检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

来源：评论

学校读者我要写书评

暂无评论

UNSUPERVISED SEMANTIC SEGMENTATION WITH SELF-SUPERVISED OBJECT-CENTRIC REPRESENTATIONS

arXiv

引用

arXiv 2022年

作者： Zadaianchuk, Andrii Kleindessner, Matthaeus Zhu, Yi Locatello, Francesco Brox, Thomas Max-Planck Institute for Intelligent Systems Tübingen Germany Amazon Web Services United States Department of Computer Science ETH Zürich Switzerland University of Freiburg Germany

In this paper, we show that recent advances in self-supervised representation learning enable unsupervised object discovery and semantic segmentation with a performance that matches the state of the field on supervised semantic segmentation 10 years ago. We propose a methodology based on unsupervised saliency masks and self-supervised feature clustering to kickstart object discovery followed by training a semantic segmentation network on pseudo-labels to bootstrap the system on images with multiple objects. We show that while being conceptually simple our proposed baseline is surprisingly strong. We present results on PASCAL VOC that go far beyond the current state of the art (50.0 mIoU), and we report for the first time results on MS COCO for the whole set of 81 classes: our method discovers 34 categories with more than 20% IoU, while obtaining an average IoU of 19.6 for all 81 categories. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Robust Generalization Against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

Robust Generalization Against Photon-Limited Corruptions via...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhuo Huang Miaoxi Zhu Xiaobo Xia Li Shen Jun Yu Chen Gong Bo Han Bo Du Tongliang Liu Sydney AI Centre The University of Sydney National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University JD Explore Academy Department of Automation University of Science and Technology of China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Department of Computer Science Hong Kong Baptist University

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Securing Industrial Internet of Things Against Botnet Attacks Using Hybrid Deep Learning Approach

TechRxiv

引用

TechRxiv 2022年

作者： Hasan, Tooba Malik, Jahanzaib Bibi, Iram Khan, Wali Ullah Al-Wesabi, Fahd N. Dev, Kapal Huang, Gaojian The Department of Computer Science COMSATS University Islamabad Pakistan The SnT University of Luxembourg Luxembourg The Department of Computer Science College of Science Art at Mahayil King Khalid University Saudi Arabia The Department of information Systems Faculty of Computer and Information Technology Sana’a University Sana’a Yemen The Department of Institute of Intelligent Systems University of Johannesburg South Africa The Physics and Electronic Information Engineering Henan Polytechnic University China

Industrial Internet of Things (IIoT) formation of richer ecosystem of intelligent interconnected devices while enabling new levels of digital innovation has essentially transformed and revolutionized global manufacturing and industry 4.0. Conversely, the prevalent distributed nature of IIoT, Industrial 5G, underlying IoT sensing devices, IT/OT convergence, Edge Computing, and Time Sensitive Networking makes it an impressive and potential target for cyber-attackers. Multi-variant persistent and sophisticated bot attacks are considered catastrophic for connects IIoTs. Besides, botnet attack detection is extremely complex and decisive. Thus, efficient and timely detection of IIoT botnets is a dire need of the day. We propose a hybrid intelligent Deep Learning (DL)-enabled mechanism to secure IIoT infrastructure from lethal and sophisticated multi-variant botnet attacks. The proposed mechanism has been rigorously evaluated with latest available dataset, standard and extended performance evaluation metrics, and current DL benchmark algorithms. Besides, cross validation of our results are also performed to clearly show overall performance. The proposed mechanisms outperforms in identifying accurately multi-variant sophisticated bot attacks by achieving 99.94% detection rate. Besides, our proposed technique attains 0.066(ms) time that also shows the promising results in terms of speed efficiency. © 2022, CC BY.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Rfid-based warehouse management system prototyping using a heterogeneous team of robots 23rd

Rfid-based warehouse management system prototyping using a h...

引用

23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020

作者： Khazetdinov, Artur Aleksandrov, Andrey Zakiev, Aufar Magid, Evgeni Hsia, Kuo-Hsien Laboratory of Intelligent Robotic Systems Intelligent Robotics Department Higher Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlevskya street Kazan420008 Russia Department of Electrical Engineering National Yunlin University of Science and Technology Tainan City74448 Taiwan

ISBN: (纸本)9781916449046

Robotic warehouse management is a promising field of research with a high practical applicability. Typically, warehouses are strictly organized and controlled environments with a high potential for rapid robot deployments. To provide system robustness every robotic deployment should be tested in advance using simulations. Such virtual testing environment should correspond well with a real world warehouse environment and be consistent with international and national standards. In this paper, we present a warehouse Gazebo simulation that was constructed in accordance with Government Standards of Russian Federation and international standards. We implemented a simplified Radio Frequency IDentification (RFID) transmitter-receiver mechanism within the Gazebo simulator and tested it in a virtual warehouse environment using a heterogeneous team of TIAGo Base mobile robot and PX4-based UAV. Our constructed virtual warehouse environment and RFID mechanism models are open-source for academic community use. © CLAWAR Association Ltd.

关键词： Robots

来源：评论

学校读者我要写书评

暂无评论

Triboelectric Nanogenerators and Hybridized systems for Enabling Next-Generation IoT Applications

引用

Research 2021年第1期2021卷 392-421页

作者： Qiongfeng Shi Zhongda Sun Zixuan Zhang Chengkuo Lee Department of Electrical&Computer Engineering National University of Singapore4 Engineering Drive 3SingaporeSingapore 117583 Center for Intelligent Sensors and MEMS National University of Singapore4 Engineering Drive 3SingaporeSingapore 117583 Smart Systems Institute National University of Singapore3 Research LinkSingaporeSingapore 117602 NUS Graduate School for Integrative Science and Engineering National University of SingaporeSingaporeSingapore 117456

In the past few years,triboelectric nanogenerator-based(TENG-based)hybrid generators and systems have experienced a widespread and flourishing development,ranging among almost every aspect of our lives,e.g.,from industry to consumer,outdoor to indoor,and wearable to implantable *** TENG technology has been extensively investigated for mechanical energy harvesting,most developed TENGs still have limitations of small output current,unstable power generation,and low energy utilization rate of multisource *** harvest the ubiquitous/coexisted energy forms including mechanical,thermal,and solar energy simultaneously,a promising direction is to integrate TENG with other transducing mechanisms,e.g.,electromagnetic generator,piezoelectric nanogenerator,pyroelectric nanogenerator,thermoelectric generator,and solar cell,forming the hybrid generator for synergetic single-source and multisource energy *** resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations,toward achieving a maximum,reliable,and stable output ***,in this review,we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems,in the aspects of operation principles,structure designs,optimization strategies,power management,and system *** recent progress of TENG-based hybrid generators and hybridized systems for the outdoor,indoor,wearable,and implantable applications is also ***,we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring,human activity sensation,human-machine interaction,smart home,healthcare,wearables,implants,robotics,Internet of things(IoT),and many other fields.

关键词： IoT generator harvesting

来源：评论

学校读者我要写书评

暂无评论

Wavelength-Division-Multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-Division-Multiplexed Transmission and Dispersion ...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Yuanbin Liu Hongyi Zhang Jiacheng Liu Liangjun Lu Jiangbing Du Yu Li Zuyuan He Jianping Chen Linjie Zhou Andrew W. Poon Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu China Department of Electronic and Computer Engineering Photonic Device Laboratory The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s $\times 9$ on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and... 详细信息

ISBN: (纸本)9798350369311

关键词： Solitons Lasers and electrooptics Wavelength division multiplexing Data communication Quantum cascade lasers Electro-optical waveguides Optical fiber dispersion Fiber lasers

来源：评论

学校读者我要写书评

暂无评论

Graph Confidence Intercalibration Network for Intracranial Aneurysm Lesion Instance Segmentation in Dsa

SSRN

引用

SSRN 2024年

作者： Ye, Haili Mo, Yancheng Tang, Chen Liao, Mingqian Zhang, Xiaoqing Dai, Limeng Li, Baihua Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Ophthalmology and Optometry Eye Hospital Wenzhou Medical University Wenzhou325027 China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen People’s Hospital Neurosurgery Department Shenzhen518020 China China

Intracranial aneurysm (IA) lesion segmentation is significant for its treatment and prognosis. Although exiting deep network-based instance methods have good IA lesion segmentation results based on digital subtraction angiography (DSA) images, they still face great challenges with instance confidence bias and imprecise boundary segmentation, which may negatively affect IA diagnosis. To tackle these problems, this paper proposes a novel graph confidence intercalibration network (GCINet) to automatically segment IA lesions from DSA images. To be specific, we design a graph confidence intercalibration (GCI) module to mitigate instance confidence bias by dynamically adjusting their confidence distributions. At the same time, we propose an edge space perception (ESP) module to correct ambiguous segmentation boundaries. Extensive experiments on a clinical IA-DSA and a publicly available LiTS dataset demonstrate that our GCINet outperforms state-of-the-art methods. Additionally, visual analysis and ablation studies are provided to verify the effectiveness of each module in GCINet. © 2024, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Medical Image Registration and Its Application in Retinal Images: A Review

arXiv

引用

arXiv 2024年

作者： Nie, Qiushi Zhang, Xiaoqing Hu, Yan Gong, Mingdao Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore State Key Laboratory of Ophthalmology Optometry and Visual Science Eye Hospital Wenzhou Medical University Wenzhou325027 China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse information of images, which may be captured under different times, angles, or modalities. Although several surveys have reviewed the development of medical image registration, these surveys have not systematically summarized methodologies of existing medical image registration methods. To this end, we provide a comprehensive review of these methods from traditional and deep learning-based directions, aiming to help audiences understand the development of medical image registration quickly. In particular, we review recent advances in retinal image registration at the end of each section, which has not attracted much attention. Additionally, we also discuss the current challenges of retinal image registration and provide insights and prospects for future research. © 2024, CC BY-NC-SA.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A framework to generate hypergraphs with community structure

arXiv

引用

arXiv 2022年

作者： Ruggeri, Nicolò Battiston, Federico De Bacco, Caterina Max Planck Institute for Intelligent Systems Cyber Valley Tübingen72076 Germany Department of Computer Science ETH Zürich8004 Switzerland Department of Network and Data Science Central European University Vienna1100 Austria

In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems. © 2022, CC BY-NC-SA.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：