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

共 4657 条记录，以下是31-40 订阅

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SwarmCatcher: An AI-Powered Anti-Drone Surveillance System for Public Safety Over 5G and Beyond Ecosystems 28

SwarmCatcher: An AI-Powered Anti-Drone Surveillance System f...

引用

28th conference on Innovation in Clouds, Internet and Networks, ICIN 2025

作者： Kalafatidis, Sarantis Tsiktsiris, Dimitrios Trakos, Dimitrios Lalas, Antonios Votis, Konstantinos Tzovaras, Dimitrios Centre for Research and Technology Hellas Information Technologies Institute Greece

ISBN: (纸本)9798331542399

The proliferation of unmanned aerial vehicles (UAVs) presents significant challenges to public safety and critical infrastructure protection. This paper introduces SwarmCatcher, an innovative AI-powered anti-drone and surveillance system designed to detect and classify UAV threats in real-time. SwarmCatcher integrates multi-sensor technologies, including optical, thermal, RF, and acoustic systems, with advanced AI algorithms to provide thorough situational awareness. Utilizing the low-latency, high-throughput capabilities of cloud-native architecture and 5G networks, the system supports scalable deployment across diverse environments, offering rapid threat mitigation. Real trials were conducted, utilizing 5G infrastructures in the FIDAL and CERTH testbeds, evaluating and demonstrating SwarmCatcher’s potential to address the requirements of public safety agencies and pave the way for integration with 6G networks. The paper investigates SwarmCatcher’s architecture, key performance metrics, and its role in advancing next-generation vertical-specific applications for public safety. © 2025 IEEE.

关键词： 5G mobile communication systems

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Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection and Early Warning Control System 3

Vehicle-Mounted Millimeter-Wave Radar Vital Signs Detection ...

引用

3rd International conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Li, Wenshan Hunan Institute of Traffic Engineering Hunan Hengyang China

ISBN: (纸本)9798331508456

Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signal processing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advanced algorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential. © 2025 IEEE.

关键词： Electronic guidance systems

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A Smart Robotic Device for Automated Environmental Monitoring: An Integrated Approach for Real-Time Data Collection and Analysis

A Smart Robotic Device for Automated Environmental Monitorin...

引用

Computational Intelligence Approaches and applications (ICCIAA), International conference on

作者： Amjad Aldweesh College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Smart robotic devices have emerged as a promising solution for automated environmental monitoring that addresses the limitations of traditional monitoring methods. We present a novel smart robotic device that integrates advanced mobility, energy management, data processing, and sensor modularity. The device features a robust chassis structure that supports a terrain-adaptive mobility assembly, a 360-degree rotational modular sensor arm, an onboard data processing unit, and a power management system with a rechargeable battery and a solar panel assembly. This integrated approach enables continuous, real-time data collection and analysis in diverse and challenging environments. The modular design of the device facilitates rapid customization and upgrades to meet specific monitoring needs, ensuring versatility and extended service life. We discuss the key components of the device, their interactions, and the advantages of this integrated approach for environmental monitoring applications.

关键词： Smart cities Power system management Data collection Robot sensing systems Smart systems Real-time systems Environmental monitoring Solar panels Resource management Assembly

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Cloud-Based Monitoring of Lithium-Ion Battery Management systems for Health Estimation in Manufacturing Industries

Cloud-Based Monitoring of Lithium-Ion Battery Management Sys...

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2025 International conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility, ADMMS 2025

作者： Zeeshan, Mohammad Akre, Vineet Wardwizard Innovations and Mobility India

The increasing reliance on lithium-ion batteries in manufacturing necessitates advanced monitoring techniques to ensure their longevity and reliability. Cloud technology offers a solution by enabling real-time data collection, analysis, and accessibility, facilitating thorough monitoring and predictive maintenance. Digital twin technology, creating a virtual replica of the physical battery system, provides a platform for simulating real-world conditions and predicting potential issues before they arise. By integrating sensor data and historical usage patterns, the digital twin model can accurately predict battery degradation, aiding in timely maintenance strategies. This proactive approach enhances battery operational efficiency and extends lifespan, leading to cost savings and improved safety. The paper explores using cloud-based monitoring systems to enhance the health estimation and management of lithium-ion batteries. A comprehensive feasibility study on adopting battery digital twin technology for electric two-wheeler and three-wheeler manufacturers examines creating a digital twin model for batteries and validating corresponding tests. Furthermore, the research discusses the technical challenges and solutions associated with implementing digital twin technology in manufacturing. Key metrics such as state of charge (SoC) and state of health (SoH) are analyzed to showcase the effectiveness of the digital twin model in real-world applications. © 2025 SAE International. All Rights Reserved.

关键词： Predictive maintenance

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks 3

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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3rd International conference on Intelligent systems, advanced Computing, and Communication, ISACC 2025

作者： Manna, Prithwish Majumder, Sudipta Singh, N. Hemarjit Dibrugarh University Dept. of CSE DUIET Assam Dibrugarh India Dibrugarh University Dept. of ECE DUIET Assam Dibrugarh India

ISBN: (纸本)9798331523893

In this paper, we propose a combination of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a hybrid optimization technique which meets k-coverage criteria in Wireless sensor Network (WSN) and Internet of Things (IoT). The goal of this approach is to increase the coverage area alongside minimizing the deployment cost of the sensors. Another aim is to reduce the power consumption. This technique is helpful to determine the optimal position of the sensors. It helps to ensure that each target point in the network is getting the coverage of at least k sensors. In this way we can enhance the reliability and robustness of the network. From experimental results, we can conclude that with optimal sensor utilization and minimized redundancy, our proposed approach achieves 97.14% coverage ratio with k-coverage equals 3. According to performance metrics, it is clear that there is consistent energy efficiency through the cycles. The approach converges in less than 3 second. So, this approach is applicable for real time applications. It is a hybrid metaheuristic approach which is very efficient in managing various challenges associated with sensor deployment. From this approach we can get solutions of coverage enhancement, energy efficiency and improved sensor utilization. © 2025 IEEE.

关键词： Energy Efficiency Genetic Algorithm K-Coverage Particle Swarm Optimization sensor Deployment Wireless sensor Network

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Sensitivity of the Rolling Bearings Diagnostic Method Depending on the Number of Measuring Points of a Multipoint Temperature sensor

Sensitivity of the Rolling Bearings Diagnostic Method Depend...

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Digital Signal Processing and its applications (DSPA), International conference on

作者： Denis K. Lebedev Laboratory of Self-Validating Sensors Systems and Advanced Instrumentation South Ural State University Chelyabinsk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

This work is aimed at research in the field of developing methods for the diagnosis of rolling bearings. This paper evaluates the influence of a number of measuring points of a multipoint temperature sensor on the performance of a rolling bearing diagnostic method. The study considers a rolling bearing diagnosis method based on analyzing a bearing temperature field and applying a neural network with an “autoencoder” architecture. The research of efficiency of the method is made on the temperature field data of the rolling bearing, which is a part of the technical equipment of the metal rolling line. As a result of this work, the hypothesis that the efficiency of the diagnostic model increases with the increase in the number of measuring points was confirmed.

关键词： Temperature measurement Temperature sensors Analytical models Temperature distribution Sensitivity Neural networks Autoencoders Rolling bearings Metals Battery charge measurement

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

引用

Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry sensor phenomena and characterization sensor systems Real-time systems Gas detectors Resilience

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A Hybrid Optimization Strategy for k-Coverage Maximization in Wireless sensor and IoT Networks

A Hybrid Optimization Strategy for k-Coverage Maximization i...

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International Symposium on advanced Computing and Communication (ISACC)

作者： Prithwish Manna Sudipta Majumder N. Hemarjit Singh Dept. of CSE DUIET Dibrugarh University Dibrugarh Assam India Dept. of ECE DUIET Dibrugarh University Dibrugarh Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

关键词： Wireless communication Wireless sensor networks Turning Energy efficiency sensor systems Real-time systems Internet of Things Particle swarm optimization Intelligent sensors Genetic algorithms

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Design and Simulation of Surface Acoustic Wave sensor in COMSOL Multiphysics for Detection of Acetone

Design and Simulation of Surface Acoustic Wave Sensor in COM...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Praneeth Borra Prasanti Korapati Vamsi Sravanam Shanmuk Bala Dikishitha Sree Tikkisetty Department of Electronics and Instrumentation Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The increasing demand for efficient and reliable gas sensors has propelled the exploration of novel materials and advanced simulation techniques. This study presents the design and development of a gas sensor that detects acetone using molybdenum disulfide $(\mathbf{MoS}_sensor)$ as the sensing material. Acetone is a major volatile organic compound that has applications in a variety of industries, including healthcare and environmental monitoring. Acetone gas is a respiratory symptom that indicates diabetes. The high sensitivity, stability and cost of $\mathbf{MoS}_{\mathbfsensor}$ make it a good sensor material. The sensor design involves the integration of $\mathbf{MoS}_{\mathbfsensor}$ thin films onto a substrate, with interdigitated electrodes facilitating the detection mechanism. COMSOL Multiphysics, a versatile simulation software, is employed to model the sensor's performance under different operating conditions and geometrical configurations. The simulation framework incorporates electrochemical processes, mass transport phenomena, and electrical properties to accurately represent the sensor's *** was found that the resistance of the sensor layer was reduced in the presence of acetone gas. The simulation was carried out using COMSOL Multiphysics.

关键词： Volatile organic compounds Transducers Surface resistance Surface acoustic waves Titanium Software Molybdenum Gas detectors Substrates Surface treatment

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Efficient Human Activity Recognition through Multi-sensor Data and Deep Learning Techniques

Efficient Human Activity Recognition through Multi-Sensor Da...

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advanced Computing Technologies (ICoACT), International conference on

作者： Rhenius A Anitha J Immanuel Alex Pandian S CSE KITS Coimbatore India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Extensive research takes place in the field of Human Activity Recognition (HAR) systems with wearable computing that helps in healthcare applications, fitness tracking and smart environments. sensor technology advances have propelled Human Activity Recognition systems to utilize innovative machine learning methods for accurate and robust activity classification. This work evaluates the effectiveness of four cutting-edge deep learning models-Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU)-through their implementation on publicly accessible accelerometer and gyroscope HAR datasets. These classification models underwent testing through different activation functions namely ReLU, Leaky ReLU, and Swish to assess their practicality for classification operations. Standard evaluation metrics including accuracy, precision, recall, ROC-AUC and F1-score demonstrate the best features of each implemented model while providing descriptive visual representations. TCN delivered 98.59% accuracy and exhibited superior training speed efficiency thus establishing itself as the suitable AI algorithm for real-time implementation. This research offers an extensive breakdown for choosing optimal models in real-time HAR systems which supports advances in wearable computing and activity recognition technologies.

关键词： Deep learning Training Visualization Accuracy Computational modeling Real-time systems Human activity recognition Convolutional neural networks Wearable devices Long short term memory

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