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Titlebook: Wireless Algorithms, Systems, and Applications; 17th International C Lei Wang,Michael Segal,Tie Qiu Conference proceedings 2022 The Editor(

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书目名称Wireless Algorithms, Systems, and Applications
副标题17th International C
编辑Lei Wang,Michael Segal,Tie Qiu
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Wireless Algorithms, Systems, and Applications; 17th International C Lei Wang,Michael Segal,Tie Qiu Conference proceedings 2022 The Editor(
描述The three-volume set constitutes the proceedings of the 17th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2022, which was held during October 28-30, 2022. The conference took place in Dalian, China.The 95 full and 62 short papers presented in these proceedings were carefully reviewed and selected from 265 submissions. The contributions in. ​algorithms, systems; and applications of internet of things;information processing and data management;.radar and sonar networks.
出版日期Conference proceedings 2022
关键词artificial intelligence; communication channels (information theory); communication systems; computer v
版次1
doihttps://doi.org/10.1007/978-3-031-19214-2
isbn_softcover978-3-031-19213-5
isbn_ebook978-3-031-19214-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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978-3-031-19213-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Robust Adaptive Cubature Kalman Filter for Attitude Determination in Wearable Inertial Sensor Networt CKF algorithm based on quaternion and gyroscope error modeling proposed in this paper can solve motion attitude solution. Lastly, we compare our method with CKF and EKF algorithm, the proposed algorithm can effectively improve the precision of attitude analysis.
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0302-9743 edings were carefully reviewed and selected from 265 submissions. The contributions in. ​algorithms, systems; and applications of internet of things;information processing and data management;.radar and sonar networks.978-3-031-19213-5978-3-031-19214-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Unsupervised Deep Learning-Based Hybrid Beamforming in Massive MISO Systemstimizing the HBF through deep learning (DL) has gained considerable attention in recent years due to its potential in dealing with the nonconvex problems. However, existing DL-based HBF methods require wider or deeper neural networks to guarantee training performance, which not only leads to higher
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