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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 36895 条记录，以下是711-720 订阅

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real-time Vision Based Obstacle Detection in Maritime Environments

Real-time Vision Based Obstacle Detection in Maritime Enviro...

引用

ieee international conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Nunes, Duarte Fortuna, Joao Damas, Bruno Ventura, Rodrigo CEiiA Ctr Engn & Desenvolvimento Dept Elect & Embedded Syst Ocean & Space Unit Matosinhos Portugal CINAV Ctr Invest Naval Almada Portugal Inst Super Tecn Inst Syst & Robot Lisbon Portugal

ISBN: (纸本)9781665482172

Automatic obstacle detection is a key feature for unmanned surface vehicles (USV) operating in a fully autonomous manner. While there are currently many approaches to obstacle detection in maritime environments (e.g., LiDAR, radar) the proposed approach resorts to standard, inexpensive RGB cameras to perform the detection of such obstacles. Recent advances in deep neural network detectors achieve state-of-theart detection results, and some one-stage networks achieve very good results while maintaining inference times small enough to be compatible with real-time capabilities on low-cost embedded processing units. In this paper, we train the YOLO v4 network to detect different types of ships, using publicly available maritime datasets. After training, we evaluate the obtained network on the processing unit located onboard the UAV with respect to detection accuracy and real-time processing capability, thus demonstrating that the presented detection method can be considered a robust, fast, flexible, and inexpensive approach to obstacle detection in USV applications.

关键词： Deep Learning Convolutional Neural Networks Object Detection embedded systems Unmanned Surface Vehicles

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Analyzing Digital Services Across the Compute Continuum Using iFogSim

Analyzing Digital Services Across the Compute Continuum Usin...

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ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Saeedeh Baneshi Ana-Lucia Varbanescu Anuj Pathania Benny Akesson Andy Pimentel Parallel Computer Systems SNE Lab University of Amsterdam The Netherlands TNO-ESI Eindhoven The Netherlands

Digital services enable users to interact with a broad range of applications and, as such, have become an essential part of our daily lives. Although convenient, their ubiquity comes at a significant cost in energy, raising sustainability concerns. We access these services by triggering a computing continuum, spanning from the device to the edge, fog, and cloud. Scheduling decisions made at each layer impact the overall quality of service (QoS) and energy consumption of digital services.

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Proceedings - 2023 ieee/ACM international conference on Connected Health: applications, systems and Engineering Technologies, CHASE 2023

Proceedings - 2023 IEEE/ACM International Conference on Conn...

引用

8th ieee/ACM international conference on Connected Health: applications, systems and Engineering Technologies, CHASE 2023

ISBN: (纸本)9798400701023

The proceedings contain 39 papers. The topics discussed include: Parkinson’s disease action tremor detection with supervised-leaning models;social visual behavior analytics for autism therapy of children based on automated mutual gaze detection;active reinforcement learning for personalized stress monitoring in everyday settings;dove: shoulder-based opioid overdose detection and reversal device;using geographic location-based public health features in survival analysis;virtual therapy exergame for upper extremity rehabilitation using smart wearable sensors;efficient and direct inference of heart rate variability using both signal processing and machine learning;interpreting high order epistasis using sparse transformers;exploring earables to monitor temporal lack of focus during online meetings to identify onset of neurological disorders;tala box: an interactive embedded system to accompany patients with cognitive disorders;and machine learning based real time detection of freezing of gait of Parkinson patients running on a body worn device.

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Intelligent Black Ice Detection and Alert System Using Thermal Imaging Camera and Drone 23

Intelligent Black Ice Detection and Alert System Using Therm...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xu, Yanan Yao, Dexiang Ren, Xue Dai, Yunhai NamSeoul University Department of Extended Reality & Immersive Technology Korea Republic of Korea Republic of PaiChai University Department of Electronic Engineering Korea Republic of NamSeoul University Department of International Trade Korea Republic of

ISBN: (纸本)9781665494571

Traffic accidents caused by black ice are frequent in winter. This paper proposed to use drone embedded with thermal imaging camera to collect road information, and conduct video analysis and segmentation based on CNN (Convolutional Neural Networks). The resulting black ice position and area are used to analyze formation time and predict vanishing time, then real-time alert and navigation information updates could be realized. The study helps to reduce traffic accidents caused by black ice and provide more security for the lives and property of travelers. © 2021 ieee.

关键词： Smart cities Thermal sensors Streaming media Cameras Thermal conductivity Ice Thermal analysis

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Algorithmic Strategies for Cyber Crime Attack Prevention Harnessing the Power of Convolutional Neural Networks 7

Algorithmic Strategies for Cyber Crime Attack Prevention Har...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Deva Kirubai, J. Christina Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Tamil Nadu Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350372816

Cybercrime is a major concern in today's cyber world, and immediate action must be taken to prevent it. Traditional cybersecurity systems are vulnerable to acclimatization issues, a high percentage of false positive alerts, and so forth. To address these problems, the study proposes a cutting-edge technique that blends CNNs with transmission characteristics for anti-cybercrime policy. The CN-based system is designed to retrieve network traffic data in real-time, with the reliability of identified hostile activity proportional to the minimizing of false alarms. The system includes components such as data collection and preprocessing, CNN architecture design, training, real-time analysis, and adaptation to emerging threats. Superior performance characteristics can be seen in comparison to existing systems, including 0.90-0.92 greater accuracy, 0.89-0.94 precision, 0.97-0.98 recall, and 0.98-0.93 F1-score with a ROC AUC score of 0.97. Furthermore, the proposed system has faster detection speeds (1200 packets per second) and lower false positive rates for various cyber threat types such as malware (0.03), phishing assaults (0.05), and DDoS attacks (0.02). Thus, the proposed system represents a big step forward in the evolution of cyber security, adopting more active threat identification and mitigation, thereby strengthening existing network security and exposing it to fewer threats. © 2024 ieee.

关键词： Phishing

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Edge Assisted real-time Instance Segmentation on Mobile Devices 42

Edge Assisted Real-time Instance Segmentation on Mobile Devi...

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42nd ieee international conference on Distributed computing systems (ICDCS)

作者： Zhang, Jialin Huang, Xiang Xu, Jingao Wu, Yue Ma, Qiang Miao, Xin Zhang, Li Chen, Pengpeng Yang, Zheng Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China HeFei Univ Technol Hefei Peoples R China China Univ Min & Technol Beijing Peoples R China

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

Accurate and real-time instance segmentation on mobile devices enables a wide spectrum of applications such as augmented reality, context-aware inspection and environmental cognition. However, the computation resource demanded by instance segmentation impedes its deployment on resource-constrained commercial mobile devices. Prior studies enable smartphones to conduct computational-intensive tasks in real-time with the assistance of an edge server. However, simply applying an edge-assisted framework hardly achieves delightful segmentation performance due to the movements of devices and targets, pixel-level precision requirements, and huge computational overhead even for edge nodes. This work proposes edgeIS, an edge-assisted system that enables real-time and accurate instance segmentation on mobile devices. edgeIS embraces the mobile device sensing ability of surroundings and its own motion, and redesigns an innovative mobile-edge collaboration paradigm suitable for segmentation tasks. We implement edgeIS on a lightweight edge node and different mobile devices. Extensive experiments are conducted under four datasets. The results show that edgeIS can run on mobile devices in real-time and achieve a 0.92 segmentation IoU, outperforming existing state-of-the-art solutions. We further embed edgeIS in an AR-based inspection system deployed in an oil field and the performance of edgeIS meets the demand of the industrial scenario.

关键词： Performance evaluation Tracking Motion segmentation Collaboration Inspection Mobile handsets real-time systems

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Enhancing Lightweight embedded systems with Network-on-Chip Based Memory Organization Unit 5

Enhancing Lightweight Embedded Systems with Network-on-Chip ...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Kumar, Aruru Sai Somineni, Rajendra Prasad Reddy, B. Naresh Kumar Varasala, Ashok Kumar Vnr Vjiet Department of Ece Bachupally500090 India Nit Tiruchirappalli Department of Ece 620015 India

ISBN: (纸本)9798350379945

In the realm of integrated circuits (ICs), efficient memory management is pivotal for enabling high-performance computing in modern applications. With the rise of lightweight processors and the Internet-of-Things (IoT) era, there is an increasing demand for multiprocessing capabilities in ultra-low-power (ULP) embedded devices. To improve the performance of the ULP devices we propose an architecture in which an memory organization unit (MOU). MOU is introduced where network-on-chip (NoC) can provide the advantage of introducing it in existing ULP devices. Segmented paging approach is used in designing MOU architecture to provide advantage in memory utilization and fragmentation. The simulated result of the current research shows implementation of segmented paging which are varied by different valid bits for each segment when accessed by processor. © 2024 ieee.

关键词： Memory management units

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 ieee.

关键词： Fruits

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Quantization and Deployment Study of Classification Models for embedded Platforms 28

Quantization and Deployment Study of Classification Models f...

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28th international conference on Automation and computing, ICAC 2023

作者： Huang, Zihan Jin, Jin Zhang, Chaolong Xu, Zhijie Xu, Yuanping Kong, Chao Wen, Qin Tang, Dan School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing & Engineering University of Hudderseld Queensgate Hudderseld United Kingdom College of Artificial Intelligence Neijiang Normal University

ISBN: (纸本)9798350335859

Deep learning models find extensive applications across various domains. However, their large number of parameters, high storage requirements, and computational overhead pose challenges for deploying these models on resource-constrained embedded devices. This study focuses on addressing this issue by exploring techniques to optimize and deploy lightweight models on embedded devices. The approach involves optimization and adjustment of the model, followed by model conversion, quantization, and quantization calibration, aimed at reducing model size and improving inference speed. Notably, improvements are made to the quantization calibration algorithm to mitigate accuracy loss caused by model quantization. The experimental results demonstrate that light quantization significantly reduces model size, facilitating storage on embedded devices. Although there is a slight reduction in accuracy, the inference speed is substantially improved, enabling real-time human face recognition in video scenarios. © 2023 ieee.

关键词： Calibration

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Leveraging UAV Technology for Enhanced Disaster Management: Innovations and applications

Leveraging UAV Technology for Enhanced Disaster Management: ...

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

作者： Upadhyay, Govind Murari Vats, Prashant Shanker, Surabhi Bhushan, Saloni Mittal, Sonu Mandot, Manju Manipal University Jaipur Dept. of Computer Application Rajasthan Jaipur India Manipal University Jaipur School of CSE Dept. of CSE Rajasthan Jaipur India K.R. Mangalam University School of Engineering & Technology Dept. of CSE Sohna Road Haryana Gurugram India Chhatrapati Shivaji Maharaj University Dept. of Computer Science/IT Maharashtra Panvel India Amity Institute of Information Technology Amity University Noida Dept. of CSE Uttar Pradesh India J. R. N. Rajasthan Vidyapeeth University Dept. of Computer Science/IT Rajasthan Udaipur India

ISBN: (纸本)9798350385199

Unmanned Aerial Vehicles (UAVs) have substantially advanced the sector by providing improved real-time monitoring, data collecting, and situational awareness capabilities when integrated into disaster management techniques. This study examines the creative uses of unmanned aerial vehicles (UAVs) in disaster relief, emphasising how they may speed up reaction times and increase operational effectiveness in emergency situations. With an emphasis on their usefulness in human detection, damage assessment, and coordination efforts, we propose a thorough framework that describes the design, deployment, and evaluation of UAV systems customised for catastrophe situations. The study shows how UAVs may be used to enhance processes of decision-making and enable more efficient recovery and catastrophe response through instances and quantitative evaluation. The results demonstrate how UAV technology may revolutionise conventional disaster management procedures and provide insightful information for future studies and real-world applications in the field. © 2024 ieee.

关键词： Disaster prevention

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