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

共 30763 条记录，以下是31-40 订阅

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computing Performance Analysis of IEC 61499 distributed Automation systems 26

Computing Performance Analysis of IEC 61499 Distributed Auto...

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26th international conference on Industrial Technology, ICIT 2025

作者： Lyu, Tuojian Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden

ISBN: (纸本)9798331521950

This work analyzes the computing performance of distributed control systems in IEC 61499 on two hardware devices, Dell XPS workstation and Raspi 4B. Based on the test IEC 61499 application, three different system configurations are tested to simulate the distribution options of singleprocess and single-thread, multi-threading, and multi-process on both hardware platforms. Experimental results show that multiprocess configurations yield more consistent execution time due to dedicated resource allocation, while multi-threading, though resource-efficient, can lead to latency variability. These results aim at help in choosing optimal configurations and selecting hardware for performance in distributed automation systems. © 2025 ieee.

关键词： Parallel processing systems

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AI-Powered Cloud computing for Performance Optimization and Scalability in distributed systems

AI-Powered Cloud Computing for Performance Optimization and ...

引用

2025 international conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Selvam, Muthu Kishan, B.S. College of Computing and Informatics University of North Carolina at Charlotte CharlotreNC United States Bms Institute of Technology and Management Department of Mechanical Engineering Bangalore India

ISBN: (纸本)9798331538538

The IT profession has observed the ingenious application of Artificial Intelligence in the field of cloud computing as a solution to increase performance along with scalability within the distributed computing environment. Conventional cloud computing context has issues with management of resources, responsiveness, and finally security. The following paper will discuss an AI based architecture which will incorporate ML algorithms to optimize the usage of the resources available in the computer cloud to balance the load between different users and ensure security. Thus, the proposed model incorporates reinforcement learning for the dynamic load distribution, predictive analytics for workload forecasting, and deep learning based on anomaly detection to improve the security features of the system. The performance comparison with the baseline and the experiments conducted show satisfactory results in more than one performance indicators. For CPU usage, it rose to 26.4%, which was achieved alongside the provision of the 48% higher request throughput and the 40% better response time under the scenarios of the heavy workload. Moreover, the security framework using CNNs enhanced the detection accuracy of intrusion to 96.7% and a reduction of the false positives by sixty-six percent. These are some of the implications suggesting that the application of artificial intelligence can bring positive changes in the goals regarding cloud performance, resource utilization and overall security features. In this way, using AI-driven automation and prediction integrated in this work is offered a powerful, adaptive, and safe cloud model for expanded usage. With the proposed approach that has been developed the businesses and the service providers will be in a position to attain efficient business operational costs together with strong system reliability that will assist in developing the next generation of intelligent cloud computing. © 2025 ieee.

关键词： distributed cloud

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Real-Time Sentiment Analysis Pipeline for Yelp Customer Feedback using ChatGPT’s LLM and distributed systems 6

Real-Time Sentiment Analysis Pipeline for Yelp Customer Feed...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Revathy, S. Shahid, J. Mohammad Salih, S. Computer Science and Engineering St.Joseph’s Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

This work proposes a real-time sentiment analysis pipeline on customer feedback using Yelp and addresses the high-volume dynamic user-generated contents processing problem. The proposal integrates state-of-the-art machine learning models and distributed systems, making use of Large Language Models within a Docker container called ChatGPT for the real-time sentiments analysis. The system ingests live Yelp data streams through TCP/IP, processes them in a scalable Apache Spark cluster, and ensures that data flows seamlessly through Apache Kafka. Enriched feedback is indexed into Elasticsearch, providing powerful analytics and visualizations through tools like Kibana, Tableau, and Power BI. The low-latency design of the pipeline gives enterprises actionable insights for real-time decision-making, enabling enhanced customer sentiment analysis and business intelligence. Scalable and efficient, this architecture fits all large volume Enterprise Feedback Management (EFM) needs when looking to achieve insights fast from customer sentiment. © 2025 ieee.

关键词： Sales

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A Survey on Consensus Algorithms for distributed Wireless Networks

A Survey on Consensus Algorithms for Distributed Wireless Ne...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Ji, Jun Cheng Lam, Chan Tong Ng, Benjamin Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

A distributed wireless communication system consists of multiple nodes. Due to the diversified resources and environmental conditions for each node, a consensus-based algorithm can be used to solve the conflicts in the wireless communication system so that each node can reach a consensus based on a consistent algorithm and a consistent result. In particular, the channel quality, signal strength, energy consumption, protocol delay, security, and reliability can be improved. Consensus algorithms can also be applied for channel decision-making, authentication problems, message routing problems, and fault tolerance problems. Consensus algorithms are becoming essential for safe and more reliable wireless communication systems. Focusing on consensus algorithms from the perspective of wireless communication applications, the survey compares current trends and emerging research opportunities in consensus algorithms. Finally, we provide future research directions for consensus algorithms for distributed wireless networks. © 2025 SPIE.

关键词： Block Chain Consensus Algorithm distributed computing Federated Learning Wireless Communication

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distributed Sensing and Collaborative Edge computing for LEO Constellations 17

Distributed Sensing and Collaborative Edge Computing for LEO...

引用

17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Jaipuria, Saumya Banerjee, Ansuman Paul, Himadri Sekhar Indian Statistical Institute India TCS Research India

ISBN: (纸本)9798331531195

In recent years, constellations of low Earth orbit (LEO) satellites are being used extensively for a wide multitude of remote sensing applications starting from forest fire imaging to maritime applications like tracking of enemy ships. These satellites fly in low earth orbits and collectively image a wide area for data collection. Traditionally, satellites are used as passive sensing devices, which sense and download observations to Earth bound ground stations. This mechanism at times faces transfer bottlenecks and significantly limits the number of image collections. Building on recent developments in orbital edge computing, wherein small satellites are also equipped with computing power, this paper proposes an integrated sense and compute framework for these satellite constellations. Given a set of regions of interest to be imaged, we propose an integer linear programming (ILP) based solution approach to set up a collect communicate compute schedule that can maximize the number of region images collected and analyzed. We present preliminary experimental results of our framework on simulated datasets. © 2025 ieee.

关键词： Small satellites

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The Application of Digital Empowerment Combined with Intelligent Algorithms of Computer Technology in Decision Support systems 3

The Application of Digital Empowerment Combined with Intelli...

引用

3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Lu, Wan Chen, Li Gao, Fei Wang, Chang Wuhan Windoor Information & Technology Co. Ltd Hubei Wuhan China

ISBN: (纸本)9798331508456

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology;the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments. © 2025 ieee.

关键词： distributed cloud

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Securing Wireless sensor Networks from Intrusions Using Machine Learning-Based Detection and Response

Securing Wireless Sensor Networks from Intrusions Using Mach...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Saranya, M. Srevarshine, S. Sujitha, V. Sobiya, T. M.Kumarasamy College Of Engineering Department of Ece Karur India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Wireless sensor Networks (WSN s) are widely used in various applications, including health care, environmental monitoring, and industrial automation. However, their distributed nature and limited resources make them vulnerable to various security threats. An Intrusion Detection System (IDS) plays a critical role in identifying and mitigating unauthorized activities within WSNs. This project focuses on developing an IDS using machine learning techniques to enhance the detection accuracy and efficiency of identifying potential intrusions. By leveraging algorithms such as decision trees, support vector machines, or neural networks, the system can classify network behaviour as normal or malicious [1]. The proposed approach is evaluated on benchmark datasets to ensure reliability and scalability in real-world scenarios [2]. This study aims to improve the security ofWSNs by providing a robust, adaptive, and energy-efficient IDS solution. © 2025 ieee.

关键词： Network intrusion

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Chengxiao Zhang, Xie Wu, Chenshu Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%. © 2025 ieee.

关键词： direct time-of-flight sensor facial expression recognition internet of things sensing AI

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A Systematic Approach for Performance Efficiency of distributed Networks using Machine Learning Techniques and Network Simulator 2 6

A Systematic Approach for Performance Efficiency of Distribu...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Parveen Akhther, A. Veera Subrahmanyam, N. Charan Sai, A. Prasanth, V.S. Dept. of CSE Sathyabama Institute of Science & Technology Chennai India Dept. of ECE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (纸本)9798331522667

In the rapidly evolving domain of distributed networks, including cloud computing and edge systems, achieving optimal performance efficiency is critical. Existing methods often fail to effectively handle dynamic policies, resource allocation, and scalability. This project presents a robust framework leveraging machine learning (ML) techniques integrated with NS2 simulation to address these challenges. Data collection from network nodes is the first step in the process. Preprocessing is then done to manage missing values, eliminate noise, and normalize the data for model training. Supervised learning methods like regression and classification optimize traffic prediction and routing protocols, while unsupervised techniques, including clustering, identify resource patterns for better allocation. Reinforcement learning dynamically adjusts policies to improve fault tolerance and resource management. NS2 simulation validates the model’s effectiveness across various network scenarios.A remarkable accuracy of 98.77% and a minimal error rate of 1.22% are attained by the claimed framework. © 2025 ieee.

关键词： Self-supervised learning

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A Soft Sensing Approach for Efficient Monitoring of Nanobody-Based Scorpion Antivenom Production 21st

A Soft Sensing Approach for Efficient Monitoring of Nanobody...

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21st international Symposium on distributed computing and Artificial Intelligence

作者： Acosta-Pavas, Juan C. Corrales, David Camilo Martinez-Menendez, Irene Villela, Susana Maria Alonso Bouhaouala-Zahar, Balkiss Georgakilas, Georgios K. Mexis, Konstantinos Xenios, Stefanos Dalamagas, Theodore Kokosis, Antonis O'donohue, Michael Fillaudeau, Luc Aceves-Lara, Cesar A. Univ Toulouse CNRS TBI INRAINSA Toulouse France INRAE UMS 1337 TWB 135 Ave Rangueil F-31077 Toulouse France Inst Pasteur Tunis Lab Biomol Venins & Applicat Theranost LBVAT 13 Pl PasteurBP-741002 Belvedere Tunis Tunisia Univ Tunis El Manar Fac Med Tunis 15 Rue Djebel Lakhdhar Bab Saadoun Tunis 10007 Tunisia Athena Res Ctr Maroussi Greece Natl Tech Univ Athens Sch Chem Engn Iroon Polytechneiou 9 Zografos 15780 Athens Greece

ISBN: (纸本)9783031820724;9783031820731

The growing demand for recombinant-based products has increased rapidly in recent years. One of the current market scopes is the production of recombinant scorpion antivenoms in E. coli. However, there are limitations in real-time quantification of Critical Quality Attributes (CQAs), such as recombinant proteins or biomass concentrations. Soft sensors merge as an alternative to solve this problem. In this work, six data-driven soft sensors were developed for online monitoring of the recombinant protein concentration. First, feature selection methods were tested to determine the relevant online variables to train the soft sensors. Then, the protein experimental dataset was augmented using a hybrid model. The K-Nearest Neighbors (KNN), and the Radial Basis Function (RBF) Support Vector Machine (SVM) obtained the best results with R-2 values higher than 0.996, RMSE and MAE values less than 0.058 and 0.051. However, the KNN soft sensor was computed in less time than the RBF SVM soft sensor. These results show the feasibility of KNN and RBF soft sensors for real-time monitoring of recombinant scorpion antivenoms.

关键词： Process Monitoring E. Coli Antivenom Production Machine learning Data-Driven Soft sensor Feature Selection

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