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数学与系统科学研究院 [3]
长春光学精密机械与物... [1]
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期刊论文 [3]
会议论文 [1]
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2022 [1]
2021 [1]
2019 [1]
2012 [1]
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Local error estimate of L1 scheme for linearized time fractional KdV equation with weakly singular solutions
期刊论文
APPLIED NUMERICAL MATHEMATICS, 2022, 卷号: 179, 页码: 183-190
作者:
Chen, Hu
;
Chen, Mengyi
;
Sun, Tao
;
Tang, Yifa
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浏览/下载:3/0
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提交时间:2023/02/07
L1 scheme
Time-fractional KdV equation
Local error estimate
Weakly singular solution
A Superconvergent Nonconforming Mixed FEM for Multi-Term Time-Fractional Mixed Diffusion and Diffusion-Wave Equations with Variable Coefficients
期刊论文
EAST ASIAN JOURNAL ON APPLIED MATHEMATICS, 2021, 卷号: 11, 期号: 1, 页码: 63-92
作者:
Fan, Huijun
;
Zhao, Yanmin
;
Wang, Fenling
;
Shi, Yanhua
;
Tang, Yifa
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浏览/下载:29/0
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提交时间:2021/01/14
Nonconforming mixed FEM
multi-term time-fractional mixed diffusion and diffusion-wave equations
L1 time-stepping method
Crank-Nicolson scheme
convergence and superconvergence
Anisotropic linear triangle finite element approximation for multi-term time-fractional mixed diffusion and diffusion-wave equations with variable coefficient on 2D bounded domain
期刊论文
COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2019, 卷号: 78, 期号: 5, 页码: 1705-1719
作者:
Zhao, Yanmin
;
Wang, Fenling
;
Hu, Xiaohan
;
Shi, Zhengguang
;
Tang, Yifa
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浏览/下载:70/0
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提交时间:2020/01/10
Multi-term time-fractional mixed diffusion-wave equations
Linear triangle finite element
L1 time-stepping method
Crank-Nicolson scheme
Unconditional stability
Convergence and superconvergence
Image coding using wavelet-based compressive sampling (EI CONFERENCE)
会议论文
2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China
Jin L.
;
Li J.
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浏览/下载:36/0
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提交时间:2013/03/25
In this paper
we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First
two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT
the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients
we allocate different measurements to low- and high-frequency component. Then
the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally
the compressed streams are transmitted. At the decoder
one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.
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