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pro vyhledávání: '"Tan, Kailin"'
Autor:
Tan, Kailin, Dai, Jincheng, Liu, Zhenyu, Wang, Sixian, Qin, Xiaoqi, Xu, Wenjun, Niu, Kai, Zhang, Ping
End-to-end image transmission has recently become a crucial trend in intelligent wireless communications, driven by the increasing demand for high bandwidth efficiency. However, existing methods primarily optimize the trade-off between bandwidth cost
Externí odkaz:
http://arxiv.org/abs/2408.14127
End-to-end visual communication systems typically optimize a trade-off between channel bandwidth costs and signal-level distortion metrics. However, under challenging physical conditions, this traditional discriminative communication paradigm often r
Externí odkaz:
http://arxiv.org/abs/2406.07390
Autor:
Dai, Jincheng, Wang, Sixian, Yang, Ke, Tan, Kailin, Qin, Xiaoqi, Si, Zhongwei, Niu, Kai, Zhang, Ping
The emerging field semantic communication is driving the research of end-to-end data transmission. By utilizing the powerful representation ability of deep learning models, learned data transmission schemes have exhibited superior performance than th
Externí odkaz:
http://arxiv.org/abs/2211.04339
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are
Externí odkaz:
http://arxiv.org/abs/2211.00937
In this paper, we propose a class of high-efficiency deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform, it can be collected under the name nonlinear transform source-channel codin
Externí odkaz:
http://arxiv.org/abs/2112.10961
Autor:
Dai, Jincheng, Tan, Kailin, Si, Zhongwei, Niu, Kai, Chen, Mingzhe, Poor, H. Vincent, Cui, Shuguang
The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codele
Externí odkaz:
http://arxiv.org/abs/2102.03828
Akademický článek
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Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Tan K; Department of Otorhinolaryngology, the Second Affiliated Hospital of Shantou University Medical College, Shantou, 515041, China., Zhang Y
Publikováno v:
Lin chuang er bi yan hou ke za zhi = Journal of clinical otorhinolaryngology [Lin Chuang Er Bi Yan Hou Ke Za Zhi] 2006 Aug; Vol. 20 (15), pp. 701-3.