检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propose a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse mechanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Recovery

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MetaSleepLearner: A pilot study on fast adaptation of bio-signals-based sleep stage classifier to new individual subject using Meta-Learning

arXiv

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arXiv 2020年

作者： Banluesombatkul, Nannapas Ouppaphan, Pichayoot Leelaarporn, Pitshaporn Lakhan, Payongkit Chaitusaney, Busarakum Jaimchariyatam, Nattapong Chuangsuwanich, Ekapol Chen, Wei Phan, Huy Dilokthanakul, Nat Wilaiprasitporn, Theerawit Bio-inspired Robotics and Neural Engineering Lab School of Information Science and Technology Vidyasirimedhi Institute of Science & Technology Rayong Thailand Department of Otolaryngology Faculty of Medicine Chulalongkorn University Thailand Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand Division of Pulmonary and Critical Care Medicine Faculty of Medicine Chulalongkorn University Thailand Head of Excellence Center for Sleep Disorders King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok Thailand The Computer Engineering Department Chulalongkorn University Bangkok Thailand The Center for Intelligent Medical Electronics Department of Electronic Engineering School of Information Science and Technology Fudan University Shanghai200433 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system due to the differences in many aspects found in individual bio-signals, causing the inconsistency in the performance of the model on every incoming individual. Thus, we aim to explore the feasibility of using a novel approach, capable of assisting the clinicians and lessening the workload. We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects (source code is available at https://***/IoBTVISTEC/MetaSleepLearner). The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system. Layer-wise Relevance Propagation (LRP) was also applied to understand the learning course of our approach. In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4% to 17.7% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. MetaSleepLearner outperformed the conventional approaches as a result from the fine-tuning using the recordings of both healthy subjects and patients. This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording. © 2020, CC BY-N

关键词： Convolutional neural networks

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Stealthy Adversaries against Uncertain Cyber-Physical Systems: Threat of Robust Zero-Dynamics Attack

Stealthy Adversaries against Uncertain Cyber-Physical System...

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作者： Park, Gyunghoon Lee, Chanhwa Shim, Hyungbo Eun, Yongsoon Johansson, Karl H. Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul136-791 Korea Republic of Research and Development Division Hyundai Motor Company Gyeonggi-do18280 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Information and Communication Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu47988 Korea Republic of ACCESS Linnaeus Centre School of Electrical Engineering KTH Royal Institute of Technology Stockholm144 28 Sweden

In this paper, we address the problem of constructing a robust stealthy attack that compromises uncertain cyber-physical systems having unstable zeros. We first interpret the (non-robust) conventional zero-dynamics attack based on Byrnes-Isidori normal form, and then present a new robust zero-dynamics attack for uncertain plants. Different from the conventional strategy, our key idea is to isolate the real zero-dynamics from the plant's input-output relation and to replace it with an auxiliary nominal zero-dynamics. As a result, this alternative attack does not require the exact model knowledge anymore. The price to pay for the robustness is to utilize the input and output signals of the system (i.e., disclosure resources). It is shown that a disturbance observer can be employed to realize the new attack philosophy when there is a lack of model knowledge. Simulation results with a hydro-turbine power system are presented to verify the attack performance and robustness. © 1963-2012 IEEE.

关键词： Cyber Physical System

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Shared Secret Key Generation via Carrier Frequency Offsets

Shared Secret Key Generation via Carrier Frequency Offsets

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IEEE Vehicular technology Conference

作者： Waqas Aman Aneeqa Ijaz M. Mahboob Ur Rahman Dushanta Nalin K. Jayakody Haris Pervaiz Electrical Engineering department Information Technology University (ITU) Lahore Pakistan School of Computer Science & Robotics National Research Tomsk Polytechnic University Russia School of Computing and Communications Lancaster University UK

This work presents a novel method to generate secret keys shared between a legitimate node pair (Alice and Bob) to safeguard the communication between them from an unauthorized node (Eve). To this end, we exploit the reciprocal carrier frequency offset (CFO) between the legitimate node pair to extract common randomness out of it to generate shared secret keys. The proposed key generation algorithm involves standard steps: the legitimate nodes exchange binary phase-shift keying (BPSK) signals to perform blind CFO estimation on the received signals, and do equi-probable quantization of the noisy CFO estimates followed by information reconciliation-to distil a shared secret key. Furthermore, guided by the Allan deviation curve, we distinguish between the two frequency-stability regimes - when the randomly time-varying CFO process i) has memory, ii) is memoryless;thereafter, we compute the key generation rate for both regimes. Simulation results show that the key disagreement rate decreases exponentially with increase in the signal to noise ratio of the link between Alice and Bob. Additionally, the decipher probability of Eve decreases as soon as either of the two links observed by the Eve becomes more degraded compared to the link between Alice and Bob.

关键词： Oscillators Entropy Stability analysis Binary phase shift keying Noise measurement Wireless communication Frequency measurement

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Multi-site, Multi-domain Airway Tree Modeling (ATM’22): A Public Benchmark for Pulmonary Airway Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Minghui Wu, Yangqian Zhang, Hanxiao Qin, Yulei Zheng, Hao Tang, Wen Arnold, Corey Pei, Chenhao Yu, Pengxin Nan, Yang Yang, Guang Walsh, Simon Marshall, Dominic C. Komorowski, Matthieu Wang, Puyang Guo, Dazhou Jin, Dakai Wu, Ya'nan Zhao, Shuiqing Chang, Runsheng Zhang, Boyu Lv, Xing Qayyum, Abdul Mazher, Moona Su, Qi Wu, Yonghuang Liu, Ying'ao Zhu, Yufei Yang, Jiancheng Pakzad, Ashkan Rangelov, Bojidar Estepar, Raul San Jose Espinosa, Carlos Cano Sun, Jiayuan Yang, Guang-Zhong Gu, Yun Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Department of Respiratory and Critical Care Medicine Department of Respiratory Endoscopy Shanghai Chest Hospital Shanghai China InferVision Medical Technology Co. Ltd. Beijing China Imperial College London London United Kingdom Alibaba DAMO Academy 969 West Wen Yi Road Zhejiang Hangzhou China Alibaba DAMO Academy USA 860 Washington Street 8F New York United States College of Medicine and Biological Information Engineering Northeastern University Shenyang China A.I R&D Center Sanmed Biotech Inc. No. 266 Tongchang Road Xiangzhou District Guangdong Zhuhai China A.I R&D Center Sanmed Biotech Inc. T220 Trade st. San DiegoCA United States ENIB UMR CNRS 6285 LabSTICC Brest29238 France Shanghai Jiao Tong University Shanghai China School of Information Science and Technology Fudan University Shanghai China University of Science and Technology of China Anhui Hefei China Dianei Technology Shanghai China EPFL Lausanne Switzerland Department of Surgery and Cancer Imperial College London London United Kingdom University of California Los AngelesCA United States Department of Computer Engineering and Mathematics University Rovira I Virgili Tarragona Spain Brigham and Women’s Hospital Harvard Medical School SomervilleMA02145 United States Medical Physics and Biomedical Engineering Department University College London London United Kingdom Center for Medical Image Computing University College London London United Kingdom

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT’09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM’22), which was held as an official challenge event during the MICCAI 2022 conference. ATM’22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM’22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://***/). Copyright © 2023, The Authors. All rights reserved.

关键词： computerized tomography

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Target localization intervention and prognosis evaluation for an individual with mild cognitive impairment

Brain‐X

引用

Brain‐X 2023年第3期1卷

作者： Weiping Wang Haiyan Zhao Chang He Yuanbo Cui Zhen Wang Alexander Hramov Ping Luan Xiong Luo Jipeng Ouyang Kurths Jürgen School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Graduate School University of Science and Technology Beijing Foshan Guangdong China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Contribution: Writing - review & editing Department of Neurology Peking University Third Hospital Beijing China Contribution: Resources Zhen Wang The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi 710072 China. Contribution: Writing - original draft The 6th Research Institute of China Electronics Corporation Beijing China The Center for Optical Imagery Analysis and Learning and School of Mechanical Engineering Northwestern Polytechnical University Xi'an Shaanxi China Chang He School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083 China. Contribution: Investigation Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia Contribution: Data curation Guangdong Second Provincial General Hospital & Health Science Center Shenzhen Guangdong China Department of Neurology Shunde Hospital Southern Medical University Foshan Guangdong China Institute of Physics Humboldt-University Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany

Currently, no specific treatments are available for Alzheimer's disease (AD). Mild cognitive impairment (MCI), the preclinical stage of AD, has a high possibility of reversing symptoms through neural regulation. A state dynamics model for single brain regions was developed to simulate blood oxygen level-dependent signals in a patient with early mild cognitive impairment. Subsequently, the analysis of functional connections was used to comprehensively consider multiple complex network centralities to locate the intervention targets, and a multiple brain region collaborative control scheme was designed. Finally, the reliability and effectiveness of the intervention were verified at the brain region and subnetwork levels. This technique provides a basis for future clinical diagnosis and treatment of AD and MCI.

关键词： adaptive pinning control Alzheimer's disease BOLD signals computational modeling Sub_network

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A Multi-Domain Feature Learning Method for Visual Place Recognition

arXiv

引用

arXiv 2019年

作者： Yin, Peng Xu, Lingyun Li, Xueqian Yin, Chen Li, Yingli Srivatsan, Rangaprasad Arun Li, Lu Ji, Jianmin He, Yuqing State Key Laboratory of Robotics Shenyang Institute of Automation University of Chinese Academy of Sciences Chinese Academy of Sciences Shenyang Beijing China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui. China Biorobotics Lab Robotics Institute Carnegie Mellon University PittsburghPA15213 United States School of Computer Science University of Beijing University of Posts and Telecommunications Beijing. China

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability to determine whether a place has been visited and where specifically. A major challenge in VPR is to handle changes of environmental conditions including weather, season and illumination. Most VPR methods try to improve the place recognition performance by ignoring the environmental factors, leading to decreased accuracy decreases when environmental conditions change significantly, such as day versus night. To this end, we propose an end-to-end conditional visual place recognition method. Specifically, we introduce the multi-domain feature learning method (MDFL) to capture multiple attribute-descriptions for a given place, and then use a feature detaching module to separate the environmental condition-related features from those that are not. The only label required within this feature learning pipeline is the environmental condition. Evaluation of the proposed method is conducted on the multi-season NORDLAND dataset, and the multi-weather GTAV dataset. Experimental results show that our method improves the feature robustness against variant environmental conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

arXiv

引用

arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： computerized tomography

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Multiple-object Tracking based on Monocular Camera and 3-D Lidar Fusion for Autonomous Vehicles

Multiple-object Tracking based on Monocular Camera and 3-D L...

引用

IEEE International Conference on robotics and Biomimetics

作者： Hao Chen Chunyue Xue Shoubin Liu Yuxiang Sun Yongquan Chen Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Science and Engineering The Chinese University of Hong Kong Shenzhen China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology

This article describes a multi-object tracking method through sensor fusion with a monocular camera and a 3-D Lidar for autonomous vehicles. Specifically, several pairwise costs from information, such as locations, movements, and poses of 3-D cues, are designed for tracking. These costs can complement each other to reduce matching errors during the tracking process. Moreover, they are efficient to be on-line computed with embedded equipment. We feed the pairwise costs to the data-association framework, which is based on the Hungarian algorithm, and then do the back-end fusion for the tracking results. The experimental results on our autonomous sightseeing car demonstrate that our tracking method could achieve accurate and robust results in real-world traffic scenarios.

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Learning to generate unambiguous spatial referring expressions for real-world environments

arXiv

引用

arXiv 2019年

作者： Doğan, Fethiye Irmak Kalkan, Sinan Leite, Iolanda The Division of Robotics Perception and Learning The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden The KOVAN Research Lab The Department of Computer Engineering Middle East Technical University Ankara Turkey

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate (∼30% better) and would prefer to use (∼32% more often). © 2019, CC BY-NC-SA.

关键词： Human robot interaction

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