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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11-20 订阅

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A Self-Assembled Flexible Strain and Temperature sensor based on the Microstructure of Pyramids with High Elasticity, Temperature Sensitivity and Wide Range 38

A Self-Assembled Flexible Strain and Temperature Sensor base...

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38th IEEE International conference on Micro Electro Mechanical systems, MEMS 2025

作者： Yu, Yangtao Yan, Bo Cui, Wenbo Li, Mengqiu Li, Chenyuan Zang, Faheng Yang, Zhuoqing Shanghai Jiao Tong University National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Department of Micro/Nano Electronics Shanghai China Harbin Institute of Technology School of Electronics and Information Engineering Harbin China

ISBN: (纸本)9798331508890

Polymers have been extensively studied in the field of flexible sensors for wearable electronics. However, polymer-based flexible sensors still face great challenges in terms of sensitivity, sensing range and stretchable properties. This paper reports a novel self-assembled flexible strain and temperature sensor, constructed from NiO molecular spheres assembled between MXene flakes. This sensor exhibits exceptional temperature sensitivity, with a sensitivity up to -15.11%°C-1. It operates a broad temperature range(-20~60°C) and boasts a high measurement gauge factor(GF) of 6.57, along with a significant elongation at break exceeding 572%. Additionally, the sensor demonstrates an ultrahigh tensile strength of over 45 MPa. The microstructure of the sensor, featuring pyramidal formations, facilitates rapid testing capabilities. Furtherly, breathing cycle test under 0°C was performed, showing robust reliability. Furthermore, an array sensing circuit has been developed and designed to precisely measure spatial temperature distributions, enhancing utility of the sensor in various applications. © 2025 IEEE.

关键词： Tensile testing

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A Novel Multiple Modes Resonant sensor Featuring Blue Sideband Excitation 38

A Novel Multiple Modes Resonant Sensor Featuring Blue Sideba...

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38th IEEE International conference on Micro Electro Mechanical systems, MEMS 2025

作者： Xu, Jiao Zheng, Zhuoyue Xi, Jingqian Zhang, Ziqian Liu, Huafeng Zhang, Pan Chen, Jianlin Wang, Chen Kraft, Michael Wang, Yuan Martins, R.P. Mak, Pui-In Huazhong University of Science and Technology Moe Key Laboratory of Fundamental Physical Quantities Measurement Pgmf School of Physics Wuhan430074 China University of Macau Institute of Microelectronics State Key Laboratory of Analog and Mixed-Signal Vlsi 999078 China Peking University National Key Laboratory of Advanced Micro and Nano Manufacture Technology School of Integrated Circuits 100871 China Shanghai University School of Microelectronics 200444 China Ku Leuven Department of Electrical Engineering-MNS Leuven3000 Belgium

ISBN: (纸本)9798331508890

This paper reports a novel MEMS resonant structure functioning as a baseline resonant electrostatic force/temperature sensor, highlighting its substantial potential in sensing applications. The resonator, characterized by a dual-cosine structure, is capable of simultaneous multiple resonant modes actuation within the 300 kHz range, through a sole-source drive approach using the recent emerging Blue Sideband Excitation (BSE) technique. This method enables a distinctive multi-modal response, essential for multi-parameter measurements. The research focuses on the sensor's temperature characteristics, electrostatic perturbation sensitivity, and noise floor. Notably, the study reveals intriguing temperature responses across different modes, where certain modes exhibit a significant frequency rise with increasing temperature, this is counter-intuitive with common MEMS resonator devices, prompting further investigation into the underlying mechanisms. The analysis of electrostatic perturbation sensitivity and noise background further unfolds the resonator's performance under various excitation conditions, laying a foundation for advancing sensor functionality, and providing solid evidence on the viability of the subject. To sum up, this study highlights the resonator's potential as a high-precision sensor, opening new opportunities for MEMS sensor design and multi-parameter measurement applications. © 2025 IEEE.

关键词： Microchannels

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A Comprehensive Survey on Traffic Light Detection sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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3rd International conference on Electronics and Renewable systems, ICEARS 2025

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 IEEE.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport systems via Optimized Capsule Attention Network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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3rd International conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of Intelligent Transportation systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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Enhanced Clustering Framework for Unveiling Hidden Patterns in IoT-Based Structured and Unstructured Data 3

Enhanced Clustering Framework for Unveiling Hidden Patterns ...

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

作者： Das, Prabhat Kovuri, Karthik Saha, Sajal The Assam Kaziranga University Department of IT India Adamas University Department of CSE India The Assam Kaziranga University Department of CSE India

ISBN: (纸本)9798331523893

Clustering algorithms play a pivotal role in uncovering hidden patterns and relationships within diverse datasets. This study discusses about an enhanced real-time clustering approach based on the Birch methodology, optimized for analyzing both structured and unstructured data. Structured data, consisting of IoT sensor readings, and unstructured image data, captured using Raspberry Pi devices deployed in various locations, were analyzed to identify meaningful patterns. For structured data, the algorithm effectively clustered environmental parameters, revealing correlations and anomalies. For unstructured data, high-dimensional image features extracted using a pre-trained VGG16 model were clustered, uncovering patterns in texture, color, and structure. Experimental results demonstrate the algorithm's scalability and efficiency, achieving high-quality clusters with average Silhouette Scores of 0.87 and 0.81 for structured and unstructured datasets, respectively. These findings highlight the algorithm's adaptability and its potential for real-time analytics in diverse applications such as environmental monitoring and image-based decision-making systems. © 2025 IEEE.

关键词： BIRCH Clustering IoT sensor Data Structured Data Unstructured Data

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Incorporating LoRa Based Wireless sensor Network and Machine Learning Technologies to Improve Precision Agriculture 3rd

Incorporating LoRa Based Wireless Sensor Network and Machine...

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3rd International conference on Intelligent systems and applications, ICISA 2024

作者： Shaikh, Aman Ranjan, Nihar M. Rai, Satayush Rao, Pranil Ashok Shinde, Ganesh Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Pimpri-Chinchwad India

ISBN: (纸本)9789819798384

In the contemporary landscape, the agricultural sector has undergone transformational changes, embracing progressive dynamics through the integration of cutting-edge technologies and methodologies. The global adoption of IoT, AI/ML, and similar innovations reflects their capacity to furnish precise, reliable, and consistent outcomes, enabling data-driven decision-making. The amalgamation of IoT and ML models emerges as a strategic choice to enhance data analytics, aiming at a substantial improvement in both the quantity and quality of agricultural yields. This paper seeks to elucidate the synergistic interaction of various agrarian elements, orchestrating the computational prowess facilitated by ML models and data derived from IoT sensors transmitted through LoRa-based Wireless sensor Network (WSN) infrastructure. The proposed system advocates a comprehensive three-tier architectural framework, wherein ML and IoT systems collaborate seamlessly to process data for field management. The envisioned system extends its utility by translating these predictions into visually comprehensible representations. The integrated approach signifies a departure from conventional methods, offering a novel pathway to ensure the sustainability of yield, encompassing both production and grading standards. This paper thus contributes to the discourse on leveraging advanced technologies for agricultural optimization in response to evolving global demands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM 27

GIIDS: Generalized Intelligent Intrusion Detection System fo...

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27th International conference on advanced Communications Technology, ICACT 2025

作者： Kabir, Fahmina Mowla, Nishat I. Doh, Inshil Division of Artificial Intelligence and Software Ewha Womans University Seoul Korea Republic of Department of Industrial Systems RISE Research Institutes of Sweden Sundsvall Sweden Department of Cyber Security Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9791188428137

Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different crossvalidations. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Recent Advances in Human Activity Recognition Techniques: A Comprehensive Review 3

Recent Advances in Human Activity Recognition Techniques: A ...

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

作者： Saha, Sukanya Banik, Debapriya ICFAI University Tripura Department of Computer Science and Engineering Agartala India

ISBN: (纸本)9798331523893

Human Activity Recognition (HAR) interprets data from healthcare, security surveillance, human-computer interaction (HCI), and sensors to recognize and categorize human actions. The rapid development of artificial intelligence (AI) and the widespread use of sensors have drawn great interest and demonstrated the immense application potential of HAR systems based on non-invasive ambient sensors. Various methods for performing HAR have been reported by researchers. In this survey, it has been noticed that data can be collected in vision and sensor-based formats. The goal of video-based HAR is to automatically identify actions in video clips. Smart wearable sensors are the most popular kind of sensor-based HAR. Surveys have identified HAR tendencies across many contexts and applications. The researchers classified HAR techniques into three categories: online versus offline, multimodal versus unimodal, and feature-based versus learning-based approaches. This survey has focused on the recognition of different activities like sports, yoga, different dance poses, human interaction, and human actions. This survey also concentrated on single-view and multiview datasets. This study compares and contrasts the most recent HAR techniques with a discussion of widely used datasets. Various criteria, including dataset size and assessment metrics, were used to categorize and analyze the chosen studies. This survey also focused on different dataset availability. © 2025 IEEE.

关键词： Computer Vision Human Activity Recognition RGB Camera Security & Surveillance sensor

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Wearable Electrodermal Activity sensor for Real-Time Stress Detection Using Machine Learning

Wearable Electrodermal Activity Sensor for Real-Time Stress ...

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18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Santos, Salvador Sousa, Joana Ferreira, João NOS Inovação Rua Actor António Silva Lisbon Portugal

This paper discusses the design and implementation of a wearable electrodermal activity (EDA) sensor intended to detect subtle changes in skin conductivity, which are indicative of emotional states such as stress and anxiety, thus monitoring stress and arousal levels through advanced machine learning techniques. The device incorporates innovative conductive lycra combined with silver-silver chloride (Ag/AgCl) electrodes, enabling optimal skin contact and enhancing signal reliability. This integration allows for effective measurement of EDA. Utilizing the XGBoost algorithm, our machine learning model was trained on the ASCERTAIN dataset, achieving an overall accuracy of approximately 77% in predicting levels of arousal. While the model exhibited some challenges in predicting intermediate arousal states, it demonstrated strong precision and recall for extreme levels of arousal, underscoring its potential applications in mental health monitoring and human-computer interaction. The capabilities of this wearable technology for continuous and long-term health monitoring pave the way for further research into stress assessment and the understanding of emotional responses, emphasizing its relevance in enhancing psychological well-being. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Silver halides

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