检索结果-内蒙古大学图书馆

9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jenifer, A. Ahmed, Syed Imran Sangeetha, G. Chakkaravarthy, A. Prabhu Kumar, Ashok Bakhare, Ruhi Rmk Engineerinh College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Mj College Department of Electronics And Communication Engineering Telangana Hyderabad500053 India R.M.K College of Engineering And Technology Department of Artificial Intelligence And Data Science Tamil Nadu Thiruvallur India Srm Institute of Science And Technology Department of Networking And Communications Tamil Nadu Kattankulathur603203 India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Dr. Ambedkar Institute of Management Studies And Research Department of Cse Deekshbhoomi Maharashtra 440010 India

ISBN: (纸本)9798350365092

In an unanticipatedly short period, a patient may seek advice from a computer prior to seeing a doctor, thus establishing the' standard' medical procedure of the future. Thanks to developments in artificial intelligence (AI), the days of misdiagnosing illnesses and treating their symptoms instead of their underlying causes may soon be over. It's possible that something like this will occur soon. To accommodate a complete 3D representation of an organ, imagine how much space would need to be cleared from your laptop or how many years' worth of blood pressure records would need to be eliminated. The image would require both of these things to be made possible. More uses of artificial intelligence and high performance data-driven medicine are made possible by the growing body of data generated in clinics and preserved in electronic medical records as a result of routine testing and medical imaging. These applications have led to the development of methods used by researchers and medical practitioners to address clinical concerns throughout time, and this trend is anticipated to continue. © 2024 IEEE.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Khattab M. Ali Alheeti Ali Hamza Najim Nurhizam Safie Mohd Satar Salah A. Aliesawi Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering mam AlKadhum College (IKC) Baghdad Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Computer Networking Systems College of Computer and Information Technology University of Anbar Anbar Iraq Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks.

关键词： Wireless communication Wireless sensor networks Protocols Energy conservation Pressing Data collection Reliability Optimization Monitoring Drones

来源：评论

学校读者我要写书评

暂无评论

Empirical Evaluation of DASH Streaming over Multipath TCP in a Mixed Traffic Environment

Empirical Evaluation of DASH Streaming over Multipath TCP in...

引用

International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Abhinaba Rakshit Dayma Khan Mohit P. Tahiliani Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Mangalore India

Multipath TCP (MPTCP) is a transport layer protocol that enables multi-homed devices to simultaneously use more than one interface. As a result, MPTCP provides a higher bandwidth to the applications and also increases resilience to potential connection failures. These aspects are crucial for real time traffic, especially streaming applications. One of the open source streaming protocols which has gained popularity in recent years is Dynamic Adaptive Streaming over HTTP (DASH). Studies evaluating the performance of DASH streaming over MPTCP have consistently shown significant performance improvements in application QoE (Quality of Experience) under various scenarios such as packet loss, shared bottleneck, and variable path latencies. However, when MPTCP is deployed for streaming over the Internet, it has to compete with several other types of traffic. So far, no study has been conducted to evaluate the performance of DASH over MPTCP under varying traffic loads. Besides, all the existing studies have used the out-of-tree Linux implementation of MPTCP. In this work, we present a detailed evaluation of streaming DASH traffic over MPTCP and also compare its performance against streaming over TCP under mixed traffic environment. We leverage Linux network namespaces for creating the testbed and stream DASH traffic. Moreover, we analyze the impact of competing traffic on the throughput of MPTCP. Our work is also the first to use the state-of-the-art upstream implementation of MPTCP in Linux. We observed that while MPTCP offers certain benefits for streaming, its performance may not be significantly better than traditional TCP under varying traffic loads. Our research indicates that the effectiveness of MPTCP for DASH streaming largely depends on segment length. MPTCP tends to perform better within a specific range of segment lengths that are neither too small nor too large.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Review of Multiple Access Techniques for Intelligent Reflecting Surface-Assisted Systems

A Review of Multiple Access Techniques for Intelligent Refle...

引用

Communications and networking (MeditCom), IEEE International Mediterranean Conference on

作者： Wei Jiang Hans D. Schotten Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Germany Department of Electrical and Computer Engineering University of Kaiserslautern (RPTU) Germany

ISBN: (数字)9798350309485

ISBN: (纸本)9798350309492

Intelligent Reflecting Surface (IRS) is envisioned to be a technical enabler for the sixth-generation (6G) wireless system. Its potential lies in delivering high performance while maintaining both power efficiency and cost-effectiveness. Previous studies have primarily focused on point-to-point IRS communications involving a single user. Nevertheless, a practical system must serve multiple users simultaneously. The unique characteristics of IRS, such as non-frequency-selective reflection and the necessity for joint active/passive beamforming, create obstacles to the use of conventional multiple access (MA) techniques. This motivates us to review various MA techniques to make clear their functionalities in the presence of IRS. Through this paper, our aim is to provide researchers with a comprehensive understanding of challenges and available solutions, offering insights to foster their design of efficient multiple access for IRS-aided systems.

关键词： Wireless communication Surveys 6G mobile communication Closed-form solutions Reviews Spectral efficiency Array signal processing

来源：评论

学校读者我要写书评

暂无评论

Joint Optimizations for Double-IRS’ Cooperative Positioning and Beamforming Over Massive-MIMOAP Based 6G Secure Mobile Wireless Networks

Joint Optimizations for Double-IRS’ Cooperative Positioning...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Jiaojie Wang Fei Wang Xi Zhang Yuanyuan Yang College of Electronic and Information Engineering Southwest University China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS’ positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs’ aggregate secrecy rates when Eve’s exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve’s exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs’ positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve’s deploying areas.

关键词： 6G mobile communication Uncertainty Array signal processing Wireless networks Predictive models Deep reinforcement learning Reflection MIMO Global communication Optimization

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Over-the-Air Computation for Federated Generative Model Fine-Tuning in Unmanned Vehicle-Assisted Disaster Relief

引用

IEEE Transactions on Cognitive Communications and networking 2025年

作者： Zhao, Yikun Feng, Lei Zhou, Fanqin Li, Wenjing Xiong, Zehui Du, Hongyang Wu, Celimuge Guo, Song Quek, Tony Q.S Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong University of Electro-Communications Meta-Networking Research Center Tokyo Japan Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The utilization of emergent generative artificial intelligence (GAI) within the realm of unmanned vehicles (UVs) can boost edge intelligence and potentially enhance the efficiency and effectiveness of rescue operations. However, to facilitate specialized generative edge intelligence in dynamic UV networks, GAI models need to tap into local data and conduct online fine-tuning, and the dispersed distribution of UVs makes distributed fine-tuning paradigms crucial. Although the federated generative model fine-tuning brings a potential solution, the large number of parameter transmissions involved in its fine-tuning process are often constrained by communication bottlenecks, which are more pronounced for resource-constrained UV networks. To cope with these challenges, we introduce an energy-efficient GAI model fine-tuning framework for the hierarchical UV networks, which employs over-the-air technology to save computational resource costs for unmanned aerial vehicle (UAV) during federated aggregation within a federated learning paradigm. Thereafter, we study the resource allocation problem in the fine-tuning process of the generative model and formulate a joint optimization problem for the bandwidth allocation, power control, computation resource allocation, and denoising factor control, aiming to minimize the system energy consumption. Then, we decouple the original problem into four tractable subproblems, and propose a block coordinate descent algorithm to solve them iteratively. Particularly, we derive a closed-form solution for the denoising factor to minimize the local model uploading transmission energy consumption under a specific AirComp communication error threshold. Simulation based on the state-of-the-art generative models shows that our AirComp-assisted federated generative model fine-tuning scheme can achieve satisfactory customized field-of-view image generation capability compared with the traditional fine-tuning scheme in an energy-efficient way. © 2015 I

关键词： Energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Optimizing Forest Fire Detection Systems: A Deep Learning Approach with UAVs and Binary Sand Cat Swarm Optimization

Optimizing Forest Fire Detection Systems: A Deep Learning Ap...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： T. Rajesh Kumar K Komala Devi P. Subramanian Mahaveerakannan R Balamanigandan R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ISE CMR Institute of Technology Bengaluru India Department of Networking Institute of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Devastating forest fires are an international environmental crisis that endangers both people and their valuable natural resources. Forest fires are becoming more common and more destructive, leading to significant depletion of natural resources. A combination of human interference with natural processes and the effects of climate alteration have contributed to a recent uptick in the frequency of wildfires. Combating this issue requires firefighters to respond swiftly and identify fires from early smoke so they can prevent the fires from spreading. An efficient system for detecting and monitoring fires is essential for reducing this risk. The purpose of this study is to investigate and assess the state of the art in forest fire detection and monitoring with the use of deep learning algorithms and drones or unmanned aerial vehicles (UAVs). An efficient and economical way to monitor in real-time and detect fires early is to use drones that are outfitted with specialised sensors and cameras. Using the suggested customised Convolutional Neural Network models, the datasets were tested. Deep Q learning (DQL) is used to extract features, which enhances the accuracy of classification. When compared to other transfer learning methods, such as VGG16, Xception, ResNet50, DenseNet121, Inception ResnetV2, besides Inception V3, their outcomes are evaluated. To analyse and solve the optimal parameter selection problem, the authors suggest using Binary Sand Cat Swarm Optimisation (bSCSO) to fine-tune the parameters of the prototypical. The suggested custom VGG16 model outstripped state-of-the-art models by a significant margin (98.14%), which was measured by its ability to generalise to unknown images.

关键词： Natural resources Wildfires Accuracy Q-learning Biological system modeling Forestry Feature extraction Real-time systems Particle swarm optimization Monitoring

来源：评论

学校读者我要写书评

暂无评论

Multi-Functional RIS for Sum-Rate Maximization in Rate-Splitting Multiple Access Networks 24

Multi-Functional RIS for Sum-Rate Maximization in Rate-Split...

引用

24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Wang, Wen Ni, Wanli Tian, Hui Yang, Zhaohui You, Changsheng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Nanyang Technological University School of Computer Science and Engineering 117583 Singapore

ISBN: (纸本)9781665496261

In this paper, we propose a multi-functional reconfigurable intelligent surface (MF-RIS) assisted rate-splitting multiple access (RSMA) scheme to improve the spectral efficiency in the downlink communications. With the ability of simultaneous signal reflection, transmission/refraction, and amplification, the proposed MF-RIS enables full-dimensional communications with enhanced signal power, thereby improving the signal reception of RSMA users in whole space. Under the constraints on the power budget, MF-RIS coefficients, and minimum rate requirement, a non-convex optimization problem is formulated to maximize the sum-rate by jointly designing the rate allocation, transmit beamforming, and MF-RIS configuration. Then, a low-complexity alternating optimization algorithm is proposed to solve this challenging problem. Simulation results show that the proposed MR-RIS-aided RSMA scheme can achieve up to 33.6% and 51.9% higher spectral efficiency than the simultaneous transmitting and reflecting RIS-aided RSMA scheme and the MF-RIS-aided non-orthogonal multiple access scheme, respectively. © 2023 IEEE.

关键词： Spectrum efficiency

来源：评论

学校读者我要写书评

暂无评论

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Error-Rate QoS-Exponents Over M-MIMO Mobile Wireless Networks Using FBC

Modeling Statistical Delay, Error-Rate, and Joint-Delay/Erro...

引用

IEEE International Symposium on Information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

To support increasing demands for real-time multimedia wireless data traffic, there have been considerable efforts toward guaranteeing stringent quality-of-service (QoS) when designing massive multiple-input and multiple-output (m-MIMO) mobile wireless network architectures for massive ultra-reliable and low-latency communications (mURLLC). One of the major design issues raised by mURLLC is how to characterize QoS metrics for upper-bounding both delay and error-rate when implementing short-packet data communications, such as finite blocklength coding (FBC), over highly time-varying m-MIMO based wireless fading channels. To efficiently accommodate statistical QoS for mURLLC traffic, it is crucial to model and investigate m-MIMO based wireless fading channels’ stochastic-characteristics by defining and identifying new statistical QoS metrics and their analytical relationships, such as delay-bound-violating probability, effective capacity, decoding error probability, etc., in the finite blocklength regime. However, how to rigorously and efficiently characterize the stochastic dynamics of m-MIMO mobile wireless networks in terms of statistically upper-bounding FBC-based both delay and error-rate QoS metrics has been neither fundamentally understood nor thoroughly studied before. To overcome these challenges, in this paper we develop analytical modeling techniques and frameworks for statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish system models using FBC. Second, we develop a set of new statistical delay and error-rate bounded QoS metrics including delay, error-rate, and joint-delay/error-rate QoS-exponents, and the corresponding ϵ-effective capacities. Finally, our simulations validate and evaluate our developed modeling schemes for statistical QoS to support 6G mURLLC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

New Intrusion Detection System to Protect MANET Networks Employing Machine Learning Techniques

New Intrusion Detection System to Protect MANET Networks Emp...

引用

2021 International Conference of Modern Trends in Information and Communication Technology Industry, MTICTI 2021

作者： Khalifa, Marwa Mohammed Ucan, Osman Nuri Ali Alheeti, Khattab M. University of Altinbaş Institute of Graduate Studies Department of Computer Engineering / Information Technologies Turkey University of Altinbaş School of Science and Engineering Department of Electrical and Computer Engineering Turkey University of Anbar College of Computer Science and Information Technology Department of Computer Networking Systems Iraq

ISBN: (纸本)9781665414449

The Intrusion Detection System (IDS) is one of the technologies available to protect mobile ad hoc networks. The system monitors the network and detects intrusion from malicious nodes, aiming at passive (eavesdropping) or positive attack to disrupt the network. This paper proposes a new Intrusion detection system using three Machine Learning (ML) techniques. The ML techniques were Random Forest (RF), support vector machines (SVM), and Naïve Bayes(NB) were used to classify nodes in MANET. The data set was generated by the simulator network simulator-2 (NS-2). The routing protocol was used is Dynamic Source Routing (DSR). The type of IDS used is a Network Intrusion Detection System (NIDS). The dataset was pre-processed, then split into two subsets, 67% for training and 33% for testing employing Python Version 3.8.8. Obtaining good results for RF, SVM and NB when applied randomly selected features in the trial and error method from the dataset to improve the performance of the IDS and reduce time spent for training and testing. The system showed promising results, especially with RF, where the accuracy rate reached 100%. © 2021 IEEE.

关键词： Support vector machines

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：