** input saturation is solved by using the Nussbaum *** dynamic surface control(DSC) technique is employed to reduce the complexity of the virtual *** is illustrated that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB).Finally,a simulation example proves the proposed results.

关键词： Input saturation Output constraints Nussbaum function Dynamic surface control

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Efficient 3D Deep LiDAR Odometry

arXiv

引用

arXiv 2021年

作者： Wang, Guangming Wu, Xinrui Jiang, Shuyang Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Artificial Intelligence of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper. In this architecture, the projection-aware representation of the 3D point cloud is proposed to organize the raw 3D point cloud into an ordered data form to achieve efficiency. The Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to estimate and refine the pose in a coarse-to-fine approach. A projection-aware attentive cost volume is built to directly associate two discrete point clouds and obtain embedding motion patterns. Then, a trainable embedding mask is proposed to weigh the local motion patterns to regress the overall pose and filter outlier points. The trainable pose warp-refinement module is iteratively used with embedding mask optimized hierarchically to make the pose estimation more robust for outliers. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask, and all operations involving point cloud sampling and grouping are accelerated by projection-aware 3D feature learning methods. The superior performance and effectiveness of our LiDAR odometry architecture are demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms all recent learning-based methods and even the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. We open sourced our codes at: https://***/IRMVLab/EfficientLO-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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Information Freshness in A Dual Monitoring system

Information Freshness in A Dual Monitoring System

引用

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Dapeng Deng Zhengchuan Chen Howard H. Yang Nikolaos Pappas Limei Hu Min Wang Yunjian Jia Tony Q.S. Quek School of Microelectronics and Communication Engineering Chongqing University Chongqing China National Mobile Communications Research Laboratory Southeast University Nanjing China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang University Haining China Department of Computer and Information Science Linkoping University Linkoping Sweden School of Optoelectronics Engineering Chongqing University of Posts and Telecommunications Chongqing China Information System Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (纸本)9781665435413

We study the average age of information (AoI) and peak AoI (PAoI) of a dual-queue status update system that monitors a common stochastic process. We capture the state transition characteristics of the considered system by establishing a Markov chain. Using the state flow graph analysis method, we derive closed-form expressions of the average peak age of information (PAoI) and the average age of information (AoI) for the dual-queue update system. The numerical results show that compared with the single-queue update system, the average PAoI of the dual-queue update system is reduced by 33.5% and the average AoI dropped by 37.5%.

关键词： Closed-form solutions Markov processes Reliability theory Information age Sensor systems Real-time systems Sensors

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SYNAPSE: An international roadmap to large brain imaging

引用

Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： Brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

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Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

arXiv

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arXiv 2024年

作者： Nguyen, Nghi Hou, Tao Amico, Enrico Zheng, Jingyi Huang, Huajun Kaplan, Alan D. Petri, Giovanni Goñi, Joaquín Zhao, Yize Duong-Tran, Duy Shen, Li Multidisciplinary Brain Research Center Bar-Ilan University Ramat Gan Israel Department of Computer Science University of Oregon EugeneOR United States Institute of Health and Neurodevelopment College of Health and Life Sciences Aston University Birmingham United Kingdom Department of Mathematics and Statistics Auburn University Alabama United States Computational Engineering Division Lawrence Livermore National Laboratory LivermoreCA United States NPLab Network Science Institute Northeastern University London London United Kingdom School of Industrial Engineering Purdue University West LafayetteIN United States School of Biomedical Engineering Purdue University W. LafayetteIN United States School of Public Health Yale University New HeavenCT United States Department of Mathematics U.S. Naval Academy AnnapolisMD United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities’ insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://***/ngcaonghi/scaffold_noise. © 2024, CC BY.

关键词： Brain mapping

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