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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

IEEE Wireless Communications Letters 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Linköping University Department of Computer and Information Science Linköping Sweden Singapore University of Technology and Design Information Systems Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. © 2012 IEEE.

关键词： Bandwidth

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A Production Model for Calculating Pollutant Concentration Considering the Influence of Precipitation Washout and Dry Deposition 17

A Production Model for Calculating Pollutant Concentration C...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Shnaider, Ilia Lapkovsky, Roman Urumbaeva, Rosa Zhabinskaya, Valeria Saratov State University Department of Mathematical Cybernetics and Computer Science Saratov Russia Institute of Precision Mechanics and Control Saratov Federal Scientific Centre of the Russian Academy of Sciences Saratov Russia Scientific and Design Organization 'Expert24' Saratov Russia V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Digital Control Systems in the Agricultural Industry Moscow Russia

ISBN: (纸本)9798350375718

The article presents the results of developing a mathematical model for computer simulation and study of the processes of atmospheric pollutant dispersion, taking into account various factors, including data on known emission sources, meteorological conditions, and monitoring results of pollutant concentrations at specific points in an industrial area. Gaussian and Ermak models were considered as the basic mathematical models for pollutant dispersion. The developed model has been implemented as a computer program that allows for automated simulation iterations based on actual measurements of the environmental situation and weather conditions in a specified area. Calculations and a comparative assessment of the accuracy of the proposed model were carried out using the program, and conclusions were drawn confirming its effectiveness for simulating pollutant dispersion in the atmosphere. © 2024 IEEE.

关键词： Digital elevation model

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Survey on Near-Space Information Networks: Channel Modeling, Transmission, and Networking Perspectives

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IEEE Communications Surveys and Tutorials 2025年

作者： Cao, Xianbin Su, Xiaoning Yang, Peng Gao, Yue Wu, Dapeng Oliver Quek, Tony Q. S. Beihang University School of Electronic Information Engineering Beijing100191 China Beihang University School of Electronic Information Engineering China Pengcheng Laboratory China State Key Laboratory of CNS/ATM Beijing100191 China Fudan University School of Computer Science Shanghai200433 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Near-space information networks (NSINs) composed of high-altitude platforms (HAPs) and high-and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control techniques, NSINs have been envisioned as an essential component of the emerging sixth-generation of mobile communication systems. This article reveals some critical issues needing to be tackled in NSINs through conducting experiments and discusses the latest advances in NSINs in the research areas of channel modeling, transmission, and networking from a forward-looking, comparative, and technical evolutionary perspective. In this article, we highlight the characteristics of NSINs and present the promising use cases of NSINs. The impact of airborne platforms’ unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advances in HAP and UAV channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. The promising techniques and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are reviewed. A comprehensive review of the networking techniques of NSINs in network deployment, handoff management, and network management aspects is provided. Besides, experiments have been conducted using practical NSINs to verify the performance of some techniques. Finally, we outline some open issues and promising directions for NSINs deserved for future study and discuss the corresponding challenges. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Geospatial exploration of geothermal reservoirs in the Souss Massa regions of the anti-atlas and Moroccan Sahara 5

Geospatial exploration of geothermal reservoirs in the Souss...

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5th Edition of the International Conference on Materials and Environmental Science, ICMES 2022

作者： El Bouazouli, Abderrahim Baidder, Lahssen Bahi, Hicham Abdehamid, Fadil Rhouzlane, Said Moussaoui, Rabia Oukrich, Nadia Baite, Wissal Hlal, Mohammed Laboratory of Geosciences Faculty of Sciences Aïn Chock University Hassan II of Casablanca Morocco School of Architecture Planning and Design Mohammed VI Polytechnic University Benguerir Morocco Morocco Polytechnic School of Montreal Canada Laboratory of Industrial Equipe Architecture de Systems ENSEM Morocco Research Laboratory in Computer Science and Education The Mohammedia School of Engineering Rabat Morocco Laboratory of Industrial Engineering Sciences and Technologies Faculty Sultan Moulay Slimane University Beni Mellal Morocco Laboratory Dynamics of Arid Environments Mohamed Premier University Oujda Morocco

Geothermal reservoirs refer to any heat stored, naturally, in the earth’s crust;it is considered as resource coming from a natural complex depending on the geological, geophysical aspect and territories geochemistry. Although this natural resource remains distinguished by his characters to be a renewable, ecological, universal and sustainable mining energy, it stills not, yet, sufficiently exploitable worldwide (El Bouazouli et al., 2020). The same finding was brought up in Moroccan context, despite the presence of some hot resources and capitalizable scientific research to develop this potential. Prospecting for underground geothermal resources is an expensive project that deserves to mobilize scientific and technical methods in order to overcome this risky impasse. Thus, Geomatics has been used, drawing on Remote Sensing for geo-spatial exploration in order to guide prospecting through the development of geothermal deposits potential maps at very low and medium depth on Moroccan territories (El Bouazouli et al., 2019). Furthermore, we explore smart tools to support short and long-term decision-making in terms of creating of clean energy mix and complementary mining energy as well as investment in energy exploration operations. And, as an experiment, we chose for this study, areas distributed mainly in the Moroccan Sahara and the Souss Massa Region, known, probably, by a potentially high geothermal gradient, and therefore constituting geothermal energy reservoirs. As for the exploration methodology adopted, it consists in processing of the imageries: LANDSAT 8, MODIS AND ASTER, in order to extract exploitation indices such as the surface temperature, the layer of lineaments and cracks density, and evidence of hydrothermal alteration that may indicate geothermal activity. The correlation between these three factors, will lead us to conclude that the Moroccan Sahara area is considered as one of the most potential geothermal reservoir areas in Morocco. In short, the a

关键词： Geothermal prospecting

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Joining Edge-Enabled Metaverse Services with Network Externality: A Stackelberg Game Approach

Joining Edge-Enabled Metaverse Services with Network Externa...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Jiang, Yuna Kang, Jiawen Niyato, Dusit Ge, Xiaohu Xiong, Zehui Xu, Minrui Deng, Ruilong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan China Guangdong University of Technology School of Automation Guangzhou China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350333336

The metaverse is defined as a three-dimensional virtual-real fusion network focused on social connection. Edge computing can empower the metaverse by providing computing resources to realize real-time motion tracking, visual or auditory rendering and virtual scene dynamic change at the edge of networks. Users enjoy the immersive experience provided by the Metaverse Service Provider (MSP), and interact through digital avatars in the virtual world by requesting metaverse services. In this paper, we formulate the edge computing resources pricing, metaverse service rendering bitrate pricing and request problem in the metaverse. Especially, we model the interactions among edge servers, the MSP and Metaverse Service Users (MSUs) as a hierarchical three-stage Stackelberg game. The problem of joining edge-enabled metaverse services is also studied with the consideration of network externality, including the network effect and congestion effect. Based on the backward induction method, we derive the optimal computing resources pricing of edge servers, rendering bitrate pricing of the MSP, and rendering bitrate requests of MSUs. We also prove the existence and uniqueness of the Stackelberg game equilibrium. Simulation results show that the network effect can bring more positive utilities to entities when the social tie coefficient among MSUs is higher. Besides, all entities in the edge-enabled metaverse are affected negatively by the congestion effect. © 2023 IEEE.

关键词： Edge computing

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

GeoReF: Geometric Alignment Across Shape Variation for Categ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Linfang Zheng Tze Ho Elden Tse Chen Wang Yinghan Sun Hua Chen Ales Leonardis Wei Zhang Hyung Jin Chang Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China School of Computer Science. University of Birmigsham UK Department of Computer Science the University of Hong Kong China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress to-wards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrep-ancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach in-tegrates an HS-Iayer and learnable affine transformations, which aims to enhance the extraction and alignment of Geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges di-verse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive ex-periments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. 1 1 Project page: https://***/***

关键词： Point cloud compression computer vision Shape Soft sensors Pose estimation computer architecture Predictive models

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Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring systems

arXiv

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arXiv 2024年

作者： Ye, Ziqiaing Gao, Yulan Xiao, Yue Xiong, Zehui Niyato, Dusit National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose Dynamic Activity-Aware Health Monitoring strategy (DActAHM) for striking a balance between optimal monitoring performance and cost efficiency, a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model to ensure precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement

arXiv

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arXiv 2024年

作者： Zheng, Linfang Tse, Tze Ho Elden Wang, Chen Sun, Yinghan Chen, Hua Leonardis, Aleš Zhang, Wei Chang, Hyung Jin Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology China Department of Computer Science The University of Hong Kong Hong Kong School of Computer Science University of Birmingham United Kingdom

Object pose refinement is essential for robust object pose estimation. Previous work has made significant progress towards instance-level object pose refinement. Yet, category-level pose refinement is a more challenging problem due to large shape variations within a category and the discrepancies between the target object and the shape prior. To address these challenges, we introduce a novel architecture for category-level object pose refinement. Our approach integrates an HS-layer and learnable affine transformations, which aims to enhance the extraction and alignment of geometric information. Additionally, we introduce a cross-cloud transformation mechanism that efficiently merges diverse data sources. Finally, we push the limits of our model by incorporating the shape prior information for translation and size error prediction. We conducted extensive experiments to demonstrate the effectiveness of the proposed framework. Through extensive quantitative experiments, we demonstrate significant improvement over the baseline method by a large margin across all metrics. © 2024, CC BY-NC-SA.

关键词： Metadata

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Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots

Fuzzy Object Ambiguity Determination and Human Attention Ass...

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International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Kevin Fan Melanie Jouaiti Kerstin Dautenhahn Chrystopher L. Nehaniv Department of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada Departments of Electrical & Computer Engineering and Systems Design Engineering Social and Intelligent Robotics Research Laboratory (SIRRL) University of Waterloo Waterloo Canada

Domestic service robots have the promising potential of bringing significant services to the general population, and more importantly, successful applications of universal domestic service robots can potentially help mitigate critical societal issues such as senior care. In order to do so, domestic service robots need to integrate seamlessly into home environments. However, home environments are dynamic, complex and filled with personal items. Therefore, ambiguity can quickly arise for robots operating in such rich environments. In this paper, we propose an object ambiguity determination system that can determine the level of ambiguity in robot object selection tasks with fuzzy logic data integration. Additionally, we propose a functional human attention assessment system with fuzzy logic that enables the robot to determine user attention before committing to general disambiguation processes. Our preliminary results show that the proposed fuzzy logic inference systems can reliably estimate the robot object selection task ambiguity from object confidence level and the number of potential target objects that satisfy the user's command. Furthermore, fuzzy inference is applied to decide human eye gaze direction robustly. These subsystems can be utilized in the context of human-robot interaction to guide the robot when to seek feedback from a human partner in order to disambiguate reference in domestic service tasks. The source code of all proposed systems is available publicly on GitHub. 1

关键词： Fuzzy logic Service robots Source coding Sociology Human-robot interaction Data integration Reliability

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Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 859-876页

作者： Ahmad Chaddad Qizong Lu Jiali Li Yousef Katib Reem Kateb Camel Tanougast Ahmed Bouridane Ahmed Abdulkadir School of Artificial intelligence Guilin Universiy of Electronic TechnologyGuilin 541004China Laboratory for Imagery Vision and Artificial IntelligenceEcole de Technologie SuperieureMontrealQuebec H3C 1K3Canada College of Medicine Taibah UniversityMadinah 42353Saudi Arabia College of Computer Science and Engineering Taibah UniversityMadinah 42353Saudi Arabia Laboratory of Design Optimization and Modeling SystemsUniversity of LorraineMetz 57070France Center for Data Analytics and Cybersecurity(CDAC) University of SharjahSharjah 27272United Arab Emirates Laboratoire de recherche en neuroimagerie Centre Hospitalier Universitaire VaudoisMont Paisible 161011LausanneSwitzerland

Artificial intelligence(AI)continues to transform data analysis in many *** in each domain is driven by a growing body of annotated data,increased computational resources,and technological *** medicine,the sensitivity of the data,the complexity of the tasks,the potentially high stakes,and a requirement of accountability give rise to a particular set of *** this review,we focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making.1)Explainable AI aims to produce a human-interpretable justification for each *** models increase confidence if the results appear plausible and match the clinicians ***,the absence of a plausible explanation does not imply an inaccurate *** in highly non-linear,complex models that are tuned to maximize accuracy,such interpretable representations only reflect a small portion of the justification.2)Domain adaptation and transfer learning enable AI models to be trained and applied across multiple *** example,a classification task based on images acquired on different acquisition hardware.3)Federated learning enables learning large-scale models without exposing sensitive personal health *** centralized AI learning,where the centralized learning machine has access to the entire training data,the federated learning process iteratively updates models across multiple sites by exchanging only parameter updates,not personal health *** narrative review covers the basic concepts,highlights relevant corner-stone and stateof-the-art research in the field,and discusses perspectives.

关键词： Domain adaptation explainable artificial intelligence federated learning

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