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检索条件"任意字段=Conference on Advanced Sensor Systems and Applications III"

共 6259 条记录，以下是61-70 订阅

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System Architecture for a Wireless sensor Network for Sustainable Agriculture, Forestry and Green Spaces in Smart Cities 12

System Architecture for a Wireless Sensor Network for Sustai...

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12th IEEE International conference on Intelligent Data Acquisition and advanced Computing systems: Technology and applications, IDAACS 2023

作者： Rosewick, Sebastian Schorlemer, Felix Kunemund, Frank Fachhochschule Dortmund Department of Computer Science Germany

ISBN: (纸本)9798350358056

This paper presents a wireless sensor network employing heterogeneous mobile robot platforms designed for data-acquisition and research in agriculture, forestry and green spaces in smart cities. Where conventional sensor networks struggle with a lack in flexibility, little variation in their reading points and no possibilities in mobile manipulation, this approach offers new capabilities in accomplishing complex tasks in outdoor and indoor scenarios. A wheel driven robot platform in combination with multiple UAVs11Unmanned Aerial Vehicle forms the basis for the mobile sensor network which can make use of a wide variety of sensors and actuators. The system benefits from new developments in controlling multi-agent systems as well as machine-learning-algorithms for local and independent data-analysis. © 2023 IEEE.

关键词： forestry machine learning mesh-networks mobile robots multi-agent systems neural networks precision agriculture UAVs wireless sensor network

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Putting the S back into IoT: Tutorial on sensor and Actor systems in Small Portable Ad-Hoc Networks 18

Putting the S back into IoT: Tutorial on Sensor and Actor Sy...

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18th ACM International conference on Distributed and Event-Based systems, DEBS 2024

作者： Norbisrath, Ulrich Machado, Renato Perotto Slavin, Anton Zorec, Matevz Borjan University of Tartu Tartu Estonia

ISBN: (纸本)9798400704437

This tutorial introduces participants to the fundamentals and advanced concepts of integrating sensor and actor systems within the Internet of Things (IoT) through hands-on experience with small, portable ad-hoc networks. We will learn to create and manage our own IoT networks by utilizing several ESP32 and ESP8266-based devices connected to various actors and sensors alongside easily configurable IoT edge network set-ups. We will orchestrate the systems using Node-RED for data flow and event-based integration. Simple dashboard and database connectivity options in Node-RED will help us build IoT applications and workflows without the complexity of traditional programming environments. © 2024 ACM.

关键词： Internet of things

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ROS Powered Autonomous Mobile Robot for Indoor applications

ROS Powered Autonomous Mobile Robot for Indoor Applications

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2024 International conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Madan, H.T. Mahesh, L. Sajjad, Mohammed Nitte India Reva University School of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350360660

In the realm of indoor robotics, navigation poses a critical challenge across service robots, humanoids, and warehouse automation. Existing techniques like line following, RFID tracking, and Aruco marker-based systems exhibit limitations in autonomy and adaptability. This has created a need for the development of an advanced and sophisticated autonomous navigation system that encompasses self-reliant perception, planning, and control capabilities. The proposed system makes use of LiDAR technology for mapping and Robot operating system for planning and control which takes place on Jetson Nano GPU platform, providing real-time processing power for environmental data. LiDAR sensors offer accurate environmental perception, obstacle detection, and adaptability to dynamic indoor settings. The system employs BLDC wheels and an aluminium base for versatility, supporting loads up to 120 kg. Additionally, it incorporates an IMU sensor with EKF sensor fusion for accurately estimating the robot's position. This flexible design has applications in various indoor robotics scenarios, ranging from complex service tasks to efficient warehouse operations. This versatile design finds applications in various indoor robotics scenarios, from complex service tasks to efficient warehouse operations. © 2024 IEEE.

关键词： Robot programming

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Existing Devices in Wrist Rehabilitation Robotics: Mechanical Design, Control systems, and User Requirements for Commercialization 20

Existing Devices in Wrist Rehabilitation Robotics: Mechanica...

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20th IEEE/ASME International conference on Mechatronic and Embedded systems and applications (MESA)

作者： Pippo, Irene Massone, Valentina Guazzotti, Marco Gnetti, Benedetta Albanese, Giulia A. Casadio, Maura Berselli, Giovanni Zenzeri, Jacopo ReWing Srl Milan Italy Univ Genoa DIME Genoa Italy Univ Genoa DIBRIS Genoa Italy Fdn Don C Gnocchi La Spezia La Spezia Italy

ISBN: (纸本)9798331516246;9798331516239

Recently, rehabilitation robotics has demonstrated the ability to improve the methodologies significantly. Robotic devices can provide accuracy, repeatability, high-dose and task-specific training, and real-time feedback. These features are essential in rehabilitation, focusing on the upper limb and the wrist, as a joint needed to perform most activities of daily living (ADLs) and thus crucial to maintaining a high quality of life. This is particularly important considering the demand for effective rehabilitation solutions due to frequent motor impairments related to neurological or orthopedic issues. This review focuses on robotic devices for wrist rehabilitation, exploring their characteristics with an eye toward significant and appealing features for commercialization. We explored relevant features of these devices such as a robust and human-centered mechanical design, an appropriate actuation system, and an advanced sensor network integrated with a sophisticated control architecture to monitor and optimize human-machine interactions. We considered usability factors as ergonomics, ease of use, and adaptability to the needs of different users, for clinical applications and broad market adoption. This work reviews existing robotic devices for wrist rehabilitation, comparing their key features to evaluate the current state of the field. It aims to identify which devices best meet the necessary criteria and highlight issues that still require further engineering research.

关键词： robotic device upper limb rehabilitation wrist mechatronic design

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A Modular sensor Fusion and Quality-Driven sensor Selection Algorithm for UAV Navigation 20th

A Modular Sensor Fusion and Quality-Driven Sensor Selection ...

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20th International conference on Intelligent Computing (ICIC)

作者： Chen, Hainuo Ni, Xuebin Gao, Ming Ma, Hui Zhang, Chen Yunsheng Intelligent Technol Co Ltd Tianjin 300457 Peoples R China Tianjin Univ Sci & Technol Tianjin 300457 Peoples R China

ISBN: (纸本)9789819755936;9789819755943

Unmanned Aerial Vehicles (UAVs) are increasingly integrated in various applications. The urgent need for accurate navigation algorithms is emphasized in various applications. In this paper, we propose an integrated framework focusing on robust attitude and position/velocity estimation for UAV navigation using Kalman filtering employing Kalman filtering (KF) technique and chi-square test based sensor fusion quality assessment. Our approach integrates sensor data through a filter-based approach, and Kalman filter handles attitude estimation and position/velocity estimation. The modal architecture enhances robustness and facilitates seamless integration of multiple sensors while prioritizing reduced attitude estimation and position/velocity estimation. Accurate attitude estimation is also prioritized to reduce the risk in case of deviation. Accurate attitude estimation is also prioritized to reduce the risk of deviation in case of higher order state estimators. Experimental validation on the "SHENG PROUAV" model proves the effectiveness of our framework and demonstrates accurate attitude estimation results.

关键词： Integrated Navigation systems Modular sensor Fusion Kalman Filters Fault Detection

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Design, Fabrication and Calibration of an Embroidery Textile Tactile sensor Array 25th

Design, Fabrication and Calibration of an Embroidery Text...

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25th Annual conference on Towards Autonomous Robotic systems, TAROS 2024

作者： Hou, Ningzhe Pontin, Marco Costi, Leone Maiolino, Perla Oxford Robotics Institute University of Oxford Oxford United Kingdom University of Genoa Liguria Genoa Italy

ISBN: (纸本)9783031720581

This paper presents the development and evaluation of an embroidery textile tactile sensor, designed for advanced pressure mapping applications. Utilizing a unique embroidery technique with conductive threads and incorporating a Velostat piezoresistive layer, the sensor array is configured in an 8×8 grid, offering multiple contact detection over large areas. The sensor’s performance is validated through several experiments, including quasi-static loading and pressure mapping with various weights and hand gestures. This research proposes a flexible, scalable solution for tactile sensing that is both cost-effective and high-performing. The findings indicate significant potential for the sensor in real-world applications, promising advancements in how tactile information is captured and utilized. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mapping

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Integrating Photonics and Fiber Bragg Grating sensors with Deep Reinforcement Learning for advanced Robotic systems 18

Integrating Photonics and Fiber Bragg Grating Sensors with D...

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18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jaideep, R. Manjula, C. Seema, V. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Mechatronics Engineering Bangalore India

ISBN: (纸本)9789380544519

This research introduces the Photonics-Enhanced Embedded Robotic Intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DRL) for advanced robotics applications. Addressing the limitations of current AI models in dynamic and complex environments, PEERIM leverages the rapid data transmission capabilities of photonics to process high-frequency sensor data, enabling real-time decision-making and enhanced automation. The methodology encompasses the development and integration of a variant of the Proximal Policy Optimization (PPO) algorithm, tailored to manage the continuous data stream and execute precise, adaptive control. The proposed model has been empirically evaluated, demonstrating a significant improvement in handling real-time sensor data with an average reward of 99.02 and a low average loss of 0.099, indicating robust performance and learning stability. These findings suggest that PEERIM provides a substantial advantage over existing AI-driven robotic systems, offering a scalable solution for a variety of challenging applications. The study's contributions lay the groundwork for future advancements in autonomous systems, promising a new era of precision and reliability in robotics. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Autonomous systems Deep Reinforcement Learning Fiber Bragg Grating sensors Photonics-Enhanced Robotics Proximal Policy Optimization Real-Time Decision Making

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Wireless Power Transfer Optimization Using FEM-GA Method 3

Wireless Power Transfer Optimization Using FEM-GA Method

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3rd International conference on advanced Electrical Engineering, ICAEE 2024

作者： Fatima, Bennia Aimad, Boudouda University of Boumerdes Department of Electrical Systems Engineering Boumerdes Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

This paper addresses the challenges associated with designing high-performance wireless power transfer (WPT) systems for various applications. The efficient operation of WPT systems is crucial, but it must meet specific restrictions imposed by the application. This is particularly true for WPT systems used in biomedical implants, sensor applications, and IoT devices, where a small receiving coil is required while maintaining high power transfer efficiency (PTE). The PTE is dependent on several design parameters, such as coil design, coil size, relative geometry, operating frequency, and load. Our paper begins by defining the finite element method and details the parametric-coil modeling in Ansys Maxwell. We then propose the optimization of the wireless power transfer efficiency via the FEM-GA. Furthermore, the suggested WPT coil design method improves the transfer efficiency by a maximum of 74%. Ultimately, we verify the accuracy of our findings by employing finite element simulation with Simplorer. © 2024 IEEE.

关键词： Inductive power transmission

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A New Multimodal and Spatio-Temporal Dataset for Traffic Control: Development, Analysis, and Potential applications

A New Multimodal and Spatio-Temporal Dataset for Traffic Con...

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International conference on advanced systems and Emergent Technologies (ICASET)

作者： Godhbani, Salah Elkosantini, Sabeur Lee, Seongkwan M. Suh, Wonho Univ Tunis ISG Tunis Tunisia Univ Carthage Fac Econ & Management Nabeul Tunisia Kunsan Natl Univ Sch Liberal Studies Jeollabuk Do South Korea Hanyang Univ ERICA Campus Seoul South Korea

ISBN: (纸本)9798350384901;9798350384895

Multimodal data, which includes various data formats such as image, video, text, and sensor data, is essential for urban traffic management. The lack of proven multimodal transportation data has been a significant challenge for urban planners, leading to biased or incomplete estimates of travel demand, mode choice, and network performance. Multimodal data integration offers a valuable resource for understanding and optimizing traffic control and management. However, the heterogeneity of the data, various kinds of noise, alignment of modalities, and techniques to handle missing data are some of the challenges that arise. This paper presents a novel multimodal dataset which is the first of its kind, its scraped from England Highways, incorporating speed, flow, and camera images for the M60, M25, and M1 motorways. The dataset provides a comprehensive view of traffic behavior at specific junctions, enabling detailed analysis and real-world applications. By integrating previously disparate data sources, this dataset offers a valuable resource for understanding and optimizing traffic control and management. The paper outlines the dataset's development, including the gathering of speed and flow data, and the use of image scraping techniques to capture CCTV images. The potential applications of the dataset for traffic control, planning, and optimization are also discussed. Overall, this multimodal dataset represents a significant contribution to the field, with implications for the development of advanced traffic management systems and the improvement of transportation infrastructure.

关键词： multimodal dataset traffic control image speed flow Machine learning

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Optimizing RF-Sensing for Drone Detection: The Synergy of Ensemble Learning and sensor Fusion 20

Optimizing RF-Sensing for Drone Detection: The Synergy of En...

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20th Annual International conference on Distributed Computing in Smart sensor systems and the Internet of Things (DCOSS-IoT)

作者： Tanveer, Laiba Alam, Muhammad Zeshan Misbah, Maham Orakzai, Farooq Alam Alkhayyat, Ahmed Kaleem, Zeeshan COMSATS Univ Islamabad Elect & Comp Engn Wah Campus Wah Pakistan Brandon Univ Dept Comp Sci Brandon MB Canada Islamic Univ Coll Engn Dept Comp Tech Engn Najaf Iraq

ISBN: (纸本)9798350369458;9798350369441

Unmanned Aerial Vehicles (UAVs) find extensive applications across various industries, surveillance, and communication services. However, concerns regarding their potential misuse have prompted the development of counter-drone measures. In this paper, we propose a counter-UAV approach centered on radio frequency (RF) signal sensing. Upon the detection of an RF signal, our system employs a Short-Time Fourier Transform (STFT)-based spectrogram (SP) generation process. This SP is further refined through adaptive windowing and logarithmic tuning to extract multi-intensity features. To classify the complex RF time-domain signals and STFT spectrograms, we utilize two deep learning classifiers: RF-Network and SP-Network, facilitating a multi-class classification process by using deep neural networks (DNN). To enhance the overall accuracy of our model, we leverage an ensemble neural network (EN-Net) by combining predictions from the RF-Network and SP-Network classifiers. Fusing data from a single sensor in both time and frequency domains enhances DNN accuracy by providing complementary information, improving robustness, and reducing overfitting, resulting in increased model performance and a deep understanding of the data. Our results demonstrate a notable improvement in accuracy-specifically, a 36% increase for multi-class models when compared to single-class models. This proves the effectiveness of our EN-Net model in addressing security threats posed by UAVs through advanced RF signal analysis and classification.

关键词： Ensemble Neural Network Radio Frequency RF Spectrogram UAVs Detection

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