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共 2750 条记录，以下是41-50 订阅

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Synergizing Underwater robotics and AI: A Tethered ROV and YOLOv7 Approach to Giant Clam Detection 2nd

Synergizing Underwater Robotics and AI: A Tethered ROV and Y...

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

作者： Badhan, Al Mominul Haque Hanif, Mariam Faudzi, Ahmad Athif Mohd Hafidz, Muhamad Hazwan Abdul Ruslan, Mohammad Izzuddin Bin Adam, Muhammad Amirul Amri Khamis, Nurulaqilla Faculty of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru81310 Malaysia Centre for Artificial Intelligence and Robotics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia A2TECH Sdn. Bhd Johor Bahru81310 Malaysia

ISBN: (纸本)9789819639489

Integrating a tethered Remotely Operated Vehicle (ROV) with the YOLOv7 object recognition algorithm offers a novel approach to marine monitoring, particularly for tracking giant clam populations in the reefs of Tioman Island, Malaysia. The ROV, designed for enhanced underwater exploration, is equipped with advanced imaging and sonar technologies, resilient tethered power management and communication systems. The control and navigation system are built on the pixhawk flight controller, supported by ArduPilot firmware and a customized QGroundControl interface, ensuring real-time operations. The primary objective of this research is to develop an algorithm capable of accurately recognizing and counting giant clams through underwater images and videos. The study compares the performance of the YOLOv7 algorithm with the manual counting. The findings indicate that the YOLOv7 algorithm demonstrates average accuracy of the model of 79.65%. The integration of YOLOv7 enables identification and quantification of giant clam populations enhancing data collection for research and conservation efforts providing practical solutions for environmental monitoring and the sustainable management of marine resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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Peer-to-Peer Integrated Smart Gardening Automation Using Cloud Technology with real-time Environmental Monitoring and Logging via Blynk Cloud Dashboard

Peer-to-Peer Integrated Smart Gardening Automation Using Clo...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Malik, Praveen Kumar Bharti, Veena Misra, Neeti Yamsani, Nagendar Mahajan, Anshul Singh, Parulpreet School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Raj Kumar Goel Institute of Technology Ghaziabad India Uttaranchal Institute of Management Uttaranchal University Department of Management Uttarakhand Dehradun India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

The authors of this article discuss the holistic method of automation of gardening operations based on IoT technology that allows real time environmental factors to be monitored, decisions to be made based on data, and those decisions to be executed at a distance. The objective of the system is to minimize the amount of water used, to improve the state of the plants, and ultimately to allow the users of the system to control their gardens from anywhere in the world with as little effort as possible. The concept makes use of the continuously monitored network of sensors to observe important parameters like soil moisture, temperature, humidity, light intensity and rainfall among others. After being received, this information is up to date and the system will use this data to manage irrigation and lighting systems for the plants efficiently. Also, the support of this cloudbased platform is essential today as it allows the users to look up the parameters in their garden remotely and go for manual intervention when required. All the advantages of cloud technology provide remote control of the system but also all the historical data sets which are important trends in future decision making. © 2025 ieee.

关键词： Green computing

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Edge AI to Edge robotics: Enhancing Human Pose Estimation with High-Performance TPU computing 4th

Edge AI to Edge Robotics: Enhancing Human Pose Estimation wi...

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Sharma, Vijeta Mupenda, Didier Numbi Thorvik, Lars Mishra, Deepti NTNU-Norwegian University of Science and Technology Gjøvik2815 Norway

ISBN: (纸本)9783031837821

The convergence of Edge AI and high-performance computing (HPC) is revolutionising intelligent systems by enabling real-time data processing and decision-making at the network’s edge. The demand for high-performance computing in resource-constrained environments opens opportunities for applications such as autonomous vehicles, smart cities, and industrial automation. This study presents the terminology that evidences the evolution of edge AI to the edge robotics landscape, and then we present the novel approach to leveraging Tensor Processing Units (TPUs) to enhance the inference capabilities of robotic systems at the edge. Using a Reachy robot as a case study, we demonstrate real-time human pose estimation powered by TPUs. The research investigates the intersection of robotics, Edge AI, and HPC, focusing on accelerating inference tasks with TPUs. We implemented deep learning-based human pose estimation models—PoseNet-ResNet50, PoseNet-MobileNet V1, MoveNet Lightning, and MoveNet Thunder—on the Robot Operating System (ROS) and benchmarked their performance. Results show that MoveNet Thunder achieved the fastest inference time (48 ms) with a pose score of 98%, while PoseNet-ResNet50 was the slowest (701 ms) with a score of 93%. Our findings highlight the significant improvements in inference speed and accuracy using TPUs over CPUs. We discuss the implications of TPU-accelerated AI in robotics and explore future research directions in this evolving field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Tensors

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Big Data Analytics in Industry 4.0: Automating Production Metrics With an Edge-Based Architecture

Big Data Analytics in Industry 4.0: Automating Production Me...

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ieee international conference on Industrial Technology (ICIT)

作者： Arthur Kretzer Denise Costa Gabriel Pardini Jardel Wolkers Renato Luz Frank Siqueira Cooperated Production Center - CPC CERTI Foundation / UFSC Florianópolis Brazil Research and Development - R&D Lenovo Indaiatuba Brazil Department of Informatics and Statistics - INE Federal University of Santa Catarina - UFSC Florianópolis Brazil

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents the development and evaluation of a Big Data Analytics (BDA) platform designed for Industry 4.0 applications, focusing on the integration, processing, and visualization of data at the edge. The platform integrates tools such as Apache Spark, Airflow, MinIO, Airbyte, and Streamlit, to facilitate automated data collection and report generation. Deployed in a real manufacturing environment in São Paulo, Brazil, the solution aims to minimize data latency by avoiding cloud transmission and leveraging edge computing. Automated daily reports have been generated since May 2024, improving decision-making and reducing human error associated with manual data integration. This work provides a practical contribution to industrial data integration, highlighting the benefits of edge-based data processing, automation, and visualization to optimize production efficiency and decision-making processes.

关键词： Measurement Decision making Data visualization Data integration Production Manuals Big Data real-time systems Fourth Industrial Revolution Edge computing

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2nd international conference on Cyber Warfare, Security and Space computing, SpacSec 2024

2nd International Conference on Cyber Warfare, Security and ...

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2nd international conference on Cyber Warfare, Security and Space computing, SpacSec 2024

ISBN: (纸本)9783031734939

The proceedings contain 27 papers. The special focus in this conference is on Cyber Warfare, Security and Space computing. The topics include: Fake Product Detection Using Blockchain with Encryption and AI;s-Defender: A Smishing Detection Approach in Mobile Environment;mathematical Socio Analysis of Cybercrimes Preparedness a Simulation Odessey with R;revolutionizing Weather Forecasting: Harnessing Machine Learning and Big Data in Upcoming Technologies;trends in Drowsiness Detection & Analysis of the Different Technologies Engaged;Detecting Local Software Issues Using NSGA Multi-optimization;deep Learning and IoT Based robotics to Monitor the Traffic;development of Lightweight and Cheaper 5G Mobile Communication System to Analyze the Performance of Espresso Ciphers and Grain Family;development of Light Weight Authentication Protocol Based on Cryptography to Access the IoT Device;development of Elliptical Cryptography Technique to Watermark Embedded and Extrusion for Healthcare Records;Step-by-Step Image Encryption Using UACI and PixAdapt;investigation of Post-Quantum Cryptography to Secure the Functionality of Vehicle Hardware Architecture;secure Data Management Using BlockChain;navigating Through Digital realm: Role of Cyberpsychology in Fostering Mental Well-Being and Digital Empathy;an Empirical Analysis of Neighborhood-Based Approaches for Trustworthy Recommendations with Apache Mahout;multilingual Sentiment Analysis over real-time Voice;Identity Verification: A Decentralized KYC Approach Using Blockchain;A Neural Network-Based Facial Expressions Detection Technique Using CK+ Dataset;cyber Security Challenges in Industrial Settings with the Internet of Things;Designing Secure Software-Defined Network, Resistant to DDoS Attack Using Non-linear Routing Rule Installation;NPQuant: A Robust Quantum Inspired Computation Algorithms as an Efficient Solution to NP-Complete Problems.

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9th international Symposium on Artificial Intelligence and robotics, ISAIR 2024

9th International Symposium on Artificial Intelligence and R...

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9th international Symposium on Artificial Intelligence and robotics, ISAIR 2024

ISBN: (纸本)9789819629138

The proceedings contain 59 papers. The special focus in this conference is on Artificial Intelligence and robotics. The topics include: Radiology Report Generation Based on Multi-Scale Feature Fusion and Enhancement;spatio-Temporal Focus with Active Learning in Sparse Black-Box Adversarial Attacks for Video Recognition;an Improved Point Cloud Registration Algorithm Based on Feature Point Extraction;YOLOv8-FGE: A Lightweight Mouse Behavior Detection Algorithm;a Dual Encoder U-Net for Multi-scale 3D Medical Image Segmentation;A Gas Concentration Prediction Model Based on SBLPformer;A MOTRv2-Based UAV Multi-Target Tracking Model EL-MOTR;prediction of Typhoon Cloud Maps Based on Self Attention Memory Spatiotemporal Model;information-Theoretic Deep Quantification for Unsupervised Cross-Modal Hashing;Research on SwinT-SOLOv2 Modeling for Instance Segmentation;an Overview of Abnormal Data Recovery in Power Systems;an Overview of Full-Cycle Data Security for Drone Inspection;a Restoration Method Based on Color Compensation and Depth Prior Using a Revised Underwater Imaging Model;real-time Student Behavior Analysis via YOLOv5 with Coordinate Attention;GD-RRT*: An Improved RRT* Path Planning Algorithm Combining Gaussian Distributed Sampling and Depth Strategy for Fruit-Picking Robot;UAV Swarm Air Combat Strategies Research Based on Multi-Agent Reinforcement Learning and Rule Coupling;photovoltaic Power Prediction Based on Machine Learning Fusion Algorithm;multi-modal Spatio-temporal Transformer for Defect Recognition of Substation Equipment;efficient Transformer-Based Visual Tracking for Edge computing Devices;a Multi-view Graph Neural Network Approach for Magnetic Resonance Imaging-Based Diagnosis of Knee Injuries;mitigating Multimodal Large Language Model Hallucinations Through Direct Preference Optimization;Briefness-Oriented Prompting Elicits Faithfulness in LVLM;Layered Clothing Detection Based on Improved YOLOV9.

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Architecture to Recreate Immersive Scenarios for Practicing Sports in Gyms 9th

Architecture to Recreate Immersive Scenarios for Practicing ...

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9th international conference on Smart City Applications, SCA 2024

作者： Pereira, Hélder Matos, Paulo Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal Research Centre in Digitalization and Intelligent Robotics (CeDRI) Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal Associate Laboratory for Sustainability and Technology in Mountains Regions (SusTEC) Polytechnic University of Bragança Campus de Santa Apolónia Bragança 5300-253 Portugal

ISBN: (纸本)9783031886522

This paper proposes an architectural solution to integrate immersive features into fitness devices, enhancing user experience and athletic performance. The architecture is designed to be open and compatible with a broad range of fitness equipment, across different brands and models. Our approach seeks to recreate real-world environments through video playback of actual routes, enriched with contextual data such as speed and altitude variations. This data facilitates the dynamic control of compatible fitness equipment by adjusting parameters like resistance, slope, and inertia, to synchronize with the video, thereby creating an authentic and immersive experience. This approach ensures that the immersive experience can be consistently achieved, regardless of the device’s inherent capabilities. The architecture is divided into two subsystems. The first is focused on video capture and collecting contextual route data, including real-time recording of speed, altitude, and environmental factors. These recordings are processed and uploaded to a cloud-based platform, to foster a community-driven repository of shared routes. The second subsystem is supported by combining cloud and edge computing, integrating artificial intelligence for personalized adjustments, SIG BLE standards for communication with fitness devices and wearables, and the mobile application. This subsystem aspires to enable real-time adaptation of fitness equipment based on the athlete’s historical performance data and current exercise metrics. The user interface for both subsystems is provided through the mobile application, although the roles of the user who creates and publishes new routes and the user who utilizes these routes are separated. This paper details the architecture, discussing the rationale behind the selected components and technologies. It also outlines the ongoing implementation process and presents preliminary results that suggest the potential effectiveness of the proposed system. © The

关键词： Artificial Intelligence Athletic Performance Cloud computing Edge computing Fitness Immersive Environments IoT SIG BLE standards

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ADMEdge: Accelerating Edge computing Through Co-design for Enhanced Data Mobility 17th

ADMEdge: Accelerating Edge Computing Through Co-design for ...

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17th Asian conference on Intelligent Information and Database Systems, ACIIDS 2025

作者： Kiet, Tran Anh Thinh, Tran Ngoc Ngo, Thai Le, Long Tan 268 Ly Thuong Kiet District 10 Ho Chi Minh City Viet Nam Thu Duc Ho Chi Minh City Viet Nam School of Computer Science The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819660049

In edge computing, optimizing data mobility is crucial for minimizing latency and maximizing bandwidth efficiency by processing data closer to its source. This reduces the need to transfer large amounts of data to centralized servers, enabling faster insights and real-time decision-making. However, data mobility challenges in embedded machine learning systems arise from inefficient data transfer, software, and algorithmic issues that result in latency, CPU utilization that hinders Internet of Things (IoTs) performance and reliability. This paper introduces ADMEdge (Accelerating Data Mobility at the Edge), a novel software/hardware co-design framework aimed at optimizing data movement in FPGA-based System-on-Chip (SoC) edge devices through the utilization of Direct Memory Access (DMA), buffer, and shared memory techniques. Testing is conducted on a Kria KV260 IoT SoC, which incorporates a PicoRV32 RISC-V core and a Deep Neural Network accelerator, using representative datasets to simulate real-world scenarios. Incorporating shared memory significantly reduces latency by removing the recompilation step, leading to a 9.5-fold speedup in real-time image processing at higher workloads. Alongside a 49% performance gain from buffering techniques, data throughput scales efficiently with larger data sizes, with DMA demonstrating up to 226.98 times faster communication in specific scenarios. Overall, these optimizations yield a 44% improvement in system performance, highlighting the potential of ADMEdge to enhance edge computing applications in the Internet of Things. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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An Optimization Approach for Dynamic Aircraft Trajectory Planning Under Multiple Constraints

An Optimization Approach for Dynamic Aircraft Trajectory Pla...

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2024 ieee international conference on real-time computing and robotics, rcar 2024

作者： Shan, Jingyuan Beijing University of Posts and Telecommunications International School Beijing100876 China

ISBN: (纸本)9798350372601

A dynamic trajectory planning approach is proposed based on a backtracking search to solve the aircraft trajectory planning problem. The objective of the proposed approach is to minimize the trajectory length and the number of calibrations while selecting calibration points. The problem faces challenges such as ample state space, low computational efficiency, significant error accumulation, and turning radius limitation. The combination of dynamic planning and backtracking search reduces the decision-making space and ensures rational trajectory planning while controlling computational costs. A "donut trap"- a forbidden zone in the decision space- is built to overcome the aircraft's minimum turning radius restriction. A geometric model is established to determine if the calibration point is inside the "donut"to reduce the decision-making space. The utility function is designed to account for path loss and the minimum turning radius for efficient trajectory optimization. The simulation results demonstrate the trajectory length and calibration error results with different weighting factors, achieving better error calibration performance than existing work. © 2024 ieee.

关键词： Dynamic programming

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A Dynamic Flocking Algorithm with a Restrictive Partnership Model to Support Mobile Ad Hoc Networks

A Dynamic Flocking Algorithm with a Restrictive Partnership ...

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

作者： Konak, Abdullah Kulturel-Konak, Sadan Graduate school of Informatics Kyoto University Kyoto Japan

ISBN: (纸本)9781538620359

This paper presents an improved flocking algorithm to increase the connectivity of a mobile ad hoc network using autonomous and intelligent agents. Flocking algorithms usually aim to simulate realistic movements of a group of agents. In this paper, however, agents use a flocking algorithm to find a solution to a computationally very difficult optimization problem in real-time as the topology of the network changes due to the mobility of users. In the improved flocking algorithm, agents select their interaction partners based on the Gabriel Graph and adjust their flocking behavior parameters dynamically. A simulation study is conducted to compare the performance of the improved flocking algorithm with a previous algorithm. Computational studies show that the recommended strategies are quite effective.

关键词： Heuristic algorithms Mobile computing Network topology Topology Mobile ad hoc networks Optimization

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