检索结果-内蒙古大学图书馆

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： M. Karthikeyan Satheeshkumar Sekar H. Anwer Basha P. Epsiba E. N. Ganesh B. Meenakshi Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science Saveetha College of Liberal Arts & Sciences SIMATS University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Indu College of Engineering and Technology Hyderabad Telangana India Department of Electronics and Communication Engineering St. Peters Institute of Higher Education and Research (SPIHER) Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Many applications, from logistics to social networks, rely on quickly navigating the shortest pathways inside large-scale graphs. For shortest route finding in huge data settings, this study aims to compare Dijkstra’s method with the Bellman-Ford algorithm. Applying these methods to large datasets will allow us to compare their speed, accuracy, and scalability. Graph density and negative weight cycles are two of the intrinsic properties of big data settings that will be used to determine which method is most suited to each. Optimal algorithm selection for given data situations is guided by insights into each algorithm’s operational strengths and shortcomings, which are revealed via rigorous testing and performance metrics analysis. This comparison helps real-world applications of each method and provides valuable information for future advancements in large data processing shortest route calculation algorithms. In a study comparing Dijkstra’s Algorithm and Bellman-Ford using the RandomGraph1 dataset, the results showed that for nodes $100-500$, the former’s ms value ranged from 5.2 to 52.3 and the latter’s ms value ranged from 6.8 to 68.9. Similarly, for nodes 100-500, the MB value range for Dijkstra’s Algorithm was 10.2-46.7 and for Bellman-Ford, it was 14.8-71.9. Data from a different SparseGraph1 dataset reveals that, for values between 100 and 500, Dijkstra’s Algorithm has a percentage range of 98.5 to 99.8, while for Bellman-Ford, it varies from 97.2 to 99.3.

关键词： Measurement Social networking (online) Navigation Scalability Big Data Data processing Computational efficiency Artificial intelligence Testing Logistics

来源：评论

学校读者我要写书评

暂无评论

MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

引用

TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

来源：评论

学校读者我要写书评

暂无评论

EFFORT: Energy efficient framework for offload communication in mobile cloud computing

EFFORT: Energy efficient framework for offload communication...

引用

作者： Malik, Saif U. R. Akram, Hina Gill, Sukhpal Singh Pervaiz, Haris Malik, Hassan Cybernetica AS Tallinn Estonia Department of Computer Science COMSATS University Islamabad Pakistan COMSATS Institute of Information Technology Islamabad Pakistan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom Department of Computer Science Edge Hill University Ormskirk United Kingdom

There is an abundant expansion in the race of technology, specifically in the production of data, because of the smart devices, such as mobile phones, smart cards, sensors, and Internet of Things (IoT). Smart phones and devices have undergone an enormous evolution in a way that they can be used. More and more new applications, such as face recognition, augmented reality, online interactive gaming, and natural language processing are emerging and attracting the users. Such applications are generally data intensive or compute intensive, which demands high resource and energy consumption. Mobile devices are known for the resource scarcity, having limited computational power and battery life. The tension between compute/data intensive application and resource constrained mobile devices hinders the successful adaption of emerging paradigms. In the said perspective, the objective of this article is to study the role of computation offloading in mobile cloud computing to supplement mobile platforms ability in executing complex applications. This article proposes a systematic approach (EFFORT) for offload communication in the cloud. The proposed approach provides a promising solution to partially solve energy consumption issue for communication-intensive applications in a smartphone. The experimental study shows that our proposed approach outperforms its counterparts in terms of energy consumption and fast processing of smartphone devices. The battery consumption was reduced to 19% and the data usage was reduced to 16%. © 2020 The Authors. Software:Practice and Experience published by John Wiley & Sons, Ltd.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Iterative Sound Source Localization for Unknown Number of Sources

arXiv

引用

arXiv 2022年

作者： Fu, Yanjie Ge, Meng Yin, Haoran Qian, Xinyuan Wang, Longbiao Zhang, Gaoyan Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Department of Electrical and Computer Engineering National University of Singapore Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan

Sound source localization aims to seek the direction of arrival (DOA) of all sound sources from the observed multichannel audio. For the practical problem of unknown number of sources, existing localization algorithms attempt to predict a likelihood-based coding (i.e., spatial spectrum) and employ a pre-determined threshold to detect the source number and corresponding DOA value. However, these threshold-based algorithms are not stable since they are limited by the careful choice of threshold. To address this problem, we propose an iterative sound source localization approach called ISSL, which can iteratively extract each source's DOA without threshold until the termination criterion is met. Unlike threshold-based algorithms, ISSL designs an active source detector network based on binary classifier to accept residual spatial spectrum and decide whether to stop the iteration. By doing so, our ISSL can deal with an arbitrary number of sources, even more than the number of sources seen during the training stage. The experimental results show that our ISSL achieves significant performance improvements in both DOA estimation and source number detection compared with the existing threshold-based algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Direction of arrival

来源：评论

学校读者我要写书评

暂无评论

Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI

Quantifying White Matter Hyperintensity and Brain Volumes in...

引用

IEEE International Symposium on Biomedical Imaging

作者： Pablo Laso Stefano Cerri Annabel Sorby-Adams Jennifer Guo Farrah Mateen Philipp Goebl Jiaming Wu Peirong Liu Hongwei Bran Li Sean I. Young Benjamin Billot Oula Puonti Gordon Sze Sam Payabavash Adam DeHavenon Kevin N. Sheth Matthew S. Rosen John Kirsch Nicola Strisciuglio Jelmer M. Wolterink Arman Eshaghi Frederik Barkhof W. Taylor Kimberly Juan Eugenio Iglesias Massachusetts General Hospital Harvard Medical School USA Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands Pioneer Centre for AI University of Copenhagen Denmark Centre for Medical Image Computing University College London UK Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA Danish Research Centre for Magnetic Resonance Denmark Yale School of Medicine USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis. Automated segmentation and quantification is desirable but existing methods require high-resolution MRI with good signal-to-noise ratio (SNR). This precludes application to clinical and low-field portable MRI (pMRI) scans, thus hampering large-scale tracking of atrophy and WMH progression, especially in underserved areas where pMRI has huge potential. Here we present a method that segments white matter hyperintensity and 36 brain regions from scans of any resolution and contrast (including pMRI) without retraining. We show results on eight public datasets and on a private dataset with paired high- and low-field scans (3T and 64mT), where we attain strong correlation between the WMH (ρ=.85) and hippocampal volumes (ρ=.89) estimated at both fields. Our method is publicly available as part of FreeSurfer, at: http://***/fswiki/WMH-SynthSeg.

关键词： Atrophy Neuroimaging Image segmentation Multiple sclerosis Image resolution Magnetic resonance imaging White matter

来源：评论

学校读者我要写书评

暂无评论

Sustainable Cloud-Based Blockchain Solution Integrated with a Supervised Attention Multi-Scale Temporal Convolutional Network for Efficient Supply Chain Management

Sustainable Cloud-Based Blockchain Solution Integrated with ...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Nidhi Sharma Alok Sharma Karthik D U Ujwal Prabhakar Nandekar Manisha Maddel Ramya Maranan Computer Engineering The Technological Institute of Textile and Sciences Bhiwani Haryana India Computer Engineering MERI CET Sampla Bahadurgarh India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka India Faculty of Law Symbiosis Centre for Advanced Legal Studies and Research Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of MBA Indira School of Business Studies Pune Maharashtra India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Organizations should prefer SCM ability, as it maintains both operating profit and market competition in dynamic markets. The current methods of analysis make poor real -time paves that give rise to sub -standard decisions that thus reduce the quality of operating performance. Research develops SA-MTCN with DSA to improve the management of the supply chain through better performance properties that deal with current control limits. SA-MTCN supply chain requires monitored attention method and multi-level temporal layers through model design to detect the necessary and sophisticated patterns correctly. The Duck Herd algorithm (DSA) acts as an adaptation system for SA-MTCN Hyperpieter adjustment to produce more stable and efficient model operations. A large data set for the supply chain has established the efficiency of the proposed function by achieving 99.9 % accuracy in evaluation tests. Better predictions emerge from this method as the efficiency of the operational supply chains is increased through time -sensitive data analysis and its functionality informed decision results. Experimental testing resulted in a better performance of the SA-MTCN model when customized by DSA which today offers an efficiently reliable solution supply chain.

关键词： Adaptation models Supply chain management Power measurement Soft sensors Supply chains Prediction algorithms Blockchains Convolutional neural networks Reliability Testing

来源：评论

学校读者我要写书评

暂无评论

Zero crosstalk in anisotropic TM leaky mode with subwavelength grating metamaterials

Zero crosstalk in anisotropic TM leaky mode with subwaveleng...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Md Faiyaz Kabir Md Borhan Mia Ishtiaque Ahmed Nafiz Jaidye Syed Z. Ahmed Sangsik Kim Department of Electrical and Computer Engineering Texas Tech University Lubbock Texas USA Department of Physics and Astronomy Texas Tech University Lubbock Texas USA School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea

We show that a TM leaky mode realized by subwavelength gratings (SWGs) can eliminate crosstalk between proximate waveguides. Oscillating fields i n t he SWGs allow coupling coefficient components to counteract each ot... 详细信息

ISBN: (纸本)9781957171258

关键词： Crosstalk Effective medium theory Electric fields Optical systems Subwavelength gratings Waveguides

来源：评论

学校读者我要写书评

暂无评论

A General Optimization Framework for Tackling Distance Constraints in Movable Antenna-Aided Systems

arXiv

引用

arXiv 2025年

作者： Jin, Yichen Lin, Qingfeng Li, Yang Zhu, Hancheng Cheng, Bingyang Wu, Yik-Chung Zhang, Rui Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen518172 China School of Computing and Information Technology Great Bay University Dongguan523000 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation in MA-aided systems faces challenges due to the non-convex and coupled constraints on antenna positions. This paper systematically reveals the challenges posed by the minimum antenna separation distance constraints. Furthermore, we propose a penalty optimization framework for resource allocation under such new constraints for MA-aided systems. Specifically, the proposed framework separates the non-convex and coupled antenna distance constraints from the movable region constraints by introducing auxiliary variables. Subsequently, the resulting problem is efficiently solved by alternating optimization, where the optimization of the original variables resembles that in conventional resource allocation problem while the optimization with respect to the auxiliary variables is achieved in closed-form solutions. To illustrate the effectiveness of the proposed framework, we present three case studies: capacity maximization, latency minimization, and regularized zero-forcing precoding. Simulation results demonstrate that the proposed optimization framework consistently outperforms state-of-the-art schemes. © 2025, CC BY-NC-ND.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Lightweight Vision Model-based Multi-user Semantic Communication Systems

arXiv

引用

arXiv 2025年

作者： Jiang, Feibo Tu, Siwei Dong, Li Wang, Kezhi Yang, Kun Liu, Ruiqi Pan, Cunhua Wang, Jiangzhou Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China School of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China Wireless and Computing Research Institute ZTE Corporation Beijing100029 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

Semantic Communication (SemCom) is a promising new paradigm for next-generation communication systems, emphasizing the transmission of core information, particularly in environments characterized by uncertainty, noise, and bandwidth constraints. However, existing image SemCom systems face several challenges, such as inefficient knowledge base construction, insufficient semantic encoding, and lack of multi-user semantic sharing. To address these issues, we propose a Lightweight Vision Model-based Multi-user Semantic Communication System (LVM-MSC). First, we construct a Lightweight Knowledge Base (LKB) based on the fast Segment Anything Model (SAM). LKB incorporates the extensive image knowledge of the SAM model while significantly reducing the number of parameters through its convolutional architecture. Next, we design an Efficient Semantic Codec (ESC) based on the Masked AutoEncoder (MAE) architecture. ESC enhances semantic compression at both the pixel and semantic levels and implements lightweight semantic decoding tailored for user devices. Furthermore, we propose a Multi-user Semantic Sharing (MSS) transmission for the multi-user SemCom. By calculating the similarity of semantic information among different users in the sharing semantic space, we unify the transmissions of similar semantic information through broadcasting, further improving the transmission efficiency. Finally, simulation results demonstrate the feasibility and effectiveness of the proposed LVM-MSC system. Copyright © 2025, The Authors. All rights reserved.

关键词： Latent semantic analysis

来源：评论

学校读者我要写书评

暂无评论

Improved Metaheuristics with Deep Autoencoders for COVID-19 Detection and Classification

Improved Metaheuristics with Deep Autoencoders for COVID-19 ...

引用

Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Manish K Assudani Nirmala B N. Kanimozhi Shanker Chandre K. K. Sunalini Anandbabu Gopatoti Department of Computer Science and Engineering G H Raisoni University Amravati Maharashtra India Department of Computer Science and Engineering Sri Ramachandra faculty of engineering and technology Sri Ramachandra Institute of Higher Education and Research Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of English Koneru Lakshmaiah Education Foundation Guntur Andhrapradesh India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

A healthcare monitoring system requires the support of current technologies like big data, artificial intelligence (AI), and machine learning (ML), particularly during the outbreak of COVID19. This pandemic caused millions of death worldwide. ML and AI are used to detect and combat COVID19 in the initial phase. Deep learning refers to a subdivision of ML that utilizes NN for making predictions and learning from data. In the context of COVID19 detection, deep learning (DL) methods can be trained for examining medical images like CT scans and chest X-rays and identify the presence of virus. This study presents an improved whale optimization algorithm with deep autoencoder (IWOA-DAE) algorithm for COVID-19 detection and classification. The presented IWOA-DAE technique makes use of DL model with metaheuristic hyperparameter optimizer for COVID-19 classification. To accomplish this, the IWOA-DAE technique employs bilateral filtering (BF) process to eliminate noisy data. Next, the IWOA-DAE technique uses NASNetLarge model for feature vector generation purposes. For COVID-19 detection, the IWOA-DAE technique uses deep autoencoder (DAE) model with IWOA based optimal hyperparameter adjustment process. To highlight the improved performance of the IWOA-DAE algorithm, a wide-ranging simulation analysis takes place. The simulation outcomes highlighted the enhanced performance of the IWOA-DAE technique over other methods.

关键词：

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：