检索结果-内蒙古大学图书馆

Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts

Science China(Information Sciences) 2021年第1期64卷 5-78页

作者： Xiaohu YOU Cheng-Xiang WANG Jie HUANG Xiqi GAO Zaichen ZHANG Mao WANG Yongming HUANG Chuan ZHANG Yanxiang JIANG Jiaheng WANG Min ZHU Bin SHENG Dongming WANG Zhiwen PAN Pengcheng ZHU Yang YANG Zening LIU Ping ZHANG Xiaofeng TAO Shaoqian LI Zhi CHEN Xinying MA Chih-Lin I Shuangfeng HAN Ke LI Chengkang PAN Zhimin ZHENG Lajos HANZO Xuemin (Sherman) SHEN Yingjie Jay GUO Zhiguo DING Harald HAAS Wen TONG Peiying ZHU Ganghua YANG Jun WANG Erik G.LARSSON Hien Quoc NGO Wei HONG Haiming WANG Debin HOU Jixin CHEN Zhe CHEN Zhangcheng HAO Geoffrey Ye LI Rahim TAFAZOLLI Yue GAO H.Vincent POOR Gerhard P.FETTWEIS Ying-Chang LIANG National Mobile Communications Research Laboratory School of Information Science and EngineeringSoutheast University Purple Mountain Laboratories Shanghai Institute of Fog Computing Technology (SHIFT) ShanghaiTech University Research Center for Network Communication Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China (UESTC) China Mobile Research Institute School of Electronics and Computer Science University of Southampton Department of Electrical and Computer Engineering University of Waterloo Global Big Data Technologies Centre (GBDTC) University of Technology Sydney School of Electrical and Electronic Engineering The University of Manchester LiFi Research and Development Centre Institute for Digital CommunicationsSchool of EngineeringThe University of Edinburgh Huawei Technologies Canada Co. Ltd. Huawei Technologies Department of Electrical Engineering (ISY) Link?ping University Institute of Electronics Communications & Information Technology (ECIT)Queen's University Belfast State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast University School of Electrical and Computer Engineering Georgia Institute of Technology 5G Innovation Centre University of Surrey Princeton University Vodafone Chair Mobile Communications Systems Technische Universit?t Dresden Center for Intelligent Networking and Communications (CINC) University of Electronic Science and Technology of China (UESTC)

The fifth generation(5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation(6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle(UAV) communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave(mmWave), terahertz(THz),and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence(AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable f

关键词： 6G vision network architecture air interface and transmission technologies space-air-ground-sea integrated network all spectra artificial intelligence network security

来源：评论

学校读者我要写书评

暂无评论

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation

arXiv

引用

arXiv 2023年

作者： Li, Xiaofan Peng, Bo Hu, Jie Ma, Changyou Yang, Daipeng Xie, Zhuyang School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Data Recovery Key Laboratory of Sichuan Province Neijiang Normal University Sichuan Neijiang641100 China

Unsupervised skin lesion segmentation offers several benefits, such as conserving expert human resources, reducing discrepancies caused by subjective human labeling, and adapting to novel environments. However, segmenting dermoscopic images without manual labeling guidance is a challenging task due to image artifacts such as hair noise, blister noise, and subtle edge differences. In this paper, we introduce an innovative Uncertainty Self-Learning Network (USL-Net) to eliminate the need for manual labeling guidance for the segmentation. Initially, features are extracted using contrastive learning, followed by the generation of Class Activation Maps (CAMs) as saliency maps. High-saliency regions in the map serve as pseudo-labels for lesion regions while low-saliency regions represent the background. Besides, intermediate regions can be hard to classify, often due to their proximity to lesion edges or interference from hair or blisters. Rather than risking potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, they are taken as uncertainty regions by exempted from pseudo-labeling and allowing the network to self-learning. Further, we employ connectivity detection and centrality detection to refine foreground pseudo-labels and reduce noise-induced errors. The performance is further enhanced by the iterated refinement process. The experimental validation on ISIC-2017, ISIC-2018, and PH2 datasets demonstrates that its performance is comparable to supervised methods, and exceeds that of other existing unsupervised methods. On the typical ISIC-2017 dataset, our method outperforms state-of-the-art unsupervised methods by 1.7% in accuracy, 6.6% in Dice coefficient, 4.0% in Jaccard index, and 10.6% in sensitivity. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Hands Activities Detection in Egocentric Interactions Using YOLOv5

Hands Activities Detection in Egocentric Interactions Using ...

引用

UK/ China Emerging Technologies (UCET)

作者： Christyn Akosua Owusu-Agyei Jin Hou School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu Sichuan P.R. China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu Sichuan P.R. China School of Information Science and Technology Southwest Jiaotong University Chengdu Sichuan P.R. China

ISBN: (纸本)9781665495769

Hands emerge quite often in egocentric video, and their structure and disposition reveal a lot as to what individuals have been engaged and showing interest to. Earlier researches in hand detection, on the other hand, have depended on significant effect that only operate in limited situations, such as lab settings or with minimal social interaction. This work investigates the recently developed YOLO architectural for object recognition in egocentric video, which offers a good balance of precision and performance. We performed an experiment of assessments utilizing various different architecture configurations and training methods. Our experiment leads to all sorts of conclusions about the most cost - effective methodologies to our strategic objectives, and leads us to further research to enhance detection of smart glasses videos. We tested these approaches on an existing dataset that contains 48 first individual footage of people interacting in situations in real life as well as the bounding box on a pixel level for more than 15,000 hand gestures.

关键词： Training Costs data models Object recognition Smart glasses

来源：评论

学校读者我要写书评

暂无评论

Joint resource management for MC-NOMA: A deep reinforcement learning approach

arXiv

引用

arXiv 2021年

作者： Wang, Shaoyang Lv, Tiejun Ni, Wei Beaulieu, Norman C. Guo, Y. Jay Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Sydney2122 Australia Beijing Key Laboratory for Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Global Big Data Technologies Centre University of Technology Sydney UltimoNSW2007 Australia

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users. © 2021, CC BY.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

引用

Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

来源：评论

学校读者我要写书评

暂无评论

CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

引用

arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Record Acceleration of the Two-Dimensional Ising Model Using High-Performance Wafer Scale Engine

arXiv

引用

arXiv 2024年

作者： Van Essendelft, Dirk Almolyki, Hayl Shi, Wei Jordan, Terry Wang, Mei-Yu Saidi, Wissam A. Advanced Computing and Artificial Intelligence The National Energy Technology Laboratory 3610 Collins Ferry Rd MorgantownWV26505 United States Computational Materials Engineering The National Energy Technology Laboratory 626 Cochrans Mill Rd PittsburghPA15236 United States HPC AI and Big Data Group Pittsburgh Supercomputing Center Carnegie Mellon University 300 S. Craig St. PittsburghPA15213 United States

The versatility and wide-ranging applicability of the Ising model, originally introduced to study phase transitions in magnetic materials, have made it a cornerstone in statistical physics and a valuable tool for evaluating the performance of emerging computer hardware. Here, we present a novel implementation of the two-dimensional Ising model on a Cerebras Wafer-Scale Engine (WSE) – a revolutionary processor that is opening new frontiers in computing. In our deployment of the checkerboard algorithm, we optimized the Ising model to take advantage of the unique WSE architecture. Specifically, we employed a compressed bit representation storing 16 spins on each int16 word, and efficiently distributed the spins over the processing units enabling seamless weak scaling and limiting communications to only immediate neighboring units. Our implementation can handle up to 754 simulations in parallel, achieving an aggregate of over 61.8 trillion flip attempts per second for Ising models with up to 200 million spins. This represents a gain of up to 148 times over previously reported single-device with a highly optimized implementation on NVIDIA V100 and up to 88 times in productivity compared to NVIDIA H100. Our findings highlight the significant potential of the WSE in scientific computing, particularly in the field of materials modeling. © 2024, CC0.

关键词： Engines

来源：评论

学校读者我要写书评

暂无评论

Encoding Global Semantic and Localized Geographic Spatial-Temporal Relations for Traffic Accident Risk Prediction

SSRN

引用

SSRN 2024年

作者： Alhaek, Fares Li, Tianrui Rajeh, Taha M. Javed, Muhammad Hafeez Liang, Weichao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

The proliferation of vehicles and the intricate layout of road systems have contributed to a significant rise in traffic accidents, posing a pressing concern globally. Despite the advancements facilitated by deep learning, several challenges persist in the domain of traffic accident prediction. Firstly, the sparsity of accident data in certain city regions presents a significant obstacle, particularly when attempting fine-grained predictions at a local level. Secondly, the intricate spatial-temporal relations inherent in traffic accident data pose a challenge for existing prediction models. In response to these formidable challenges, this paper presents GLST-TARP, a novel model for predicting traffic accident risk in urban environments by leveraging both global semantic and localized geographic spatial-temporal relations. Specifically, we construct multi-graphs to encode global static and dynamic spatial relations, incorporating attention mechanisms to adaptively focus on relevant information and temporal relations. Additionally, we employ channel-wise convolutional neural network blocks to extract localized geographic features and enhance predictive accuracy. The proposed model is trained using a Huber loss function tailored for regression tasks, relieving the impact of zero values during optimization. Experimental results demonstrate that GLST-TARP outperforms state-of-the-art methods in predicting traffic accident risks, showcasing its potential for enhancing urban safety and transportation management. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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：