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检索条件"机构=Dept. Computer and Robotics Engineering"

共 498 条记录，以下是1-10 订阅

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

引用

30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Survey on Blockchain P2P Network 24

Survey on Blockchain P2P Network

引用

24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Kim, Aeri Essaid, Meryam Ju, Hongtaek Dept. of Computer Engineering Keimyung University Daegu Korea Republic of Dept. of Robotics Engineering Keimyung University Daegu Korea Republic of

ISBN: (纸本)9788995004395

Blockchain P2P solutions are becoming more specialized in architecture, peer type, and data propagation techniques. Interoperability on the Blockchain is becoming increasingly important, which has prompted the creation of protocols and frameworks for communication across various chains. In this study, we investigate the development and application of P2P technology in Bitcoin, Ethereum, EOS, and Cosmos are four blockchain platforms. Our findings demonstrate that the various blockchain P2P networks concentrate on finding practical solutions to the scalability issue. Copyright 2023 KICS.

关键词： Interoperability

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A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System 12

A Vision-Based Autonomous AI Tic-Tac-Toe Manipulation System

引用

12th International Electrical engineering Congress, iEECON 2024

作者： Rakprayoon, Panjawee Ounsiri, Akechai Chongyoa, Ratchanon Bangkok University Faculty Of Engineering Dept. Ai Engineering And Data Science Pathum Thani Thailand Dna Robotics And Automation System Company Limited Pathum Thani Thailand Bangkok University Faculty Of Engineering Dept. Computer And Robotics Engineering Pathum Thani Thailand

ISBN: (纸本)9798350383591

For an AI tic-tac-toe manipulator application, a real-time vision-based approach is proposed. The technique employs the RealSense camera to capture color and dept. images. Combining object detection and image processing techniques to identify X's and O's positions on the game board. However, we recognize that errors can occur during object movement when using only 2D images. To solve this problem, a calibration method between RealSense and the manipulator based on the point-to-plane ICP algorithm is presented. This method provides the 3D object position in the robot coordinate, resulting in increasing the accuracy from 86.25% to 97.25%. Our experiment shows that this technique is highly effective and can accurately identify position objects. © 2024 IEEE.

关键词： Object detection

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Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis with Stereo Cameras in Unpredictable Environments

Towards Autonomous Robotic Grasping: 3D Point Cloud Analysis...

引用

2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Patel, Shrey Kovarovics, Aiden More, Aaditya Chun, Carrington Fletcher, Montreal Tanveer, M. Hasan Chakravarty, Sumit Voicu, Razvan Kennesaw State University Dept. of Robotics & Mechatronics Engineering Marietta United States Kennesaw State University Dept. of Electrical and Computer Engineering Marietta United States

ISBN: (纸本)9798331504847

Current robotic arm systems typically depend on pretrained models or function within controlled environments using known positions or fiducial markers for object manipulation. Although these methods achieve high accuracy, they face limitations due to lengthy training processes and the need for carefully controlled setups. This research introduces a methodology that leverages stereo cameras to develop a generalized algorithm aimed at overcoming these constraints and enhancing robotic manipulation in uncontrolled environments. The proposed solution utilizes an Intel RealSense stereo camera in conjunction with YOLO segmentation models for initial object identification. It combines segmentation meshes with the camera's dept. values to create a detailed three-dimensional point cloud of the target object's surface. This point cloud is analyzed through Random Sample Consensus (RANSAC) and geometry-based feature extraction techniques, which allow the algorithm to approximate the object's shape as a combination of fundamental 3D forms, such as cuboids, cylinders, and spheres. A grasp point is then estimated by identifying suitable surfaces for grasping with a two-pronged gripper, followed by calculating an optimal approach angle and position kinematics for the robotic arm. This innovative approach enables robotic arm systems to autonomously adapt to various objects in uncontrolled environments, significantly expanding their practical applications. For example, the methodology could support modular robotic systems designed for sample collection in extraterrestrial missions, where adaptability and autonomous operations are essential. Ultimately, this research aims to enhance the efficiency and capabilities of robotic systems in dynamic and unpredictable settings. © 2025 IEEE.

关键词： Robotic arms

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling 2

On the Enhancement of Energy Efficiency of Wireless Recharge...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Kamaruzzaman, Md. Chandra, Abhijit Azharuddin, Md. The Neotia University Dept.of Robotics and Automation Kolkata India Jadavpur University Dept. of Instrumentation and Electronics Engineering Kolkata India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382952

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme. © 2024 IEEE.

关键词： Energy efficiency

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Comparative Study Between CNN and LSTM Approaches for Sign Language Recognition 6

Comparative Study Between CNN and LSTM Approaches for Sign L...

引用

6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Edward, Phoebe Sameeh, Bola Alexan, Wassim Universities of Canada in Egypt Computer Science Dept. Egypt German International University Robotics and Automation Engineering Germany The German University in Cairo Communications Dept. Egypt

ISBN: (纸本)9798350378511

Image recognition technology has advanced to the point where it can help address challenges such as communication barriers faced by individuals with hearing impairments. Due to the societal rarity of learning and practicing Sign Languages (SLs), Machine Learning (ML) techniques have come to light to ease and automate this process through Sign Language Recognition (SLR) systems. This paper compares two SLR approaches;namely: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). These two ML approaches are used to accurately identify the English alphabet, numbers, and specific words in American Sign Language (ASL). Results show that both techniques achieve very good accuracy values using test dataset, however in real-time tests CNN has outperformed LSTM. © 2024 IEEE.

关键词： Convolutional neural networks

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Probabilistic Modelling and Safety Assurance of an Agriculture Robot Providing Light-Treatment 19

Probabilistic Modelling and Safety Assurance of an Agricultu...

引用

19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Adam, Mustafa Ye, Kangfeng Anisi, David A. Cavalcanti, Ana Woodcock, Jim Morris, Robert Robotics Group Faculty of Science & Technology Norway University of York Dept. of Computer Science United Kingdom Faculty of Engineering and Science Dept. of Mechatronics Norway Saga Robotics As Norway

ISBN: (纸本)9798350320695

Continued adoption of agricultural robots postulates the farmer's trust in the reliability, robustness and safety of the new technology. This motivates our work on safety assurance of agricultural robots, particularly their ability to detect, track and avoid obstacles and humans. This paper considers a probabilistic modelling and risk analysis framework for use in the early development phases. Starting off with hazard identification and a risk assessment matrix, the behaviour of the mobile robot platform, sensor and perception system, and any humans present are captured using three state machines. An auto-generated probabilistic model is then solved and analysed using the probabilistic model checker PRISM. The result provides unique insight into fundamental development and engineering aspects by quantifying the effect of the risk mitigation actions and risk reduction associated with distinct design concepts. These include implications of adopting a higher performance and more expensive Object Detection System or opting for a more elaborate warning system to increase human awareness. Although this paper mainly focuses on the initial concept-development phase, the proposed safety-assurance framework can also be used during implementation, and subsequent deployment and operation phases. © 2023 IEEE.

关键词： Agricultural robots

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features 6

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

引用

6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Sami, Abdullah Kamal Al-Dulaimi, Khamael Al-Nahrain University Computer Science Dept. Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (纸本)9798350389692

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models. © 2024 IEEE.

关键词： Machine learning

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Zaid, Mohd Ahmad, Nadeem Aljamal, Mohammed Patel, Sarosh Khan, Asif Dept. of Computer Science and Engineering Integral University Lucknow India Dept. of Electronics and Communication Engg. Goel Institute of Technology and Managment Lucknow India School of Engineering University of Bridgeport United States Integral Robotics Lab Dept. of Computer Application Integral University Lucknow India

ISBN: (纸本)9798350374933

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people. © 2024 IEEE.

关键词： Wi-Fi

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Enhancing robotics Performance: Exploring the Role of Cloud Computing in Planning Tasks 29

Enhancing Robotics Performance: Exploring the Role of Cloud ...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Melo, Eduardo Pinto, Rui Tavares, Pedro University of Porto Dept. of Electrical and Computer Engineering Faculty of Engineering Porto Portugal Faculty of Engineering University of Porto Systec Arise Porto Portugal Sarkkis Robotics Porto Portugal

ISBN: (纸本)9798350361230

The rapid growth of the robotics field is driven by increasing demand for enhanced performance, characterized by high reliability and repeatability capabilities. As industries strive to maintain elevated quality performance throughout their production pipelines, the complexity of the challenges they face grows in tandem. This complexity extends to processing algorithms, needing greater computational power for faster and more effective solutions. In this context, emerging technologies such as Cloud Computing present promising opportunities to augment planning tasks in robotics. This literature review explores the intersection of robotics and Cloud Computing, investigating how leveraging cloud resources can enhance planning performance in response to evolving industry demands. © 2024 IEEE.

关键词： Cloud platforms

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