IEEE Transactions on Consumer Electronics最新文献_第2页

IEEE Consumer Technology Society Board of Governors 电气和电子工程师学会消费技术协会理事会

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3493276

引用次数: 0

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3493274

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Guest Editorial of the Special Section on Consumer Electronics in the Era of the Internet of Everything (IoE) and Massive Data 万物互联和大数据时代的消费电子专题特约编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3416153

Hui Xia;Feng Hong;Feng Li;Zhipeng Cai;Jiwei Zhang;Rui Chen

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Metaverse and Digital Twins for Consumer Electronics 消费电子产品的元宇宙和数字双胞胎

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3411469

Zhihan Lyu;Jaime Lloret;Houbing Song;Wojciech Mazurczyk;Huihui Wang;James J. Park

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Guest Editorial of the Special Section on Secure Artificial Intelligence in 6G Consumer Electronics 6G消费电子产品安全人工智能专题特约编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3447072

Shalli Rani;Celestine Iwendi;Syed Hassan Shah;Ali Kashif Bashir

{"title":"Guest Editorial of the Special Section on Secure Artificial Intelligence in 6G Consumer Electronics","authors":"Shalli Rani;Celestine Iwendi;Syed Hassan Shah;Ali Kashif Bashir","doi":"10.1109/TCE.2024.3447072","DOIUrl":"https://doi.org/10.1109/TCE.2024.3447072","url":null,"abstract":"Unmanned aerial vehicles (UAVs) with AI-enabled logistics are gradually demonstrating their special benefits for upcoming smart cities. However, current research on logistics UAV path planning fails to take into account the limits on UAV energy consumption, customer time windows, and the effects of wind direction and speed at the same time. As a result, we study how wind direction and speed affect UAV flight states, determine relevant parameters and how wind conditions affect them, and explore the logistics problem of UAV path planning that simultaneously takes into account the constraints on UAV energy consumption, customer time windows, and wind conditions [1]. The ubiquitous monitoring and intelligent control capabilities of the Internet of Things (IoT) are mainly dependent on inexpensive wireless sensors with low energy consumption. Nevertheless, remote terminals that are not covered by wireless can be connected to IoT networks using unmanned aerial vehicles (UAVs). With the help of this solution, IoT networks may reach a wider audience and have more options for control and monitoring. Notwithstanding this advantage, the UAV’s onboard battery has a modest capacity [2].","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 3","pages":"5698-5701"},"PeriodicalIF":4.3,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guest Editorial Advanced Learning Intelligence in Quantum-Enabled Consumer Applications 量子支持的消费者应用中的高级学习智能

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3411822

Ashish Singh;Muhammad Khurram Khan;Abhinav Kumar

引用次数: 0

Guest Editorial of the Special Section on Multimodal Data-Driven Decision-Making for Next-Generation Consumer Electronics 新一代消费电子产品的多模式数据驱动决策特别部分客座编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3447281

Wei Wang;Shahid Mumtaz;Joe Xiang;Kai Fang;Tingting Wang

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IEEE Consumer Technology Society Officers and Committee Chairs IEEE消费技术协会官员和委员会主席

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3493278

引用次数: 0

Guest Editorial of the Special Section on Digital Twin and Metaverse for Consumer Health (MCH) 消费者健康的数字孪生和元宇宙（MCH）专题特约编辑

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-12-13 DOI: 10.1109/TCE.2024.3415493

M. Shamim Hossain;Diana P. Tobón;Josu Bilbao;Abdulmotaleb El Saddik

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Learned Image Compression With Adaptive Channel and Window-Based Spatial Entropy Models 基于自适应信道和基于窗口的空间熵模型的学习图像压缩

IF 4.3 2区计算机科学

IEEE Transactions on Consumer Electronics Pub Date : 2024-10-23 DOI: 10.1109/TCE.2024.3485179

Jian Wang;Qiang Ling

{"title":"Learned Image Compression With Adaptive Channel and Window-Based Spatial Entropy Models","authors":"Jian Wang;Qiang Ling","doi":"10.1109/TCE.2024.3485179","DOIUrl":"https://doi.org/10.1109/TCE.2024.3485179","url":null,"abstract":"Image compression is essential for reducing the cost to save or transmit images. Recently, learned image compression methods have achieved superior compression performance compared to traditional image compression standards. Many learned image compression methods utilize convolutional entropy models to remove local spatial and channel redundancy in the latent representation. Some recent methods incorporate transformer to further eliminate non-local redundancy. However, these methods employ the same transformer structure to model both spatial and channel correlations, thereby failing to take advantage of the difference between the spatial characteristics and the channel characteristics of the latent representation. To resolve this issue, we propose novel adaptive channel and window-based spatial entropy models. The adaptive channel entropy model, which consists of the channel transformer module and the channel excitation module, dynamically fuses and excites channel information to implicitly predict channel context. More specifically, we first establish the relationship between the decoded channels and the channels to be encoded. Based on that channel relationship, the channel transformer module adaptively updates the predicted channel context. Finally, the channel excitation module is employed to emphasize informative channel context and suppress irrelevant channel context. Furthermore, we introduce a window-based spatial entropy model to capture global semantic information within the window and generate the spatial context of non-anchor features based on the decoded anchor features. The spatial context and channel context are combined to predict the Gaussian parameters of the latent representation. Experimental results demonstrate that our method outperforms some state-of-the-art image compression methods on Kodak, CLIC and Tecnick datasets.","PeriodicalId":13208,"journal":{"name":"IEEE Transactions on Consumer Electronics","volume":"70 4","pages":"6430-6441"},"PeriodicalIF":4.3,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0