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作者： Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

Fluid simulation is an important research topic since it is widely used in virtual training, education, computer games, and digital entertainment. Generating fluid simulation results with high degree of visual realism is challenging in these applications. We present a visualization approach for fluid simulation in this paper. Our method allows students to understand the complex natural phenomena so as to solve the fluid engineering problems intuitively. In this study, an SPH (Smoothed Particle Hydrodynamics)-based multi-resolution method is proposed for fluid simulation. The method can keep the accuracy while improve the efficiency significantly. We construct a multi-resolution fluid model from bottom to top by combining fluid computation and surface reconstruction. We first compute multi-resolution particles with a novel adaptive SPH model in the bottom layer. Next, surface particles are extracted and pre-processed to provide samples for surface extraction. Then, the multi-resolution surface reconstruction model is built in the top layer. Meshes from different reconstruction resolutions are merged to obtain the final fluid surface. Experimental results show that our approach can effectively represent multi-scale fluid details and efficiently produce visual-pleasing fluid simulation results. © 2018 John Wiley & Sons, Ltd.

关键词： Visualization

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White blood cell segmentation based on dual path network and channel attention

White blood cell segmentation based on dual path network and...

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International Conference on information Science and Control Engineering (ICISCE)

作者： Yan Lu Xinrong Cao Zuoyong Li Tao Wang Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Mathematics and Computer Science Fuzhou University Fuzhou China Fuzhou Digital Healthcare Industry Technology Innovation Center Fuzhou China

White blood cell (WBC, leukocyte) in blood smear image plays a crucial role in the diagnosis of many blood diseases. WBC segmentation is an important preprocessing step in blood disease diagnosis. However, due to some factors such as different dying technologies and different WBC appearances, WBC segmentation is still challenging. To improve accuracy of WBC segmentation, we propose an end-to-end WBC segmentation network. Specifically, we first introduce Dual Path Network (DPN) as a context-aware feature encoder to extract multi-scale image features. Then, we use a channel attention module to capture the interdependencies between channel maps, and further enhance feature representation capability of the proposed network. Finally, we reconstruct WBC feature maps by a feature decoder, and use skip connections to reduce image information loss. Experimental results on three datasets show that the proposed method obtains higher segmentation accuracy than the other methods.

关键词： White blood cells Image segmentation information science Control engineering Fuses Feature extraction Decoding

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Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches

arXiv

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arXiv 2025年

作者： Zhu, Fenghao Wang, Xinquan Zhu, Chen Gong, Tierui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhang, Zhaoyang Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Polytechnic Institute Zhejiang University Hangzhou310015 China Nanyang Technological University Singapore639798 Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Deep learning (DL) has emerged as a transformative technology with immense potential to reshape the sixth-generation (6G) wireless communication network. By utilizing advanced algorithms for feature extraction and pattern recognition, DL provides unprecedented capabilities in optimizing the network efficiency and performance, particularly in physical layer communications. Although DL technologies present the great potential, they also face significant challenges related to the robustness, which are expected to intensify in the complex and demanding 6G environment. Specifically, current DL models typically exhibit substantial performance degradation in dynamic environments with time-varying channels, interference of noise and different scenarios, which affect their effectiveness in diverse real-world applications. This paper provides a comprehensive overview of strategies and approaches for robust DL-based methods in physical layer communications. First we introduce the key challenges that current DL models face. Then we delve into a detailed examination of DL approaches specifically tailored to enhance robustness in 6G, which are classified into data-driven and model-driven strategies. Finally, we verify the effectiveness of these methods by case studies and outline future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches

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IEEE Network 2025年

作者： Zhu, Fenghao Wang, Xinquan Zhu, Chen Gong, Tierui Yang, Zhaohui Huang, Chongwen Chen, Xiaoming Zhang, Zhaoyang Debbah, Merouane Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Zhejiang University Polytechnic Institute Hangzhou310015 China Nanyang Technological University 639798 Singapore Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates

关键词： Feature Selection

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An Adaptive Model Update Object Tracking Algorithm based on DenseNet Features

An Adaptive Model Update Object Tracking Algorithm based on ...

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2020 International Conference on Networking and Network Applications, NaNA 2020

作者： Ma, Sugang Zhang, Lei Pu, Lei Yang, Xiaobao Hou, Zhiqiang Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an shaanxi710121 China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an shaanxi710121 China School of Information and Navigation Air Force Engineering University Xi'an shaanxi710077 China

ISBN: (纸本)9781728189543

In order to further improve the ability to deal with complex scenes, a visual tracking algorithm based on DenseNet features and model adaptive updating is proposed. Aiming to improve the feature representation ability of correlation filtering algorithm, a more discriminative DenseNet model is used as feature extractor. Meanwhile, in order to improve the long-term tracking performance, an adaptive model update strategy is proposed. The average peak correlation energy (APCE) based on response map is used to judge and evaluate the tracking result and the model is updated only when the confidence is high. The experimental results show that the proposed algorithm achieves higher success rate and accuracy compared to the other five related algorithms, and improves the robustness of object tracking in complex scenes such as occlusion and deformation. © 2020 IEEE.

关键词： Tracking (position)

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Ripple-Effect Analysis of Ontology Evolution Based on CK-Modes Algorithm

Ripple-Effect Analysis of Ontology Evolution Based on CK-Mod...

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International Conference on Industrial Engineering and Applications (ICIEA)

作者： Jianghe Gong Haidong Fu School of Computer Science and Technology Wuhan University of Science and Technology Key Laboratory of Intelligent Information Processing a nd Real-time Industrial System in Hubei Province Wuhan China

ISBN: (数字)9781728167855

ISBN: (纸本)9781728167862

Ontology users can accurately and comprehensively analyze the construction and evolution of the ontology by quantitatively analyzing the Ripple-Effect of the concepts or entities in the ontology, and provide a basis for the subsequent updating of the ontology. Existing methods quantify the Ripple-Effect in ontology, but do not consider the impact of ontology semantic structure on the Ripple-Effect. Existing methods preliminary quantify the Ripple-Effect in ontology, but do not consider the impact of ontology semantic structure on the Ripple-Effect. Aiming at the above problems, this paper proposes a method for analyzing the Ripple-Effect of ontology based on CK-Modes clustering. This method transforms the ontology into an ontology labeled attribute graph, cut the ontology according to the CK-Modes clustering algorithm, multiplied by different quantization coefficients according to different cutting modules, thus quantify the semantic from the perspective of semantic structure. Then, the transformation from the ontology to the semantic relation matrix is completed through the quantified semantic relations. Finally, the effect of each concept or entity is calculated by the improved Freud algorithm. The experimental result show, CK-Modes clustering-based ontology evolution Ripple-Effect analysis method can more accurately measure the concept or entity Ripple-Effect during the ontology evolution process. At the same time, the results of the measurement of the common nodes in different versions are more stable.

关键词： Ontologies Semantics Vegetation Forestry Clustering algorithms Quantization (signal) Current measurement

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Efficient differentiated storage architecture for large-scale flow tables in openflow networks 21

Efficient differentiated storage architecture for large-scal...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xiong, Bing Wu, Rengeng Liao, Zhuofan Wang, Jin School of Computer AND Communication Engineering Changsha University of Science AND Technology Changsha410114 China Department of Computer and Information Sciences Temple University Philadelphia19122 United States Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science AND Technology Changsha China

ISBN: (纸本)9781728120584

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. However, it leads to large-scale flow tables and poses serious challenges on their storage resources and lookup performance in OpenFlow switches. This paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow networks. Firstly, we investigate into the impact of wildcards in match fields on packet-in-batch property within a flow based on network traffic locality. Then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. The experimental results indicate that our proposed storage architecture obviously outperforms the traditional one applying the elephant/mice flow differentiation method in terms of TCAM hit rates and average flow table access time. © 2019 IEEE.

关键词： Software defined networking

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Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

arXiv

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arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Skediff: Skeleton 3d Ct Diffusion Reconstruction Using 2d X-Ray

SSRN

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SSRN 2024年

作者： Gao, Yuan Ge, Rongjun Wu, Zhan Li, Yuanhang Gu, Jiabing Chen, Yang Zhou, Mingle Li, Min Li, Gang Coatrieux, Jean-Louis Qilu University of Technology Shandong Academy of Sciences No. 19 Keyuan Road Lixia District Shandong Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science No. 19 Keyuan Road Lixia District Shandong Jinan250013 China Shandong SCICOM Information and Economy Research Institute Co. Ltd. No. 2000 Shunhua Road High-tech Zone Shandong Jinan250101 China Southeast University No. 2 Sipailou Xuanwu District Jiangsu Nanjing210096 China The Affiliated Hospital of Qingdao University No. 16 Jiangsu Road South District Shandong Qingdao266001 China University of Rennes 1 Breizh Rennes35042 France

In orthopedic disease examination, 2D X-ray imaging is the major source used in emergency situations, due to its wide accessibility and clinical effectiveness as well as its easy operation and low radiation effect. However, the 2D nature of these images do not provide a full access to the skeletal structures available through 3D CT. To address this issue, we propose an algorithm, named SkeDiff, to achieve a skeleton 3D CT reconstruction from two standard 2D X-ray images. SkeDiff establishes a mapping between the 2D space and the 3D space by means of a skeleton feature latent spatial diffusion process called $DM_{3DL}$. To fully utilize the information of a given X-ray image to guide the reconstruction direction of the diffusion process, we design a cross-dimensional conditional encoder $E_{Cond}$ in order to get 2D image priors for the 3D diffusion process $DM_{3DL}$. Furthermore, we propose a diffusion-based scoliosis classifier $D_{SC}$ for orthopedic to achieve scoliosis disease classification during the reconstruction of 3D CT images. The SkeDiff solution is evaluated on spine, hip and knee datasets by using standard metrics. These experiments show a better performance of SkeDiff when quantitatively and visually compared to recent algorithms. Our code is available on GitHub: https://***/YuanGao1201/SkeDiff. © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Manifold partition discriminant analysis

arXiv

引用

arXiv 2020年

作者： Zhou, Yang Sun, Shiliang Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 500 Dongchuan Road Shanghai200241 China

We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA). It aims to find a linear embedding space where the within-class similarity is achieved along the direction that is consistent with the local variation of the data manifold, while nearby data belonging to different classes are well separated. By partitioning the data manifold into a number of linear subspaces and utilizing the first-order Taylor expansion, MPDA explicitly parameterizes the connections of tangent spaces and represents the data manifold in a piecewise manner. While graph Laplacian methods capture only the pairwise interaction between data points, our method capture both pairwise and higher order interactions (using regional consistency) between data points. This manifold representation can help to improve the measure of within-class similarity, which further leads to improved performance of dimensionality reduction. Experimental results on multiple real-world data sets demonstrate the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Discriminant analysis

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