机构

• 数学与系统科学研究院 [3]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2022 [1]
• 2021 [1]
• 2019 [1]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Local error estimate of L1 scheme for linearized time fractional KdV equation with weakly singular solutions 期刊论文  OAI收割 APPLIED NUMERICAL MATHEMATICS, 2022, 卷号: 179, 页码: 183-190 作者:  Chen, Hu;  Chen, Mengyi;  Sun, Tao;  Tang, Yifa   |  收藏  |  浏览/下载：20/0  |  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  OAI收割 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   |  收藏  |  浏览/下载：27/0  |  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  OAI收割 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   |  收藏  |  浏览/下载：86/0  |  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  OAI收割 2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China 作者:  Li J.;  Li J.;  Li J. 收藏  |  浏览/下载：43/0  |  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.