Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networ...Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.展开更多
Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by p...Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.展开更多
In the field of knowledge science,understanding the structure and dynamic evolution of knowledge is essential for advancing disciplinary development and anticipating research trends.However,current methodologies lack ...In the field of knowledge science,understanding the structure and dynamic evolution of knowledge is essential for advancing disciplinary development and anticipating research trends.However,current methodologies lack a unified semantic framework for the structured representation of knowledge,which impedes the quantitative analysis of its evolution and limits the ability to uncover complex relationships among knowledge entities.展开更多
On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering loca...On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering localization method based on dual-stream feature fusion.Our approach employs a dualstream encoder to simultaneously extract features from both the RGB stream and the noise stream,enabling the localization of forged regions.By introducing an attention mechanism,these two feature streams are fused,further enhancing the detection performance.Additionally,the Atrous Spatial Pyramid Pooling(ASPP)module is integrated to expand the receptive field and extract contextual information at different scales.Finally,the decoder generates a tamper region localization map.Experimental results demonstrate that the proposed method exhibits significant performance improvements on three widely used datasets,affirming its effectiveness in the field of image tampering detection.展开更多
基金supported by the Natural Science Foundation of China under Grants 61971084,62025105,62001073,62272075the National Natural Science Foundation of Chongqing under Grants cstc2021ycjh-bgzxm0039,cstc2021jcyj-msxmX0031+1 种基金the Science and Technology Research Program for Chongqing Municipal Education Commission KJZD-M202200601the Support Program for Overseas Students to Return to China for Entrepreneurship and Innovation under Grants cx2021003,cx2021053.
文摘Semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity limit.Despite the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for SC.In this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are summarized.Next,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are summarized.Applications of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are summarized.Finally,some challenges and future research directions are presented to provide guidance for further research of SC.
基金supported by the National Natural Science Foundation of China(Grant No.62172033).
文摘Urban traffic generates massive and diverse data,yet most systems remain fragmented.Current approaches to congestion management suffer from weak data consistency and poor scalability.This study addresses this gap by proposing the Urban Traffic Congestion Unified Metadata Model(UTC-UMM).The goal is to provide a standardized and extensible framework for describing,extracting,and storing multisource traffic data in smart cities.The model defines a two-tier specification that organizes nine core traffic resource classes.It employs an eXtensible Markup Language(XML)Schema that connects general elements with resource-specific elements.This design ensures both syntactic and semantic interoperability across siloed datasets.Extension principles allow new elements or constraints to be introducedwithout breaking backward compatibility.Adistributed pipeline is implemented usingHadoop Distributed File System(HDFS)and HBase.It integrates computer vision for video and natural language processing for text to automate metadata extraction.Optimized row-key designs enable low-latency queries.Performance is tested with the Yahoo!Cloud Serving Benchmark(YCSB),which shows linear scalability and high throughput.The results demonstrate that UTC-UMM can unify heterogeneous traffic data while supporting real-time analytics.The discussion highlights its potential to improve data reuse,portability,and scalability in urban congestion studies.Future research will explore integration with association rulemining and advanced knowledge representation to capture richer spatiotemporal traffic patterns.
基金partially supported by EU H2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement(Project-DEEP,grant no.101109045)the National Natural Science Foundation of China(nos.NSFC 62401334 and 62442106)+1 种基金funded by the German Federal Ministry of Education and Research(BMBF)(project AITT,AI-assisted Technology Transfer,no.03LB3058B)the Program of Jiangsu Province under grant NTACT-2024-Z-001.
文摘In the field of knowledge science,understanding the structure and dynamic evolution of knowledge is essential for advancing disciplinary development and anticipating research trends.However,current methodologies lack a unified semantic framework for the structured representation of knowledge,which impedes the quantitative analysis of its evolution and limits the ability to uncover complex relationships among knowledge entities.
文摘On the internet,image tampering has become awidespread issue,leading to a series of adverse effects on the trustworthiness of image information.In response to this challenge,this paper proposes an image tampering localization method based on dual-stream feature fusion.Our approach employs a dualstream encoder to simultaneously extract features from both the RGB stream and the noise stream,enabling the localization of forged regions.By introducing an attention mechanism,these two feature streams are fused,further enhancing the detection performance.Additionally,the Atrous Spatial Pyramid Pooling(ASPP)module is integrated to expand the receptive field and extract contextual information at different scales.Finally,the decoder generates a tamper region localization map.Experimental results demonstrate that the proposed method exhibits significant performance improvements on three widely used datasets,affirming its effectiveness in the field of image tampering detection.
