Proceedings of the IEEE最新文献_第10页

Proceedings of the IEEE Publication Information IEEE出版信息学报

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328669

引用次数: 0

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial 超可靠低延迟通信的统计工具和方法-教程

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328920

Onel L. A. López;Nurul H. Mahmood;Mohammad Shehab;Hirley Alves;Osmel Martínez Rosabal;Leatile Marata;Matti Latva-Aho

{"title":"Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication—A Tutorial","authors":"Onel L. A. López;Nurul H. Mahmood;Mohammad Shehab;Hirley Alves;Osmel Martínez Rosabal;Leatile Marata;Matti Latva-Aho","doi":"10.1109/JPROC.2023.3328920","DOIUrl":"10.1109/JPROC.2023.3328920","url":null,"abstract":"Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":20.6,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323296","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138293579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Future Special Issues/Special Sections of the Proceedings 未来的特刊/会议记录的特别部分

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328673

引用次数: 0

Proceedings of the IEEE: Stay Informed. Become Inspired. IEEE会刊:保持信息灵通。成为灵感。

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-11-20 DOI: 10.1109/JPROC.2023.3328677

引用次数: 0

Deep-Learning-Based 3-D Surface Reconstruction—A Survey 基于深度学习的三维曲面重建研究综述

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-30 DOI: 10.1109/JPROC.2023.3321433

Anis Farshian;Markus Götz;Gabriele Cavallaro;Charlotte Debus;Matthias Nießner;Jón Atli Benediktsson;Achim Streit

{"title":"Deep-Learning-Based 3-D Surface Reconstruction—A Survey","authors":"Anis Farshian;Markus Götz;Gabriele Cavallaro;Charlotte Debus;Matthias Nießner;Jón Atli Benediktsson;Achim Streit","doi":"10.1109/JPROC.2023.3321433","DOIUrl":"10.1109/JPROC.2023.3321433","url":null,"abstract":"In the last decade, deep learning (DL) has significantly impacted industry and science. Initially largely motivated by computer vision tasks in 2-D imagery, the focus has shifted toward 3-D data analysis. In particular, 3-D surface reconstruction, i.e., reconstructing a 3-D shape from sparse input, is of great interest to a large variety of application fields. DL-based approaches show promising quantitative and qualitative surface reconstruction performance compared to traditional computer vision and geometric algorithms. This survey provides a comprehensive overview of these DL-based methods for 3-D surface reconstruction. To this end, we will first discuss input data modalities, such as volumetric data, point clouds, and RGB, single-view, multiview, and depth images, along with corresponding acquisition technologies and common benchmark datasets. For practical purposes, we also discuss evaluation metrics enabling us to judge the reconstructive performance of different methods. The main part of the document will introduce a methodological taxonomy ranging from point- and mesh-based techniques to volumetric and implicit neural approaches. Recent research trends, both methodological and for applications, are highlighted, pointing toward future developments.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":null,"pages":null},"PeriodicalIF":20.6,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10301359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135260924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Future Special Issues/Special Sections of the Proceedings 未来的特别问题/诉讼程序的特别部分

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3316236

引用次数: 0

Affective Computing [Scanning the Issue] 情感计算[扫描问题]

IF 20.6 1区计算机科学

Proceedings of the IEEE Pub Date : 2023-10-12 DOI: 10.1109/JPROC.2023.3318028

Björn W. Schuller;Matti Pietikäinen

引用次数: 0

Proceedings of the IEEE Publication Information IEEE出版信息汇编

IF 20.6 1区计算机科学