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3rd IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2023

作者： Zhang, Wenjun Chen, Yuan Wei, Xia Yan, Yongrui Shaanxi Branch of National Computer Network Center China Emergency Response Technical Team/Coordination Xi'an China School of Information Engineering Xi'An Mingde Institute of Technology Xi'an China China Mobile Communications Group Shanxi Co. Ltd. Taiyuan China

ISBN: (纸本)9798350317022

With the development of the Internet of Things technology, the smart home industry is rapidly developing, and the collection and use of sensitive user data are also increasing. The issue of user privacy protection is becoming increasingly prominent and has received widespread attention. However, related research is still in its infancy. Firstly, the technical architecture of the smart home system was introduced, and an in-depth analysis was conducted on the privacy issues and the reasons for their increasing severity in the system. Secondly, based on the degree of use of privacy data such as user personal information and behavior, existing smart home systems were classified. Once again, the research status of different technologies in smart home privacy protection, such as access control, policy control, data minimization, network traffic obfuscation, federated learning, homomorphic encryption, and zero knowledge proof, was summarized and organized, and a comparative analysis was conducted on various technologies. Finally, corresponding suggestions were proposed for privacy protection in smart home systems. © 2023 IEEE.

关键词： data security federal learning internet of things privacy protection smart home

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

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6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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Rerouting in Cognitive Optical networks

Rerouting in Cognitive Optical Networks

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IEEE International Conference on Communications (ICC)

作者： Anny Xijia Zheng Vincent W.S. Chan Department of Electrical Engineering and Computer Science Claude E. Shannon Communication and Network Group Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

We investigate the design of an efficient rerouting scheme for dynamic bursty high granularity traffic as a part of an agile network management and control scheme in cognitive optical networks. An efficient rerouting scheme allows more efficient use of wavelengths resources. The design includes the fast reconfiguration of wavelengths in the existing lit and spatially routed fibers, and the setting up and lighting of new fibers. Rerouting is necessary to maintain users' quality of service when the network load exceeds the load limit of the primary path. Our design is to trigger reconfiguration when a load threshold is crossed, and the threshold is determined by requirements on both the delay and the blocking (i.e. no available lightpath) probability. The delay is dependent on the probability of exhausting all wavelengths on the primary route, the availability of secondary routes, the holder (buffer) size, and the topology of the network. A dynamic rerouting scheme implemented with a shortest-path algorithm with immediate wavelength availability is proposed to find the alternate paths. It is desirable that the alternate paths for rerouting have small numbers of hops and are disjoint with other busy paths. In addition, a good candidate network topology should have high edge-connectivity. Wavelength reservation for rerouted traffic does not maximize wavelength utilization. Traffic with different sizes should be broken up into multiclasses with dedicated partitioned resources and will have different maximum load thresholds for triggering rerouting to meet the performance requirements.

关键词： Integrated optics network topology Heuristic algorithms Wavelength assignment Lighting Quality of service Optical fiber networks

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A middleware protocol for time-critical wireless communication of large data samples 42

A middleware protocol for time-critical wireless communicati...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Peeck, Jonas Möstl, Mischa Ishigooka, Tasuku Ernst, Rolf TU Braunschweig Institute of Computer and Network Engineering Braunschweig Germany Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9781665428026

We present a middleware-based protocol that reliably synchronizes large samples consisting of multiple frames efficiently and within application level QoS requirements over a lossy wireless channel. The protocol uses a custom retransmission scheme, exploiting the latency requirements on sample level for frame level scheduling. It can be integrated into the popular DDS middleware. We investigate some technical limits of such a protocol and compare it to existing error protocols in the software stack and in the wireless protocol and combinations thereof. The comparison is based on an Omnet++ simulation using an established wireless channel error model. For evaluation, we take a use case from automated valet parking where infrastructure data provided via a wireless link augments in-vehicle sensor data. The use case respects the related safety requirements. Results show that the application awareness of the presented protocol, significantly improves service availability by transmitting data efficiently in time even under higher frame error rates. © 2021 IEEE

关键词： Middleware

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground engineering

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computer Modeling in engineering & Sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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Designing a Distributed Multi-agent System for Compiler Optimization

Designing a Distributed Multi-agent System for Compiler Opti...

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2021 Zooming Innovation in Consumer Technologies Conference, ZINC 2021

作者： Sari, Alparslan Sahin, Cagri Butun, Ismail University of Delaware Electrical and Computer Engineering Computer Systems and Networking NewarkDE United States Gazi University Computer Engineering Software Research Group Ankara Turkey Chalmers University of Technology Computer Science and Engineering Network and Systems Division Gothenburg Sweden

ISBN: (纸本)9781665404174

This paper explores the run time performance improvements using different GCC optimization flags in program compilation. As multi-core microprocessor systems replacing legacy single-core ones, tremendous effort is needed to address to optimize the associated compilers for newly designed architectures in order to suit them for running parallel programming on multiple cores. Therefore, the aim of this paper is to address this challenge by designing an optimum distributed multi-agent system to perform compiler optimization. A multi-agent framework is adopted to utilize random and genetic algorithm-based search algorithm to find the best GCC optimization flags for a given program. The framework is highly scalable and can be extended with distributed system concept to perform code compilation in parallel to find the best-optimized code sequence in a short amount of time. The initial performance results have promising indicators which clearly show that the performance improvement is achieved. © 2021 IEEE.

关键词： Multi agent systems

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A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

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computers, Materials & Continua 2022年第11期73卷 4291-4309页

作者： Kais Tissaoui Sahbi Boubaker Waleed Saud Alghassab Taha Zaghdoudi Jamel Azibi University of Ha’il Applied CollegeHail CitySaudi Arabia University of Tunis El Manar Faculty of Economic Sciences and Management of Tunisthe International Finance GroupTunisTunisia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Unit on Study of Systems and Renewable Energy National College of Engineering of MonastirUniversity of MonastirMonastirTunisia LAREQUAD&FSEGT University of Tunis El ManarTunisTunisia University of Jendouba Law FacultyManagement and Economic Sciences of JendoubaTunisia

The application of optimization methods to prediction issues is a continually exploring *** line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting *** complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown *** use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein *** indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,*** performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

关键词： Forecasting Cboe’s volatility index COVID-19 pandemic nonlinear polynomial hammerstein model hybrid particle swarm optimization

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D2D Resource Allocation Problem in Full - Load Cellular networks 8th

D2D Resource Allocation Problem in Full - Load Cellular Netw...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Yi, Haimin He, Xiaoli Li, Lun Song, Yu Li, Hongwei Zeng, Zhen Yin, Xiaodong School of Computer Science Sichuan University of Science and Engineering Zigong643000 China Department of Network Information Management Center Sichuan University of Science and Engineering Zigong643000 China China Mobile Group Sichuan Company Limited Zigong Branch Zigong643000 China

ISBN: (纸本)9783031067662

With the increasing demand of local service load, how to improve resource utilization is an important problem. D2D (Device-to-Device) communication is considered to be an effective solution. The letter allows users in close proximity to communicate directly without passing through the base station.D2D communication technology has improved user experience, reduced delay and reduced power consumption and other potential advantages. However, cellular users will be disturbed when D2D users reuse channel resources, resulting in complex interference environment in the cell, so how to allocate resources and interference management of D2D users is an urgent problem to be solved. Aiming at the cellular network scenario, this paper studies the wireless resource allocation algorithm of D2D communication in cellular network, which reduces the interference of D2D users to cellular users and improves the total throughput of cell. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Resource allocation

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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BloodComm: A Peer-to-Peer Blockchain-based Community for Blood Donation network

BloodComm: A Peer-to-Peer Blockchain-based Community for Blo...

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International Conference on computer and Information Technology (ICCIT)

作者： Chowdhury Mohammad Abdullah Minhaz Kamal Fairuz Shaiara Abu Raihan Mostofa Kamal Md Azam Hossain Department of Computer Science and Engineering Network and Data Analysis Group (NDAG) Islamic University of Technology Gazipur Bangladesh

Blood transfusion is an integral part of the healthcare system that plays an important role in ensuring the quality of care for patients undergoing a variety of medical procedures and treatments. A large portion of this blood comes from voluntary donors. The existing blood donor management systems are unable to offer a reliable audit trail and traceability. Hence, there is a significant risk that patients may get transfusion of blood from unreliable sources. In this paper, we propose a system built on Ethereum with the goal of creating a decentralized, transparent, traceable, and secure network of blood donors. The platform uses smart contracts to facilitate peer-to-peer interactions. To encourage donors to donate blood more regularly, the system also offers rewards in the form of tokens. Our source code is available in a public Github repository 1 .

关键词： Source coding computer network reliability Smart contracts Medical services Peer-to-peer computing Reliability Information technology

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