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检索条件"任意字段=5th IEEE International Conference on Distributed Computing in Sensor Systems"

共 5705 条记录，以下是21-30 订阅

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Optimizing RF-Sensing for Drone Detection: the Synergy of Ensemble Learning and sensor Fusion 20

Optimizing RF-Sensing for Drone Detection: The Synergy of En...

引用

20th Annual international conference on distributed computing in Smart sensor systems and the Internet of things (DCOSS-IoT)

作者： Tanveer, Laiba Alam, Muhammad Zeshan Misbah, Maham Orakzai, Farooq Alam Alkhayyat, Ahmed Kaleem, Zeeshan COMSATS Univ Islamabad Elect & Comp Engn Wah Campus Wah Pakistan Brandon Univ Dept Comp Sci Brandon MB Canada Islamic Univ Coll Engn Dept Comp Tech Engn Najaf Iraq

ISBN: (纸本)9798350369458;9798350369441

Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. this SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy-specifically, a 36% increase for multi-class models when compared to single-class models. this proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.

关键词： Ensemble Neural Network Radio Frequency RF Spectrogram UAVs Detection

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Securing distributed Power Dispatching systems: A Framework for Cybersecurity and 5G Integration 5

Securing Distributed Power Dispatching Systems: A Framework ...

引用

5th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Zhang, Xiao He, Yuxiong He, Yuhong Power Dispatch Control Center State Grid Hubei Electric Power Co. Ltd. Wuhan China

ISBN: (纸本)9798331533694

With the increasing integration of distributed energy resources (DERs) into power grids, the security of distributed power dispatching control systems becomes a critical concern. this paper presents a comprehensive security framework to address the unique challenges posed by these systems. the study focuses on four key areas: (1) security partitioning strategies, (2) layered network security defense architecture, (3) 5G-based secure communication for distributed power dispatching systems, and (4) security monitoring and emergency response technologies. the proposed solutions aim to mitigate cyber threats and enhance the resilience of distributed power dispatching control systems in modern power grids. © 2025 ieee.

关键词： 5G mobile communication systems

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Optimization of distributed Algorithms in Cloud computing Environment 5

Optimization of Distributed Algorithms in Cloud Computing En...

引用

5th international conference on Artificial Intelligence and Computer Engineering

作者： Cao, Yulong Heilongjiang Int Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9798331528911;9798331528928

this thesis aims to explore the optimization strategies of distributed algorithms in cloud computing environment. With the rapid development of cloud computing, traditional distributed algorithms face many challenges, such as network latency, resource scheduling and data consistency. By analyzing the limitations of existing algorithms, scheduling strategies based on resource optimization, algorithm adaptation schemes in heterogeneous environments, and load balancing techniques are proposed. the study shows that these optimization strategies can significantly improve the performance and scalability of the algorithms, providing a theoretical basis and practical guidance for future cloud computing applications.

关键词： distributed algorithm optimization strategy resource scheduling

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distributed Training Optimization for DCU 5

Distributed Training Optimization for DCU

引用

5th international conference on Information Science, Parallel and distributed systems, ISPDS 2024

作者： Chen, Biaoyuan Li, Jianan Li, Pengfei Xie, Jingming Zhao, Rongcai Gao, Wei Han, Lin Zhengzhou University Zhengzhou China National Supercomputing Center Zhengzhou China National Supercomputing Center Zhengzhou Zhengzhou China

ISBN: (纸本)9798350352719

With the continuous advancement of large-scale models and expanding volumes of data, a single acceleration hardware is no longer sufficient to meet the training demands. Simply stacking multiple acceleration hardware together in parallel does not enhance training efficiency, but rather results in a waste of computing resources. therefore, for existing distributed training parallel methods, we first establish the definition of parallel strategies and construct cost boundaries to enable the automatic selection of efficient parallel strategies. Additionally, we implement a time-balanced partition algorithm based on pipeline parallelism to achieve more equitable model partitioning at each stage and reduce the overall cost of model training time. Experiments conducted on a single machine with four DCU cards and a dual machine with eight DCU cards demonstrate that multiple models, supported by the automatic parallel strategy, exhibit outstanding distributed training performance. Simultaneously, the average acceleration ratio can reach 1.13× with pipeline optimization. In summary, automatic selection of strategies and pipeline parallelism optimization significantly enhances the efficiency of distributed training for large models. © 2024 ieee.

关键词： Digital storage

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Research on sensor Data Spatiotemporal Synchronization Technology for Onboard Visual Navigation systems 5

Research on Sensor Data Spatiotemporal Synchronization Techn...

引用

5th ieee international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Yuan, Yanna Liu, Huimin Zhao, Chenqiang Aviation Industry Computing Technology Research Institute Xi'an China

ISBN: (纸本)9798331533694

Against the backdrop of the rapid development of unmanned aerial vehicle (UAV) technology, onboard visual navigation systems have become the core support technology for achieving precise positioning and efficient map construction of UAVs. this paper deeply explores the key technologies of sensor data spatiotemporal synchronization in onboard visual navigation systems, aiming to solve the synchronization issues in multi-sensor data fusion in variable environments, and enhance the navigation accuracy and reliability of UAVs. the research covers both spatial and temporal synchronization dimensions, with a focus on the technical implementation of Visual-Inertial Odometry (VIO). For spatial synchronization, a target-based method is proposed to construct an error equation for solving the extrinsic parameters between the camera and the Inertial Measurement Unit (IMU), achieving precise alignment of sensor coordinate systems through the identification and matching of visual target feature points. In terms of temporal synchronization, considering the data frequency differences between the camera and IMU, this paper designs a time alignment strategy that realizes efficient temporal alignment of data through data buffering and timestamp matching. Additionally, the initialization and local sliding window optimization process of the Visual-Inertial Odometry are detailed, achieving high-precision fusion of visual and inertial data through feature extraction, optical flow tracking, and motion structure recovery. Experimental results show that the methods proposed in this paper can technically provide strong support for the autonomous flight of UAVs, effectively improving the navigation performance of UAVs in complex environments, and enhancing the robustness of the system. © 2025 ieee.

关键词： Unmanned aerial vehicles (UAV)

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Knowledge Extraction from distributed Heterogeneous Data Sources 5

Knowledge Extraction from Distributed Heterogeneous Data Sou...

引用

5th ieee international conference for Emerging Technology, INCET 2024

作者： Ramachandran, Raji Abhijith, K. Karthik, J.J. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361155

the growing volume and complexity of unstructured and semi-structured data pose a significant challenge in extracting meaningful and relevant information. Information Extraction (IE) emerges as a powerful technique to transform unstructured data into structured format, facilitating further analysis and knowledge discovery. IE techniques can effectively extract information from unstructured data in the form of entities, relationships, objects, and events. Our objective is to develop a method for extracting or filtering out the most relevant information from data sources which are heterogeneous in nature as well as they are distributed over many systems and hence build a unified schema based on the collective knowledge extracted from the data sources. Several existing methods have been developed to extract knowledge from heterogeneous datasets. We focuses on datasets in the medical field that are distributed over multiple systems. Specifically, we aim to extract valuable information from patient records, including patient details, medical conditions and medications consumed. the unified schema can be utilized to study a new patient's condition and assist doctors in making a pre-examination of a patient by querying it. © 2024 ieee.

关键词： Data mining

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LGDCloudSim: A resource management simulation system for large-scale geographically distributed cloud data center scenarios 17

LGDCloudSim: A resource management simulation system for lar...

引用

ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Liu, Jiawen Xu, Yuehao Feng, Binbin Ding, Zhijun Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350368543;9798350368536

Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build experimental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the largescale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Experiments show that LGDCloudSim can simulate up to 5x10(8) hosts and 107 request concurrency. It also supports diverse scheduling architectures and different request types.

关键词： large-scale cloud geographically distributed data centers cloud simulation system resource management scheduling architecture

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Survey and Analysis of Anomaly Detection for Smart Home using Microcontroller 5

Survey and Analysis of Anomaly Detection for Smart Home usin...

引用

5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Rami, Ruchika Malek, Zakiyabanu Gujrat Law Society Faculty of Computer Application IT Dept Gujrat Ahmedabad India Centennial College TorontoON Canada

ISBN: (纸本)9798350395235

An increasingly prominent issue in recent times is the utilization of the Internet of things (IoT) for home automation systems. Home automation, also known as smart home technology, refers to the wireless and intelligent control and operation of various household appliances such as lights, fans, doors, refrigerators, and washing machines. In this system, all the devices are connected to the Internet and form a part of the Internet of things (IoT) model. the objective of implementing a wireless sensor network-based home automation system is to effectively supervise and regulate various parameters such as voltage, current, and temperature. the primary goal of this research work is to minimize the energy utilization of a smart home. It enhances the efficiency of the control network. this study explores several home automation techniques and examines the manual control of these applications with the use of remote controls. © 2024 ieee.

关键词： Wireless sensor networks

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Research on Standardized Information Model of distributed Energy Grid-Connected Communication Considering Social Factors 5

Research on Standardized Information Model of Distributed En...

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5th ieee international conference on Power, Intelligent computing and systems, ICPICS 2023

作者： Zhang, Hengrong Liu, Yinliang Zhang, Ruifeng Lin, Xinhao Xiao, Xiaobing Li, Zhangcheng Electric Power Research Institute of Guizhou Power Grid Co. Ltd Guiyang China Guangzhou China

ISBN: (纸本)9798350333442

the large-scale grid connection of distributed energy resources is an effective way to solve the problems of power supply shortage and environmental pollution, and there is no unified standard for the communication protocol when the existing distributed energy resources are connected to the grid. these standards focus on the electrical characteristics of the transformer itself, ignoring social factors. Firstly, this paper analyzes the business data requirements of distributed energy grid-connected communication. then, the 'phenomenon/data/data-provider' pattern is extracted, and a standardized information model of distributed energy grid-connected communication is constructed. Finally, the summary and application prospect are carried out. © 2023 ieee.

关键词： Energy resources

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Use of sensor Networks with Self-Organizing Algorithm to Increase the Agricultural Productivity 5

Use of Sensor Networks with Self-Organizing Algorithm to Inc...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Lahoti, Saurabh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350383522

Traditional farming methods are becoming incapable of keeping up with global population growth. As a result, innovative farming ideas are desperately necessary to meet the food needs of a growing population. Intelligent farming systems based on sensor networks with self-organizing algorithms have gained popularity in recent years as a means of increasing agricultural production. In this study, we will use sensor networks with self-organizing algorithms to boost agricultural productivity. Finally, the paper describes the future applications of appropriate technology in agricultural production. © 2024 ieee.

关键词： Agricultural Productivity Self-Organizing Algorithm sensor Networks

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