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Titlebook: Internet of Things. IoT through a Multi-disciplinary Perspective; 5th IFIP Internation Luis M. Camarinha-Matos,Luis Ribeiro,Leon Strous Con

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發(fā)表于 2025-3-21 16:59:48 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Internet of Things. IoT through a Multi-disciplinary Perspective
副標(biāo)題5th IFIP Internation
編輯Luis M. Camarinha-Matos,Luis Ribeiro,Leon Strous
視頻videohttp://file.papertrans.cn/473/472537/472537.mp4
叢書名稱IFIP Advances in Information and Communication Technology
圖書封面Titlebook: Internet of Things. IoT through a Multi-disciplinary Perspective; 5th IFIP Internation Luis M. Camarinha-Matos,Luis Ribeiro,Leon Strous Con
描述This book constitutes the refereed post-conference proceedings of the Fifth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2022, held in Amsterdam in October 2022..The 20 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in the following topical sections: IoT for Smart Villages, Security and Safety, Smart Home, Development, Engineering, Machine Learning, and Applications..
出版日期Conference proceedings 2022
關(guān)鍵詞architecture types; artificial intelligence; computer hardware; computer networks; computer systems; data
版次1
doihttps://doi.org/10.1007/978-3-031-18872-5
isbn_softcover978-3-031-18874-9
isbn_ebook978-3-031-18872-5Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2022
The information of publication is updating

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ves Verfahren birgt es jedoch die Gefahr ernsthafter Komplikationen. Als nicht invasives und somitsichereres und schneller durchführbares Verfahren eignet sich die kardiale Magnetresonanztomographie. Diese Studie überprüft mittels Volumetrie, Strainanalyse und kardialem Mapping nun zun?chst, ob die
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Internet of Things. IoT through a Multi-disciplinary Perspective5th IFIP Internation
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aGROdet: A Novel Framework for?Plant Disease Detection and?Leaf Damage Estimation systems in the proposed Agriculture Cyber Physical System. A convolutional neural network-based model has been proposed to detect different plant diseases. The model has been trained with large publicly available datasets. More than . accuracy has been achieved in the initial phase of the experimen
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Sensor-Based PUF: A Lightweight Random Number Generator for?Resource Constrained IoT Devicesom Number Generator (RNG) based on sensor Physical Unclonable Functions (PUF) in smart logistics scenario. PUF is a secure and lightweight source of randomness and hence suitable for constrained devices. Data is collected from various sensors and processed to extract cryptographically secure seed. N
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