检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Deshpande, Nitish Vikas Castellanos, Miguel Rodrigo Khosravirad, Saeed R. Du, Jinfeng Viswanathan, Harish Heath, Robert W. Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Department of Electrical Engineering and Computer Science The University of Tennessee KnoxvilleTN37996 United States Nokia Bell Laboratories Murray HillNJ07974 United States

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. It does not address the problem of physical realizability. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. We then derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. We show that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. We simplify the beamforming optimization problem using a shift-of-origin transformation to the typical unit-modulus beamforming weight. With this transformation, we derive a closed-form solution for the reconfigurable parasitic reactance. We generalize this solution to the multi-active multi-parasitic hybrid array operating in a multi-path channel. Our proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency. © 2025, CC BY.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Unleashing Collaborative Computing for Adaptive Video Streaming with Multi-objective Optimization in Satellite Terrestrial Networks

arXiv

引用

arXiv 2024年

作者： Shen, Zhishu Zheng, Qiushi Rong, Ziqi Jin, Jiong Tagami, Atsushi Xiang, Wei The School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia KDDI Research Inc. Japan The School of Computing Engineering and Mathematical Sciences La Trobe University Melbourne Australia

Satellite-terrestrial networks (STNs) are anticipated to deliver seamless IoT services across expansive regions. Given the constrained resources available for offloading computationally intensive tasks like video streaming, it is crucial to establish collaborative computing among diverse components within STNs. In this paper, we present the task offloading challenge as a multi-objective optimization problem, leveraging the collaboration between ground devices/users and satellites. We propose a collaborative computing scheme that optimally assigns computing tasks to various nodes within STNs to enhance service performance including quality of experience (QoE). This algorithm initially dynamically selects an end-to-end path that balances service time and resource utilization. For each selected path, a multi-agent soft actor-critic (MA-SAC)-based algorithm is introduced to make adaptive decisions and collaboratively assign optimal heterogeneous resources to the given computing tasks. In this algorithm, the ground station bridging satellite network and terrestrial network is treated as agent to extract the information from both STNs and users. Through MA-SAC, multiple agents cooperate to determine the adaptive bitrate and network resources for the arriving tasks. The numerical results demonstrate that our proposal outperforms comparative schemes across various computing tasks in terms of various criteria. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids

arXiv

引用

arXiv 2025年

作者： Albustami, Abdallah Alalem Taha, Ahmad F. Bou-Harb, Elias Vanderbilt University Civil and Environmental Engineering Department NashvilleTN37235 United States Louisiana State University Division of Computer Science and Engineering Baton RougeLA70803 United States

Smart grids are inherently susceptible to various types of malicious cyberattacks that have all been documented in the recent literature. Traditional cybersecurity research on power systems often utilizes simplified models that fail to capture the interactions between dynamic and steady-state behaviors, potentially underestimating the impact of cyber threats. This paper presents the first attempt to design and assess stealthy false data injection attacks (FDIAs) against nonlinear differential algebraic equation (NDAE) models of power networks. NDAE models, favored in industry for their ability to accurately capture both dynamic and steady-state behaviors, provide a more accurate representation of power system behavior by coupling dynamic and algebraic states. We propose novel FDIA strategies that simultaneously evade both dynamic and static intrusion detection systems while respecting the algebraic power flow and operational constraints inherent in NDAE models. We demonstrate how the coupling between dynamic and algebraic states in NDAE models significantly restricts the attacker’s ability to manipulate state estimates while maintaining stealthiness. This highlights the importance of using more comprehensive power system models in cybersecurity analysis and reveals potential vulnerabilities that may be overlooked in simplified representations. The proposed attack strategies are validated through simulations on the IEEE 39-bus system. © 2025, CC BY.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Efficient In-Training Adaptive Compound Loss Function Contribution Control for Medical Image Segmentation

Efficient In-Training Adaptive Compound Loss Function Contri...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Abdullah F. Al-Battal Soan T. M. Duong Chanh D. Tr. Nguyen Steven Q. H. Truong Chien Phan Truong Q. Nguyen Cheolhong An Electrical and Computer Engineering Department UC San Diego La Jolla California USA Electrical Engineering Department King Fahd University of Petroleum and Minerals Saudi Arabia Vinbrain JSC Hanoi Vietnam Department of Computer Science Le Quy Don Technical University Hanoi Vietnam College of Engineering and Computer Science VinUniversity Hanoi Vietnam Diagnostic Imaging Department University Medical Center at Ho Chi Minh City Vietnam

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Image segmentation plays a crucial role in many clinical applications, including disease diagnosis and monitoring. Current state-of-the-art segmentation approaches use deep neural networks that are trained on their target tasks by minimizing a loss function. Class imbalance is one of the major challenges that these networks face, where the target object is significantly underrepresented. Compound loss functions that incorporate the binary cross-entropy (BCE) and Dice loss are among the most prominent approaches to address this issue. However, determining the contribution of each individual loss to the overall compound loss function is a tedious process. It requires hyperparameter fine-tuning and multiple iterations of training, which is highly inefficient in terms of time and energy consumption. To address this issue, we propose an approach that adaptively controls the contribution of each of these individual loss functions during training. This eliminates the need for multiple fine-tuning iterations to achieve the desired precision and recall for segmentation models.

关键词： Training Image segmentation Adaptation models Energy consumption Computational modeling Compounds Medical diagnosis Monitoring Medical diagnostic imaging Faces

来源：评论

学校读者我要写书评

暂无评论

Exploring Delay Tolerant Network: A Simulation Approach 4

Exploring Delay Tolerant Network: A Simulation Approach

引用

4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Salam, Mohammad Abdus Deniz, Arif Furkan Aghadinuno, Chinwe Kassouf-Short, Robert Hylton, Alan G. Chicago State University Department of Computing Information and Mathematical Sciences and Technology ChicagoIL60628 United States Southern University and A&M College Department of Computer Science Baton RougeLA United States Southern University and A&M College Department of Electrical Engineering Baton RougeLA United States NASA Glenn Research Center ClevelandOH United States NASA Goddard Space Flight Center GreenbeltMD United States

ISBN: (纸本)9798350395914

This paper provides an in-depth exploration of Delay Tolerant Networks (DTNs), focusing particularly on the development and functionality of High Delay Tolerant Networks (HDTNs). The distinction between DTNs and HDTNs is illuminated, emphasizing the greater resilience and performance of HDTNs. The role and significance of HDTN simulators were thoroughly explained, explaining their essential role in modeling, and predicting the behavior of these advanced networks. A detailed walkthrough is offered on the installation, configuration, and simulation process of the HDTN Simulator, making this study a valuable resource for practitioners in the field. The paper also incorporates the use of DTNSim to further extend the simulation capabilities of HDTNs. The research also delves into the results and network topology obtained from the simulation, offering valuable insights into the practical applications and implications of HDTNs. The findings contribute to a growing body of knowledge surrounding the design, implementation, and management of high delay tolerant networks, thereby aiding future developments in this critical aspect of network engineering. © 2024 IEEE.

关键词： Delay tolerant networks

来源：评论

学校读者我要写书评

暂无评论

Theoretical Bound and Compensation for Residual Error Probability of GRAND-CRC-Based Functional Safety Communication

引用

IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, Shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (SPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded SPDU can minimize SPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 Standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to SPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an SPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Health care data analysis and visualization using interactive data exploration for sportsperson

引用

science China(Information sciences) 2022年第6期65卷 85-97页

作者： Hao LIU Yang ZHANG Ke LIAN Yifei ZHANG Oscar Sanjuán MARTíNEZ Rubén González CRESPO Department of Sport and Exercise Sciences Kunsan National University Department of Naval Architecture and Ocean Engineering Ludong University Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja

Sports have scored significant attention among the public in this multifaceted world. Diverse training strategies are followed by many athletics and even flexible to adapt comfortable and optimal techniques. This fact has led physicians and educators to encourage remote health surveillance as one of the core strategies in athletic training. The need for innovative data exploration methodologies capable of facing Big Data's influence to make remote monitoring services viable has been raised by the growing ties of networks that deliver high quantities of real-time data. This paper presents an interactive healthcare data exploration and visualization(IHDEV) model to enhance multi-scaling data analysis and visualization in the athletic health vision platform. This paper aims to simplify optimization methods to measure sportsperson muscle tension. This model illustrates a three-layer architecture with a raw data acquisition layer, data analysis layer, and visualization layer. The first layer considers the acquisition of health-related data from the athletes for remote monitoring using IoT and stores it into the cloud. The data analysis layer adapts artificial intelligence(AI) in data mining. The final layer introduces an intelligent interactive data visualization model assisted by a reactive workflow mechanism, enabling analysis and visualization solutions to be composed in a personalized data flow appropriate to the athletic training. This experimental study extended with two healthcare datasets to show the feasibility of IHDEV in promoting healthcare based athletic monitoring and improves the accuracy ratio of 96.7%, prediction ratio of 96.2%, an efficiency ratio of 96.8%, Pearson correlation coefficient of 98.2%, and reduces the error rate of 18.7% compared to other conventional models.

关键词： artificial intelligence health monitoring data visualization data analysis athletics

来源：评论

学校读者我要写书评

暂无评论

Towards an Effective and Efficient Transformer for Rain-by-snow Weather Removal

arXiv

引用

arXiv 2023年

作者： Gao, Tao Wen, Yuanbo Zhang, Kaihao Cheng, Peng Chen, Ting School of Information Engineering Chang’an University Xi’an710064 China School of Computing Australian National University CanberraACT Australia Department of Computer Science and Information Technology La Trobe University Australia the University of Sydney Australia

Rain-by-snow weather removal is a specialized task in weather-degraded image restoration aiming to eliminate coexisting rain streaks and snow particles. In this paper, we propose RSFormer, an efficient and effective Transformer that addresses this challenge. Initially, we explore the proximity of convolution networks (ConvNets) and vision Transformers (ViTs) in hierarchical architectures and experimentally find they perform approximately at intra-stage feature learning. On this basis, we utilize a Transformer-like convolution block (TCB) that replaces the computationally expensive self-attention while preserving attention characteristics for adapting to input content. We also demonstrate that cross-stage progression is critical for performance improvement, and propose a global-local self-attention sampling mechanism (GlaSM) that down-/up-samples features while capturing both global and local dependencies. Finally, we synthesize two novel rain-by-snow datasets, RSCityScape and RS100K, to evaluate our proposed RSFormer. Extensive experiments verify that RSFormer achieves the best trade-off between performance and time-consumption compared to other restoration methods. For instance, it outperforms Restormer with a 1.53% reduction in the number of parameters and a 15.6% reduction in inference time. Datasets, source code and pre-trained models are available at https://***/chdwyb/RSFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Dealing with noise in crowdsourced GPS human trajectory logging data

Dealing with noise in crowdsourced GPS human trajectory logg...

引用

作者： Adhinugraha, Kiki Rahayu, Wenny Hara, Takahiro Taniar, David Department of Computer Science and Information Technology School of Engineering and Mathematical Sciences La Trobe University MelbourneVIC Australia Department of Multimedia Engineering Osaka University Osaka Japan Faculty of Information Technology Monash University MelbourneVIC Australia

As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real-world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non-map-matching approach convex hull-based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories. © 2020 John Wiley & Sons, Ltd.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Towards an Effective and Efficient Transformer for Rain-by-Snow Weather Removal

SSRN

引用

SSRN 2023年

作者： Gao, Tao Wen, Yuanbo Zhang, Kaihao Cheng, Peng Chen, Ting School of Information Engineering Chang'an University Xi’an710064 China School of Computing Australian National University CanberraACT Australia Department of Computer Science and Information Technology La Trobe University Australia The University of Sydney Australia

Rain-by-snow weather removal is a specialized task in weather-degraded image restoration aiming to eliminate coexisting rain streaks and snow particles. In this paper, we propose RSFormer, an efficient and effective Transformer that addresses this ***, we explore the proximity of convolution networks (ConvNets) and vision Transformers (ViTs) in hierarchical architectures and experimentally find they perform approximately at intra-stage feature *** this basis, we utilize a Transformer-like convolution block (TCB) from Conv2Former that replaces the computationally expensive self-attention while preserving attention characteristics for adapting to input *** focus on demonstrating that cross-stage progression is critical for performance improvement, and propose a global-local self-attention sampling mechanism (GlaSM) that down-/up-samples features while capturing both global and local dependencies. Finally, we synthesize two novel rain-by-snow datasets, RSCityScape and RS100K, to evaluate our proposed *** experiments verify that RSFormer achieves the best trade-off between performance and time-consumption compared to other restoration *** instance, it outperforms Restormer with a 1.53% reduction in the number of parameters and a 15.6\% reduction in inference ***, source code and pre-trained models are available at https://***/chdwyb/RSFormer. © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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：