检索结果-内蒙古大学图书馆

arXiv 2024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 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 The Frontiers Science Center for Mobile Information Communication and Security National Mobile Communications Research Laboratory Southeast University Nanjing China The Purple Mountain Laboratories Nanjing China

Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction;2) Latent semantic redundancy;and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3D SC (GAM-3DSC) system. Firstly, we introduce a 3D Semantic Extractor (3DSE), which employs generative AI models, including Segment Anything Model (SAM) and Neural Radiance Field (NeRF), to extract key semantics from a 3D scenario based on user requirements. The extracted 3D semantics are represented as multi-perspective images of the goal-oriented 3D object. Then, we present an Adaptive Semantic Compression Model (ASCM) for encoding these multi-perspective images, in which we use a semantic encoder with two output heads to perform semantic encoding and mask redundant semantics in the latent semantic space, respectively. Next, we design a conditional Generative adversarial network and Diffusion model aided-Channel Estimation (GDCE) to estimate and refine the Channel State information (CSI) of physical channels. Finally, simulation results demonstrate the advantages of the proposed GAM-3DSC system in effectively transmitting the goal-oriented 3D scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

引用

Computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

来源：评论

学校读者我要写书评

暂无评论

Analysis of river morphology change in Zhengzhou reach of the Yellow River based on long time series remote sensing images

Analysis of river morphology change in Zhengzhou reach of th...

引用

2024 International Conference on Remote Sensing, Mapping, and Geographic information Systems, RSMG 2024

作者： Li, Ziwei Liang, Bingjie Chen, Yi Ge, Wen Deng, Xingran Jianzhong, Guo The College of Geography and Environmental Science Henan University No. 379 North Mingli Road Henan Province Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Henan Province Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent processing Ministry of Natural Resources Henan Province Zhengzhou450001 China Yellow River Institute of Hydraulic Research Yellow River Conservancy Commission Zhengzhou450003 China China Construction Eighth Engineering Division Rall Transit Construction Co Ltd Zhongjian Building No.6 Wenlan Road Xianlin University Town Qixia District Jiangsu Province Nanjing City210023 China

ISBN: (纸本)9781510685826

The Zhengzhou reach of the Yellow River is a typical meandering section with frequent changes in river morphology. In this paper, using satellite remote sensing technology and Landsat 8 satellite imagery data, the morphological changes of the Yellow River in Zhengzhou (Taohuayu - Huayuankou) in the lower reaches from 2013 to 2022 were quantitatively studied and analyzed. The overall water surface area of the study segment changes significantly (61.8 square kilometers), while the length remains stable. The variations in the area and length of the river segment exhibit a pattern of phased changes. Regarding the main meanders, the first and third meanders showed a trend of initial strengthening followed by weakening, while the second meander showed an upward trend. The intensity of the river's morphological changes followed a pattern of stability, followed by a period of drastic alteration, and then a return to stability. Overall, the tendency for the river segment in the study area to meander has diminished over the study period. © 2024 SPIE.

关键词： Remote sensing Satellites Satellite imaging Floods Earth observing sensors Feature extraction Landsat Clouds Statistical analysis Roads

来源：评论

学校读者我要写书评

暂无评论

Binary Representation via Jointly Personalized Sparse Hashing

arXiv

引用

arXiv 2022年

作者： Wang, Xiaoqin Chen, Chen Lan, Rushi Liu, Licheng Liu, Zhenbing Zhou, Huiyu Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology China College of Electrical and Information Engineering Hunan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g. color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, firstly, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our proposed JPSH, we incorporate the proposed PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters, but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. We apply the proposed representation learning algorithm JPSH to the similarity search task. Extensive experiments on four benchmark datasets verify that the proposed JPSH outperforms several state-of-the-art unsupervised hashing algorithms. Cop

关键词： Hash functions

来源：评论

学校读者我要写书评

暂无评论

Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning

arXiv

引用

arXiv 2022年

作者： Chen, Weiling Lin, Rongfu Liao, Honggang Zhao, Tiesong Gu, Ke Le Callet, Patrick Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Peng Cheng Laboratory Shenzhen518000 China The Faculty of Information Technology Beijing University of Technology Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Équipe Image Perception et Interaction Laboratoire des Sciences du Numérique de Nantes Université de Nantes France

The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience;while for other tasks such as popular object recognition, there exists a low correlation between utilities and perceptions. In such cases, the fidelity-based and aesthetics-based IQA methods cannot be directly applied. To address this issue, this paper proposes a utility-oriented IQA in object recognition. In particular, we initialize our research in the scenario of underwater fish detection, which is a critical task that has not yet been perfectly addressed. Based on this task, we build an Underwater Image Utility Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM). Inspired by the top-down design of fidelity-based IQA, we exploit the deep models of object recognition and transfer their features to our UIUM. Experiments validate that the proposed transfer-learning-based UIUM achieves promising performance in the recognition task. We envision our research provides insights to bridge the researches of IQA and computer vision. Copyright © 2022, The Authors. All rights reserved.

关键词： Multimedia systems

来源：评论

学校读者我要写书评

暂无评论

A Dual-Functional Single-Atom Fe Nanozyme-Based Sensitive Colorimetric Sensor for Tannins Quantification in Brandy

SSRN

引用

SSRN 2023年

作者： Hou, Changjun Wang, Xinrou Liu, Huan Qiao, Cailin Ma, Yi Luo, Huibo Huo, Danqun Key Laboratory for Biorheological Science and Technology of Ministry of Education Bioengineering College of Chongqing University Chongqing400044 China Chongqing Institute for Food and Drug Control Chongqing401121 China Liquor Making Biology Technology Application of Key Laboratory of Sichuan Province College of Bioengineering Sichuan University of Science and Engineering 188 University Town Yi bin644000 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

Detection of tannins can be utilized as an approach for regulating food quality. Herein, a single-atom Fe nanozyme (Fe@CN-20)-based sensitive colorimetric sensor for tannins was initially developed. Fe@CN-20 had both laccase-like and oxidase-like activities, and has a high affinity for TMB with a Km value of 6.191×10-7 M. Therefore, the Fe@CN-20/TMB system can be sensitive to tannic acid due to the fact that tannins are easily oxidized. The linear range was 5 - 100 mg/L, and the detection limit was 0.13 mg/L (S/N=3). The entire testing process takes approximately 30 minutes. The sensing platform was further successfully utilized for quantifying tannins in five different brands of brandy. The sensing platform is characterized by great environmental stability, recyclability, remarkable sensitivity, and stability, all of which demonstrate its immense potential for rapid on-site detection of tannins. © 2023, The Authors. All rights reserved.

关键词： Tannins

来源：评论

学校读者我要写书评

暂无评论

Large AI Model-Based Semantic Communications

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

引用

arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 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 Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

来源：评论

学校读者我要写书评

暂无评论

Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor

arXiv

引用

arXiv 2023年

作者： Jiang, San Ma, Yichen Li, Qingquan Jiang, Wanshou Guo, Bingxuan Li, Lelin Wang, Lizhe The School of Computer Science China University of Geosciences Wuhan430074 China Shenzhen518060 China The Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430078 China The College of Civil and Transportation Engineering Shenzhen University Shenzhen518060 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China The Provincial Key Laboratory of Geo-information Engineering in Surveying Mapping and Remote Sensing Hunan University of Science and Technology Xiangtan411201 China

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation. © 2023, CC BY.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Eu-Mof Based Fluorescence Probe for Ratiometric and Visualization Detection of Copper Ions

SSRN

引用

SSRN 2022年

作者： Li, Zhihua Deng, Jiaxi Zhang, Jing Qiao, Cailin Yang, Mei Xu, Guoren Luo, Xiaogang Huo, Danqun Hou, Changjun Ministry of Education College of Bioengineering Chongqing University Chongqing400044 China State Key Laboratory of Urban Water Resources & Environment Harbin Institute of Technology Harbin150090 China Chongqing Key Laboratory of Bio-perception & Intelligent Information Processing School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China

In this work, a novel and effective ratio fluorescent probe Eu-BTB was prepared for the detection of Cu2+ using 1,3, 5-tri (4-carboxyphenyl) benzene (H3BTB) as ligand and Eu3+ as metal framework, which showed excellent fluorescence performance in the detection of Cu2+. The BTB of the probe can achieve "antenna effect" with Eu3+, which can sensitize Eu3+ emission effectively while maintaining its own emission. Copper ions can competitively bind to BTB, thus inhibiting the antenna effect. In the concentration range of 0.05-10 μM, the ratio of fluorescence intensity and copper ion concentration showed a good linear relationship, and the detection limit was as low as 10 nM, corresponding time was 3 minutes. The fluorescence probe has been successfully applied to the detection of copper ions in water in different environments, the standard method Inductively coupled plasma mass spectrometry(ICP-MS) proved that the results were reliable. which confirmed the potential of Eu-BTB probe in the rapid and accurate detection of Cu2+ in different environmental water samples. © 2022, The Authors. All rights reserved.

关键词： Fluorescence

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：