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检索条件"任意字段=IEEE Conference on Local Computer Networks"

共 18622 条记录，以下是271-280 订阅

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Video Description Model Based on Feature Reinforcement and Knowledge Supplement 7

Video Description Model Based on Feature Reinforcement and K...

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7th ieee Information Technology, Networking, Electronic and Automation Control conference, ITNEC 2024

作者： Li, Ming Cao, Wanwan Li, Longyue Wu, Ji Yu, Dongbo Zhang, Min State Grid Anhui Electric Power Co.LTD. Information & Telecommunication Branch Hefei China State Grid Anhui Electric Power Co. Ltd Hefei China

ISBN: (纸本)9798350370782

The video captured in low illumination environment often has low contrast, much noise, unclear details and other problems, which seriously affects the subsequent target detection, segmentation and other computer vision tasks. Most of the existing low-light video enhancement methods are built based on convolutional neural networks. Because convolutional can not make full use of the long-term dependence between pixels, the generated video often has the problem of detail loss and color distortion in some areas. To solve the above problems, a twin low-illumination video enhancement network model integrating local and global is proposed. The local feature extraction module based on deformable convolution is used to capture the local features of video frames, and a lightweight self-attention module is designed to capture the global features of video frames. Finally, the feature fusion module fuses the extracted local features and global features to guide the model to generate more realistic and detailed enhanced videos. The experimental results show that it has certain engineering value. © 2024 ieee.

关键词： Convolutional neural networks

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Classification of Red Blood Cells Randomly Selected from Dataset of National Institutes of Health 31

Classification of Red Blood Cells Randomly Selected from Dat...

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31st ieee conference on Signal Processing and Communications Applications (SIU)

作者： Bashi, Wasem Q. Ince, Erhan A. Eastern Mediterranean Univ Elect & Elect Engn Dept Famagusta North Cyprus Turkiye

ISBN: (纸本)9798350343557

Millions of people worldwide suffer from malaria, a potentially fatal disease. Early and precise diagnosis is essential for the medical condition to be successfully treated and managed. This paper employs three computer aided methods to determine percentages of red blood cells that are either parasitic or uninfected given test set(s) randomly obtained from National Institutes of Health (NIH) dataset. The three methods employed are traditional image processing, Support Vector Machine (SVM), and Convolutional Neural networks based Deep Learning (CNN-DL). The simulations were performed using a dataset that had 27,558 images of red blood cells. The traditional image processing method achieves an accuracy of 91.97%. SVM classifier using Histogram of Oriented Gradients (HOG) features had accuracy of 88.6% and with features extracted using local Binary Patterns (LBP) accuracy had improved to 92.5%. The two previous methods were proved to be inferior when compared with the CNN- DL classification that gave an accuracy of 95.7%.

关键词： Support Vector Machine local Binary Patterns Histogram of Oriented Gradients Convolutional Neural networks Based Deep Learning

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Efficient Two-Way Edge Backscatter with Commodity Bluetooth

Efficient Two-Way Edge Backscatter with Commodity Bluetooth

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ieee conference on computer Communications (ieee INFOCOM)

作者： Jiang, Maoran Liu, Xin Li, Dong Gong, Wei Univ Sci & Technol China Hefei Anhui Peoples R China Ohio State Univ Columbus OH USA Macau Univ Sci & Technol Macau Peoples R China

ISBN: (纸本)9798350383515;9798350383508

Two-way backscatter is essential to general-purpose backscatter communication as it provides rich interaction to support diverse applications on commercial devices. However, existing Bluetooth backscatter systems suffer from unstable uplinks due to poor carrier-identification capability and inefficient downlinks caused by packet-length modulation. This paper proposes EffBlue, an efficient two-way backscatter design for commercial Bluetooth devices. EffBlue employs a simple edge backscatter server that alleviates the computational burden on the tag and helps build efficient uplinks and downlinks. Specifically, efficient uplinks are designed by introducing an accurate synchronization scheme, which can effectively eliminate the use of non-compliant packets as carriers. To break the limitation of packet-level modulation, we design a new symbol-level WiFi-ASK downlink where the edge sends ASK-like WiFi signals and the tag can decode such signals using a simple envelope detector. We prototype the edge server using commodity WiFi and Bluetooth chips and build two-way backscatter tags with FPGAs. Experimental results show that EffBlue can identify the target excitations with more than 99% precision. Meanwhile, its WiFi-ASK downlink can achieve up to 124 kbps, which is 25x better than FreeRider.

关键词： Wireless local area networks (WLAN)

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Identify IoT Devices from Backbone networks Using Lightweight Neural networks 47

Identify IoT Devices from Backbone Networks Using Lightweigh...

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47th ieee conference on local computer networks (LCN)

作者： Wu, Hua Fan, Xingmeng Cheng, Guang Hu, Xiaoyan Southeast Univ Sch Cyber Sci & Engn Nanjing Peoples R China Purple Mt Labs Network & Commun Secur Nanjing Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Nanjing Peoples R China Jiangsu Prov Engn Res Ctr Secur Ubiquitous Networ Nanjing Peoples R China

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Due to the heterogeneity, fragmentation, and lack of visibility, Internet of Things has become the new target for attacks. Therefore, it is necessary for Internet Service Providers to identify IoT devices to prevent attacks and protect the entire network in time. In this paper, we propose an IoT device identification approach based on lightweight deep learning models using a single feature. Specifically, we analyze the traffic pattern specific to IoT devices and use one feature to characterize this pattern, reducing the time consumption. Moreover, we select multiple time scales to extract this feature for different IoT devices, achieving an accurate characterization and improving the accuracy. Furthermore, we use unidirectional flows as analysis objects, suitable for backbone networks. The evaluation results on real-world datasets show that our approach achieves an accuracy of over 99%, with one-seventeenth of the time consumption of the state-of-the-art approach, realizing the lightweight and real-time requirements.

关键词： Internet of Things devices identification specific traffic pattern deep learning algorithm

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An Enhanced Grey Wolf Optimizer with Opposition, Mutation, and local Search Strategy for Feature Selection 13

An Enhanced Grey Wolf Optimizer with Opposition, Mutation, a...

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13th ieee International conference on Communication, networks and Satellite, COMNETSAT 2024

作者： Sirmayanti Prastyo, Pulung Hendro Mahyati Politeknik Negeri Ujung Pandang Department of Electrical Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Informatics and Computer Engineering Makassar Indonesia Politeknik Negeri Ujung Pandang Department of Chemical Engineering Makassar Indonesia

ISBN: (纸本)9798350368086

Feature selection is a pivotal process in machine learning and data analysis, aimed at improving model performance by identifying and utilizing the most relevant features while eliminating redundant and irrelevant ones. The Grey Wolf Optimizer (GWO), a metaheuristic algorithm based on the social hierarchy and hunting behavior of grey wolves, has proven to be an effective method for feature selection. However, it is also easy to get trapped in the local optima. Therefore, this study presents an enhanced Grey Wolf Optimizer with Opposition, Mutation, and local Search Strategy (GWO-OMLSS) to boost exploration and exploitation of GWO in order to avoid the algorithm falling into local optima. The proposed method was validated using ten becnhmark data sets from the UCI machine learning repository and compared with state-of-the-art metaheuristic algorithm-based feature selections, such as GWO, PSO, ESSA, and CS. The Experimental results demonstrate that GWO-OMLSS outperforms the standard of GWO and other metaheuristic algorithms on all data sets except Ionosphere and Wine. The proposed method achieves the best an accuracy of 99.7% and 99.8% on the Exactly and M-of-n data sets, respectively, while also significantly reducing the number of features. It indicates that the proposed method not only obtains high accuracy, but also reduces the dimensionality of features effectively. It means that the proposed method can be considered as a promising algorithm for feature selection. © 2024 ieee.

关键词： Heuristic algorithms

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Implications of Wireless networks Using the PROMETHEE Method 5

Implications of Wireless Networks Using the PROMETHEE Method

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5th International conference on Sustainable Communication networks and Application, ICSCNA 2024

作者： Agrawal, Rohit Pathak, Pooja Gore, Santosh Goel, Akanksha Wani, Priyanka Sabale, Pratiksha Dadasaheb GLA University Department of Computer Engineering & Applications Mathura India Sai Info Solution Maharashtra Nashik India Dr D. Y. Patil School of Science and Technology Dr D. Y. Patil Vidyapeeth Tathawade Pune411033 India Dr. D. Y. Patil Institute of Technology Department of Electronics and Telecommunication Pimpri Pune411018 India Dr. D. Y. Patil Institute of Pharmaceutical Sciences & Research Pimpri Pune41101 India

ISBN: (纸本)9798331530013

This research study investigates the selection of the most suitable wireless network technology among various options, including Wireless local Area networks (WLANs) and Wireless Metropolitan Area networks (WMANs). The study utilizes the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method to evaluate the performance of different wireless technologies based on criteria such as bandwidth, security, cost, and reliability. By comparing the performance of each technology across these criteria, the PROMETHEE method helps identify the most suitable solution for specific applications and deployment scenarios. The findings of this research provide valuable insights for selecting the most appropriate wireless technology for various applications, such as home networks, enterprise networks, and public Wi-Fi hotspots. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

Improving Out-of-Distribution Generalization in Graphs via H...

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ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Piao, Yinhua Lee, Sangseon Lu, Yijingxiu Kim, Sun Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea Seoul Natl Univ Inst Comp Technol Seoul South Korea AIGENDRUG Co Ltd Seoul South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350353006

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more semantically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical semantic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the input graph to generate proxy predictions on local environments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Particularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.

关键词： Graph Neural networks Molecular Representation Learning Out-of-Distribution Generalization

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Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification

Transformer Based Multi-Grained Features for Unsupervised Pe...

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23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Li, Jiachen Wang, Menglin Gong, Xiaojin Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China

ISBN: (纸本)9798350320565

Multi-grained features extracted from convolutional neural networks (CNNs) have demonstrated their strong discrimination ability in supervised person re-identification (Re-ID) tasks. Inspired by them, this work investigates the way of extracting multi-grained features from a pure transformer network to address the unsupervised Re-ID problem that is label-free but much more challenging. To this end, we build a dual-branch network architecture based upon a modified Vision Transformer (ViT). The local tokens output in each branch are reshaped and then uniformly partitioned into multiple stripes to generate part-level features, while the global tokens of two branches are averaged to produce a global feature. Further, based upon offlineonline associated camera-aware proxies (O2CAP) that is a top-performing unsupervised Re-ID method, we define offline and online contrastive learning losses with respect to both global and part-level features to conduct unsupervised learning. Extensive experiments on three person Re-ID datasets show that the proposed method outperforms stateof-the-art unsupervised methods by a considerable margin, greatly mitigating the gap to supervised counterparts. Code will be available soon at https://***/ RikoLi/WACV23-workshop- TMGF

关键词： computer vision Codes conferences computer architecture Network architecture Feature extraction Transformers

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Vehicle-to-Vehicle Charging Coordination over Information Centric Networking 47

Vehicle-to-Vehicle Charging Coordination over Information Ce...

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47th ieee conference on local computer networks (LCN)

作者： Thompson, Robert Ismail, Muhammad Shannigrahi, Susmit Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination.

关键词： Protocols Greenhouse effect Information-centric networking Urban areas Fault tolerant systems Vehicular ad hoc networks Packet loss

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Complementary Dynamics of Global and local Centrality for Influence Maximization in Social networks

Complementary Dynamics of Global and Local Centrality for In...

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2024 International conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Yadav, Rajesh Kumar Arora, Aaryan Patel, Aayush Gupta, Aditya Computer Science And Engineering Delhi Technological University Dtu Delhi India

ISBN: (纸本)9798350371567

With the growing interest and ease of communication, the number of people using various social networks to disseminate information daily has increased exponentially. Social networking platforms have experienced exponential growth in recent years. To maximize the reach of information, there is a need to effectively identify influential nodes in social networks. Still, existing centrality measures and other methods have drawbacks relating to generalizing across networks and providing consistently accurate identification. In this study, a novel method is proposed by using the Global local Influence (GLI) approach in particular combining closeness centrality and eigenvector centrality respectively. This aims to get more accurate identification of influential nodes across various networks. To evaluate the performance of the proposed work, experiments were conducted on synthetic and real-world datasets. The results were evaluated with the help of the diffusion models, namely Susceptible-Infected-Recovered (SIR) model and Node Removal Procedure (NRP). The results showed that the proposed algorithm outperforms the existing centrality measures by over 9% across the datasets supported by both the evaluation metrics used. © 2024 ieee.

关键词： Eigenvalues and eigenfunctions

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