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

共 18609 条记录，以下是171-180 订阅

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Task Replication in Unreliable Edge networks 47

Task Replication in Unreliable Edge Networks

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47th ieee conference on local computer networks (LCN)

作者： Amer, Ibrahim M. Oteafy, Sharief M. A. Hassanein, Hossam S. Queens Univ Sch Comp Kingston ON Canada Depaul Univ Sch Comp Chicago IL USA

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

Edge networks provide ample resources for low-latency service recruitment, unlike remote resources in the Cloud. As such, smart devices and Internet of Things (IoT) nodes form a pool of Extreme Edge Devices (EED) that are within reach of Mist and Fog networks, providing significant advantages in latency, geographic cognizance, and reduced communication costs. EEDs are often recruited in Edge networks assuming they are reliable in their commitment to tasks. However, many EEDs may fail to fulfill their tasks because they operate under opportunistic approaches and are prone to intermittent connectivity. To ameliorate task failure, we aim to optimize task allocation under the assumption of failure. Additionally, we optimize CPU utilization to engage reliable EEDs, resorting to replication when needed to exceed a tunable reliability margin. We demonstrate the efficacy of our model in multiple scenarios and present future work in EED utilization.

关键词： Edge Computing Extreme Edge Device Uncertainty Task Replication System Reliability

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Distributed Algorithms for Electromagnetic Compatibilities on local Area networks 2

Distributed Algorithms for Electromagnetic Compatibilities o...

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2nd International conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Seth, Jyoti Makhija, Priya Pandey, Arvind Kumar Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Mangement - UG Department of Management Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Disbursed algorithms are realistic solutions for resolving the Electromagnetic Compatibilities (EMC) troubles to bob up from wireless neighborhood place networks (WLANs). Such algorithms rent allotted techniques which are relevant to network structures, a good way to provide an effective "electromagnetic noise"discount method by considering the topology of the community and incorporating information from the proximate wireless nodes. Those algorithms consist of the usage of more than one receiver, frequency hopping, and space-time decoding. Furthermore, distributed algorithms allow EMC troubles to be addressed from a local attitude, as a consequence, imparting an extra accurate and adaptable manner to lessen electromagnetic interference. By way of using distributed algorithms, accurate optimization of the EMC parameters of WLANs is finished, resulting in progressed signal great and decreased radiation publicity. This progressed signal satisfaction and reduced radiation publicity result in higher usage of the WLAN's potential, in the long run offering an extra efficient universal community performance. © 2024 ieee.

关键词： Wireless local area networks (WLAN)

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Evaluation of MIMO Multipath Fading Channels Based on the ieee 802.11n Channel Models for Indoor Wireless local Area networks 4

Evaluation of MIMO Multipath Fading Channels Based on the IE...

引用

4th International conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2024

作者： Patra, Trilochan Mitra, Swarup Kumar Dream Institute of Technology Samali Kolkata700104 India Guru Nanak Institute of Technology Kolkata700114 India

ISBN: (纸本)9781643685519

The topic in this paper deals with the performance of a multi-input multi-output multi-path dissipating medium in indoor cellular computer network environments on the basis of the ieee 802.11n channel model. The MIMO multipath fading channel system utilized for this study comprises four transferring and accepting aerials and incorporates the Bell Doppler spectrum object. The channel model encompasses six distinct outlines marked A through F, that cover various schemes such as flat and large areas, fields, small and large offices, indoors and outdoors. Every medium incorporates a fading model containing shadowing and MIMO multi-path decaying, taking into account the distribution of multiple delays, spatial power, K-factor dispersal, and Doppler range. © 2024 The Authors.

关键词： Wireless local area networks (WLAN)

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TinyML for Edge networks: Challenges and Future Directions

TinyML for Edge Networks: Challenges and Future Directions

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2025 International conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Tummala, Veera Manikantha Rayudu Korada, Sonish Lingamallu, Sai Pavan Hari, Bandaru Sai Hazra, Abhishek Indian Institute of Information Technology Sri City India

ISBN: (纸本)9798331513894

TinyML, deploying low-complexity Machine Learning (ML) models on microcontroller units, which have constrained the availability of resources, is revolutionizing the technologies for extremely low-end devices. This study investigates the importance, categorization, and uses of TinyML in edge networks, providing an overview of workflow and the importance of different compression techniques. TinyML can be applied in various industries such as healthcare, Industrial Internet of Things (IIoT), and agriculture where it demonstrates the capability of doing local data processing and fast decision making which means need to provide the results there on spot. Nevertheless, TinyML encounters limitations and challenges such as interoperability, memory usage, energy efficiency, and standardization, which are also discussed in this article. © 2025 ieee.

关键词： Decision making

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DRSU-net: Depth-Residual Separable U-net model for Semantic Segmentation

DRSU-net: Depth-Residual Separable U-net model for Semantic ...

引用

International Joint conference on Neural networks (IJCNN)

作者： Arbane, Mohamed Khanouche, Mohamed Essaid Khodabandelou, Ghazaleh Abdelghani, Chibani Amirat, Yacine Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

ISBN: (纸本)9781665488679

In recent years, semantic segmentation has become an increasingly important task in computer vision, with numerous applications in image analysis and object recognition. One popular approach for semantic segmentation is the use of convolutional neural networks (CNNs), such as the U-net architecture, which is known for its ability to capture both local and global contexts in images. However, the U-Net model can be computationally intensive and time-consuming, especially for large or highresolution images. This paper proposes a depth-residual separable U-net (DRSU-net) model to improve the U-net model for semantic segmentation tasks. The proposed approach introduces an additional regularisation technique instead of dropout and L2 regularisation and modifies the network's structure to reduce the risk of overfitting and the number of model parameters. This reduces training time without losing model information, especially for large datasets. The effectiveness of the DRSU-net method is demonstrated through extensive experiments on several semantic segmentation benchmarks. The results show improved performance and lower temporal complexity compared to the basic U-net and state-of-the-art architectures.

关键词： Deep learning computer vision semantic segmentation U-net

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Quantifying the Quality Attenuation of WiFi 47

Quantifying the Quality Attenuation of WiFi

引用

47th ieee conference on local computer networks (LCN)

作者： Teigen, Bjorn Ivar Davies, Neil Ellefsen, Kai Olav Skeie, Tor Torresen, Jim Univ Oslo Dept Informat Oslo Norway Predictable Network Solut Ltd Stonehouse England

ISBN: (纸本)9781665480017

WiFi is one of the most widely deployed networking technologies, and understanding WiFi performance is therefore of great importance. The WiFi MAC layer sometimes introduces significant and variable delays. No existing models of the WiFi protocol describe WiFi performance in terms of complete latency distributions. In this work, we present a novel model of WiFi performance. We explicitly define our model in terms of the latency introduced at each step in the protocol state machine, and the model produces complete latency distributions. We validate the model by comparing its outputs to previous modeling work and real-world measurements. Finally, we use our results to quantify the latency distribution of WiFi as a function of the duration of transmit opportunities and the number of stations competing for the channel. Quantifying this relation represents a significant improvement in our understanding of WiFi performance that would not be possible with existing models.

关键词： Multimedia and real-time communication Performance evaluation of networks Delta Q Quality attenuation Ergodicity networking WiFi 802.11

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X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System 59

X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusio...

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59th Annual ieee International conference on Communications (ieee ICC)

作者： Kaya, Kiymet Ak, Elif Bas, Sumeyye Canberk, Berk Oguducu, Sule Gunduz Istanbul Tech Univ Dept Comp Engn Istanbul Turkiye Istanbul Tech Univ Dept Artificial Intelligence & Data Engn Istanbul Turkiye BTS Grp Istanbul Turkiye Edinburgh Napier Univ Sch Comp Engn & Built Environm Edinburgh Midlothian Scotland

ISBN: (纸本)9781728190549

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.

关键词： intrusion detection system graph neural networks explainable artificial intelligence self-supervised learning edge embedding

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An Efficient Hybrid Deep Learning Model for Detecting Musculoskeletal Abnormalities

An Efficient Hybrid Deep Learning Model for Detecting Muscul...

引用

2025 ieee International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Deo, Arpit Gupta, Priyasha Deveradi, Karnika Patidar, Kashish Kumawat, Ashish Malik, Pankaj Medi-Caps University Department of Computer Science and Engineering Madhya Pradesh Indore453331 India Institute of Engineering & Technology Devi Ahilya Vishwavidyalaya Department of Computer Engineering Madhya Pradesh Indore452017 India

ISBN: (纸本)9798331512965

Deep Convolutional Neural networks (DCNNs) have made tremendous advances in healthcare, but their limited perceptual capacities make it difficult to capture extensive structural details. To solve this restriction, this study presents a new technique which integrates the benefits of EfficientNet to perform local data extraction with DenseNet201 for processing global characteristics in pictures. The MURA dataset, which contains 40,005 radiographic pictures, is used in the project with the goal of enhancing radiographic image categorization, notably for identifying Musculoskeletal disorders. The goal of the project is to accomplish a more thorough analysis of radiological pictures by successfully collecting global as well as local information via the fusion of EfficientNet and DenseNet201. The proposed approach shows positive outcomes, with the combined approach obtaining an overall accuracy of 96.38% on the test dataset and a sensitivity of 0.90. © 2025 ieee.

关键词： DCNNs DenseNet201 EfficientNet Hybrid Model MURA

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MVNet:Mutil-view Feature Representation based Image Classification 5

MVNet:Mutil-view Feature Representation based Image Classifi...

引用

5th International conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Gao, Yuan Nanjing University of Information Science & Technology Nuist Nanjing China

ISBN: (纸本)9798350366174

Image classification remains a fundamental challenge in computer vision, demanding models to improve accuracy and efficiency. This paper introduces a novel approach by integrating convolutional neural networks (CNNs) and Transformer models to exploit their respective strengths in feature extraction. We propose a Multi-view Feature Extraction method that leverages local CNN feature detection capabilities, along with awareness of the global context of Transformers. To further improve model performance, we've developed a Feature Fusion module that integrates features from multiple perspectives, providing a more comprehensive representation of input images. © 2024 ieee.

关键词： Convolutional neural networks

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The Adaptive Fault-tolerant Routing Based on an Improved local Security Information Model of the Exchanged Hypercube 21

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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21st ieee International Symposium on Parallel and Distributed Processing with Applications, 13th ieee International conference on Big Data and Cloud Computing, 16th ieee International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Yazhi Luo, Chuanwen Wang, Guijuan Zhang, Li Lv, Mengjie Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Beijing Forestry University School of Information Science and Technology Beijing China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Nanjing University of Posts and Telecommunications College of Computer Nanjing China Qilu University of Technology Shandong Academy of Sciences Big Data Research Institute Jinan China

ISBN: (纸本)9798350329223

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length. © 2023 ieee.

关键词： Hypercube networks

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