中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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浏览/检索结果: 共6条,第1-6条 帮助

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Lightweight Multiattention Recursive Residual CNN-Based In-Loop Filter Driven by Neuron Diversity 期刊论文  OAI收割
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 11, 页码: 6996-7008
作者:  
Li, Mingxuan;  Ji, Wen
  |  收藏  |  浏览/下载:3/0  |  提交时间:2024/05/20
Doppler-robust high-spectrum-efficiency VCM-OFDM scheme for low Earth orbit satellites broadband data transmission 期刊论文  OAI收割
IET COMMUNICATIONS, 2018, 卷号: 12, 期号: 1, 页码: 35-43
作者:  
Li, Jionghui;  Xiong, Weiming;  Sun, Geng;  Wang, Zhugang;  Huang, Yonghui
  |  收藏  |  浏览/下载:75/0  |  提交时间:2018/03/02
EcoUp: Towards Economical Datacenter Upgrading 期刊论文  OAI收割
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2016, 卷号: 27, 期号: 7, 页码: 1968-1981
作者:  
Yan, Guihai;  Ma, Jun;  Han, Yinhe;  Li, Xiaowei
  |  收藏  |  浏览/下载:40/0  |  提交时间:2019/12/13
共线SPDC下剩余脉冲泵浦光滤光特性研究1 期刊论文  OAI收割
时间频率学报, 2014, 卷号: 37, 期号: 1, 页码: 1
作者:  
白云;  权润爱;  张羽;  侯飞雁;  刘涛
  |  收藏  |  浏览/下载:16/0  |  提交时间:2023/12/13
Sort optimization algorithm of median filtering based on FPGA (EI CONFERENCE) 会议论文  OAI收割
2010 International Conference on Machine Vision and Human-Machine Interface, MVHI 2010, April 24, 2010 - April 25, 2010, Kaifeng, China
作者:  
Li S.
收藏  |  浏览/下载:30/0  |  提交时间:2013/03/25
Image parallel processing based on GPU (EI CONFERENCE) 会议论文  OAI收割
2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
作者:  
Wang J.-L.;  Wang J.-L.
收藏  |  浏览/下载:31/0  |  提交时间:2013/03/25
In order to solve the compute-intensive character of image processing  based on advantages of GPU parallel operation  parallel acceleration processing technique is proposed for image. First  efficient architecture of GPU is introduced that improves computational efficiency  comparing with CPU. Then  Sobel edge detector and homomorphic filtering  two representative image processing algorithms  are embedded into GPU to validate the technique. Finally  tested image data of different resolutions are used on CPU and GPU hardware platform to compare computational efficiency of GPU and CPU. Experimental results indicate that if data transfer time  between host memory and device memory  is taken into account  speed of the two algorithms implemented on GPU can be improved approximately 25 times and 49 times as fast as CPU  respectively  and GPU is practical for image processing. 2010 IEEE.