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Titlebook: Communications, Signal Processing, and Systems; Proceedings of the 1 Qilian Liang,Wei Wang,Baoju Zhang Conference proceedings 2023 The Edit

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書目名稱Communications, Signal Processing, and Systems
副標(biāo)題Proceedings of the 1
編輯Qilian Liang,Wei Wang,Baoju Zhang
視頻videohttp://file.papertrans.cn/231/230550/230550.mp4
概述Unifies Communications, Signal Processing, and Systems.Incorporates the latest development in these areas.Presents system approaches for communications and signal processing
叢書名稱Lecture Notes in Electrical Engineering
圖書封面Titlebook: Communications, Signal Processing, and Systems; Proceedings of the 1 Qilian Liang,Wei Wang,Baoju Zhang Conference proceedings 2023 The Edit
描述.This book brings together papers presented at the 2022 International Conference on Communications, Signal Processing, and Systems,?online, July 23-24, 2022,?which provides a venue to disseminate the latest developments and to discuss the interactions and links between these multidisciplinary fields. Spanning topics ranging from communications, signal processing and systems, this book is aimed at undergraduate and graduate students in Electrical Engineering, Computer Science and Mathematics, researchers and engineers from academia and industry as well as government employees (such as NSF, DOD and DOE). .
出版日期Conference proceedings 2023
關(guān)鍵詞Wireless communications; Wireless networks; Internet of Things; Wireless sensor networks; Signal process
版次1
doihttps://doi.org/10.1007/978-981-99-2653-4
isbn_softcover978-981-99-2655-8
isbn_ebook978-981-99-2653-4Series ISSN 1876-1100 Series E-ISSN 1876-1119
issn_series 1876-1100
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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,Cross-Modal Two-Stream Target Focused Network for?Video Anomaly Detection, video. Training an unsupervised learning model on normal data is one of the most prevalent VAD paradigms owning to rare abnormal snippets. However, anomaly related and irrelated information sharing feature space will degrade the representation ability of the model for abnormal events. In this artic
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,Fall Detection in?the?Wild: An Intelligent Emergency Assistance System,s of serious injuries caused by falls. In this paper, we propose an IEAS, a framework that integrates deep learning-based fall detection and edge computing-based local processing. First, we introduce a novel challenging benchmark dataset, called WildFall, to better evaluate fall detection models in
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