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2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)最新文献

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Indoor Path Loss Modeling for 5G Communications in Smart Factory Scenarios Based on Meta-Learning 基于元学习的智能工厂5G通信室内路径损耗建模
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528530
Pei Wang, Hyukjoon Lee
{"title":"Indoor Path Loss Modeling for 5G Communications in Smart Factory Scenarios Based on Meta-Learning","authors":"Pei Wang, Hyukjoon Lee","doi":"10.1109/ICUFN49451.2021.9528530","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528530","url":null,"abstract":"Millimeter waves (mmWaves) of the 28 GHz frequency bands have been selected for the 5G communications with special usage scenarios such as smart factories. Indoor path loss prediction plays an important role in configuring a base station to be able to utilize the full capacity of the new technology. Although machine learning has attracted much attention recently in path loss modeling thanks to its ability to make accurate predictions, its performance can be limited by the size of available measurement data set used for training. In this paper, we propose a new training strategy to train path loss models based on convolutional neural network (CNN). The proposed strategy is based on meta-learning which performs well in few-shot learning scenarios with multiple tasks comprising a meta-task. It is shown that the indoor path loss model based on a CNN configured as a metatask of multiple beams can outperform the CNN models by a conventional training algorithm as well as empirical models.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Metal Defect Classification Using Deep Learning 基于深度学习的金属缺陷分类
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528702
Aji Teguh Prihatno, Ida Bagus Krishna Yoga Utama, J. Kim, Y. Jang
{"title":"Metal Defect Classification Using Deep Learning","authors":"Aji Teguh Prihatno, Ida Bagus Krishna Yoga Utama, J. Kim, Y. Jang","doi":"10.1109/ICUFN49451.2021.9528702","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528702","url":null,"abstract":"In the era of Industry 4.0, the vast development of Smart Factory is always followed by the advancement of Deep Learning technology. To avoid the smart factory system from unwanted losses because of defects in its output production in the steel factory, defect classification on steel sheets based on Deep Learning should be developed precisely. This paper explains how the Deep Learning technique was used to implement defect detection in a smart factory. For this study, we used an open dataset of steel defects. The result of the Deep Learning method for the defect detection system generates 96% accuracy, 0.95 recall, and a precision of 0.97 on the training process. This research goal may contribute to enhancing efficiency and cost reduction in the smart steel factory environment.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121596725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A Distributed Resource Allocation Algorithm for Task Offioading in Fog-enabled IoT Systems 雾支持物联网系统中任务负载的分布式资源分配算法
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528792
Hoa Tran-Dang, Dong-Seong Kim
{"title":"A Distributed Resource Allocation Algorithm for Task Offioading in Fog-enabled IoT Systems","authors":"Hoa Tran-Dang, Dong-Seong Kim","doi":"10.1109/ICUFN49451.2021.9528792","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528792","url":null,"abstract":"In the IoT-based systems, the integration of fog computing allows the fog nodes to offload and process tasks requested from IoT-enabled devices in a distributed manner to reduce the response delay. However, achieving such a benefit is still challenging in the heterogeneous fog systems in which long task queues of powerful fogs can contribute to an average long delay of task execution. To handle the conflict of resource request for task processing this paper proposes a distributed fog resource allocation algorithms, namely MaxRU (Maximum Resource Allocation). Through the simulation analysis, MaxRU show potential advantages in reducing the average delay in the heterogeneous fog environment compared with the existing solutions.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122207279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Heart Rate Monitoring System Using Feature Extraction in Electrocardiogram Signal by Convolutional Neural Network 基于卷积神经网络的心电图信号特征提取心率监测系统
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528584
Hsing-Chung Chen, K. Shouryadhar
{"title":"Heart Rate Monitoring System Using Feature Extraction in Electrocardiogram Signal by Convolutional Neural Network","authors":"Hsing-Chung Chen, K. Shouryadhar","doi":"10.1109/ICUFN49451.2021.9528584","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528584","url":null,"abstract":"A new deep learning architecture, which is heart rate monitoring system using feature extraction in electrocardiogram signal by Convolutional Neural Network (CNN). Electrocardiogram based healthcare applications is presented in a federated context. The proposed system correctly diagnoses arrhythmias using an auto encoder and a classifier, both based on CNN. The module is provided to explain the classification findings in which the proposed classifier via employing an auto encoder and a classifier could check whether the rhythms of heart are normal, paced up or the heartbeat rate is irregular depending on the patient's situations. The module could offer the explanations of the classification findings in order to allow medical practitioners to quickly make the trustworthy judgments in preliminary diagnoses. Finally, the result shows that the proposed classifier could explain the classification for finding the two arrhythmias conditions which allow healthcare practitioners to rapidly make the correct conclusions.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132828583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Balancing the Detection of Malicious Traffic in SDN Context 平衡SDN环境下的恶意流量检测
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528577
Bruno Salgado Machado, J. M. Silva, S. R. Lima, P. Carvalho
{"title":"Balancing the Detection of Malicious Traffic in SDN Context","authors":"Bruno Salgado Machado, J. M. Silva, S. R. Lima, P. Carvalho","doi":"10.1109/ICUFN49451.2021.9528577","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528577","url":null,"abstract":"Huge efforts and resources are spent every year on prevention and recovery of cyberattacks targeting users, services and network infrastructures. Software-Defined Networking (SDN) is a technology providing advances to the field of security with the ability of programming the network, promoting highperformance solutions and efficient resource utilization at low costs, as the use of specialized hardware is avoided. The present paper aims at exploring the SDN paradigm to develop an SDN-based framework for prevention and mitigation of malicious attacks throuhgt the network. The framework design and proposal has concerns regarding the efficient use of network and computational resources, distributing the inspection of suspicious flows by distinct Intrusion Detection Systems. For this purpose, a load-balancing strategy for traffic inspection is devised, allowing to balance both the usage of resources and the analysis of traffic flows. In this way, this paper also sheds light on the usage of OpenFlow messages to build distributed SDN-based applications with the mentioned properties.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"475 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Time-Compressed Synchronization Sequence for Future Spectrally Efficient Transmission Schemes 未来频谱高效传输方案的时间压缩同步序列
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528611
Myungsup Kim, Ji-Won Jung, Ki-Man Kim
{"title":"Time-Compressed Synchronization Sequence for Future Spectrally Efficient Transmission Schemes","authors":"Myungsup Kim, Ji-Won Jung, Ki-Man Kim","doi":"10.1109/ICUFN49451.2021.9528611","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528611","url":null,"abstract":"In this paper, we propose a technique for generating a synchronization sequence for estimating time and frequency offsets for spectrum efficient transmission schemes. The synchronization sequence should have excellent auto-correlation characteristics without deteriorating the efficiency of various spectrum efficient transmission schemes. Since the columns of the discrete filter matrix obtained from the coefficients of the binomial function have a very short finite length and limit the band, the performance can be accurately estimated. In this paper, we propose a scheme for generating a sequence with excellent auto-correlation characteristics while being band-limited and, for example, verify the accuracy of the scheme.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133719463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptive V2X platform for guaranteed QoS/QoE service based on cloud computing and deep reinforcement learning 基于云计算和深度强化学习的自适应V2X保障QoS/QoE服务平台
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528541
Bokyun Jo, S. Jeong
{"title":"Adaptive V2X platform for guaranteed QoS/QoE service based on cloud computing and deep reinforcement learning","authors":"Bokyun Jo, S. Jeong","doi":"10.1109/ICUFN49451.2021.9528541","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528541","url":null,"abstract":"An industry where technology pushes behind and the market pulls ahead succeeds. In this repect, autonomous driving is a global industry that is rapidly growing in line with the advancement of wireless communication, vision, AI technologies and the smart infrastructure market, including automobiles. In particular, V2X technology is an international communication standard for providing autonomous driving services at the level of mobility 4.0. In this paper, we propose an adaptive V2X service methodology. Autonomous driving is a technology belonging to the MCS field which is directly connected to human life, so we provide enhanced QoS/QoE through proposed method which is guaranteed the real-time based on V2X standard.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning Based Pilot Assisted Channel Estimation for Rician Fading Massive MIMO Uplink Communication System 基于深度学习的无线衰落海量MIMO上行通信系统导频辅助信道估计
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528814
Md. Habibur Rahman, M. Shahjalal, Md. Osman Ali, Sukjin Yoon, Y. Jang
{"title":"Deep Learning Based Pilot Assisted Channel Estimation for Rician Fading Massive MIMO Uplink Communication System","authors":"Md. Habibur Rahman, M. Shahjalal, Md. Osman Ali, Sukjin Yoon, Y. Jang","doi":"10.1109/ICUFN49451.2021.9528814","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528814","url":null,"abstract":"Massive multiple input multiple output (MIMO) communication is one of the promising candidates for the successful deployment of Fifth-generation communication which offers an extensive improvement in spectral efficiency as well as data rate. The estimation of massive MIMO channel is very arduous due to its enormous diversity gain and enlarged capacity. However, channel estimation for uplink Rician fading massive MIMO system, where the channel is occupied with both Line of sight and non-line of sight component is not properly investigated yet. In this article, we have studied deep learning based channel estimation scheme for the massive MIMO system in Rician fading environment. Unlike the traditional approach, we have developed an optimized neural network model which can intelligently design pilot and estimate channels. We have simulated massive MIMO system at different signal to noise ratio values varying number of transmitted antennas and also investigated the performance of our proposed scheme by analyzing simulation results.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"125 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132230289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Design of Ridge Waveguide Array Antenna for Radar 雷达用脊状波导阵列天线的设计
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528566
In-Hee Han, J. Woo
{"title":"Design of Ridge Waveguide Array Antenna for Radar","authors":"In-Hee Han, J. Woo","doi":"10.1109/ICUFN49451.2021.9528566","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528566","url":null,"abstract":"In this paper, we suggest a wide-angle waveguide array antenna for radar operation and designed a ridge waveguide antenna to enlarge the beam steering angle. A miniaturization rate of 48.2% was obtained compared to the WR-90 waveguide. Using this, a 1×8 array antenna was designed and manufactured. The average beam-width of each antenna was 120°. A number of phase shifters corresponding to the beam steering angle were fabricated, and it confirmed that the measured beam steering angle was consistent with the designed beam steering angle. Accordingly, the designed antenna has characteristics suitable for beam steering of a hexagonal array structure.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134292669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
FPGA based approach for Heterogenous Sensors Data Fusion in Autonomous Vehicles 基于FPGA的自动驾驶汽车异构传感器数据融合方法
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN) Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528721
D. Créno, B. Senouci, Rafik Zitouni
{"title":"FPGA based approach for Heterogenous Sensors Data Fusion in Autonomous Vehicles","authors":"D. Créno, B. Senouci, Rafik Zitouni","doi":"10.1109/ICUFN49451.2021.9528721","DOIUrl":"https://doi.org/10.1109/ICUFN49451.2021.9528721","url":null,"abstract":"The new era of autonomous vehicles is considered as one of hot topics in Cyber Physical Systems exploration. It uses many sensors and functions to improve vehicle perception. The decision maker offers a flexible way to define the vehicle behaviour whereas the convoy driving mode is one important use case to explore more driving related issues. This paper reports an overview of on-going work on FPGA prototyping to improve the overall speed of the decision-making process and enable the convoy to drive at the higher speed safely. We present a data fusion methodology using heterogeneous Sensors (Lidar & Camera). Our methodology is based on an FPGA approach to speed up the processing time. A prototype has been built to explore new issues and solutions.","PeriodicalId":318542,"journal":{"name":"2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114543033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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