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检索条件"任意字段=14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022"

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Transmit Beamforming for Dual-Function Radar- communication System Based on FDA 14

Transmit Beamforming for Dual-Function Radar- Communication ...

引用

14th international conference on Signal Processing systems, ICSPS 2022

作者： Wu, Haozheng Jin, Biao Xu, Zhaoyang Zhu, Xiaohua Ocean College Jiangsu University of Science and Technology Zhenjiang China 723th Research Institute China Shipbuilding Industry Corporation Yangzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350336313

Dual-function radar-communication (DFRC) methods employed phased array and multiple-input multiple- output (MIMO) systems are 'point-to-line'. the enemy hidden in coverage can intercept the transmit signals leading to poor confidentiality. Frequency diversity array (FDA) can provide an angle-time-dependent transmit steering vector by introducing a stepping frequency increment among each array element. this paper proposes an anti-interception DFRC system based on FDA transmit beamforming. the proposed system adopts a sub-pulse transmit mode to realize a 'point-to-point' detection and communication. the information symbols are embedded into the transmit beampattern by beam level control and orthogonal waveform transmission. the receiver can obtain the original information by detecting the received signal amplitude in multiple channels. the simulation results demonstrate that the proposed system can perform 'point-to-point' detection and communication capability with a low bit error rate and high confidentiality. © 2022 IEEE.

关键词： Bit error rate

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Binary classification architecture for Edge Computing based on cognitive services and deep neural networks 22

Binary classification architecture for Edge Computing based ...

引用

14th international conference on Management of Digital Ecosystems (MEDES)

作者： Chancusig, Cristian Tumbaco, Sergio Alulema, Darwin Iribarne, Luis Criado, Javier Univ Fuerzas Armadas ESPE Sangolqui Ecuador Univ Fuerzas Armadas ESPE Appl Comp Grp TIC 211 Sangolqui Ecuador Univ Almeria Appl Comp Grp TIC 211 Almeria Spain

ISBN: (纸本)9781450392198

systems based on computer vision and artificial intelligence are an alternative for repetitive inspection processes. However, it is possible to extend the learning capacity of these systems to classify multiple objects using edge computing. this allows combining local processing with cloud processing to expand the possibilities and reduce the response time. In this work, a classification architecture based on remoteweb services and local neural networks is proposed. To test this architecture, Microsoft Azure cognitive web services and its Computer Vision API have been used, combined with the use of transfer learning and ResNet 50. the cloud service allows the identification and labelling of image content, while the Edge service, based on the neural network, allows the generation of classification models for those objects not identified or incorrectly identified by the remote service. the architecture allows to extend the possibility of image recognition by integrating web services that combined with edge processing accelerate the identification process. the proposed architecture is composed of three layers;(a) a physical layer, for the mechanical and electronic structure;(b) a logical layer, which defines the interaction of the remote and local image recognition web services, and (c) an application layer, for the integration of the monitoring and control interfaces. Finally, the architecture was evaluated through functionality testing and performance metrics of classification models, as well as load and usability testing.

关键词： Microservices Cyber-Physical systems Edge Computing Computer Vision Neural Network

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Machine Learning and thermal Imaging in Precision Agriculture 14th

Machine Learning and Thermal Imaging in Precision Agricult...

引用

14th international conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Mirzaev, Kostas-Gkouram Kiourt, Chairi International Hellenic University Kavala Greece Research and Innovation Center in Information Communication and Knowledge Technologies Xanthi Greece

ISBN: (纸本)9783031674259

thermal imaging has emerged as a pivotal technology in modern agriculture, offering farmers and domain experts advanced tools for monitoring crop health and detecting stress, pests, and diseases at early stages. Its integration with machine learning algorithms further enhances its potential, revolutionizing agricultural practices by providing real-time, data-driven insights. this paper delves into the recent advancements in the application of thermal imaging in agriculture, boosted by machine learning techniques. We explore some of the most important key areas, such as: Crop monitoring: utilizing thermal imaging for identifying and quantifying crop stress, aiding in the refinement of crop management strategies to boost *** management: leveraging thermal imaging data to fine-tune irrigation schedules, thereby conserving water and enhancing water use efficiency. Pest and disease detection: employing thermal imaging in conjunction with machine learning algorithms for the early detection and automated identification of pests and diseases, enabling prompt and effective interventions. this paper aims to provide a comprehensive overview of current applications and emerging trends in the use of thermal imaging and machine learning in agriculture, highlighting their benefits, challenges, and potential for future development. Moreover we conclude the paper by discussing the challenges and opportunities of using thermal imaging in agriculture applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Study on Latency Prediction in 5G network 14

A Study on Latency Prediction in 5G network

引用

14th international conference on Ubiquitous and Future networks, ICUFN 2023

作者： Choi, Seunghan Kim, Changki Electronics and Telecommunications Research Institute Packet Network Research Section Daejeon Korea Republic of

ISBN: (纸本)9798350335385

these days, due to the increase in the use of mobile terminals such as smartphones, tablets, and XRM(Extended Reality and Media) service terminals, heterogeneous networks for various services are often connected to the 5G network. Low latency should be supported on the network for these services. At the time of measuring the latency at the current time point, recalculating the end-to-end QoS path, or informing the XRM service application, it can be a past value, which can lead to an inaccurate situation. To overcome this situation, 5G network needs to predict latency in advance, recalculate end-to-end QoS paths based on this information, or informs XRM applications to meet more effective QoS requirements. In this paper, we have evaluated the performance of several machine learning models for predicting latency, and introduce the results of experimenting with performance. © 2023 IEEE.

关键词： 5G mobile communication systems

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Deep Denoising Channel Extrapolation for IRS-Assisted OFDM systems 14

Deep Denoising Channel Extrapolation for IRS-Assisted OFDM S...

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14th international conference on Information and communication Technology Convergence, ICTC 2023

作者： Khan, Mubasher Ahmed Kang, Huijin Awais, Muhammad Kim, Yun Hee Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of

ISBN: (纸本)9798350313277

Intelligent reflecting surface (IRS) has emerged as a promising solution for future networks in improving the coverage and spectral efficiency by adapting its reflection pattern to the channel environments. To enable this, it is essential to acquire channel state information for which deep learning networks have been popularly applied recently. In this paper, we apply and optimize a deep denoising network to estimate the IRS-cascaded frequency selective fading channel with a reduced reflection pilot pattern for an IRS-enhanced orthogonal frequency division multiplexing system. the proposed network designed to support various noise levels with a single network model is shown to provide a reasonably good channel extrapolation performance when compared with the benchmark networks optimized for each noise level. © 2023 IEEE.

关键词： Channel estimation deep learning intelligent reflecting surface orthogonal frequency division multiplexing

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Energy-Efficient Optimization for Multi-cell Distributed Massive MIMO URLLC systems 14

Energy-Efficient Optimization for Multi-cell Distributed Mas...

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14th IEEE international conference on Wireless communications and Signal Processing (WCSP)

作者： Zhu, Xiaoyue Liu, Bo Zhu, Pengcheng Li, Jiamin Wang, Yan Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

ISBN: (纸本)9781665450850

One of the most critical services in the fifth generation (5G) mobile communication is ultra-reliable and low-latency communication (URLLC). In this paper, we investigate the energy efficiency (EE) maximization problem of URLLC for the multi-cell distributed massive MIMO (DM-MIMO) system through a joint optimization of beamforming and remote radio unit (RRU) selection. the formulated problem is a mixed integer nonlinear program (MINLP) and then we solve it by applying efficient approaches, including the Dinkelbach method, semidefinite relaxation (SDR), reweighted l1-norm and auxiliary variable approximation. By combining these measures in one iteration, an energy-efficient optimization algorithm is proposed to find the suboptimal solution of the problem. Simulation results show that the proposed algorithm converges fast and significantly improves the EE of the multi-cell DM-MIMO URLLC system.

关键词： URLLC DM-MIMO EE beamforming RRU selection

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Text Mining on Hospital Stay Durations and Management of Sickle Cell Disease Patients 14

Text Mining on Hospital Stay Durations and Management of Sic...

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14th IEEE international conference on Computational Intelligence and communication networks, CICN 2022

作者： Gollapalli, Mohammed Alabdullatif, Latifa Alsuwayeh, Farah Aljouali, Moodhi Alhunief, Alhanoof Batook, Zaina Ccsit Imam Abdulrahman Bin Faisal University Department of Computer Information Systems Dammam Saudi Arabia Ccsit Imam Abdulrahman Bin Faisal University Department of Computer Engineering Dammam Saudi Arabia

ISBN: (纸本)9781665487719

Sickle cell disease (SCD) is a genetic blood disorder characterized by clumping of red blood cells, preventing blood and oxygen from reaching all parts of the body. SCD disease is very common in Sub-Saharan Africa, the Mediterranean basin, and the eastern regions of Saudi Arabia due to high consanguineous marriage practices. Patients are frequently admitted due to the prevalence of multiple organ damage among SCD patients as a result of repeated vascular occlusion, resulting in a large amount of medical notes recorded by doctors and nurses during each clinical trial. In this study, 12 years of SCD patient de-identified data (2018-2020) were obtained officially from the hospital and experimented with in relation to SCD patient medical notes. We used a text mining framework to analyze and predict the length of stay (LoS) of SCD patients using three machine learning (ML) models: XGBoost, Decision Tree, and KNN. the most frequently occurring words were extracted from 62,847 SCD medical screening records using text mining. Furthermore, feature models were created to investigate the effect of increasing or decreasing the number of terms on model performance. the XGBoost algorithm produced the best results, with 94.3% accuracy, while the other algorithms produced results of 93.5% for Decision Tree and 90.7% for KNN. the findings suggest that predicting the length of stay of SCD patients is highly feasible, allowing for better utilization of medical personnel and resources. © 2022 IEEE.

关键词： Decision trees

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Modeling and Analysis of Full Duplex Wireless systems with Interference 13

Modeling and Analysis of Full Duplex Wireless Systems with I...

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13th international conference on Information and communication systems (ICICS)

作者： Baltazar, Georgino da Silva Brito, Jose Marcos C. Natl Inst Telecommun Inatel Santa Rita Sapucai Santa Rita Do Sapucai Brazil

ISBN: (纸本)9781665480970

Numerous technologies have been developed to meet the performance requirements of fifth generation cellular mobile communications networks (5G), among them Full Duplex communication. In this paper, we proposed an analytical Markovian model to analyze the performance of Full Duplex and Half Duplex technology combined with omnidirectional and directional antennas in a multi-hop wireless network with unidirectional traffic considering interference, it was considered that no buffer is available on the servers. Several performance metrics to evaluate the network performance were considered: throughput, Capacity, Block Probability, Drop Probability and Average Number of Elements in the system. We show that in scenarios that SNR is lower, half duplex communication can be better perform than full duplex communication in terms of capacity and throughput.

关键词： 5G 6G Full duplex communications Performance analysis Markovian models

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Fake News Detection Using Machine Learning Models 14

Fake News Detection Using Machine Learning Models

引用

14th IEEE international conference on Computational Intelligence and communication networks, CICN 2022

作者： Aljabri, Malak Alomari, Dorieh M. Aboulnour, Menna College of Computer and Information Systems Umm Al-Qura University Department of Computer Science Makkah21955 Saudi Arabia Imam Abdulrahman Bin Faisal University Department of Computer Science College of Computer Science and Information Technology Dammam31441 Saudi Arabia College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Saudi Aramco Cybersecurity Chair Department of Computer Engineering P.O. Box 1982 Dammam31441 Saudi Arabia

ISBN: (纸本)9781665487719

Nowadays, with the widespread use of technology, fake news and rumors are spreading too. People and society are greatly impacted by fake news, which also can be used as phishing attempts and a way of stealing their information. In many areas of our lives, Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated their effectiveness. Furthermore, Natural Language Processing (NLP) has shown promising results in text classification applications. In this study, we proposed an experimental study for detecting fake news using ML models. the proposed model analyzes the main text of the news using NLP techniques and then classifies the news into fake or real news. We used a new dataset that combined multiple fake news datasets. Moreover, we studied the impact of features extraction methods on the performance of the developed models. Eight experiments were performed using Random Forest (RF) and Support Vector Machines (SVM) models, each with a different features extraction technique. the SVM model resulted in the best performance with an accuracy level of 98%. this result proves the model ability to be deployed and used in real-world with high reliability, to detect fake news. © 2022 IEEE.

关键词： Support vector machines

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Impact of UAV Wobbling on the Air-to-Terrestrial Wireless communication Channels in Various Frequency Bands 14

Impact of UAV Wobbling on the Air-to-Terrestrial Wireless Co...

引用

14th international conference on Signal Processing systems, ICSPS 2022

作者： Shen, Hongjie Wang, Qi Gao, Shang Yu, Hualong School of Computer Jiangsu University of Sci. and Tech. Zhenjiang China

ISBN: (纸本)9798350336313

Recently, unmanned aerial vehicles (UAVs) have stroke great interest in the field of wireless communication due to their unique capabilities, for instance, flexibility, maneuverability, and adaptive altitude adjustment. However, the impact of the UAV jitter on parameters related to air-to-terrestrial wireless communication channel characteristics and how to reasonably quantify this impact have not been widely explored. In this paper, we assume a single UAV scenario under the Rician multipath channel model to derive the relationship between channel capacity and channel quality with the number of antennas and carrier frequency, the sensitivity of channels at different carrier frequencies to UAV random jitter, and the comparative results of channel availability at the same jitter level through the experiments. Finally, it is concluded that under the condition of the same carrier frequency, the channel capacity and transmission rate are proportional to the number of transmitting and receiving antennas, while the maximum pitch angle of the UAV swing is inversely proportional to the length of the channel coherence time;under the condition of the same number of transmitting and receiving antennas, the carrier frequency of the channel is inversely proportional to the channel coherence time and the channel stability, especially in millimeter-wave conditions. © 2022 IEEE.

关键词： Millimeter waves

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