检索结果-内蒙古大学图书馆

IEEE Conference on Global Communications (GLOBECOM)

作者： Ben Chen Ke Guan Danping He Pengxiang Xie Zhangdui Zhong Jianwu Dou Shahid Mumtaz Wael Bazzi School of Electronic and Information Engineering Beijing Jiaotong University Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Engineering Nottingham Trent University UK Electrical and Computer Engineering Department American University in Dubai Dubai UAE

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Object-Attribute-Relation Representation Based Video Semantic Communication

arXiv

引用

arXiv 2024年

作者： Du, Qiyuan Duan, Yiping Yang, Qianqian Tao, Xiaoming Debbah, Mérouane The Department of Electronic Engineering Tsinghua University Beijing China Beijing China The State Key Laboratory of Space Network and Communications Beijing China School of Computer Science and Technology Xinjiang University Xinjiang China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel coding

来源：评论

学校读者我要写书评

暂无评论

Leveraging Machine Learning to Identify Synergistic Drug Combinations for Effective Cancer Treatment

Leveraging Machine Learning to Identify Synergistic Drug Com...

引用

International Conference on Image Information Processing

作者： P. Sujatha K Saravanan Mohammed Ali Sohail A Basi Reddy Rohit R Dixit Nallam Krishnaiah Department of AI/ML Amrita Vishwa Vidyapeetham Amaravati Guntur India Department of Information Technology R.M.K Engineering College Tiruvallur District Tamil Nadu India Department of Computer & Network Engineering College of Computer Science & Information Technology Jazan University Jazan K.S.A Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Senior Data Scientist Siemens Healthineers New Hampshire USA Department of AI&ML St.Martin’s Engineering College Dhulapally Secunderabad Telangana India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The potential of drug combinations to treat and overcome medication resistance complex genetic diseases is evident. Synergistic drug combinations offer a promising way to enhance drug therapy efficacy and reduce the required medication dosage. However, developing effective combination medication therapies with synergistic effects has been challenging, despite numerous ongoing clinical investigations. Current models and approaches to detect medication synergy outlined in the literature lack the expected consistency in outcomes. to better comprehend the impact of particular medication combinations, it is essential to be familiar with the vocabulary used to describe synergy. In this study, a combinational drug screen is utilized to identify useful features for locating synergistic or efficient drug combinations. The feature selection algorithm (Boruta) helps select the most relevant features, and machine learning models are then trained using the selected feature dataset. Performance assessment metrics like sensitivity, accuracy, and specificity are used to compare the trained models, and the Random Forest model stands out for its significantly better performance compared to other models.

关键词： Drugs Vocabulary Sensitivity Machine learning algorithms Cancer treatment Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Adaptive Delay Optimization of Multicast-Enabled Coded Caching in Device-to-Device networks

引用

China Communications 2020年第1期17卷 42-48页

作者： Liang Zhong Xueqian Zheng Jie Lin Yang Cao Shenggen Ju China University of Geosciences School of Mechanical Engineering and Electronic Information Department of Electronics and Information EngineeringWuhan 430027China Huazhong University of Science and Technology Wuhan 430027China Sichuan University Department of Computer Network and CommunicationChengdu 610207China

This letter investigates a delay optimization problem in device-to-device(D2D)networks where users have pre-downloaded subfiles following a coded caching *** to acquiring rest subfiles,users may suffer both receiving and transmitting *** achieve the delay minimization,we first propose a delay-aware mode-selection strategy to adaptively choose multicast or D2D communications mode to reduce the receiving-caused *** matching these transmit modes with distinct subfile sizes,we further formulate a min-max optimization problem to minimize the delivery ***,numerical results prove that the proposed scheme outperforms existing ones in terms of both receiving and transmitting delays.

关键词： coded caching delay optimization multicast device-to-device

来源：评论

学校读者我要写书评

暂无评论

A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

引用

IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

来源：评论

学校读者我要写书评

暂无评论

RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

引用

Expert Systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural networks (GNN) for efficient traffic engineering (TE) in Software Defined networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Safeguarding Next Generation Multiple Access Using Physical Layer Security Techniques: A Tutorial

arXiv

引用

arXiv 2024年

作者： Lv, Lu Xu, Dongyang Hu, Rose Qingyang Ye, Yinghui Yang, Long Lei, Xianfu Wang, Xianbin Kim, Dong In Nallanathan, Arumugam The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China The Department of Electrical and Computer Engineering Utah State University LoganUT84322 United States The Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an710121 China The School of Information Science and Technology Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Electronic Engineering and Computer Science Queen Mary University of London E1 4NS United Kingdom

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultra-low latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next generation multiple access schemes to facilitate provisioning of these stringent demands. This inspires the embrace of non-orthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats, due to the open nature of the air interface, the broadcast characteristic of radio propagation as well as intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security aware design, for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview on the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio waves

来源：评论

学校读者我要写书评

暂无评论

A novel cooperative game for reinforcing obesity awareness amongst children in UAE

A novel cooperative game for reinforcing obesity awareness ...

引用

International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020

作者： Alnaqbi, Fatema Alzahmi, Sarah Alharmoozi, Ayesha Alshehhi, Fatema Zia, Muhammad Talha Ouhbi, Sofia Belkacem, Abdelkader Nasreddine Department of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain United Arab Emirates Department of Computer and Network Engineering CIT United Arab Emirates University Al Ain United Arab Emirates Department of Information Technology CIT United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789811557835

This paper presents a funny cooperative game that makes kids interact with their parents to indirectly educate both of them about the importance of making their own choices of eating unhealthy and healthy food. The game-based learning and cultivation of an informed decision-making approach throughout our proposed game design was utilized to achieve the obesity awareness objectives. This paper describes the design, implementation, and evaluation of our proposed cooperative game "ObeseGo" that was developed to enhance the obesity awareness for kids at an early age and educate them regarding their choices concerning eating a balanced meal to be able to adopt a healthy lifestyle. A survey questionnaire was conducted to evaluate our game. The results of the survey allowed us to analyze the impact and usefulness of the proposed game and to plan for improvement in future work. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of Indoor VLC network with Secure Downlink NOMA for Body Blockage Model

arXiv

引用

arXiv 2023年

作者： Shen, Tianji Yachongka, Vamoua Hama, Yuto Ochiai, Hideki B2B Division Hithink Royalflush Information Network Co. Ltd. Zhejiang Hangzhou310023 China Department of Computer Science and Engineering University of Texas at Arlington ArlingtonTX76010 United States Department of Electrical and Computer Engineering Yokohama National University Kanagawa Yokohama240-8501 Japan Graduate School of Engineering Osaka University Osaka565-0871 Japan

In this work, we investigate the performance of indoor visible light communication (VLC) networks based on power domain non-orthogonal multiple access (NOMA) for mobile devices, where multiple legitimate users are equipped with photodiodes (PDs). We propose a body blockage model for both the legitimate users and eavesdropper to address scenarios where the communication links from transmitting light-emitting diodes (LEDs) to receiving devices are blocked by the bodies of all parties. Furthermore, we propose a novel LED arrangement that improves secrecy without requiring knowledge of the channel state information (CSI) of the eavesdropper. This arrangement reduces the overlapping areas covered by different LED units supporting distinct users. We also suggest two LED transmission strategies, i.e., simple and smart LED linking, and compare their performance with the conventional broadcasting in terms of transmission sum rate and secrecy sum rate. Through computer simulations, we demonstrate the superiority of our proposed strategies to the conventional approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile telecommunication systems

来源：评论

学校读者我要写书评

暂无评论

Cube Sampled K-Prototype Clustering for Featured Data 18

Cube Sampled K-Prototype Clustering for Featured Data

引用

18th IEEE India Council International Conference, INDICON 2021

作者： Jain, Seemandhar Shastri, Aditya A. Ahuja, Kapil Busnel, Yann Singh, Navneet Pratap Indian Institute of Technology Indore Computer Science and Engineering Indore India Information Technology NMIMS Shirpur India Lab Indore India Institut Mines-Telecom Atlantique Network Systems Cybersecurity and Digital Law Department Rennes France Computer Science and Engineering NMIMS Indore Indore India

ISBN: (纸本)9781665441759

Clustering large amount of data is becoming increasingly important in the current times. Due to the large sizes of data, clustering algorithm often take too much time. Sampling this data before clustering is commonly used to reduce this time. In this work, we propose a probabilistic sampling technique called cube sampling along with K-Prototype clustering. Cube sampling is used because of its accurate sample selection. K-Prototype is most frequently used clustering algorithm when the data is numerical as well as categorical (very common in today's time). The novelty of this work is in obtaining the crucial inclusion probabilities for cube sampling using Principal Component Analysis (PCA). Experiments on multiple datasets from the UCI repository demonstrate that cube sampled K-Prototype algorithm gives the best clustering accuracy among similarly sampled other popular clustering algorithms (K-Means, Hierarchical Clustering (HC), Spectral Clustering (SC)). When compared with unsampled K-Prototype, K-Means, HC and SC, it still has the best accuracy with the added advantage of reduced computational complexity (due to reduced data size). © 2021 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：