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arXiv 2023年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed reality, and the Internet of Everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model (LAM) provides new solutions to overcome the above issues. Here, we propose a LAM-based SC framework (LAM-SC) specifically designed for image data, where we first apply the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semanticaware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the possibility of applying the LAM-based KB in future SC paradigms. Copyright © 2023, The Authors. All rights reserved.

关键词： Latent semantic analysis

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A UAV-Enabled Time-Sensitive Data Collection Scheme for Grassland Monitoring Edge Networks

arXiv

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arXiv 2024年

作者： Jiao, Dongbin Wang, Zihao Fan, Wen Yang, Weibo Yang, Peng Shang, Zhanhuan Yan, Shi School of Information Science and Engineering Lanzhou University Lanzhou730000 China Key Laboratory of Tourism Information Fusion Processing and Data Ownership Protection Ministry of Culture and Tourism Lanzhou University Lanzhou730000 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China School of Automobile Chang’an University Xi’an710064 China State Key Laboratory of Grassland Agro-Ecosystem College of Ecology Lanzhou University Lanzhou730000 China

Grassland monitoring is essential for the sustainable development of grassland resources. Traditional Internet of Things (IoT) devices generate critical ecological data, making data loss unacceptable, but the harsh environment complicates data collection. Unmanned Aerial Vehicle (UAV) and mobile edge computing (MEC) offer efficient data collection solutions, enhancing performance on resource-limited mobile devices. In this context, this paper is the first to investigate a UAV-enabled time-sensitive data collection problem (TSDCMP) within grassland monitoring edge networks (GMENs). Unlike many existing data collection scenarios, this problem has three key challenges. First, the total amount of data collected depends significantly on the data collection duration and arrival time of UAV at each access point (AP). Second, the volume of data at different APs varies among regions due to differences in monitoring objects and vegetation coverage. Third, the service requests time and locations from APs are often not adjacent topologically. To address these issues, We formulate the TSDCMP for UAV-enabled GMENs as a mixed-integer programming model in a single trip. This model considers constraints such as the limited energy of UAV, the coupled routing and time scheduling, and the state of APs and UAV arrival time. Subsequently, we propose a novel cooperative heuristic algorithm based on temporal-spatial correlations (CHTSC) that integrates a modified dynamic programming (MDP) into an iterated local search to solve the TSDCMP for UAV-enabled GMENs. This approach fully takes into account the temporal and spatial relationships between consecutive service requests from APs. Systematic simulation studies demonstrate that the mixed-integer programming model effectively represents the TSDCMP within UAV-enabled GMENs. Moreover, the proposed CHTSC algorithm outperforms two superior algorithms across twelve different scale instances. © 2024, CC BY.

关键词： Spatio-temporal data

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Visual Language Model based Cross-modal Semantic Communication Systems

arXiv

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arXiv 2024年

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China The Department of Computer Science Brunel University London United Kingdom The School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years, successfully transcending the Shannon physical capacity limits through innovative semantic transmission concepts. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low semantic density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Digital medical education empowered by intelligent fabric space

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National Science Open 2022年第1期1卷 68-77页

作者： Min Chen Rui Wang Rui Wang Yingting Zhou Zicheng He Xiaojuan Liu Muyao He Jiaxi Wang Chaolin Huang Huamin Zhou Ping Hong Chong Hou Ning Zhou Dingyu Zhang Guangming Tao Sport and Health Initiative Optical Valley Laboratory and Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Wuhan Jinyintan Hospital Wuhan 430048China State Key Laboratory of Material Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Beijing Sport University Beijing 100091China School of Optics and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China Tongji Medical College Huazhong University of Science and TechnologyWuhan 430074China Hubei Provincial Health and Health Committee Wuhan 430015China

Medical education plays an important role in promoting the development of global medical ***,the intrinsic gap existing between institutional medical teaching and practical clinical tasks causes low education efficiency and students’weak *** developments of sensing fabric and embedded computing,along with the advances in artificial intelligence(AI)and digital twin technology are paving the way for the transformation of medical research towards *** this work,we present an intelligent fabric space based on novel functional fabric materials and digital twin networking enabled by 5G and internet of things(IoT)*** this space,medical students can learn knowledge with collaborative mapping of the digital and real world,cyber-physical interaction and real-time tactile *** the proposed service system will evaluate and feedback students’operational behaviors to improve their experimental *** provide four typical applications of intelligent fabric space for medical education,including medical education training,health and behavior tracking,operation playback and reproduction,as well as medical knowledge *** proposed intelligent fabric space has the potential to promote innovative technologies for training cutting-edge medical students by effective and efficient ways.

关键词： medical education intelligent fabric digital twin tactile interaction

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Autonomous migration of virtual machine based on OpenStack

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International Journal of Hybrid information technology 2016年第8期9卷 65-84页

作者： Zhou, Li Yu, Zhuoer Zhang, Jilin Wan, Jian Wu, Yusen School of Computer and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou310058 China School of Information and Electronic engineering Zhejiang University of Science & Technology Hangzhou310023 China Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis HangzhouZhejiang China

For the problem of compute nodes load unbalance leads to the overload node impact VM performance and the underload node waste electrical energy. Dynamic VM consolidation is an efficient approach for improving the utilization of servers and reducing energy costs. In this paper, we introduce dynamic consolidation into OpenStack and implement AMVM based on OpenStack. AMVM collect and analyze all performance data of VM and compute node in OpenStack. Then AMVM automatically schedules VM live migrate to the most appropriate node according to the real-time load of all compute nodes. Then we use hybrid-copy instead of pre-copy in AMVM to improve the efficiency of VM live migration. Finally, we analyze the performance of hybrid-copy, hybrid-copy can reduce page faults compared with post-copy and reduce total data transferred compared with pre-copy. © 2016 SERSC.

关键词： Virtual machine

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Biparty Multiobjective Optimal Power Flow: The Problem Definition and an Evolutionary Approach

SSRN

引用

SSRN 2023年

作者： Chang, Yatong Luo, Wenjian Lin, Xin Song, Zhen Coello Coello, Carlos A. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Artificial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Departamento de Computación CINVESTAV-IPN Mexico City Mexico School of Engineering and Sciences Tecnologico de Monterrey Nuevo León Monterrey Mexico

The multiobjective optimal power flow (MOOPF) problem consists of adjusting the generator power and the voltage state of each node within the feasible range in the process of power transmission and, finally of achieving the objectives of optimizing the cost, loss and stability, etc. In the MOOPF, two key decision makers are usually involved, which are the power generation sector and the transmission sector. Thus, it is more suitable to model an MOOPF as a biparty multiobjective optimal power flow (BPMOOPF) problem. However, so far, there is no work on treating and solving the MOOPF problem from the perspective of biparty multiobjective optimization. In this paper, we propose the definition of the BPMOOPF problem as well as a novel evolutionary biparty multiobjective optimization algorithm for solving the BPMOOPF problem, which we call BPMOOPF-EA. Our experimental results show that compared with respect to two state-of-the-art algorithms (C-MOEA/D and A-NSGA-III), our proposed BPMOOPF-EA has a better performance when solving the BPMOOPF problem. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Ai driven heterogeneous MEC system for dynamic environment - Challenges and solutions

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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dugMatting: Decomposed-Uncertainty-Guided Matting

arXiv

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arXiv 2023年

作者： Wu, Jiawei Zhang, Changqing Li, Zuoyong Fu, Huazhu Peng, Xi Zhou, Joey Tianyi College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou China College of Intelligence and Computing Tianjin University Tianjin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Institute of High Performance Computing Agency for Science Technology and Research Singapore College of Computer Science Sichuan University Chengdu China Singapore

Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing. Due to the highly ill-posed issue, additional inputs, typically user-defined trimaps or scribbles, are usually needed to reduce the uncertainty. Although effective, it is either time consuming or only suitable for experienced users who know where to place the strokes. In this work, we propose a decomposed-uncertainty-guided matting (dugMatting) algorithm, which explores the explicitly decomposed uncertainties to efficiently and effectively improve the results. Basing on the characteristic of these uncertainties, the epistemic uncertainty is reduced in the process of guiding interaction (which introduces prior knowledge), while the aleatoric uncertainty is reduced in modeling data distribution (which introduces statistics for both data and possible noise). The proposed matting framework relieves the requirement for users to determine the interaction areas by using simple and efficient labeling. Extensively quantitative and qualitative results validate that the proposed method significantly improves the original matting algorithms in terms of both efficiency and efficacy. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment

arXiv

引用

arXiv 2024年

作者： Zhou, Fei Gu, Tianhao Huang, Zhicong Qiu, Guoping College of Electronic and Information Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China SZU-AFS Joint Innovation Center for Artificial Intelligence Technology Shenzhen518000 China Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Shenzhen518060 China School of Computer Science The University of Nottingham Ningbo China Zhejiang315100 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

The visual quality of an image is confounded by a number of intertwined factors including its semantic content, distortion characteristics and appearance properties such as brightness, contrast, sharpness, and colourfulness. Distilling high level knowledge about all these quality bearing attributes is crucial for developing objective Image Quality Assessment (IQA). While existing solutions have modeled some of these aspects, a comprehensive solution that involves all these important quality related attributes has not yet been developed. In this paper, we present a new blind IQA (BIQA) model termed Self-supervision and Vision-Language supervision Image QUality Evaluator (SLIQUE) that features a joint vision-language and visual contrastive representation learning framework for acquiring high level knowledge about the images semantic contents, distortion characteristics and appearance properties for IQA. For training SLIQUE, we have developed a systematic approach to constructing a first of its kind large image database annotated with all three categories of quality relevant texts. The Text Annotated Distortion, Appearance and Content (TADAC1) database has over 1.6 million images annotated with textual descriptions of their semantic contents, distortion characteristics and appearance properties. The method for constructing TADAC and the database itself will be particularly useful for exploiting vision-language modeling for advanced IQA applications. Extensive experimental results show that SLIQUE has superior performances over state of the art, demonstrating the soundness of its design principle and the effectiveness of its implementation. The link to the TADAC dataset is https://***/documents/tadactextannotated-distortion-appearance-and-content Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

引用

arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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