Journal of Ambient Intelligence and Smart Environments最新文献

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Enhancing shallow water quality monitoring efficiency with deep learning and remote sensing: A case study in Mar Menor 利用深度学习和遥感技术提高浅层水质监测效率：梅诺尔湾案例研究

Journal of Ambient Intelligence and Smart Environments Pub Date : 2024-03-01 DOI: 10.3233/ais-230461

José G. Giménez, Martín González, Raquel Martínez-España, José M. Cecilia, J. López-Espín

{"title":"Enhancing shallow water quality monitoring efficiency with deep learning and remote sensing: A case study in Mar Menor","authors":"José G. Giménez, Martín González, Raquel Martínez-España, José M. Cecilia, J. López-Espín","doi":"10.3233/ais-230461","DOIUrl":"https://doi.org/10.3233/ais-230461","url":null,"abstract":"Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency in assessing shallow lakes has been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze remote sensing data to measure chlorophyll-a (Chl-a) concentration levels. Focused on a shallow coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour and compares Machine Learning and Deep Learning techniques to enhance efficiency and accuracy data of this satellite. Convolutional Neural Networks (CNNs) stand out as a robust choice, capable of delivering excellent results even in the presence of anomalous events. Our findings demonstrate that the CNN-based approach directly utilizing satellite data yields promising results in monitoring shallow lakes, offering enhanced efficiency and robustness. This research contributes to optimizing remote sensing data to and produce a continuous information flow addressed to monitoring shallow aquatic ecosystems with potential environmental management and conservation applications.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"77 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review 低成本物联网室内空气质量监测系统：系统综述

Journal of Ambient Intelligence and Smart Environments Pub Date : 2024-02-01 DOI: 10.3233/ais-220577

João Peixe, Gonçalo Marques

{"title":"Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review","authors":"João Peixe, Gonçalo Marques","doi":"10.3233/ais-220577","DOIUrl":"https://doi.org/10.3233/ais-220577","url":null,"abstract":"Indoor air quality (IAQ) is a critical challenge much less controlled in comparison with outdoor air quality. Bad IAQ is related to significant health complications such as respiratory problems, heart disease, and cancer. Many people spend most of their days inside buildings and don’t have air quality monitoring systems. Therefore, the occupants don’t know when the space has a higher quantity of pollutants than recommended, saturating the environment, and compromising people’s health. This is a problem that can be addressed by using Internet of Things (IoT) technologies to develop monitoring systems that allow a greater number of possibilities regarding the storage and processing of data and access to information by the end user, assisting the decision-making process regarding the indoor air pollution problem. Real-time data can be compared to default values, alerting the user of that situation, and suggesting an action to decrease the air pollutants concentration. There already are multiple solutions involving IoT-based technologies, many of them using low-cost sensors. Those are analyzed in this systematic review. Furthermore, the COVID-19 pandemic pointed out the importance of IAQ monitoring to evaluate the risk of contamination. The microcontrollers, IAQ parameters, sensors, data storage and visualization methods used in monitoring systems have been analyzed. The results show that most of the studies store data in Cloud systems and use Web platforms for data consulting. However, sensor calibration and efficient energy consumption are challenges that still exist.","PeriodicalId":508128,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"50 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139688011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods for volume inference of non-medical objects from images: A short review 从图像推断非医疗物体体积的方法：简评

Journal of Ambient Intelligence and Smart Environments Pub Date : 2024-01-17 DOI: 10.3233/ais-230193

Baticté Nabitchita, N. Gonçalves, Paulo Jorge Simães Coelho, Luís Pimenta, Eftim Zdravevski, Petre Lameski, Mónica Costa, Paulo Alexandre Neves, Ivan Miguel Pires

引用次数: 0

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