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

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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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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...

引用

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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Design a Touch sensor Using Conductive Spandex Fabric 7th

Design a Touch Sensor Using Conductive Spandex Fabric

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7th International conference on Green Technology and Sustainable Development

作者： Minh Ky Tran Thi Tuyet Nga Ly Bao Hy Nguyen Chi Cuong Vu HCMC Univ Technol & Educ Dept Facil Elect & Elect Engn 01 Vo Ngan St Ho Chi Minh City Vietnam

ISBN: (纸本)9783031762314;9783031762321

Recent advancements in sensor technology have propelled the e-textile industry to the forefront of innovation. E-textiles, also known as smart fabrics, possess physical and dimensional flexibility, making them suitable for wearable sensor technology. These technologies are developed to cater to the needs of advanced daily life, which aligns with the IoT development trend. This paper introduces an applied resistive sensor that uses an electronic fabric made from elastic fabric and carbon nanotubes (CNTs) fabric as the electrical layer. The sensor achieves high sensitivity (2.6241 N-1 with force less than 1 N), thickness (similar to 3 mm), and versatility make it ideal for load performance and fast resistance switching without waiting for capacitor charging and discharging as capacitive sensors, facilitating faster interaction with e-touch. The study provides an overview of the sensor's characteristics and future directions, emphasizing the creation of soft sensors based on pyralux for medical devices and health monitoring applications in the future.

关键词： Touch sensor Carbon nanotubes (CNTs) Conductive spandex fabric Force-sensing resistor (FSR)

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Evaluating the Impact of Lossy Compression on ADAS Deep Learning Models using Fisheye Cameras 26

Evaluating the Impact of Lossy Compression on ADAS Deep Lear...

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26th Irish Machine Vision and Image Processing conference, IMVIP 2024

作者： Simha, Srinidhi Mukanahallipatna Molloy, Dara Fahy, Darren Valeo Vision Systems Ireland University of Galway Ireland

ISBN: (纸本)9781837242672

The increasing deployment of advanced Driver Assistance systems (ADAS) alongside the continual rise in camera sensor resolution has led to high bandwidth, and generally high cost, computation, and intra-vehicle communication. While the sensor bandwidth impacts the vehicle architecture, it also affects the data collection, storage, deep learning model training, and validation infrastructures. However, if the bandwidth was low, while still achieving the goal of high accuracy ADAS perception, the time and cost associated with creating and deploying the system would be greatly reduced. This study investigates the influence of lossy compression on multi-task deep learning models for real-time perception in ADAS employing fisheye cameras. We leverage a large-scale dataset and train a representative multi-task ADAS perception model for pedestrian, kerb, line, and soiling classification. The testing dataset is subjected to compression using the popular H.264 video codec at varying compression ratios. Through rigorous evaluation, we analyse the effects of compression on model performance, providing insights into the feasibility of employing lossy compression techniques in ADAS applications. Our results reveal that lossy compression could be deployed in automotive perception applications and that a compression ratio of up to 98% (720Mb/s to 12Mb/s), could be utilised with negligible performance degradation. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： advanced driver assistance systems

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Quantitative Analysis and Optimization of sensor Deployment for Enhanced Activity Recognition in Smart Home Environments: A Multidimensional Approach to Data-Driven Intelligence and Efficiency in Universal Computing Spaces

Quantitative Analysis and Optimization of Sensor Deployment ...

引用

2024 International conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Aziz, Khalid Dua, Sakshi Gupta, Prabal Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350363289

This paper presents a comprehensive study on the enhancement of smart home automation and monitoring systems through the application of advanced activity recognition algorithms and sensor deployment optimization. We start by taking a CASAS dataset capturing a wide range of daily living activities, detected through an array of environmental sensors. This study conducts an in-depth analysis of activity recognition performance, leveraging precision, recall, and f1score metrics to identify areas for improvement in existing machine learning models. Further, we investigate sensor activation data to propose optimizations in sensor deployment, aimed at increasing the accuracy of activity recognition while reducing unnecessary sensor data processing. Through temporal analysis of activity patterns, we develop predictive models that forecast future activities with significant accuracy, facilitating smarter resource management and personalized automation strategies. This paper also explores the implications of our findings on user experience enhancement. Our methodology showcases the integration of machine learning techniques, statistical analysis, and practical considerations for deploying smart home technologies. The results indicate a promising avenue for future research and development in smart environments, with potential applications extending beyond residential settings to include healthcare facilities and office spaces. The findings underscore the importance of adaptive and intelligent systems in enhancing the quality of life through technology. © 2024 IEEE.

关键词： Smart homes

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The Optimal Design of a Rotor System with Elastic Ring 10

The Optimal Design of a Rotor System with Elastic Ring

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10th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, DependSys 2024

作者： Shi, Yihang Yang, Zhongyu Feng, Yinli Liu, Di University of Chinese Academy of Sciences Chinese Academy of Sciences School of Aeronautics and Astronautics Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences School of Engineering Sciences Natl. Key Lab. of Sci. and Technol. on Adv. Light-duty Gasturbine Inst. of Engineering Thermophysics Beijing China Chinese Academy of Sciences Institute of Engineering Thermophysics National Key Laboratory of Science and Technology on Advanced Light-duty Gasturbine Beijing China

ISBN: (纸本)9798331540500

Elastic ring (ER) is widely adopted for its compact size and ease of assembly and have become a prevalent type of elastic support within rotor-support systems. However, it may exhibit stiffness nonlinearity under specific operating conditions, potentially compromising the safety of the rotor system. This paper examines the influence of elastic ring stiffness on the critical speed of a specific aero-engine rotor support structure, focusing on related nonlinear phenomena. The Levenberg-Marquardt method was employed to optimize key parameters, including the stiffness and clearance of the elastic ring, with the second-order critical speed as the optimization goal. Finite element analysis was performed to validate the optimization results, which were found to be both effective and practically applicable. The finding provides a significant reference for the design and analysis of aero-engine rotor systems and similar applications. © 2024 IEEE.

关键词： Optimal systems

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Investigation of Hardware-Software Solutions for an Energy-Efficient Wireless sensor Network 12

Investigation of Hardware-Software Solutions for an Energy-E...

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

作者： Yatsenko, Anastasiia Parkhomenko, Anzhelika Wolff, Carsten Tulenkov, Artem Parkhomenko, Andriy National University Zaporizhzhia Polytechnic Zaporizhzhia Ukraine Institute for the Digital Transformation of Application and Living Domains University of Applied Sciences and Arts Dortmund Germany

ISBN: (纸本)9798350358056

This work is devoted to the study of methods and tools for minimizing power consumption of wireless sensor networks. The results of processing experimental data for various operating modes of network nodes, as well as options for organizing their power supply and software settings are given. Practical recommendations have been developed to improve the energy efficiency of wireless sensors networks for the Smart Home systems. © 2023 IEEE.

关键词： communication module energy efficiency Smart Home system solar panel wireless sensors network

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A Predictive Maintenance Platform for a Conveyor Motor sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural Computing for advanced applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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