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检索条件"任意字段=2024 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2024"

共 2746 条记录，以下是101-110 订阅

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Influence of user's body in olfactory virtual environment generated by real-time CFD 31

Influence of user's body in olfactory virtual environment ge...

引用

ieee conference on Virtual reality and 3D User Interfaces (VR)

作者： Uda, Masafumi Nakamoto, Takamichi Tokyo Inst Technol Sch Engn Tokyo Japan Tokyo Inst Technol Inst Innovat Res Tokyo Japan

ISBN: (纸本)9798350374025;9798350374032

We have developed a virtual olfactory environment using an olfactory display and computational fluid dynamics (CFD) simulation to provide odor concentration change according to user's movement in real-time in a virtual space. Although CFD can calculate the odor distribution in the complicated geometry, its computational cost was expensive and did not work in real-time in the previous study. In this study, real-time CFD based on GPU calculation was introduced to generate olfactory VR environment. Using real-time CFD we investigated influence of the user's body with its location and orientation changing irregularly. In the sensory test to find the odor direction, the correct answer rate was over 70% when the body influence was considered, while it was just a chance hit when the influence was not considered. The experimental result indicates the usefulness of considering the effect of the user's body since we cannot avoid that influence.

关键词： sense of smell olfactory information computational fluid dynamics computing methodologies-Modeling and simulation evaluation Human-centered computing-Interaction paradigms-Virtual reality

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Using Hypervectors for Efficient Anomaly Detection in Graph Streams 11

Using Hypervectors for Efficient Anomaly Detection in Graph ...

引用

ieee 11th international conference on Data Science and Advanced Analytics (DSAA)

作者： Arliss, William Godbehere, Andrew B. Mueller, Graham Leidos Arlington VA 22203 USA

ISBN: (纸本)9798350364941;9798350364958

We present an online algorithm for detecting changes in computer network activity. Anomalous activity in IT systems often appear as changes in network topology as edges evolve in a dynamic graph;identifying these behavioral changes can be a challenging task. We propose a method that uses principles of Hyperdimensional computing to encode graphs to a real valued space where anomalies are easily identifiable. With reasonable assumptions of the baseline edge generating process, our approach operates in real time and can produce an anomaly score primitive for one sample independently of all others. This score lends itself easily to an online Bayesian confidence estimate in constant memory, which is essential for real-world applications where networks are extremely large and interpretable predictions are needed in real time. We demonstrate the effectiveness of our approach on both synthetic and real-world datasets.

关键词： Anomaly detection graph sketching streaming data hyperdimensional computing

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Proceedings of the 3rd ieee international conference on Micro/Nano Sensors for AI, Healthcare and robotics, NSENS 2024

Proceedings of the 3rd IEEE International Conference on Micr...

引用

3rd ieee international conference on Micro/Nano Sensors for AI, Healthcare and robotics, NSENS 2024

ISBN: (纸本)9798350375213

The proceedings contain 27 papers. The topics discussed include: high performance conductive composite hydrogel interface for epidermal electrophysiological monitoring;implementation of Kirigami design in flexible wearable electronic by laser cutting for conformability;cuffless continuous BP monitoring from photoplethysmography signals using UNET and attention mechanism;composite graphene/Cu pressure sensor with one-step laser-fabricated microstructure: a one stone two birds strategy;an active interface pressure adapter for enhancing sensing capacity and stability of wearable electronics;3D porous stacked organic electrochemical transistor for wearable glucose sensing: enhancing comfort, breathability, and real-time monitoring;tracking of dual gas concentration using carbon nanotubes based reservoir computing;and a novel fluid classification unit based on moisture electricity generation mechanism.

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An Empirical Study on AI-Powered Edge computing Architectures for real-time IoT Applications 48

An Empirical Study on AI-Powered Edge Computing Architecture...

引用

48th Annual ieee international Computers, Software, and Applications conference (COMPSAC) - Digital Development for a Better Future

作者： Yasmin, Awatif Mahmud, Tarek Debnath, Minakshi Ngu, Anne H. H. Texas State Univ San Marcos TX 78666 USA

ISBN: (纸本)9798350376975;9798350376968

AI-Powered Edge computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machine learning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machine learning prediction on the server using the TensorFlow native model format has achieved the best model accuracy without draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.

关键词： service framework Edge computing IoT Applications Software Architectures Fall Detection

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Edge computing Empowered Digital Twin: An End-to-End computing Task Scheduling Approach 20

Edge Computing Empowered Digital Twin: An End-to-End Computi...

引用

ieee 20th international conference on Automation Science and Engineering (CASE)

作者： Zhong, Ruirui Feng, Yixiong Song, Xiuju Hu, Bingtao Wang, Yong Li, Puyan Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Hangzhou Dianzi Univ Key Lab Discrete Ind Internet Things Zhejiang Pro Hangzhou 310018 Peoples R China

ISBN: (纸本)9798350358513;9798350358520

The emerging of Digital Twin (DT) technology facilitates the further development of industrial automation. However, real-time and accurate DTs modeling and updating require massive communication and computing resources, which poses a challenge to limited resources. Edge computing as a distributed computing architecture offers the possibility of high-efficient resource scheduling in DTs. Motivated by this gap, this paper aim to solve the problem of real-time and high fidelity DTs modeling and updating. First, we represent the computing tasks of DTs in the form of Heterogeneous computing Task Graph (HCTG). Then, a Hierarchical Attention Mechanism (HAT) is proposed to obtain the latent representation vectors of the HCTG. Finally, we design Markov Decision Process (MDP), and propose Deep Reinforcement Learning (DRL)-based computing task scheduling approach (HAT-DRL) to satisfy the minimum total completion time requirement of different DTs. Experimental results demonstrate that the proposed algorithm has promising scheduling performance and outperforms other task scheduling algorithms.

关键词： Scheduling algorithms

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real-time Driver Alertness Monitoring with Optimized Deep Learning and Haar-Cascade Methods

Real-Time Driver Alertness Monitoring with Optimized Deep Le...

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2024 ieee international conference on Electronic Systems and Intelligent computing, ICESIC 2024

作者： Rawat, Karan Singh Kumar, Shubham Kowsigan, M. Ramamoorthy, S. Madhavan, P. School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu Kattankulathur603203 India

ISBN: (纸本)9798331522988

Driver fatigue is a leading cause of road accidents. This research presents a real-time driver drowsiness detection system using deep learning, optimized for low-power embedded systems. By compressing a complex model into a lightweight version, the system achieves an 85.5% accuracy in detecting various alertness states while maintaining energy efficiency. Utilizing facial landmark tracking and the Haar-Cascade method, the approach ensures fast and accurate detection, making it ideal for real-time vehicle safety applications. The system's integration into modern vehicles promises enhanced driver fatigue management and accident prevention. © 2024 ieee.

关键词： Deep learning

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[conference Info]

[Conference Info]

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ieee international conference on real-time computing and robotics (rcar)

Provides general information of interest to conference attendees.

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Provides general information of interest to conference attendees.

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Dynamic scheduling method of computing resources in Internet of Vehicles based on an improved NSGA-II algorithm 6

Dynamic scheduling method of computing resources in Internet...

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6th international conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Li, Huiyong Yang, Yang Han, Shuhe Wu, Xiaofeng School of Intelligent Manufacturing and Information Jiangsu Shipping College Nantong China

ISBN: (纸本)9798331541699

The Internet of Vehicles (IoV) represents a critical component of modern mobile edge computing systems. IoV computing resources encompass backend cloud resources, roadside edge nodes, and vehicle-mounted units. Traditional static scheduling methods struggle to meet the real-time performance and resource utilization demands imposed by the IoV's dynamic and heterogeneous environment. This study introduces a dynamic scheduling approach for IoV computing resources leveraging an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) to address multi-objective optimization challenges in resource allocation. First, the study develops a multi-objective optimization model that integrates key performance metrics, including task delay, energy consumption, and resource utilization. Subsequently, an improved NSGA-II framework incorporating adaptive encoding, selection, crossover, and mutation strategies is formulated to enhance the algorithm's global optimization capabilities in dynamic IoV scenarios. Experimental results demonstrate that the proposed method outperforms traditional approaches in multi-objective optimization efficacy, convergence rate, and algorithmic stability, offering an efficient solution for managing IoV computing resources. © 2024 ieee.

关键词： Multiobjective optimization

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Evolution of Neuromorphic computing 4

Evolution of Neuromorphic Computing

引用

4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sai Sree Vaishnavi, Vakada G. Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Surathkal Mangalore575025 India

ISBN: (纸本)9798350343670

With the advancement of artificial intelligence (AI) technologies, novel and inventive approaches for addressing complex problems are coming to the forefront. Neuromorphic computing based on AI technologies stands as an exemplar, endeavoring to mimic the human brain's intricate neural architecture and computational principles within electronic devices. Contrary to conventional Von Neumann architecture, neuromorphic computing architecture offers a promising solution for building intelligent and efficient computational systems that excel in tasks requiring low power consumption, real-time processing, and adaptability. Subsequently, it is employed in various applications such as robotics, sensory processing, neuromorphic vision, edge computing, etc. This paper explores the conventional Von Neumann architecture and outlines its shortcomings. Next, neuromorphic architecture as an alternative and its evolution are described. Next, the characteristics of neuromorphic computing and its diverse applications are illustrated. The paper also addresses the key challenges hindering neuromorphic computing development. © 2024 ieee.

关键词： Neuromorphic computing Neuromorphic computing Architecture Neuromorphic computing Challenges SpiNNaker Von Neumann Architecture

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Cloud-Edge Collaboration For Integration of Sensing, computing, and Communication in Industrial Internet

Cloud-Edge Collaboration For Integration of Sensing, Computi...

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ieee/CIC international conference on Communications in China (ICCC)

作者： Guo, Chunyu Zhang, Yutong Di, Boya Song, Lingyang Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Dept Networked Intelligence Shenzhen Peoples R China Peking Univ Sch Elect Beijing Peoples R China

ISBN: (纸本)9798350378412

With the development of industrial Internet, numerous real-time applications emerge where a huge amount of sensing data should be captured from the environment and processed. In this paper, we consider a heterogeneous mobile edge computing (Het-MEC) network via cloud-edge collaboration, which integrates data sensing, computing, and communication. Considering the real-time demand of industrial production, the freshness and validity of data need to be guaranteed. Therefore, we formulate the average peak age of information (PAoI) minimization problem. A Proximal Policy Optimization (PPO) based deep reinforcement learning algorithm is proposed to solve it online by dynamically optimizing the task offloading strategy and the joint sensing-computing-communication resource allocation scheme. Simulation results show that compared with local computing and cloud computing schemes, the proposed algorithm can achieve a lower average PAoI of the system. Benefited from the learning and transfer characteristic of deep model, the time complexity can also be reduced in a long-term view compared to the heuristic algorithm.

关键词： Industrial Internet mobile edge computing integrated sensing computing and communication deep reinforcement learning

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