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检索条件"任意字段=Proceedings - 3rd International Conference on Distributed Computing Systems."

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616978

The proceedings contain 27 papers. The special focus in this conference is on Machine Learning, Cloud computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking Control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative Learning Control for Nonlinear Switched Continuous-Time systems.with Time-Varying Delay;Model Migration and Joint Communication and computing Resource Allocation in Native AI Wireless Networks;FTB-RRP Control for 2-D Continuous-Discrete systems.in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI Networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural Networks;research and Application of Computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization Control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection Control;state Estimation and Control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time;distributed Consensus Control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco Networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation Control in Multi-agent systems.with Disturbance Rejection Using RISE Controller;model Predictive Control for Contour Control in Biaxial System.

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819624676

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616930

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distributed, Parallel and Biologically Inspired systems. 7th IFIP TC 10 Working conference, DIPES 2010, and 3rd IFIP TC 10 international conference

引用

2014年

作者： Mike G Hinchey Bernd Kleinjohann Lisa Kleinjohann Peter Lindsay Franz Josef Rammig Jon Ian Timmis Marilyn Wolf

ISBN: (纸本)9783642422928

st This volume contains the proceedings of two conferences held as part of the 21 IFIP World Computer Congress in Brisbane, Australia, 2023 September 2010. th The first part of the book presents the proceedings of DIPES 2010, the 7 IFIP conference on distributed and Parallel Embedded systems. The conference, int- duced in a separate preface by the Chairs, covers a range of topics from specification and design of embedded systems.through to dependability and fault tolerance. rd The second part of the book contains the proceedings of BICC 2010, the 3 IFIP conference on Biologically-Inspired Collaborative computing. The conference is concerned with emerging techniques from research areas such as organic computing, autonomic computing and self-adaptive systems. where inspiraton for techniques - rives from exhibited behaviour in nature and biology. Such techniques require the use of research developed by the DIPES community in supporting collaboration over multiple systems. We hope that the combination of the two proceedings will add value for the reader and advance our related work.

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On Constructing a Byzantine Linearizable SWMR Atomic Register from SWSR Atomic Registers 25

On Constructing a Byzantine Linearizable SWMR Atomic Registe...

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26th international conference on distributed computing and Networking, ICDCN 2025

作者： Kshemkalyani, Ajay D. Piduguralla, Manaswini Peri, Sathya Misra, Anshuman University of Illinois Chicago ChicagoIL United States Indian Institute of Technology Hyderabad Hyderabad India Purdue University Fort Wayne Fort WayneIN United States

ISBN: (纸本)9798400710629

The SWMR atomic register is a fundamental building block in shared memory distributed systems.and implementing it from SWSR atomic registers is an important problem. While this problem has been solved in crash-prone systems. it has received less attention in Byzantine systems. Recently, Hu and Toueg gave such an implementation of the SWMR register from SWSR registers. While their definition of register linearizability is consistent with the definition of Byzantine linearizability of a concurrent history of Cohen and Keidar, it has several drawbacks. In this paper, we give a stronger definition of a Byzantine linearizable register that overcomes these drawbacks. The construction of a Byzantine linearizable SWMR atomic register from SWSR registers that meets our stronger definition is given in the full arxiv report. The construction is correct when n > 3f, where n is the number of readers, f is the maximum number of Byzantine readers, and the writer can also be Byzantine. The construction relies on a public-key infrastructure. © 2025 Copyright held by the owner/author(s).

关键词： distributed computer systems

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2022 3rd international conference on Embedded and distributed systems. EDiS 2022

2022 3rd International Conference on Embedded and Distribute...

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3rd international conference on Embedded and distributed systems. EDiS 2022

ISBN: (纸本)9798350397543

The proceedings contain 23 papers. The topics discussed include: designing neuromorphic architectures: towards an ultra-low power AI;creating an edge-to-cloud computing continuum: status and perspective;observability of software computing systems. challenges and opportunities;challenges in real-time scheduling for energy harvesting embedded systems.optimizing deep learning application for edge computing;toward memory-centric scheduling for prem task on multicore platforms, when processor assignments are specified;embedded Raspberry Pi vehicle detection and classification using single shot detector and dilated multi-column convolutional neural network;a combination of multi and univariate anomaly detection in urban irrigation systems.an innovative smart and sustainable low-cost irrigation system for smallholder farmers' communities;and Bayesian convolutional neural networks for image classification with uncertainty estimation.

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Efficient Signal Processing: Harnessing distributed Arithmetic for High-Speed FFT Operations 3

Efficient Signal Processing: Harnessing Distributed Arithmet...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Bharathi, M. Mohanarangam, Krithikaa Shirur, Yasha Jyothi M. Andhra Pradesh Tirupati India Symbiosis Institute of Technology Pune India Bnm Institute of Technology Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331515911

The present article introduces a novel approach to significantly enhance the computational efficiency of modern Digital Signal Processing (DSP) processors. The algorithm enhances the efficiency of Sum of Products (SOP) operations, which are essential for real-time signal processing applications, by utilizing distributed Arithmetic (DA) techniques for the Fast Fourier Transform (FFT) and its inverse (Inverse FFT) (IDFT) computations. The present study undertakes an exhaustive quantitative analysis to assess the computational delays of the proposed 2, 4, and 8-point Fast Fourier Transform (FFT) and Inverse Discrete Fourier Transform (IDFT) across various bit representations (4, 8, and 16-bit). For example, when using 4-bit representations, the computational delays for a 2-point Fast Fourier Transform (FFT) are measured to be 13.318ns, 30.629ns for a 4-point FFT, and 27.779ns for an 8-point FFT. Similarly, the delays for the 2-point Fast Fourier Transform (FFT) in an 8-bit representation are 13.727ns, the 4-point FFT in 22.276ns, and the 8-point FFT in 30.592ns. The study further includes the addition of appropriate delays for IDFT operations, which enhances the understanding of the effectiveness of the approach suggested in various scenarios. Furthermore, the research presents and evaluates three distinct categories of transforms, namely 2-point, 4-point, and 8-point FFT and IDFT structures. These structures utilize different quantities of Look-Up Tables (LUTs) for computational purposes. The research showcases the exceptional computing efficiency of the suggested design through detailed quantitative analysis, emphasizing its potential advantages in improving DSP implementations in practical situations. © 2025 IEEE.

关键词： Inverse problems

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Multi-Agent systems.for Autonomous IoT Network Management Using distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd international conference on Intelligent systems. Advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems.are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems.can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 IEEE.

关键词： Resource allocation

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2024 3rd international conference on distributed computing and High Performance computing, DCHPC 2024

2024 3rd International Conference on Distributed Computing a...

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3rd international conference on distributed computing and High Performance computing, DCHPC 2024

ISBN: (纸本)9798350381580

The proceedings contain 21 papers. The topics discussed include: enhancing the performance of photonic crystal and gates with machine learning optimization;bitcoin price prediction based on financial data, technical indicators, and news headlines sentiment analysis using CNN and GRU deep learning algorithms;promoting cybersecurity knowledge via gamification: an innovative intervention design;modeling and verification of the causal broadcast algorithm using colored Petri Nets;GPU-based parallel technique for solving n-similarity problem in textual data mining;a novel approach for specification and verification of symmetric distributed algorithms using spin;optimizing geophysical workloads in high-performance computing: leveraging machine learning and transformer models for enhanced parallelism and processor allocation;and rational Jacobi kernel functions: a novel massively parallelizable orthogonal kernel for support vector machines.

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