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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 2742 条记录，以下是21-30 订阅

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2024 ieee 3rd international conference on computing and Machine Intelligence, ICMI 2024 - Proceedings

2024 IEEE 3rd International Conference on Computing and Mach...

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3rd ieee international conference on computing and Machine Intelligence, ICMI 2024

ISBN: (纸本)9798350372977

The proceedings contain 157 papers. The topics discussed include: a self-scaling dynamic blockchain model for IoT;advanced generative ai methods for academic text summarization;data augmentation for entity resolution: a comparative evaluation;mitigation of user-prompt bias in large language models: a natural language processing and deep learning based framework;evaluating the effectiveness of an object detection pipeline to support surveillance of unintended passage;automated scripting for real-time responses to suspicious user actions;highway merging control using multi-agent reinforcement learning;using machine learning to predict student success in undergraduate engineering programs;NeuroAqua: developing an optimized artificial intelligence and Internet of Things-based aquaponics system;and precision fish farming to mitigate pond water quality through IoT.

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Proceedings - 2024 ieee 8th international conference on Fog and Edge computing, ICFEC 2024

Proceedings - 2024 IEEE 8th International Conference on Fog ...

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8th ieee international conference on Fog and Edge computing, ICFEC 2024

ISBN: (纸本)9798350361353

The proceedings contain 16 papers. The topics discussed include: computation offloading for precision agriculture using cooperative inference;AMBIT: an efficient pruning technique in federated learning for edge computing systems;asynchronous federated split learning;federated learning deployments of industrial applications on cloud, fog, and edge resources;VATE: edge-cloud system for object detection in real-time video streams;enabling adaptive video streaming via content steering on the edge-cloud continuum;thinking out of replication for geo-distributing applications: the sharding case;WoW-IO: a gaming-based storage trace generator for edge computing;knowledge distillation based on monoclass teachers in edge infrastructure using unlabeled data;migration of isolated application across heterogeneous edge systems;and synergizing fuzzy-based task offloading with machine learning-driven forecasting for IoT.

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A Research on Fast Acquisition Technology of Spatio-Temporal Data of Railway Infrastructure Based on Measurable real Image

A Research on Fast Acquisition Technology of Spatio-Temporal...

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2024 international conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Xu, Xiaolei Wang, Yaoyao Feng, Boqing Cui, Mengzhen Institute of Computing Technology China Academy of Railway Sciences Corporation Limited China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

The spatiotemporal data of railway infrastructure plays an important role in the development of railway informatization, but existing collection technologies have problems such as low efficiency, high cost, and many limitations. Starting from different business application scenarios in railways, this article first conducts a comprehensive investigation and analysis of the business requirements for spatiotemporal data of railway infrastructure. Then, by studying new surveying and mapping technologies such as GNSS+IMU combined positioning technology, laser point cloud scanning technology, and real scene video acquisition technology, a railway measurable real scene image acquisition device is developed to achieve the integrated collection of device operation trajectory positioning data, point cloud data, and real scene video data. At the same time, real scene image calculation technology is used to obtain measurable real scene image data along the railway line, thereby achieving rapid collection of railway infrastructure spatiotemporal data based on measurable real scene images. Finally, experimental verification was conducted on the circular railway line of the National Railway Test Center, successfully collecting and obtaining the spatial and mileage coordinates of various professional infrastructure along the circular railway, as well as accurately measuring the geometric dimension information of various facilities and structures. © 2025 SPIE.

关键词： Mapping

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Vehicle Pseudonym Change Mechanism Based on Dynamic Game

Vehicle Pseudonym Change Mechanism Based on Dynamic Game

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2024 international conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhao, Zheng Wang, Junren Mao, Qian Zhao, Qi Xie, Xin Dalian Maritime University Dalian China Liaoning University Shenyang China Northeastern University Shenyang China Hunan University of Information Technology Changsha China Hunan Province Higher Education Key Laboratory of Intelligent Sensing and Computing Changsha China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Intelligent Transportation Systems (ITS), particularly vehicular ad-hoc networks, play a crucial role in improving road safety and traffic efficiency. However, transmitting vehicle location and identity data poses significant privacy risks, potentially allowing attackers to exploit this information. In this study, we developed a user-centric privacy model based on information entropy, enabling vehicles to monitor real-time location privacy levels. We also proposed a decision algorithm for pseudonym changes, dynamically identifying a cooperative set of vehicles for pseudonym alteration. Comparative experiments using SUMO and NS-3 simulations show that our scheme enhances location privacy while reducing pseudonym change frequency. This research provides valuable theoretical and technical foundations for ITS. © 2025 SPIE.

关键词： Vehicular ad hoc networks

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Residual-INR: Communication Efficient On-Device Learning Using Implicit Neural Representation 24

Residual-INR: Communication Efficient On-Device Learning Usi...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Chen, Hanqiu Yao, Xuebin Subedi, Pradeep Hao, Cong Georgia Institute of Technology United States Samsung Semiconductor Inc. United States

ISBN: (纸本)9798400710773

Edge computing is a distributed computing paradigm that collects and processes data at or near the source of data generation. The on-device learning at edge relies on device-to-device wireless communication to facilitate real-time data sharing and collaborative decision-making among multiple devices. This significantly improves the adaptability of the edge computing system to the changing environments. However, as the scale of the edge computing system is getting larger, communication among devices is becoming the bottleneck because of the limited bandwidth of wireless communication leads to large data transfer latency. To reduce the amount of device-to-device data transmission and accelerate on-device learning, in this paper, we propose Residual-INR, a fog computing-based communication-efficient on-device learning framework by utilizing implicit neural representation (INR) to compress images/videos into neural network weights. Residual-INR enhances data transfer efficiency by collecting JPEG images from edge devices, compressing them into INR format at the fog node, and redistributing them for on-device learning. By using a smaller INR for full image encoding and a separate object INR for high-quality object region reconstruction through residual encoding, our technique can reduce the encoding redundancy while maintaining the object quality. Residual-INR is a promising solution for edge on-device learning because it reduces data transmission by up to 5.16 × across a network of 10 edge devices. It also facilitates CPU-free accelerated on-device learning, achieving up to 2.9 × speedup without sacrificing accuracy. Our code is available at: https://***/sharclab/Residual-INR. © 2024 Copyright is held by the owner/author(s).

关键词： Edge computing

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Advancing Air Quality Monitoring: TinyML-Based real-time Ozone Prediction with Cost-Effective Edge Devices 2nd

Advancing Air Quality Monitoring: TinyML-Based Real-Time Ozo...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Ken, Huam Ming Behjati, Mehran Rafsanjani, Ahmad Sahban Aslam, Saad Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The escalation of urban air pollution necessitates innovative solutions for real-time air quality monitoring and prediction. This paper introduces a novel TinyML-based system designed to predict ozone concentration in real-time. The system employs an Arduino Nano 33 BLE Sense microcontroller equipped with an MQ7 sensor for carbon monoxide (CO) detection and built-in sensors for temperature and pressure measurements. The data, sourced from a Kaggle dataset on air quality parameters from India, underwent thorough cleaning and preprocessing. Model training and evaluation were performed using Edge Impulse, considering various combinations of input parameters (CO, temperature, and pressure). The optimal model, incorporating all three variables, achieved a mean squared error (MSE) of 0.03 and an R-squared value of 0.95, indicating high predictive accuracy. The regression model was deployed on the microcontroller via the Arduino IDE, showcasing robust real-time performance. Sensitivity analysis identified CO levels as the most critical predictor of ozone concentration, followed by pressure and temperature. The system’s low-cost and low-power design makes it suitable for widespread implementation, particularly in resource-constrained settings. This TinyML approach provides precise real-time predictions of ozone levels, enabling prompt responses to pollution events and enhancing public health protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Edge Intelligence for Wildlife Conservation: real-time Hornbill Call Classification Using TinyML 2nd

Edge Intelligence for Wildlife Conservation: Real-Time Hornb...

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2nd international conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Online Optimization of Offloading Video Analytics Tasks to Multiple Edges for Accuracy Maximization

Online Optimization of Offloading Video Analytics Tasks to M...

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

作者： Liang, Yu Zhang, Sheng Wu, Jie School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University China State Key Laboratory for Novel Software Technology Nanjing University China Center for Networked Computing Temple University United States

ISBN: (纸本)9798350368741

real-time video analytics (VA) presents challenges due to its computational intensity and latency sensitivity, especially when processed on mobile devices with limited local resources. We propose to offload VA tasks to edge servers with diverse computational capabilities. We present a "detect + track" approach with on-device object tracking and edge-assisted object detection. We formulate a long-term nonlinear integer programming to maximize the overall accuracy within detection frequency and latency constraints. We then design a queue-based online optimization algorithm to solve it: relax the original problem from the integer domain to the real domain, then employ a queue-based adaptation and randomized rounding strategy. Via rigorous proof, both dynamic regret regarding detection accuracy and the real-time requirement are ensured. Evaluation results also demonstrate the effectiveness of our approach. © 2025 ieee.

关键词： online optimization resource allocation Video analytics

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Contrast Memory for Unsupervised Anomaly Detection

Contrast Memory for Unsupervised Anomaly Detection

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

作者： Li, Jiahao Chen, Yiqiang Xing, Yunbing Gu, Yang Lan, Xiangyuan Institute of Computing Technology Chinese Academy of Sciences Beijing China Pengcheng Laboratory Shenzhen China University of Chinese Academy of Sciences Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China Guangzhou China

ISBN: (纸本)9798350368741

real-world multivariate time series unsupervised anomaly detection is a challenging problem due to intricate temporal correlations. Recently, impressive progress have been made in tackling this issue through the design of large-scale models, facilitated by the growing model parameters. However, in resource-constrained scenarios such as ubiquitous computing and edge computing, the large-scale models suffer from issues like high parameter complexity and expensive training overheads. Existing methods can only strive for a direct tradeoff between model size and performance. To address this challenge, we propose DiMER (Diminutive Memory-Enhanced Reconstruction), a model with parameters of the order of 0.1M. In DiMER, we introduce a novel contrast memory mechanism to learn normal patterns with diminutive network and propose a temporal reconstruction loss nto add the autocorrelation information. In addition, we introduce a multi-space composite detection criterion, an anomaly score calculation that takes into account both memory space and data space. Extensive experiments on real-world datasets across various domains demonstrate that the proposed model achieves comparable or even superior performance to large-scale models while maintaining lightweight. © 2025 ieee.

关键词： Anomaly timeseries Unsupervised Learning

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Tawna Yachaq an Intelligent System for the safe movement of people with visual disabilities

Tawna Yachaq an Intelligent System for the safe movement of ...

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2025 ieee international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Carrasco, Victor Diaz Melgar, Bryan Barzola Sayan, Luis Quispe Palomares, Ricardo National Technological University of Lima South School of Electronic Engineering and Telecommunications Lima Peru National Technological University of Lima South Research Group of Computing and Cognitive Neuroscience Lima Peru

ISBN: (纸本)9798331529833

The development of the intelligent system "Tawna Yachaq"is presented for the safe movement of a volunteer with visual disabilities. This system includes a cane with an obstacle detection system, an artificial vision system for the identification of objects and people, with an alert and position warning system in a controlled environment. The mechanical system consists of an intelligent system protection box developed in Fusion 360 software. The electronic-electrical design of the intelligent system, based on the Raspberry Pi 4B microprocessor. Includes a Neo 6M GPS module to provide real-time user location. Using HC-SR04 ultrasonic sensors and a buzzer module, it emits an alert sound to the user. In addition, an ESP32-Cam camera that uses computer vision algorithms detects predefined objects in the university campus environment. The system is powered by 7.4V Lion rechargeable batteries, with an LM2596 voltage regulator ensuring stable operation. A patent is proposed as future work. © 2025 ieee.

关键词： Sustainable development

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