检索结果-内蒙古大学图书馆

International Test Conference in Asia (ITC-Asia)

作者： Xiaoze Lin Liyang Lai Huawei Li The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Electrical Engineering Shantou University Shantou Guangdong China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9781665413350

Static learning is a learning algorithm for finding additional implicit implications between gates in a netlist. In automatic test pattern generation (ATPG) the learned implications help recognize conflicts and redundancies early, and thus greatly improve the performance of ATPG. Though ATPG can further benefit from multiple runs of incremental or dynamic learning, it is only feasible when the learning process is fast enough. In the paper, we study speeding up static learning through parallelization on heterogeneous computing platform, which includes multi-core microprocessors (CPUs), and graphics processing units (GPUs). We discuss the advantages and limitations in each of these architectures. With their specific features in mind, we propose two different parallelization strategies that are tailored to multi-core CPUs and GPUs. Speedup and performance scalability of the two proposed parallel algorithms are analyzed. As far as we know, this is the first time that parallel static learning is studied in the literature.

关键词： Scalability Microprocessors Redundancy Graphics processing units Computer architecture Automatic test pattern generation Heterogeneous networks

来源：评论

学校读者我要写书评

暂无评论

On the Performance Trade-off of Distributed Integrated Sensing and Communication Networks

arXiv

引用

arXiv 2023年

作者： Li, Xuran Guo, Shuaishuai Li, Tuo Zou, Xiaofeng Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing School of Physics and Electronics Shandong Normal University Jinan250061 China School of Control Science and Engineering Shandong University Jinan250061 China Shandong Provincial Key Laboratory of Wireless Communication Technologies China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. China

In this letter, we analyze the performance tradeoff in distributed integrated sensing and communication (ISAC) networks. Specifically, with the aid of stochastic geometry theory, we derive the probability of detection of that of the coverage given user number. Based on the analytical derivations, we provide a quantitative description of the performance limits and the performance trade-off between sensing and communication in a distributed ISAC network under the given transmit power and bandwidth budget. Extensive simulations are conducted and the numerical results validate the accuracy of our derivations. Copyright © 2023, The Authors. All rights reserved.

关键词： Economic and social effects

来源：评论

学校读者我要写书评

暂无评论

Advancing Cross-Subject Domain Generalization in Brain–Computer Interfaces With Multiadversarial Strategies

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Liu, Yici Qin, Lang Chen, Xin Le Bouquin Jeannes, Regine Louis Coatrieux, Jean Shu, Huazhong Southeast University LIST Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Ministry of Education Nanjing210096 China INSERM RennesF-3502 France The First Affiliated Hospital With Nanjing Medical University Department of Radiology Nanjing210029 China Laboratoire Traitement du Signal et de l’Image Universit de Rennes 1 Rennes35000 France Centre de Recherche en Information Biomedicale Sino-Francais Rennes35042 France National Institute for Health and Medical Research Rennes35000 France

A cross-subject domain generalization (DG) approach with multiadversarial strategies (DGMA) is introduced to reduce brain-computer interfaces (BCIs) systems' dependency on high-quality, subject-specific electroencephalographic (EEG) data, making it adaptable to unseen domains. DGMA leverages annotated training data from other subjects and consists of three modules: 1) prefeature extraction (PFE), enhancing EEG signal separability through preprocessing, data augmentation, and tangent space mapping;2) distribution feature updater (DFU), aligning intersubject feature distributions with marginal maximum mean discrepancy (MMD);and 3) multiadversarial training (MAT), initially using gradient reversal layer (GRL) to amplify domain differences and classification loss, allowing the model to learn diverse domain-specific features before minimizing these differences to balance domain transferability and discriminability. DGMA is capable of better capturing domain-specific features while achieving stronger generalization compared with traditional methods focused solely on minimizing domain differences. Validated on four motor imagery datasets, DGMA achieved state-of-the-art accuracies of 76.1% on BCI Competition IV 2a and 72.4% on the 002-2014 dataset. Additional tests on a private fatigue dataset and the SEED dataset yielded accuracies of 99.5% and 86.6%, respectively. © 1963-2012 IEEE.

关键词： signal reconstruction

来源：评论

学校读者我要写书评

暂无评论

Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

引用

Computers, Materials & Continua 2021年第1期66卷 499-515页

作者： Lejun Zhang Minghui Peng Weizheng Wang Yansen Su Shuna Cui Seokhoon Kim College of Information Engineering Yangzhou UniversityYangzhou225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou362000China Division of Computer Science University of AizuAizu–Wakamatsu9658580Japan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Medical College of Yangzhou University Yangzhou225001China Department of Gynecology and Obstetrics Affiliated Hospital of Yangzhou UniversityYangzhouChina Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea

In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical *** traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data *** storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low *** address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double *** our scheme,we encrypt the original EMR and store it in the *** storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the *** with different attributes can make requests to different blockchains to share different parts according to their own *** experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original *** Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original *** symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of *** analysis and performance analysis verified the feasibility and effectiveness of our scheme.

关键词： Cloud storage blockchain electronic medical records access control data sharing privacy

来源：评论

学校读者我要写书评

暂无评论

Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzheng Zeng Yang Xiao Sicheng Wei Jinfang Gan Xintao Zhang Zhiguo Cao Zhiwen Fang Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Passively Mode-Locked Er-Doped Fiber Laser with Single and Double Wavelength Pulses Based on Germanene Saturable Absorber

SSRN

引用

SSRN 2023年

作者： Ding, Jiashuai Liu, Xueyao Li, Tuo Zou, Xiaofeng Yu, Yanfeng Xu, Nannan Sahng, Xinxin Lu, Hua Cheng, Chen Huang, Pu Zhang, Huanian Li, Dengwang Shandong Key Laboratory of Medical Physics and Image Processing Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine School of Physics and Electronics Shandong Normal University Shandong Jinan250358 China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Jinan China School of Physics and Optoelectronic Engineering Shandong University of Technology Shandong Zibo255049 China

In this paper, we focus on the single crystal material germanium (Ge), which is fabricated into saturable absorbers (SA) employing liquid phase exfoliation, and validate it with an erbium-doped fiber laser (EDFL). The Ge SA was obtained with a modulation depth of 9.8 % and a saturation intensity of 11.02 MW/cm2. The single wavelength mode-locked pulse with a minimum pulse width of 847 fs was obtained at a cavity length of 10.5 m. In addition, at a cavity length of 106 m, a dual wavelength mode-locked phenomenon was obtained in which the central wavelengths were located at 1559.20 nm and 1561.31 nm. The experimental results show that Ge nanosheets in an EDFL provide a strong basis for the development of nonlinear optics and have a wide range of applications in the field of pulsed fiber lasers. © 2023, The Authors. All rights reserved.

关键词： Saturable absorbers

来源：评论

学校读者我要写书评

暂无评论

Detection of White Matter Hyperintensities in Magnetic Resonance Imaging by Hyperspectral Subpixel Detection

引用

IEEE Access 2024年 12卷 111992-112007页

作者： Yung-Chieh Chang Chein-I Chang Yen-Chieh Ouyang Jyh-Wen Chai Wen-Hsien Chen Kuan-Jung Pan Hsin-Che Wang Clayton Chi-Chang Chen Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Sciences Central Taiwan University of Science and Technology Taichung Taiwan Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore County Baltimore MD USA Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Department of Industrial Engineering and Enterprise Information Tunghai University Taichung Taiwan Department of Medical Research Center for Quantitative Imaging in Medicine (CQUIM) Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging China Medical University Hospital Taichung Taiwan

White matter hyperintensities (WMHs) are lesion in brain magnetic resonance images generally associated with Alzheimer’s disease (AD) and cognitive decline. Finding WMHs of AD poses a great challenge for diagnosis. This paper interprets a brain MR image as a hyperspectral image so that a well stablished hyperspectral subpixel detection algorithm, constrained energy minimization (CEM), is applicable to solving the WMHs detection problem at mixed pixel and subpixel level. To resolve nonlinear mixing in detecting WMHs nearby boundaries, a nonlinear CEM, called kernel CEM (KCEM) is also developed. Since CEM is a hyperspectral technique without taking spatial correlation into account, CEM was also extended to iterative CEM (ICEM) by including spatial filters to capture spatial information for WMHs detection. This paper combines ICEM and KCEM to derive a new WHMs detection algorithm, iterative KCEM (IKCEM) to improve ICEM and KCEM on WMHs detection. To evaluate the WMHs detection performance, two criteria, Dice similarity index (DSI) and 3D ROC analysis are used as evaluation tools. In order to show the superiority of IKCEM, two commonly used software packages, statistical parametric mapping (SPM)-based algorithms, SPM-lesion growth algorithm (SPM-LGA) and SPM-lesion prediction algorithm (SPM-LPA) are implemented for validation and comparison.

关键词： Hyperspectral imaging Magnetic resonance imaging Iterative methods medical diagnostic imaging White matter Manuals Energy management Neuroimaging Brain modeling Alzheimer's disease Energy measurement

来源：评论

学校读者我要写书评

暂无评论

Restoration-enhanced reversible information steganography network for CT images in the Internet of medical Things

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Kai Chen, Yang Coatrieux, Gouenou Wu, Jiasong Coatrieux, Jean-Louis School of Cyber Science and Engineering Southeast University Nanjing210096 China Southeast University Nanjing210096 China IMT Atlantique Inserm LaTIM UMR1101 Brest29000 France Laboratoire Traitement du Signal et de L'Image Université de Rennes 1 RennesF-35000 France Centre de Recherche en Information Biomédicale Sino-francais Rennes35042 France National Institute for Health and Medical Research RennesF-35000 France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Ministry of Education Nanjing210096 China Ministry of Education Nanjing210096 China

Smart health and emotional care powered by the Internet of medical Things (IoMT) are revolutionizing the healthcare industry by adopting several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The security of medical images containing private patient information distributed in IoMT is of paramount importance in social communication, and guaranteeing their reliability has attracted widespread attention. Some existing medical image steganography methods fail to guarantee the original image's integrity, authenticity, and lossless restoration. To solve this problem, a restoration-enhanced reversible adaptive information steganography network, MediSR-Net, is proposed. Specifically, MediSR-Net consists of an analysis module and an encoding module. The image degradation-restoration strategy is exploited by the analysis module to improve the effectiveness of information embedding and perceptual field to reduce the error of pixel prediction. An information distillation and attention machine-based approach are adopted to achieve degradation-restoration of CT medical images at any scale and focus more on the degradation-restoration effect with small magnification factors. The encoding module is responsible for the embedding and extraction of privacy information. Ten state-of-the-art are compared with the MediSR-Net method concerning steganographic effect, embedding rate, distortion analysis, and prediction error. The qualitative and quantitative results demonstrate the effectiveness and superiority of MediSR-Net. © 1963-2012 IEEE.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Deep recursive embedding for high-dimensional data

arXiv

引用

arXiv 2021年

作者： Zhou, Zixia Wang, Yuanyuan Lelieveldt, Boudewijn P.F. Tao, Qian The Department of Electronic Engineering Fudan University Shanghai200433 China The Department of Electronic Engineering Fudan University Shanghai200433 China Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai Shanghai200032 China The Division of Image Processing Department of Radiology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands The Division of Image Processing Department of Radiology Leiden University Medical Center Albinusdreef 2 Leiden2333 ZA Netherlands

t-distributed stochastic neighbor embedding (t-SNE) is a well-established visualization method for complex high-dimensional data. However, the original t-SNE method is nonparametric, stochastic, and often cannot well prevserve the global structure of data as it emphasizes local neighborhood. With t-SNE as a reference, we propose to combine the deep neural network (DNN) with the mathematical-grounded embedding rules for high-dimensional data embedding. We first introduce a deep embedding network (DEN) framework, which can learn a parametric mapping from high-dimensional space to low-dimensional embedding. DEN has a flexible architecture that can accommodate different input data (vector, image, or tensor) and loss functions. To improve the embedding performance, a recursive training strategy is proposed to make use of the latent representations extracted by DEN. Finally, we propose a two-stage loss function combining the advantages of two popular embedding methods, namely, t-SNE and uniform manifold approximation and projection (UMAP), for optimal visualization effect. We name the proposed method Deep Recursive Embedding (DRE), which optimizes DEN with a recursive training strategy and two-stage losse. Our experiments demonstrated the excellent performance of the proposed DRE method on high-dimensional data embedding, across a variety of public databases. Remarkably, our comparative results suggested that our proposed DRE could lead to improved global structure preservation. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Combination of global and nonlocal sparse regularization priors for MR image reconstruction

Combination of global and nonlocal sparse regularization pri...

引用

IEEE Region 10 International Conference TENCON

作者： Raji Susan Mathew Joseph Suresh Paul Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management Kerala

Magnetic resonance (MR) image recovery from the undersampled k-space data using fixed basis transforms such as wavelet utilizes the global sparsity of images. On the other hand, redundant representation schemes such as block-matching and 3D filtering (BM3D) make use of the the non-local sparsity of images, resulting in better preservation of finer details. But, use of any of these individual methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an algorithm for efficient combination of the global and non-local sparsity through the use of the fast composite splitting denoising (FCSD) algorithm. Here wavelets and BM3D are used for global and non-local sparsity, respectively. Additionally, we use adaptive iterative soft thresholding algorithm (ISTA) for wavelet recovery that uses an adaptive regularization parameter (threshold) in each iteration. The proposed adaptive fast composite splitting denoising (AFCSD) algorithm combines the merits of both BM3D with adaptive ISTA. This is because, it utilizes the self-repetitive structure in images by enforcing nonlocal sparsity as well as entire consistency of the image. The proposed method is superior to any of the individual methods in terms of reconstructed image quality, computation complexity and compute time. Furthermore, we show that the combined sparsity constraint can yield further improvements in reconstruction with significant reduction in the compute time when used as an auxiliary constraint in Dictionary Learning (DL), compared to a standard DL reconstruction.

关键词： image reconstruction Wavelet transforms signal processing algorithms Convergence Three-dimensional displays Noise reduction

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：