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Guard Interval's Length Prediction in 802.11ay Systems in Indoor Environments 31

Guard Interval's Length Prediction in 802.11ay Systems in In...

引用

31st International conference on Software, Telecommunications and computer networks, SoftCOM 2023

作者： Drozdowska, Monika Cardona, Narcis ITEAM Research Institute Universitat Politecnica de Valencia Valencia Spain

ISBN: (纸本)9798350301076

The optimisation of 60 GHz Wireless networks requires reducing the end-to-end (E2E) latency, which depends very much on the configuration of the physical layer protocols. In the ieee 802.11ay, the standard for the 60 GHz frequency band local area networks (LAN), three lengths of guard intervals (GI) are established. Its proper selection in every environment will affect the E2E latency and the overall performance. This work proposes a method to choose the length of GI, based on the Root Mean Square Delay Spread (RMS-DS) prediction model and the indoor environment geometry, to optimise the latency of the wireless access system. © 2023 University of Split, FESB.

关键词： ieee Standards

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Addressing data scarcity in local photovoltaic datasets: A GAN-based workflow

Addressing data scarcity in local photovoltaic datasets: A G...

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2024 ieee International conference on Big Data, BigData 2024

作者： Izzi, Federico Martella, Cristian Longo, Antonella University of Salento Dept. of Engineering for Innovation Lecce Italy

ISBN: (纸本)9798350362480

Digital twins are increasingly being implemented in smart cities, where they are designed to provide 3D digital representations for near-real time interactivity and status feedback of the twinned physical assets. The ability to provide accurate predictions in what-if scenarios plays a key role in driving the advances in resources optimization and risk mitigation. To this end, prediction models require large datasets to train, and such datasets are usually scarce at local level. In this paper, we propose a generative AI-based workflow to infer domestic photovoltaic energy production data, based on a process that uses Generative Adversarial networks to generate an enriched meteorological dataset. The generated meteorological data captures essential statistical properties of real data that can be later used to infer realistic energy production data points. The resulting output dataset is validated against a real photovoltaic system, highlighting the ability to deliver high-fidelity time series out of scarce input datasets. © 2024 ieee.

关键词： Generative adversarial networks

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Multi-Level Graph Convolutional networks with Enhanced Partitioning Strategies for Skeleton-Based Action Recognition

Multi-Level Graph Convolutional Networks with Enhanced Parti...

引用

2024 ieee International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Xinyu Yao, Huaxiong Peng, Qin College of Computer Central China Normal University WuHan China

ISBN: (纸本)9781665410205

The methods of skeleton-based human action recognition (HAR) have gained widespread interest due to their robustness in capturing actions amidst changing environments and intricate backgrounds. Utilizing graph convolutional networks (GCNs) to describe the human skeleton for HAR has been shown to achieve impressive performance. However, most GCN-based methods only consider the relationships between adjacent joints, overlooking the relationships between joints that are not connected by natural physical links. Therefore, we propose a novel method called Multi-level Graph Convolutional Network, abbreviated as ML-GCN. We have refined the original partitioning strategy, introducing three novel strategies to more effectively enhance the connectivity among distant joints. Additionally, we incorporate a multi-level graph convolutional network and a non-local temporal convolutional network to better extract spatio-temporal features. Experiments conducted on the NTU-RGB+D and Kinetics datasets demonstrate that our model achieves a certain improvement in accuracy. © 2024 ieee.

关键词： Network theory (graphs)

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An Enhanced Bat Algorithm Using Clustering Coefficient for Community Detection in Complex networks 1

An Enhanced Bat Algorithm Using Clustering Coefficient for C...

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1st International conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Taibi, Salaheddine Bouamama, Salim Toumi, Lyazid University Sétif 1 - Ferhat Abbas Department of Computer Science Setif19000 Algeria

ISBN: (纸本)9798331541064

The discrete bat algorithm has gained significant interest in solving a variety of combinatorial optimization problems. Discovering community structures in complex networks is particularly challenging, as it is an NP-hard problem. To address this, various adaptations of the discrete bat algorithm have been developed. This work proposes CC-DBAT, an improved discrete bat algorithm utilizing the clustering coefficient to identify community structures in complex networks. The proposed algorithm generates an initial population of solutions using the clustering coefficient metric, followed by a fast local move technique to enhance the intensification process. Additionally, a random neighbor communities technique is employed to generate a random solution that guides the search process toward the global optimum. Experiments conducted on synthetic and real-world networks demonstrate that the proposed approach outperforms existing bat algorithms and exhibits improved convergence rates. © 2024 ieee.

关键词： Combinatorial optimization

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Efficient Load Balancing in Software Defined networks Using Unsupervised Meta-Reinforcement Learning

Efficient Load Balancing in Software Defined Networks Using ...

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

作者： Kulkarni, Prerita Doohan, Nitika Vats Medicaps University Department of Computer Science Indore India Sage University Department of Computer Science Indore India

ISBN: (纸本)9798331512965

New technologies, such as cloud computing and big data, make traditional network management more difficult, while the network's user base continues to expand. Consequently, changes to the traditional network architecture are required. To address this issue and enhance network management compliance, a new idea called software-defined network (SDN) has been proposed. Due to limited network resources and to satisfy the criteria of quality of service, it is necessary to address numerous difficulties, one of which is load balancing (LB), which distributes data traffic among several resources to maximize their efficiency and reliability. The local information of a network is used to design LB in a typical network. Consequently, it is not very accurate. Still, software-defined network (SDN) controllers can see the network from anywhere in the world and make better LB. We provide a set of unsupervised meta- RL algorithms to build smart optimization architecture for SDN LB. © 2025 ieee.

关键词： load balancing Optimization Software-defined networking (SDN) Unsupervised meta-reinforcement learning

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Integrating Ad-Hoc networks with 5G for Enhanced Communication Capabilities 2

Integrating Ad-Hoc Networks with 5G for Enhanced Communicati...

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2nd ieee International conference on Advances in Information Technology, ICAIT 2024

作者： Purushothaman, N. Sivasankar, C. Subramanian, E.K. Sarala, V. Moorthy, A. Department of CSE SKP Engineering College Tiruvannamalai India Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Simats Chennai India Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Thandalam Chennai India

ISBN: (纸本)9798350383867

It has become customary that 5G telecommunications systems merge with IoT these days because of their high data rates, minimal delay, and vastly distributed connectivity. The interference and the network renewal issues still last, though. The current application of this 5G in IoT applications and its main obstacles or limitations are discussed as this. The integration of ad-hoc networks in 5G environment is one viable option for future indl strongest data transmission. Various ad hoc networks can give a chance for a local communication channel and continuous capability of self-organising, which can help 5G networks which have a centralized architecture. Furthermore, this symmetric network model can increase coverage, decrease the need for the amount of the transmission power and therefore will help to enrich the communication capabilities of the IoT devices. Among the topics that call for the further research are designing efficient routing protocols, constructing a cybersecurity framework, and establishment a mechanism for interaction between intrewireless networks and 5G data network infrastructure. In general, 5G technology can successfully solve the problem of limited communication over the IoT networks thus, create superior platforms for communication in the future. © 2024 ieee.

关键词： Ad hoc networks

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Understanding a Power Grid's Cyber-Physical Interdependence Through Higher-order Motifs

Understanding a Power Grid's Cyber-Physical Interdependence ...

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2024 ieee Power and Energy Society Innovative Smart Grid Technologies conference, ISGT 2024

作者： Huang, Hao Poor, H. Vincent Flynn, David Al-Muhaini, Mohammad Princeton University Department of Electrical and Computer Engineering PrincetonNJ United States University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom King Fahd University of Petroleum & Minerals Electrical Engineering Department Eastern Province Saudi Arabia

ISBN: (纸本)9798350313604

Power grid consists of interconnected cyber and physical networks. The complexity of which is increasing as a result several factors including: The convergence of low carbon technologies, the increased coupling of other critical networks, and a need for new distributed control and forecasting capabilities. These trends are creating unprecedented complexity in our critical networks, as well as introducing new threats to their functionalities. Hence, the network design is crucial to ensure the power grid's inherent security and resilience. Given its multilayered nature, this necessitates an understanding of the interdependence between cyber and physical networks. However, the heterogeneity between these networks makes it challenging to holistically analyze the power grid's cyber-physical architecture without losing granularity. To address this, higher-order motifs, defined as small connected subgraphs, can be employed to disclose the topological interdependence of heterogeneous networks at a local level. This paper uses an augmented cyber-physical WSCC 9-Bus System to investigate its 4-node motif patterns under different cyber attack scenarios. Certain 4-node motifs demonstrate their necessity to secure power grid functionality. © 2024 ieee.

关键词： Complex networks

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Adaptive Decentralized Federated Learning in LEO Satellite networks 24

Adaptive Decentralized Federated Learning in LEO Satellite N...

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24th ieee International conference on Communication Technology, ICCT 2024

作者： Li, Dong Yan, Zhigang Hao, Yuyu Niyato, Dusit Quek, Tony Q. S. Macau University of Science and Technology School of Computer Science and Engineering Macau China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

ISBN: (纸本)9798350363760

In Federated Learning (FL) for the low Earth orbit (LEO) satellite networks with parameters aggregated by a ground station, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the latency within the FL for the satellite-ground communication, Decentralized Federated Learning (DFL) has appeared as an effective solution. However, considering the heterogeneity of satellites, the problem on how to efficiently leverage the limited resources available to enhance the model performance of DFL has drawn little attention, which motivates this work. Specifically, the proposed solution involves optimizing the number of local training rounds across diverse satellites with varying resource budgets to enhance the DFL performance by utilizing the resource more efficiently. Simulation results show that, the proposed scheme achieves a much better performance than the conventional schemes with fixed rounds of local training. © 2024 ieee.

关键词： Federated learning

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computer-Aided Load Forecasting and Optimal Dispatching Algorithm for Power Systems 1

Computer-Aided Load Forecasting and Optimal Dispatching Algo...

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1st International conference on Intelligent Systems and Computational networks, ICISCN 2025

作者： Zhang, Zhiqiang Pang, Xiaoling Jia, Yongmin Haidu College Qingdao Agricultural University Shandong Laiyang China

ISBN: (纸本)9798331529246

In order to solve the problems of low load forecasting accuracy and low operating efficiency caused by strong load fluctuation and rapid changes in electricity demand in the power system, this paper introduces a computer-aided load forecasting and optimization scheduling algorithm to improve the overall operating efficiency of the system and reduce the risk of unbalanced power supply. Firstly, big data analysis technology is used to clean and extract features of historical load data. Then, a load forecasting model based on multivariate time series is constructed by combining support vector machine (SVM) and long short-term memory network (LSTM) to capture the nonlinear characteristics of load changes and improve the accuracy of prediction. Secondly, this paper adopts a hybrid optimization strategy of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to globally search for the optimal scheduling solution and achieve rapid convergence through local fine-tuning. Among them, the SVM-LSTM model has the highest accuracy with an error of 10%. The GA-PSO algorithm performs best, with energy consumption as low as 900 kWh and calculation time of only 30 seconds. The computer-aided power system load forecasting and optimal dispatch algorithm proposed in this paper can operate stably in complex power system environments. © 2025 ieee.

关键词： Power systems computer aided design

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local Feature Enhanced Adversarial Network for the Blind Image Quality Assessment 48

Local Feature Enhanced Adversarial Network for the Blind Ima...

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48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shi, Xiaomei Zhang, Min Xia, Shouhai Zhang, Ruxue Feng, Jun Northwest University School of Mathematics China Northwest University School of Information Science and Technology China

ISBN: (纸本)9781728163277

As a hot research topic in the field of computer vision, blind image quality assessment (BIQA) can provide high-quality images for end-users and promote the development of other fields of computer vision. Although the existing BIQA based on convolution neural networks has made significant progress in synthetic distortion evaluation, it still cannot be well extended to authentic distortion and algorithm-related distortion. Therefore, this paper proposes a BIQA adversarial network with local feature enhancement to deal with this challenge. First, the ResNeSt50 network with local feature enhancement is used to extract the features of images, which effectively combines the overall semantic information and the local features of the images. Then, the mapping of distorted images to their quality scores is learned by the adversarial network. Extensive experiments demonstrate that the proposed method performs best on three categories of distorted scenario databases (nine databases) compared with state-of-the-art BIQA methods. © 2023 ieee.

关键词： computer vision

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