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Surface registration algorithm for rapid detection of surface thermal deformation of paraboloid antennas
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作者 马开锋 Huang Guiping +1 位作者 Hu Qingfeng He Peipei 《High Technology Letters》 EI CAS 2018年第3期266-271,共6页
In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rap... In order to obtain and master the surface thermal deformation of paraboloid antennas,a fast iterative closest point( FICP) algorithm based on design coordinate guidance is proposed,which can satisfy the demands of rapid detection for surface thermal deformation. Firstly,the basic principle of the ICP algorithm for registration of a free surface is given,and the shortcomings of the ICP algorithm in the registration of surface are analysed,such as its complex computation,long calculation time,low efficiency,and relatively strict initial registration position. Then an improved FICP algorithm based on design coordinate guidance is proposed. Finally,the FICP algorithm is applied to the fast registration test for the surface thermal deformation of a paraboloid antenna. Results indicate that the approach offers better performance with regard to fast surface registration and the algorithm is more simple,efficient,and easily realized in practical engineering application. 展开更多
关键词 paraboloid antenna surface thermal deformation ICP algorithm fast iterative closest point (FICP) algorithm surface registration
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Efficient Concurrent L1-Minimization Solvers on GPUs 被引量:1
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作者 Xinyue Chu Jiaquan Gao Bo Sheng 《Computer Systems Science & Engineering》 SCIE EI 2021年第9期305-320,共16页
Given that the concurrent L1-minimization(L1-min)problem is often required in some real applications,we investigate how to solve it in parallel on GPUs in this paper.First,we propose a novel self-adaptive warp impleme... Given that the concurrent L1-minimization(L1-min)problem is often required in some real applications,we investigate how to solve it in parallel on GPUs in this paper.First,we propose a novel self-adaptive warp implementation of the matrix-vector multiplication(Ax)and a novel self-adaptive thread implementation of the matrix-vector multiplication(ATx),respectively,on the GPU.The vector-operation and inner-product decision trees are adopted to choose the optimal vector-operation and inner-product kernels for vectors of any size.Second,based on the above proposed kernels,the iterative shrinkage-thresholding algorithm is utilized to present two concurrent L1-min solvers from the perspective of the streams and the thread blocks on a GPU,and optimize their performance by using the new features of GPU such as the shuffle instruction and the read-only data cache.Finally,we design a concurrent L1-min solver on multiple GPUs.The experimental results have validated the high effectiveness and good performance of our proposed methods. 展开更多
关键词 Concurrent L1-minimization problem dense matrix-vector multiplication fast iterative shrinkage-thresholding algorithm CUDA GPUS
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Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques
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作者 苏伍各 王宏强 阳召成 《Journal of Central South University》 SCIE EI CAS 2014年第1期223-231,共9页
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
关键词 attributed scatter center model sparse representation sparse Bayesian learning fast Bayesian matching pursuit smoothed l0 norm sparse reconstruction by separable approximation fast iterative shrinkage-thresholding algorithm
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