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

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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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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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Labeled Multi-Bernoulli Filter for Distributed multi-target Tracking with Detection and Class Measurements 27

Labeled Multi-Bernoulli Filter for Distributed multi-target ...

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27th International conference on Information Fusion (FUSION)

作者： Klupacs, James Gostar, Amirali K. Bab-Hadiashar, Alireza Hoseinnezhad, Reza RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9798350371420;9781737749769

advanced machine vision and deep learning models are now widely used as virtual sensors in various applications to detect and classify objects in image measurements. Typically, these virtual sensors output measurements as a set of class probabilities for each detected object within the sensor's field of view. However, integrating this type of data into multi-target tracking systems, traditionally based on point measurement detections, presents some challenges. This paper proposes a solution by introducing a labeled multi-Bernoulli filter formulated to handle detections with class measurements from virtual sensors for multi-target tracking. Furthermore, we explore the application of this filter for multi-sensor multi-target tracking within a distributed sensor network. Through numerical experiments involving vehicles equipped with cameras and deep learning classification modules navigating complex roads, we demonstrate that incorporating class information into the tracking process improves tracking accuracy. This improvement is further enhanced when vehicles share and fuse their information in a distributed manner. Our findings highlight the benefits of integrating class probability data into multi-target tracking filters, offering substantial improvements in the accuracy of automated monitoring systems in dynamic and complex environments.

关键词： Random Finite Sets Intelligent Transport systems Multi-Object Tracking Information Fusion Class-Based systems

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Enhanced Navigation Based Obstacle Avoidance and Target Tracking with Raspberry-Pi for Mobile Robots 6th

Enhanced Navigation Based Obstacle Avoidance and Target Trac...

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6th conference on Computing systems and applications

作者： Belaidi, Hadjira Demim, Fethi Mezhary, Adel Rouigueb, Abdenebi Yahoui, Sofiane Nemra, Abdelkrim Univ MHamed Bougara Boumerd Signals & Syst Lab Inst Elect Elect Engn Boumerdes Algeria Ecole Mil Polytech Lab Guidance & Nav Algiers Algeria Ecole Mil Polytech Lab Artificial Intelligence & Virtual Real Algiers Algeria

ISBN: (纸本)9783031718472;9783031718489

Our study focuses on developing a self-navigating mobile robot using advanced computer vision techniques and a microprocessor. The innovative integration of these technologies results in an advanced robotic system. Our main objective is to create a seamless navigation system from a starting point to a predetermined target, with real-time obstacle avoidance and dynamic decision-making capabilities. The robot efficiently detects and adjusts its trajectory to avoid obstacles by continuously scanning its surroundings with computer vision. Precise obstacle identification is ensured through intricate embedded algorithms, enabling secure and collision-free navigation. The robot autonomously halts upon reaching the target, and if the target is hidden, it initiates comprehensive environmental exploration. Powered by the Raspberry Pi, our system offers a robust and cost-effective solution for mobile robot navigation. This strategic amalgamation of technologies greatly enhances the robot's operational prowess, enabling adept navigation and focused target pursuit in complex environments.

关键词： Autonomous navigation mobile robot computer vision system collision avoidance image processing and sensor fusion

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Hybrid Near-Field Scanning Microwave Microscope with an Optical Confocal sensor 25

Hybrid Near-Field Scanning Microwave Microscope with an Opti...

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25th International conference on Electromagnetics in advanced applications, ICEAA 2024

作者： Vyshatko, Nikolai Tselev, Alexander CICECO-Aveiro Institute of Materials University of Aveiro Aveiro Portugal

ISBN: (纸本)9798350360974

There are a number of micro- and nanometer-scale manufacturing processes, which demand or would highly benefit from tools allowing high-spatial-resolution quality control for identification of defect spots and defective parts. These may include, for example, fabrication of microfluidic components or micro-electro-mechanical systems (MEMS) as well as certain processes and technological steps in semiconductor and electronics industries. Addressing this need, we have developed a scanning probe microscope that utilizes optical confocal distance sensor and near-fields of microwaves to probe and image samples and parts with a sub-micrometer spatial resolution, which exceeds the resolution of the available optical tools. The near-field microwave probe remains out of contact with the part/sample surface during imaging to prevent or dramatically reduce the probe damage and wear-out. The main foreseen use of the microscope is high-spatial-resolution inspection of parts with varying surface shape and/or electrical properties (dielectric constant and conductivity). As demonstrate in Fig. 1, the lateral spatial resolution of the microwave near-field probing of the tool is better than 1 um, which exceeds the resolution of about 2 um of far-field optical systems currently employed in industry. The tool can be used for in- and off-line inspection of topographic features, such as channels and protrusions in microfluidic systems and MEMS. Due to the ability of microwaves to penetrate through dielectric materials the microscope can be utilized for imaging of buried structures, for example, metal lines or voids in a dielectric material (see, e.g., the review [1]), which can find applications in non-destructive failure analysis of semiconductor device packaging. © 2024 IEEE.

关键词： Semiconductor devices

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Solarux Wireless Mesh Network and Application for Monitoring Photovoltaic Solar systems 13

Solarux Wireless Mesh Network and Application for Monitoring...

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13th International conference on Renewable Energy Research and applications

作者： Toygar, M. Evren Incesu, Ozgu Bayram, Tufan Dokuz Eylul Univ Dept Mech Engn Izmir Turkiye Solarux Alternat Energy Syst Izmir Turkiye

ISBN: (纸本)9798350375596;9798350375589

Wireless mesh network is a technology that can dynamically self-organize and self-heal, provide flexible integration, rapid installation, easy maintenance, low cost, high scalability, and secure service capabilities. This technology includes mesh routers and mesh clients. They enable integration into networks such as the Internet, IEEE 802.11, IEEE 802.15, IEEE 802.16 and sensor networks through bridging capabilities in gateways and mesh routers. Wireless mesh networks provide wireless services for personal, local, campus and metropolitan area applications. In this paper, a method and an apparatus to deploy a wireless mesh network its and network nodes are described. In particular, a network with network node neighborhoods is described. A node consists of an antenna and a transceiver controller. Nodes are configured for installation with MAC addresses and geographic coordination with the network. In addition, the software architecture for the mesh network is described.

关键词： Wireless mesh networking wireless mesh network architecture wireless mesh network applications routing metrics routing protocols

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Machine Learning based Wild-Animal Detection near Roads using IoT sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

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2024 IEEE International conference on Intelligent systems and advanced applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

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