Zobrazeno 1 - 10 of 17 pro vyhledávání: '"Tiexing Wang"'
2
Harvesting Ambient RF for Presence Detection Through Deep Learning
Autor: Yuexin Jiang, Biao Chen, Yang Liu, Tiexing Wang
Publikováno v: IEEE Transactions on Neural Networks and Learning Systems. 33:1571-1583
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich in
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::94873155770af607352bf159d5140e32
https://doi.org/10.1109/tnnls.2020.3042908
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4
On Exponentially Consistency of Linkage-Based Hierarchical Clustering Algorithm Using Kolmogrov-Smirnov Distance
Autor: Biao Chen, Yang Liu, Tiexing Wang
Publikováno v: ICASSP
This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distribution
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f274ca0daa3583aec11d89f5ba9680b2
https://doi.org/10.1109/icassp40776.2020.9053708
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6
Closed-loop SRME based on 3D L1-norm sparse inversion
Autor: Bin Hu, Chengming Liu, Deli Wang, Jing Sun, Tiexing Wang
Publikováno v: Acta Geophysica. 65:1145-1152
In many situations, the quality of seismic imaging is largely determined by a proper multiple attenuation as preprocessing step. Despite the widespread application of surface-related multiple elimination (SRME) and estimation of primaries by sparse i
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a91587b7f811417874b0f7851ec0cf3d
https://doi.org/10.1007/s11600-017-0098-6
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7
Inverse data-space multiple elimination with 3D curvelet sparsity promotion
Autor: Mi Tian, Tiexing Wang, Deli Wang, Tong Wang
Publikováno v: Acta Geophysica. 65:1197-1205
This paper describes an effective implementation of the inverse data-space multiple elimination method via the three-dimensional (3D) curvelet domain. The method can separate the surface-related operator (A) and primaries (P 0) through seismic data m
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f9f4c9fbf667ff23b9798f72024b4314
https://doi.org/10.1007/s11600-017-0096-8
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9
Exponentially Consistent K-Means Clustering Algorithm Based on Kolmogrov-Smirnov Test
Autor: Pramod K. Varshney, Yingbin Liang, Donald J. Bucci, Tiexing Wang, Biao Chen
Publikováno v: ICASSP
This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a35ff467e5d8d0dcdd86cc5d3c6e76cf
https://doi.org/10.1109/icassp.2018.8461730
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10
Clustering under composite generative models
Autor: Donald J. Bucci, Yingbin Liang, Tiexing Wang, Pramod K. Varshney, Biao Chen
Publikováno v: CISS
This paper studies clustering of data samples generated from composite distributions using the Kolmogorov-Smirnov (KS) based K-means algorithm. All data sequences are assumed to be generated from unknown continuous distributions. The maximum intra-cl
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b0330a815b8cc72d343d2048ef6f9970
https://doi.org/10.1109/ciss.2018.8362256
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