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检索条件"任意字段=IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

共 36864 条记录，以下是351-360 订阅

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Software Architecture for IoT-based Indoor Positioning systems for Ambient Assisted Living 20

Software Architecture for IoT-based Indoor Positioning Syste...

引用

ieee 20th international conference on Software Architecture (ICSA)

作者： Mendes, Lucas F. Aguilar, Paulo A. C. Bezerra, Carla I. M. Univ Fed Ceara Dept Comp Sci Quixada Brazil

ISBN: (纸本)9798350397499

Indoor positioning is an important element in context-aware applications, especially based on the internet of things (IoT). Elderly people or people with specific needs can benefit from such applications by providing them with an ambient assisted living (AAL). Indoor positioning system (IPS) applications can be deployed under an IoT infrastructure to provide location awareness to other applications, such as AAL solutions. However, software patterns and architectures in the context of IoT applications are still open research topics. This paper proposes a software architecture for IoT-based IPS applications to support AAL systems. We performed a systematic literature review (SLR) to identify solutions and non-functional requirements (NERs) to incorporate into the architecture of these applications. From the SLR results, we developed an architecture for IoT-based IPS applications, using Fog computing elements, to support AAL systems. We evaluate the architecture using the iFogSim simulation environment regarding latency, network usage, and energy consumption. The proposed approach was compared to a cloud-based deployment. Experimental results show that the proposed fog-based architecture significantly reduces latency, network usage, and operational costs and is suitable for real-time response scenarios. In addition, the energy consumption of cloud data centers is reduced by employing fog computing.

关键词： ambient assisted living fog computing indoor positioning system internet of things software architecture

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AI Driven Predictive Functional Safety Leveraging Key Performance Indicators in Autonomous Vehicles

AI Driven Predictive Functional Safety Leveraging Key Perfor...

引用

2024 international conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Sawarkar, Nikhilesh Harman International Automotive Intelligent Cockpit Platform Bangalore India

ISBN: (纸本)9798331517953

As the automotive industry transitions towards Software-Defined Vehicles (SDVs) with centralized computing architectures, managing functional safety becomes increasingly complex. This paper examines how AI-driven decision systems can enhance predictive functional safety in autonomous vehicles especially with the architectural shift from traditional to zonal and centralized computing. A combined approach utilizing Predictive Artificial Intelligence (AI) and Key Performance Indicators (KPIs) is proposed to ensure functional safety and optimize system performance. KPIs track real-time performance of critical functions, serving as diagnostic tools during development and enabling AI-based predictive monitoring post-deployment. This approach facilitates proactive risk mitigation and continuous system optimization. © 2024 ieee.

关键词： Automotive industry

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A Transfer Learning Approach for Enhancing Road Surface Classification in Intelligent Driving systems

A Transfer Learning Approach for Enhancing Road Surface Clas...

引用

2024 ieee international conference on Signal Processing and Advance Research in computing, SPARC 2024

作者： Judeson Antony Kovilpillai, J. Jayanthy, S. Syed Mohamed, Sulaiman Mir, Mahmood Hussain Pragya Dhar, Soumi Alliance University Dept. of CSE Bangalore India Sri Ramakrishna Engineering College Dept. of ECE Coimbatore India

ISBN: (纸本)9798350385199

Road surface classification is very critical in the domain of intelligent driving systems as it ensures enhanced safety and comfort in autonomous vehicles. In this paper, a transfer learning approach is proposed for effectively classifying the road surface with enhanced accuracy and real-time performance mitigating the constraints of traditional image processing techniques A deep learning model is developed and trained in this research using an open-source dataset, encompassing 72,400 images capturing distinct road surface characteristics present across varying terrains. This research adopts pre-trained models like Mobilenet, VGG-19 and Resnet101 as feature extractors to optimize and improve the classification performance of the deep learning model significantly. The proposed methodology is evaluated using different key performance metrics such as loss, accuracy, average rate of improvement and change in accuracy, and total training time. The proposed deep learning technique achieved an accuracy of 98.77 %, signifying its critical importance for real-world applications and practical deployment. © 2024 ieee.

关键词： Adversarial machine learning

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Waste Sorting System Using AI: Development of An embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

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6th ieee international conference on Image Processing, applications and systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 ieee.

关键词： AI models Computer vision Waste detection Waste management Waste sorting

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Efficient Resource Management for real-time AI systems in the Cloud using Reinforcement Learning 7

Efficient Resource Management for Real-time AI Systems in th...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Mallikarjunaradhya, Vinay Sreeramulu, Madhusudhan Dasari Mohammed, Abdul Sajid Boddapati, Nagesh Gupta, Ketan Natarajan, Yuvaraj Thomson Reuters Toronto Canada American Express Department of Cyber Security United States University of the Cumberlands Department of School of Computer and Information Sciences KY United States Microsoft Department of Computer Science CharlotteNC United States University of The Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350350067

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning (RL) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The third way our proposed approach utilizes the weapon of RL entails allowing for dynamic resource allocation so that resources are allocated when needed rather than from a static method as set at design time. © 2024 ieee.

关键词： Adversarial machine learning

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AI-Driven Patient Monitoring in Smart Hospitals: A Survey on Edge computing Integration and applications 2

AI-Driven Patient Monitoring in Smart Hospitals: A Survey on...

引用

2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ranjani, R. Naresh, R. SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur Chengalpattu Tamilnadu Chennai603 203 India

ISBN: (纸本)9798331505745

The integration of artificial intelligence (AI) and edge computing in healthcare is transforming patient monitoring systems, particularly within smart hospitals. These technologies enable real-time data processing, enhanced decision-making, and improved patient outcomes by shifting computational tasks closer to data sources, reducing latency, and alleviating the burden on centralized cloud infrastructures. This survey paper provides a comprehensive review of the current state of AI-driven patient monitoring systems in smart hospitals, focusing on the role of edge computing. It explores key applications, such as vital sign monitoring, early disease detection, and predictive analytics, while highlighting the benefits of edge computing, including real-time analytics, enhanced data privacy, and resource efficiency. Furthermore, the paper examines challenges in the deployment of AI and edge computing in clinical settings, such as data security, interoperability, scalability, and the need for robust AI algorithms tailored to healthcare environments. The survey concludes with a discussion of future research directions and potential advancements to further enhance AI-driven patient monitoring systems in smart hospitals. This review serves as a valuable resource for researchers and healthcare professionals aiming to understand the intersection of AI and edge computing in modern healthcare infrastructures. © 2025 ieee.

关键词： Data Analytics

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An Efficient Integrated Negative Fractional Counting Sort Algorithm

An Efficient Integrated Negative Fractional Counting Sort Al...

引用

2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Sarker, Dip Gomes, Dipta Hossain, Shahadat Hasan, Md. Manzurul American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Daffodil International University Department of Computer Science Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331529765

The time complexity of the Counting Sort algorithm is linear and is famous for sorting non-negative integers (0-positive). It runs well for a possible small difference between the smallest and largest number in the input data series to avoid redundant unused memory space. So far studied in the literature, the traditional counting sort algorithm may also be applied to sort all integers (negative-0-positive) in such a way that positive and negative integers will be dealt with separately to sort. In this paper, we propose a integrated form of counting sort algorithm to sort all real numbers and we call it the "negative fractional counting sort"method that can efficiently sort arrays with negative and positive integers along with fractional numbers. While the input array has been extended from the integer range (0toK) to the fractional range from -ve fractional number to +ve fractional number, the time complexity remains linear. The proposed method involves separately sorting the negative and positive components of the array, followed by an innovative merging process. This integrated approach eliminates the need for additional arrays or multiple sorting passes, thus optimizing memory usage and computational efficiency. Experimental results demonstrate the algorithm's capability to sort mixed numeric arrays effectively, with performance metrics indicating minimal execution time. Our contribution offers a versatile and efficient sorting solution, extending the practical utility of Counting Sort to a broader spectrum of real-world data scenarios. © 2024 ieee.

关键词： Counting Sort Fractional Counting Sort Linear-time time Complexity Mixed Numeric Arrays Negative Counting Sort real Numbers

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Mastering Beaglebone AI & AI-64 on its Multifaceted applications 4

Mastering Beaglebone AI & AI-64 on its Multifaceted Applicat...

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4th international conference on Soft computing for Security applications, ICSCSA 2024

作者： Sivakumar, P. Premkumar, T. Ani Sowntharya, A. Navin, S. Pavithra, T. PSG College of Technology Department of EEE Coimbatore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515720

The rapid growth of embedded systems has led to the integration of artificial intelligence capabilities at the edge of computer devices. The Beagle-Bone AI and AI64 platform is a potent development board intended to make AI and machine learning activities easier in embedded environments. This study investigates the device's diverse possibilities. The paper provides a comprehensive guide about configuring the Beagle-Bone AI and AI64, offering insights into hardware specifications, software setup, and communication interfaces. Beyond technical configuration, this paper delves into a wide spectrum of applications and beyond. Through case studies and practical implementations, this paper demonstrates the platform's utility as a catalyst for innovation and research at the intersection of edge computing and artificial intelligence. © 2024 ieee.

关键词： Application programs

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real-time Bandwidth-efficient Occupancy Grid Map Synchronization for Multi-Robot systems

Real-time Bandwidth-efficient Occupancy Grid Map Synchroniza...

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2024 international conference on Intelligent Robots and systems

作者： Shi, Liuyu Yin, Longji Kong, Fanze Ren, Yunfan Zhu, Fangcheng Tang, Benxu Zhang, Fu Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

ISBN: (纸本)9798350377712;9798350377705

Robot swarms are increasingly being applied in various domains. However, due to the inherent limitation imposed by low real-time communication bandwidth, the synchronization of environmental information among multiple robots remains a persistent and challenging problem in practical applications. In response to this challenge, we introduce a comprehensive framework for synchronizing occupancy grid maps (OGMs) in practical multi-robot systems that operate under communication bandwidth constraints. In our research, we elaborately design the data structure of transmitted local OGMs and employ the Hilbert space-filling curve for voxel sorting. By adopting this approach, data redundancy is effectively increased, resulting in lower information entropy for compression and significantly reducing the volume of communication data. Finally, our framework outperforms the benchmark method by reducing the average and maximum bandwidth usage by more than 10 times in high-resolution scenarios. Moreover, our method has been successfully applied in the multi-UAV autonomous navigation application, demonstrating its real-time and bandwidth-efficient nature, as well as its practical value.

关键词： Bandwidth

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A ROS 2 and TwinCAT Based Digital Twin Framework for Mechatronics systems 11

A ROS 2 and TwinCAT Based Digital Twin Framework for Mechatr...

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11th international conference on Control, Mechatronics and Automation (ICCMA)

作者： Sterk-Hansen, Alexander Saghaug, Bjorn H. H. Hagen, Daniel Aftab, Muhammad Faisal Univ Agder Dep Engn Sci Grimstad Norway

ISBN: (纸本)9798350315684

This paper presents a digital twin framework as a first step towards developing digital twin-based control systems for autonomous lifting and load-handling operations. The proposed digital twin framework has been divided into three main parts: a) a real-time control and simulation environment using TwinCAT 3 running on a Beckhoff embedded PC with EtherCAT Fieldbus, b) a ROS 2 interface (middleware), and c) a virtual environment using the state-of-the-art game engine software NVIDIA Isaac Sim. The investigated case study demonstrates that the proposed architecture offers scalability and forms a promising baseline for further developing a general-purpose digital twin development platform suitable for developing autonomous control systems for various mechatronics systems and applications.

关键词： Mechatronics digital twin real-time control ROS 2 TwinCAT 3 NVIDIA Isaac Sim computer vision

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