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In-Network Computing and Split-AI in 6G: Enablers and Proof-of-Concept Studies 3

In-Network Computing and Split-AI in 6G: Enablers and Proof-...

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3rd international conference on 6G Networking, 6GNet 2024

作者： Spina, M.G. Soto Lebron, D.V. Schwarzmann, S. Guerzoni, R. Trivisonno, R. Iera, A. De Rango, F. Karetsos, G.T. Zinner, T. Corujo, D. University of Calabria Italy CNIT Italy Huawei Technologies Munich Germany DOCOMO Communications Laboratories Europe Germany University of Thessaly Greece NTNU Norway Department of Information Security and Communication Technology Norway University of Aveiro Instituto de Telecomunicações Portugal

ISBN: (纸本)9798350378597

Split-AI proposes a paradigm shift in which Neural Networks (NN)-relevant tasks are distributed across multiple networking entities. The capabilities envisaged for 6G mobile networking such as In-Network Computing (INC) can facilitate a smooth deployment of this technique. 6G's Access Nodes (ANs) and User Plane Functions (UPFs) will be able to execute parts of an NN while performing their traditional functions (e.g. transmitting packets). Recent studies show that INC-assisted SplitAI has the potential to enhance Key Performance Indicators (KPIs) like inference time, network traffic load, and UE energy consumption. The incorporation of Split-AI in 6G networks needs to fulfill significant design and performance requirements such as optimal partitioning strategies for distributing NN parts across network elements. Additionally, the 6G User Plane must enable the dynamic and flexible allocation and deployment of these split NN parts. In this work we: i) propose possible enhancements for the Control Plane (CP) and UP, to make possible the integration of INC-assisted Split-AI in 6G networks;ii) present the design of a Proof-Of-Concept (PoC) simulation model aimed at demonstrating the feasibility of the technique with a comprehensive study on the impact that different NN partitioning options have on the network utilization, inference time, and the resources of both UE and network entities involved. © 2024 IEEE.

关键词： Neural networks

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Efficient Microservice Deployment with Dependencies in Multi-Access Edge Computing 29

Efficient Microservice Deployment with Dependencies in Multi...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Lu, Shuaibing Yan, Ran Wu, Jie Beijing University of Technology Faculty of Information Technology Beijing China Temple University Center for Networked Computing Philadelphia United States

ISBN: (纸本)9798350330717

In the context of mobile edge computing, efficiently deploying microservices to reduce finish time and enhance user service quality is a challenging task. However, existing research still has certain deficiencies in considering microservice deployment within edge server clusters and communication link constraints. To address this issue, we propose three microservice deployment strategies by offering flexibility and adaptability for various application scenarios. We initially consider two straightforward scenarios: one with unlimited storage resources under the bandwidth constraint, and the other with unlimited bandwidth resources under the storage constraint. For each of these two scenarios, we introduce a novel enhanced graph construction method and design two optimal solutions. For Scenario 3, which involves complex constraints on server capacity, computational capability, and communication resources, we present an optimization method based on main path partitioning and the simulated annealing algorithm. We effectively tackle challenges arising from server capacity, computational capability, and communication resource limitations. Across multiple experimental setups, our approach significantly improves microservice deployment efficiency and overall performance compared to traditional strategies. © 2023 IEEE.

关键词： dependency high-efficient microservice deployment multi-access edge computing

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Defect Identification and Localization Method Based on All-fiber Photoacoustic Non-destructive Testing System 3

Defect Identification and Localization Method Based on All-f...

引用

3rd international conference on Digital Signal and Computer Communications, DSCC 2023

作者： Zhang, Pengyu Fu, Xuelei Wang, Honghai School of Information Engineering Wuhan University of Technology 122 Luoshi Road Hubei Wuhan430070 China National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

The all-fiber photoacoustic system can work properly in the harsh environment of electromagnetic interference and has the potential advantage of achieving a wide range of distributed detection. However, the ultrasound signal of all-fiber system is affected by signal mode mixing, which will have an impact on defect identification and localization, and the time varying filtering based empirical mode decomposition (TVF-EMD) algorithm is used to solve the effect of mode mixing. According to different modes with different signal characteristics, this work proposes a defect identification and localization method based on an all-fiber photoacoustic NDT system. After experimental testing, the method can identify and locate the number and shape of defects in a thin metal plate with a bar-shaped penetration defect of aluminum alloy 6061 of size 50*50*0.1 cm3 © 2023 SPIE.

关键词： Nondestructive examination

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LiteCrypt: Enhancing IoMT Security with Optimized HE and Lightweight Dual-Authorization 30

LiteCrypt: Enhancing IoMT Security with Optimized HE and Lig...

引用

30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Xie, Qipeng Wang, Weizheng Huang, Yongzhi Zheng, Mengyao Shang, Shuai Jiang, Linshan Khan, Salabat Wu, Kaishun IoT Thrust Guang Zhou China Hong Kong University of Science and Technology Hong Kong City University of Hong Kong Computer Science Engineer Hong Kong Dsa Thrust Information Hub Guang Zhou China Harvard T.H. Chan School of Public Health Boston United States University of Electronic Science and Technology of China Chengdu Chengdu China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798331515966

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural Networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt's superior performance, achieving a 233-fold increase in processing speed, a 96% reduction in encrypted message size, and a 28-fold speed increase using GPUs. © 2024 IEEE.

关键词： Convolutional neural networks

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An Improved BiTCN Model Merging Multi-Head Attention-BiGRU for Photovoltaic Power Generation Prediction Based on Meteorological Data 3

An Improved BiTCN Model Merging Multi-Head Attention-BiGRU f...

引用

3rd international conference on Communications, information System, and Data Science, CISDS 2024

作者： Gan, Jianhong Lin, Xi Fan, Changyuan Qu, Youming Wei, Peiyang Huo, Yaoran Li, Zhibin Chengdu University of Information Technology Chengdu China Hunan Meteorological Bureau Information Center Changsha China Information & Communication Company State Grid Sichuan Electric Power Company Chengdu China Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences Xinjiang Urumqi China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The widespread application of photovoltaic power generation in smart grids makes accurate generation forecasting essential for grid management and planning. Aiming at the randomness and unpredictability of solar energy in PV power generation prediction, this paper proposes a short-term PV power generation prediction model with higher accuracy based on BiTCN, multi-focus mechanism, and BiGRU model. In addition, DRSN is introduced to improve the residual block of BiTCN so as to extract the important features of PV power generation and reduce the redundant features. In addition, the combination of multiple attention mechanisms enables the model to analyze all aspects of the data in parallel, ensuring a comprehensive examination of the information. The BiGRU model accurately captures the long-term dependencies and inherent characteristics of time series data. In order to further improve the prediction accuracy, the AR model was used to optimize the linear extraction ability of the model. The experimental results show that the MAE, RMSE, and R2 of the proposed model are superior to the traditional model in complex data sets, including weather forecast data, station weather data, and power data. MAE, RMSE and R2 evaluation indexes show that the model has good prediction accuracy. © 2025 SPIE.

关键词： Prediction models

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Research on Computer Processing Technology of Big Data Based on Cloud Environment 3

Research on Computer Processing Technology of Big Data Based...

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3rd international conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)

作者： Du, Chenglong Guangdong Univ Sci & Technol Dongguan 523083 Guangdong Peoples R China

ISBN: (纸本)9781665454551

With the development of The Times, the Internet has become an essential part of people's *** order to meet the needs of the market, it is necessary to continuously optimize and innovate in technology and display various advantages in the era of big *** methods such as distributed computing or parallel computing are limited by various conditions and cannot meet specific computing ***, in order to solve this problem, people gradually begin to adopt the computer processing technology of big data based on the background of cloud environment, so as to break these limitations and carry out data calculation more quickly and *** the background of the era of big data, the choice of vigorously promoting the use of advanced computer information processing technology can continue to ensure the security of different types of data storage and application, so that the application value of data information can be presented in an all-round way.

关键词： cloud environment computer technology big data processing calculate data storage information technology

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Synthesis of information System Elements with Ensuring Their Security 3

Synthesis of Information System Elements with Ensuring Their...

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3rd international conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Gromov, Yuri Minin, Yuri Kopylov, Sergey Ivanova, Olga El-Eissawi, Badr Khalil Mahmoud Institute of Automation and Information Technology Tambov State Technical University Tambov Russia Tambov Regional State Budget Institution Center for Determining the Cadastral Value of Real Estate Tambov Russia

ISBN: (纸本)9781665439817

A model is proposed for the synthesis of optimal logical structures of distributed databases in an organization's information system, in which, due to the lack of sufficient statistical information on the parameters of the system's functioning at the initial stage of development, some of the variables are assigned as fuzzy LR-Type numbers. In connection with the need to ensure information security requirements for such systems, the determination of the optimal structure of a distributed database is expanded by the task of determining forbidden trajectories of transitions between its elements by analyzing the correctness of access matrices and determining errors in them. © 2021 IEEE.

关键词： information systems

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parallel Architecture Based on Transformers and CNNs for Renal Cell Carcinoma Image Segmentation 3

Parallel Architecture Based on Transformers and CNNs for Ren...

引用

3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Ao, Bin Liu, Rong Wen, Qing Wu, Xin Li, Kuan Yin, Jianping School of Cyberspace Security Dongguan University of Technology Dongguan China

ISBN: (纸本)9781510667563

Image segmentation of Renal Cell Carcinoma is a crucial prerequisite for pathologists to diagnose the disease and implement treatment. In various previous medical image segmentation tasks, convolutional neural networks(CNNs) have been widely used and have become a practical benchmark with significant success. However, the inherently local nature of the convolution operation leads to its inability to balance long-range relations and local dependencies in the modeling process, resulting in redundancy-deepening networks and the loss of local details. With its innate global self-attention mechanism, transformers were designed for sequence-to-sequence prediction to better extract global information. However, on account of the lack of low-level details, it may cause limited localization abilities. In this paper, we propose a parallel architecture based on Transformers and CNNs, combining Transformers and CNNs in a parallel manner to obtain global and local information by utilizing location-encoded Transformers model and local convolution neural networks, respectively. So that global contextual information can be captured more efficiently while maintaining a strong grasp of low-level spatial detailed information for Renal Cell Carcinoma image segmentation. Through a series of comparative experiments and extension experiments, the effectiveness and progressiveness of our method were validated, demonstrating remarkable potential in the image segmentation task of Renal Cell Carcinoma. © 2023 SPIE.

关键词： Semantic Segmentation

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PCU-Net: An enhanced U-network by combining PPM and CBAM for Medical Image Segmentation 29

PCU-Net: An enhanced U-network by combining PPM and CBAM for...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Chen, Hejian Fu, Zhongming Liu, Li He, Mengsi Deng, Jiayi Tang, Zhuo University of South China School of Computer Science and Technology Hunan Hengyang421001 China Hunan University National Supercomputing Center School of Information Science and Engineering Hunan Changsha410082 China

ISBN: (纸本)9798350330717

U-network is a kind of full convolutional neural network, which is widely used in the field of medical image segmentation. However, it still has the problem of small targets being unable to be segmented and resulting in unsatisfactory segmentation effect. In order to solve this problem, this paper proposes an enhanced U-network by combining pyramid pooling module (PPM) and convolutional block attention module (CBAM). It's whole network is U-Net architecture, where PPM with bin sizes of 1×1, 2×2 and 3×3 are used in the downsampling part of the network, which can extract input image features of various dimensions. And CBAM is used in the downsampling part of the network, which combines convolution and attention mechanism, and can pay attention to the image from two aspects of space and channel to improve the segmentation ability of the network. Experimental results show that our network outperforms traditional Ushaped segmentation networks by 30% to 40% in metrics IoU, MAE, and Dice, respectively. © 2023 IEEE.

关键词： convolutional block attention module medical image segmentation pyramid pooling module U-network

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Joint Planning Method of Virtual Power Plant for distributed Generation and Grid in AC-DC Medium and Low Voltage Distribution Network 3

Joint Planning Method of Virtual Power Plant for Distributed...

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2023 3rd international conference on Electronics, Circuits and information Engineering, ECIE 2023

作者： Liu, Bei Shen, Xu Zhang, Ruosong Chen, Haofei Zhou, Xiaohang Huaneng Zhejiang Energy Sales Company China

The high proportion of virtual power plants (VPPs) used for distributed generation may cause problems such as power fluctuations and voltage distribution. In contrast, AC/DC hybrid power grid has strong transmission capacity due to its power information transmission. However, Its power grid structure and the location and capacity of VPP have an impact on the consumption capacity of the system, and the relationship between them are closely coupled. In addition, Under different operation modes, power grids of different voltage levels also have great differences. Therefore, the cooperative planning of medium and low voltage AC and DC distribution networks can improve the transmission capacity of the power grid Further improve the ability of distribution network to consume distributed power, A method is proposed in this paper about siting and capacity determination and AC-DC distribution grid frame transformation planning for medium and low voltage systems. The upper-layer model mainly focuses on AC-DC transformation scheduling in the power grid, distributed node location in the access grid and transmission line transmission capacity, while the lower-layer model effectively communicates with the low-voltage system through information flow and changes the operation of the power grid to a certain *** results show that the distributed generation system can reduce the operation cost and improve the overall operation efficiency of the power grid. © Published under licence by IOP Publishing Ltd.

关键词： Electric power transmission

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