IEEE Journal of Selected Topics in Signal Processing最新文献_第8页

IEEE Journal of Selected Topics in Signal Processing Publication Information IEEE 信号处理选题期刊》出版信息

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-11-01 DOI: 10.1109/JSTSP.2023.3324776

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Index-Modulated Metasurface Transceiver Design Using Reconfigurable Intelligent Surfaces for 6G Wireless Networks 利用可重构智能表面为 6G 无线网络设计索引调制元表面收发器

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-10-06 DOI: 10.1109/JSTSP.2023.3322655

John A. Hodge;Kumar Vijay Mishra;Brian M. Sadler;Amir I. Zaghloul

引用次数: 0

DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality DeepAdaIn-Net:用于增强现实的深度自适应设备边缘协同推理

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-09-22 DOI: 10.1109/JSTSP.2023.3312914

Li Wang;Xin Wu;Yi Zhang;Xinyun Zhang;Lianming Xu;Zhihua Wu;Aiguo Fei

{"title":"DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality","authors":"Li Wang;Xin Wu;Yi Zhang;Xinyun Zhang;Lianming Xu;Zhihua Wu;Aiguo Fei","doi":"10.1109/JSTSP.2023.3312914","DOIUrl":"10.1109/JSTSP.2023.3312914","url":null,"abstract":"The object inference for augmented reality (AR) requires a precise object localization within user's physical environment and the adaptability to dynamic communication conditions. Deep learning (DL) is advantageous in capturing highly-nonlinear features of diverse data sources drawn from complex objects. However, the existing DL techniques may have disfluency or instability issues when deployed on resource-constrained devices with poor communication conditions, resulting in bad user experiences. This article addresses these issues by proposing a deep adaptive inference network called DeepAdaIn-Net for the real-time device-edge collaborative object inference, aiming at reducing feature transmission volume while ensuring high feature-fitting accuracy during inference. Specifically, DeepAdaIn-Net encompasses a partition point selection (PPS) module, a high feature compression learning (HFCL) module, a bandwidth-aware feature configuration (BaFC) module, and a feature consistency compensation (FCC) module. The PPS module minimizes the total execution latency, including inference and transmission latency. The HFCL and BaFC modules can decouple the training and inference process by integrating a high-compression ratio feature encoder with the bandwidth-aware feature configuration, which ensures that the compressed data can adapt to the varying communication bandwidths. The FCC module fills the information gaps among the compressed features, guaranteeing high feature expression ability. We conduct extensive experiments to validate DeepAdaIn-Net using two object inference datasets: COCO2017 and emergency fire datasets, and the results demonstrate that our approach outperforms several conventional methods by deriving an optimal 123x feature compression for \u0000<inline-formula><tex-math>$640times 640$</tex-math></inline-formula>\u0000 images, which results in a mere 63.3 ms total latency and an accuracy loss of less than 3% when operating at a bandwidth of 16 Mbps.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135650942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VR+HD: Video Semantic Reconstruction From Spatio-Temporal Scene Graphs VR+HD:基于时空场景图的视频语义重构

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-09-01 DOI: 10.1109/JSTSP.2023.3323654

Chenxing Li;Yiping Duan;Qiyuan Du;Shiqi Sun;Xin Deng;Xiaoming Tao

{"title":"VR+HD: Video Semantic Reconstruction From Spatio-Temporal Scene Graphs","authors":"Chenxing Li;Yiping Duan;Qiyuan Du;Shiqi Sun;Xin Deng;Xiaoming Tao","doi":"10.1109/JSTSP.2023.3323654","DOIUrl":"10.1109/JSTSP.2023.3323654","url":null,"abstract":"With the development of computer science and deep learning networks, AI generation technology is becoming increasingly mature. Video has become one of the most important information carriers in our daily life because of their large amount of data and information. However, because of their large amount of information and complex semantics, video generation models, especially High Definition (HD) video, have been a difficult problem in the field of deep learning. Video semantic representation and semantic reconstruction are difficult tasks. Because video content is changeable and information is highly correlated, we propose a HD video generation model from a spatio-temporal scene graph: the spatio-temporal scene graph to video (StSg2vid) model. First, we enter the spatio-temporal scene graph sequence as the semantic representation model of the information in each frame of the video. The scene graph used to describe the semantic information of each frame contains the motion progress of the object in the video at that moment, which is equivalent to a clock. A spatio-temporal scene graph transmits the relationship information between objects through the graph convolutional neural network and predicts the scene layout of the moment. Lastly, the image generation model predicts the frame image of the current moment. The frame at each moment depends on the scene layout at the current moment and the frame and scene layout at the previous moment. We introduced the flow net, wrapping prediction model and the spatially-adaptive normalization (SPADE) network to generate images of each frame forecast. We used the Action genome dataset. Compared with the current state-of-the-art algorithms, the videos generated by our model achieve better results in both quantitative indicators and user evaluations. In addition, we also generalized the StSg2vid model into virtual reality (VR) videos of indoor scenes, preliminarily explored the generation method of VR videos, and achieved good results.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136258951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","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 Signal Processing for XR Communications and Systems XR通信与系统信号处理

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-09-01 DOI: 10.1109/JSTSP.2023.3326801

Yongpeng Wu;Erik G. Larsson;Jing Li;Angel Lozano;Luce Morin;Mai Xu;Chengshan Xiao;Wei Yang

引用次数: 0

IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-09-01 DOI: 10.1109/JSTSP.2023.3296231

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IEEE Signal Processing Society Information IEEE信号处理学会信息

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-09-01 DOI: 10.1109/JSTSP.2023.3296235

引用次数: 0

Adaptive Semantic-Bit Communication for Extended Reality Interactions 扩展现实交互的自适应语义位通信

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-08-31 DOI: 10.1109/JSTSP.2023.3310654

Chaowei Wang;Yehao Li;Feifei Gao;Danhao Deng;Jisong Xu;Yuhan Liu;Weidong Wang

{"title":"Adaptive Semantic-Bit Communication for Extended Reality Interactions","authors":"Chaowei Wang;Yehao Li;Feifei Gao;Danhao Deng;Jisong Xu;Yuhan Liu;Weidong Wang","doi":"10.1109/JSTSP.2023.3310654","DOIUrl":"10.1109/JSTSP.2023.3310654","url":null,"abstract":"Semantic communication is a novel paradigm that conveys intention or goal from the source to the destination. It can greatly improve communication efficiency, especially for the applications that require extremely low latency and high reliability, such as augmented reality (AR), virtual reality (VR) or extended reality (XR). An adaptive semantic-bit communication structure based on resource efficiency enhancement for XR is proposed, in which part of the XR users employ semantic communication, while others employ the conventional way. We utilize adaptive communication and power allocation to maximize the system-level achievable performance indicated by equivalent semantic rate. The formulated problem is addressed by a two-step optimization. First, we propose a signal-to-interference-plus-noise ratio (SINR)-based paradigm selection scheme as the semantic communication outperforms the conventional way in low and moderate SINR regimes. Then we propose a genetic algorithm-based power allocation to solve the non-convex optimization. Simulation results demonstrate that the proposed scheme achieves a higher equivalent semantic rate against the baseline schemes.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76618608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Orientation and Location Tracking of XR Devices: 5G Carrier Phase-Based Methods XR设备的方向和位置跟踪:基于5G载波相位的方法

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-08-28 DOI: 10.1109/JSTSP.2023.3309463

Jukka Talvitie;Mikko Säily;Mikko Valkama

{"title":"Orientation and Location Tracking of XR Devices: 5G Carrier Phase-Based Methods","authors":"Jukka Talvitie;Mikko Säily;Mikko Valkama","doi":"10.1109/JSTSP.2023.3309463","DOIUrl":"10.1109/JSTSP.2023.3309463","url":null,"abstract":"Accurate knowledge of the three-dimensional (3D) orientations and 3D locations of the user devices, such as wearable glasses, is of paramount importance in different extended reality (XR) use cases and applications. In this article, we address the corresponding six degrees-of-freedom (6DoF) tracking challenge of 5G-empowered XR devices. We describe a new uplink (UL) carrier phase measurements based estimation approach, allowing for low-latency 3D orientation and 3D location tracking directly at the 5G network base-stations or gNodeBs (gNBs). extended Kalman filter (EKF) based practical signal processing algorithms are described while also the applicable Cramér-Rao lower-bounds (CRLBs) are derived and presented. Also, the related aspect of over-the-air estimation of the XR headset antenna constellation or antenna geometry is addressed. Additionally, the important practical challenges related to user equipment (UE) clock drifting as well as integer ambiguities in carrier phase based methods are both considered. Finally, an extensive set of numerical results is provided in an example indoor factory like environment, covering both 3.5 GHz and 28 GHz network deployments. The obtained results demonstrate the feasibility of continuous 6DoF tracking through the proposed approach, with root mean squared error (RMSE) accuracies below one degree for the 3D orientation and below one centimeter for the 3D location, respectively. The results also demonstrate that UE clock drifting and carrier phase integer ambiguities can both be efficiently estimated and tracked, as part of the overall proposed concept and methods.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10232971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79758062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Sensing and Communication for Wireless Extended Reality (XR) With Reconfigurable Intelligent Surface 基于可重构智能表面的无线扩展现实(XR)集成传感与通信

IF 7.5 1区工程技术

IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-08-14 DOI: 10.1109/JSTSP.2023.3304846

Teng Ma;Yue Xiao;Xia Lei;Ming Xiao

引用次数: 0