首届"设计在中国:瑞士雷达表新锐设计师大奖"(DESIGNES IN CHINA:The Rado Young DesignPrize)于10月15日在"100%设计"上海展上揭晓。经过国际评委会的严格甄选,张抱一和胡玲玲的"时间之光"挂钟以及沈倩...首届"设计在中国:瑞士雷达表新锐设计师大奖"(DESIGNES IN CHINA:The Rado Young DesignPrize)于10月15日在"100%设计"上海展上揭晓。经过国际评委会的严格甄选,张抱一和胡玲玲的"时间之光"挂钟以及沈倩倩的DIY长椅最终摘得桂冠。瑞士雷达表全球市场营销副总裁Patric Zingg先生为获奖设计师颁发了由英国工业设计大师Jasper Morrison设计的雷达r5.5系列腕表,并对这些中国设计师新锐所表现出的创新设计理念、作品的审美价值和功能性给予了高度评价。展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
文摘首届"设计在中国:瑞士雷达表新锐设计师大奖"(DESIGNES IN CHINA:The Rado Young DesignPrize)于10月15日在"100%设计"上海展上揭晓。经过国际评委会的严格甄选,张抱一和胡玲玲的"时间之光"挂钟以及沈倩倩的DIY长椅最终摘得桂冠。瑞士雷达表全球市场营销副总裁Patric Zingg先生为获奖设计师颁发了由英国工业设计大师Jasper Morrison设计的雷达r5.5系列腕表,并对这些中国设计师新锐所表现出的创新设计理念、作品的审美价值和功能性给予了高度评价。
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
