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Distribution-flexible subset quantization for post-quantizing super-resolution networks
《Science China(Information Sciences)》2025年第3期159-176,共18页Yunshan ZHONG Mingbao LIN Jingjing XIE Yuxin ZHANG Fei CHAO Rongrong JI 
supported by National Key R&D Program of China(Grant No.2022ZD0118202);National Science Fund for Distinguished Young Scholars(Grant No.62025603);National Natural Science Foundation of China(Grant Nos.U21B2037,U22B2051,62176222,62176223,62176226,62072386,62072387,62072389,62002305,62272401);Natural Science Foundation of Fujian Province of China(Grant Nos.2021J01002,2022J06001)。
This paper introduces distribution-flexible subset quantization(DFSQ),a post-training quantization method for super-resolution networks.Our motivation for developing DFSQ is based on the distinctive activation distrib...
关键词:SUPER-RESOLUTION post-training quantization distribution-fexible subset quantization neural network 
Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training被引量:3
《Science China(Information Sciences)》2011年第7期1456-1470,共15页ZHANG Han DAI XianHua Pan DaRu 
supported by the National Natural Science Foundation of China (Grant No.61002012);the Project of NSF of Guangdong Province (Grant No.10451063101006074);the Industry-Universities-Research Cooperation Project of Guangdong Province and Ministry of Education of China (Grant No.2007A090302116)
We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST)....
关键词:OFDM/MIMO systems linearly time-varying channel estimation superimposed training training power allocation 
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