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检索条件"任意字段=7th International Conference on Real Time Computing Systems and Applications"

共 13062 条记录，以下是61-70 订阅

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FDA-TO-FCA: Flow Direction Algorithm-Based Task Offloading in Fog-Cloud Architecture 4th

FDA-TO-FCA: Flow Direction Algorithm-Based Task Offloading i...

引用

4th international conference on Smart IoT systems: Innovations and computing, SSIC 2023

作者： Shukla, Prashant Pandey, Sudhakar Gupta, Shivani Agrawal, Deepika Department of IT NIT Chhattisgarh Raipur492010 India Department of CSE IIT Bihar Patna801106 India

ISBN: (纸本)9789819751990

With the development of a lot of delay-sensitive applications, it is very difficult to perform all the tasks locally because our devices have less computation power. Traditional Cloud Server (CS) has large computing power and storage but it is very far from IoT devices that’s why it consumes a lot of energy and take a huge time to execute a task. For these types of applications, local devices and CSs are not good choices. So, to overcome this delay-sensitive problem, the Fog computing (FC) concept was introduced. In FC, we can offload our task to Fog Nodes (FN) that are close to the user device and take less execution time and energy than a CS. To minimize the execution time for delay-sensitive applications, we have modeled our problem and converted our solutions from a bounded real space to a finite discrete space that uses the Flow Direction Algorithm (FDA). Our algorithm performance is 12 \% better than cloud-only, 6 \% better than Fog-only, and 6 \% better than IoT-only on average. In this, the task can execute locally or on a FN or CS. the proposed approach performed better to reduce the execution time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Delay-sensitive applications

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"Cropmaster" - real-time Coordination of Multirobot systems for Autonomous Crop Harvesting: Design and Implementation 5

"Cropmaster" - Real-Time Coordination of Multirobot Systems ...

引用

5th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Pramod, Isuranga Arachchi, Avishka Mallawa Rashen, Chemuthu Chinthaka, Gayan Pandithage, Dinithi Gamage, Narmada Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331533694

the CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. the MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields. © 2025 IEEE.

关键词： Robotic arms

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HoloMoCap: real-time Clinical Motion Capture with HoloLens 2 7

HoloMoCap: Real-Time Clinical Motion Capture with HoloLens 2

引用

7th IEEE international conference on Artificial Intelligence and eXtended and Virtual reality, AIxVR 2025

作者： Zaccardi, Silvia Arapi, Anxhela Frantz, Taylor Brahimetaj, Redona Debeuf, Ruben Beckwée, David Swinnen, Eva Jansen, Bart Vrije Universiteit Brussel Belgium Vrije Universiteit Brussel Imec Belgium

ISBN: (纸本)9798331521578

Low-cost, portable motion capture (MoCap) systems struggle to achieve the same accuracy as the marker-based gold standard, and often fail to provide real-time feedback on patients' motion parameters. To address these challenges, we present HoloMoCap, a novel marker-based MoCap system enabling clinicians to track and visualize human movements in real-time through a head-mounted Augmented reality (AR) display. HoloMoCap is a HoloLens 2 stand-alone application, requiring no external tracking systems or additional servers for motion analysis. the application utilizes the HoloLens' depth sensor, operating at 5 frames per second (fps), to perform inside-out tracking of infrared markers attached to the patient's skin. At each frame, the system detects reflective markers, uses an on-device Deep Learning (DL) model to associate each marker with its corresponding body landmark, and calculates anatomical joint angles (hip and knee flexion, abduction, and rotation). Validation against Vicon was performed during rehabilitation exercises (squats and hip abduction), showing that estimated joint angles maintain root-mean-square error (RMSE) and mean absolute error (MAE) below 2° for most angles. HoloMoCap accurately estimated the range of motion (ROM) for hip abduction and knee flexion, with average MAEs of 0.4° and 1.2°, respectively. However, for hip flexion, the MAE can exceed 10° at maximum flexion during squats. HoloMoCap shows promise as a portable and cost-effective solution for motion capture, although further improvements in accuracy and frame rate are necessary to broaden its clinical applications. © 2025 IEEE.

关键词： Motion capture

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Challenges and Innovations in Multimedia and real-time Networking: A Review of Modeling Approaches 4

Challenges and Innovations in Multimedia and Real-Time Netwo...

引用

4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Shah, Milind Parmar, Kajal Padhiyar, Priyanka Parmar, Naina Parikh, Monali Patni, Dhruvansh Gujarat Vasad India Changa388421 India Changa388421 India Gujarat Vadodara India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. these platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. the demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. this review paper explores into the basics and introduction of Multimedia and real-time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. the paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies. © 2025 the Authors. Published by Elsevier B.V.

关键词： Challenges Innovations Intelligent Networking Multimedia real time Networking

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Intelligent System for real-time Processing of High Resolution Aerospace Data 16th

Intelligent System for Real-Time Processing of High Resoluti...

引用

16th international conference on Application of Fuzzy systems, Soft computing and Artificial Intelligence Tools, ICAFS 2023

作者： Ibrahimova, Sevda R. Zeynalova, Adila B. Babayeva, Sevinj S. Hasanova, Sadagat I. Aliyeva, Gulshan R. National Aviation Academy BakuAZ1045 Azerbaijan Azerbaijan Technical University BakuAZ1073 Azerbaijan Azerbaijan University of Oil and Industry BakuAZ1010 Azerbaijan Azerbaijan University Baku Azerbaijan

ISBN: (纸本)9783031725050

Currently, data from various satellite systems are used in Azerbaijan. therefore, the growing demand for satellite data can only be utilized by intelligent, real-time processing systems. Based on the models developed in this study, the possibilities of intelligent processing aerospace data in real-time studied. In this paper, the proposed intelligent system architecture is trained under sample conditions on an artificially enlarged remote sensing dataset and is adapted to recognize vegetation cover categories. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aerospace Intelligent Processing real-time Recognition Remote sensing Resolution

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Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios 24

Scalable Machine Learning Architectures for IPA-Driven Maint...

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7th international conference on Information Science and systems, ICISS 2024

作者： Huang, Zhimeng Liu, Xiaodong Romdhani, Imed Shih, Chi-Sheng School of Computing Engineering and Built Environment Edinburgh Napier University Edinburgh United Kingdom College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (纸本)9798400717567

this research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. this innovative model harnesses the synergies between Artificial Intelligence (AI), Machine Learning (ML), and Internet of things (IoT) technologies to transition from reactive to proactive and predictive building maintenance strategies. the study highlights the critical gap in current BMM practices—the absence of intelligent systems for anticipating and addressing maintenance issues before they escalate. through an extensive literature review, the transformative potential of AI and IoT for enhancing building maintenance management within smart cities is explored, establishing a foundation for the IBMM model’s application. the core of this research lies in its novel application of scalable machine learning architectures to automate and optimize maintenance task allocation in large-scale building portfolios. the practicality of the IBMM model is demonstrated via a proof of concept (POC) in an industrial setting, evidencing its capacity to improve efficiency, reduce costs, and bolster sustainability in building maintenance operations. the model epitomizes a paradigm shift in BMM by integrating IPA, which combines AI and ML, facilitating automated, intelligent decision-making and task allocation. Among its advancements, the IBMM model introduces enhanced predictive maintenance through real-time data analysis, adaptive learning and optimization, automated decision-making, and human-machine collaboration, contributing to energy efficiency and alignment with smart city objectives. the paper delineates the methodology, design, and implementation of a machine learning model for engineer task assignments, culminating in a case study that validates the model’s efficacy. this research not only signifies a significant advancement in BMM by leveraging IPA technologies for autonomous p

关键词： Predictive maintenance

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A Framework for real-time Monitoring of Power Consumption of Distributed Calculation on Computational Cluster

A Framework for Real-Time Monitoring of Power Consumption of...

引用

15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

this paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. the framework allows to visualize and analyze the provider results based on the context information about the performed calculation. the first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. the second part is dedicated to the testing of distributed data storage, called Scalable Distributed Two-Layered Data Structure (SD2DS). the results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Lightweight real-time vehicle detection algorithm on edge computing platform 5

Lightweight real-time vehicle detection algorithm on edge co...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Xu, Xingzhen Gao, Qiangye Zhou, Qifeng Chu, Yating Xie, Yongdong Branch of Suzhou Construction & Communications Jiangsu Union Technical Institute Jiangsu Suzhou China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

To address these issues, this study proposes a lightweight and highly deployable vehicle detection model. the model builds upon the single-stage YOLOv7 architecture, replacing the original convolutional layers with the inverted residual structure and depth-wise separable convolutions from MobileNetv3. this streamlines the network width and computational parameters. Furthermore, an ECA attention mechanism is incorporated into both the backbone network and multi-scale feature branches to reduce computational overhead while enhancing the model's cross-channel feature extraction capabilities. Additionally, data augmentation techniques were applied to the vehicle dataset, and the Focal Loss strategy was employed for model training and evaluation. Experimental results demonstrate that the proposed model reduces the parameter count from 37.62M to 10.217M, achieving a competitive MAP of 77.59%. the proposed model achieves a real-time inference speed of 40.04 FPS when deployed on a Jetson Xavier NX edge platform, representing a 77.8% performance improvement over the original YOLOv7 model. the lightweight and high-performance characteristics of the proposed detection model enable its seamless integration into resource-constrained edge computing infrastructure for ITS applications. this work provides a valuable technical foundation for realizing the full potential of intelligent. © 2025 SPIE.

关键词： Mobile edge computing

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Scheduling Energy-Aware Multi-function Serverless Workloads in OpenFaaS 20th

Scheduling Energy-Aware Multi-function Serverless Workloads ...

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20th international conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Chiorescu, Raulian Djemame, Karim School of Computing University of Leeds LeedsLS2 9JT United Kingdom

ISBN: (纸本)9783031812255

the paper investigates the prediction capabilities of a Machine Learning model in real-time scheduling applications on Kubernetes in a serverless computing environment with the aim to achieve a degree of energy efficiency. A highly pluggable framework for integrating a learning-based model into the Kubernetes scheduler is proposed and evaluated in a serverless setup on OpenFaaS. the experimental results in a cloud-native deployment demonstrate that, while maintaining Quality of Service for the application, an overall 8% in power reduction is achieved at a minimal performance loss. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Kubernetes Machine Learning Power consumption Serverless computing

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Enhancing Predictive Maintenance with AI: applications and Impact 6

Enhancing Predictive Maintenance with AI: Applications and I...

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

作者： Kaur, Chitranjanjit Sharma, Anurag GNA University Phagwara India

ISBN: (纸本)9798331522667

Industrial maintenance is crucial for companies due to its significant impact on operational costs and efficiency. Many industrial firms find that a substantial portion of their expenses stems from equipment breakdowns, failures, or suboptimal performance over time. these costs can sometimes account for up to 50% of overall expenditures. Additionally, companies face various losses, including downtime from breakdowns, challenges in inventory and purchase management, and potential risks of injuries. the objective of this writing is to investigate how Predive maintenance is a footstep towards industry 5.0 where automation of predictive maintenance is evolved to autonomy in maintenance. In addition to it various machine learning algorithms are suggested to identify between healthy and poor data of a machine which is first step in Predictive maintenance then Deep learning algorithms to construct XAI (Explainable artificial Intelligence). Predictive maintenance, production scheduling, problem detection, predictive quality, and increased energy efficiency are highlighted in the typical use cases of the selected AI applications. Data from the real environment is transmitted to be virtually recreated. Efficient industrial maintenance helps mitigate these costs by reducing downtime, improving equipment reliability, and lowering the need for emergency repairs. By implementing a well-designed and optimized maintenance strategy, production plants can ensure their equipment operates as reliably as possible, minimizing disruptions and enhancing overall operational efficiency. Machine Learning (ML) methods have been appeared as a crucial tool in Predictive Maintenance (PdM) applications to prevent failures in equipment that make up the production lines. However, the performance of PdM applications depends on the appropriate choice of the ML method. To save expenses, find inefficiencies, duplicate tool tracking systems, and do other tasks, Digital Twin evaluates material utilization

关键词： Predictive maintenance

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