现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching...现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)和SLR相结合的跳频信号参数估计方法。该方法将接收到的样本数据均匀分段,对每段数据用OMP算法预处理,检测出发生频率跳变的数据段以及估计出没有发生跳变的数据段的频率;对这些发生跳变的数据段分别用SLR算法估计得到各段的跳时和频率;拼接可以得到整个样本的跳时、跳频图案等。仿真结果表明,该方法在在保持SLR精确估计性能的同时,能有效减少计算量。展开更多
Recent experience has shown that interior-point methods using a log barrierapproach are far superior to classical simplex methods for computing solutions to large parametricquantile regression problems. In many large ...Recent experience has shown that interior-point methods using a log barrierapproach are far superior to classical simplex methods for computing solutions to large parametricquantile regression problems. In many large empirical applications, the design matrix has a verysparse structure. A typical example is the classical fixed-effect model for panel data where theparametric dimension of the model can be quite large, but the number of non-zero elements is quitesmall. Adopting recent developments in sparse linear algebra we introduce a modified version of theFrisch-Newton algorithm for quantile regression described in Portnoy and Koenker[28]. The newalgorithm substantially reduces the storage (memory) requirements and increases computational speed.The modified algorithm also facilitates the development of nonparametric quantile regressionmethods. The pseudo design matrices employed in nonparametric quantile regression smoothing areinherently sparse in both the fidelity and roughness penalty components. Exploiting the sparsestructure of these problems opens up a whole range of new possibilities for multivariate smoothingon large data sets via ANOVA-type decomposition and partial linear models.展开更多
启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免...启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免了从低分辨率到高分辨率图像块映射的病态性问题;通过局部自相似实例图像块学习获得非线性映射函数的一阶近似,从而获得低分辨率图像块相对应的高分辨率图像块,克服了实例图像块算法不足的问题;实验采用峰值信噪比(Peak Signal to Noise Ratio,PSNR)和均方误差(Root-mean-square error,RMSE)比较各算法效果;从实验结果数据可以看出,大多数情况下,提出的算法具有最高的峰值信噪比和最低的均方根误差,从实验结果图可以看出,提出的算法的纹理保留的最好,图像自然性最好,且运行时间也少于其他几种较新的算法,表明提出的算法更适合用于解决实际问题。展开更多
文摘现有多跳频信号参数估计方法稀疏线性回归(Sparse Linear Regression,SLR)存在计算量大、内存消耗大的缺点。事实上,频率跳变只在少数几个数据点上发生,大部分数据不包含跳变信息。基于此,提出一种基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)和SLR相结合的跳频信号参数估计方法。该方法将接收到的样本数据均匀分段,对每段数据用OMP算法预处理,检测出发生频率跳变的数据段以及估计出没有发生跳变的数据段的频率;对这些发生跳变的数据段分别用SLR算法估计得到各段的跳时和频率;拼接可以得到整个样本的跳时、跳频图案等。仿真结果表明,该方法在在保持SLR精确估计性能的同时,能有效减少计算量。
基金This research was partially supported by NSF grant SES-02-40781.
文摘Recent experience has shown that interior-point methods using a log barrierapproach are far superior to classical simplex methods for computing solutions to large parametricquantile regression problems. In many large empirical applications, the design matrix has a verysparse structure. A typical example is the classical fixed-effect model for panel data where theparametric dimension of the model can be quite large, but the number of non-zero elements is quitesmall. Adopting recent developments in sparse linear algebra we introduce a modified version of theFrisch-Newton algorithm for quantile regression described in Portnoy and Koenker[28]. The newalgorithm substantially reduces the storage (memory) requirements and increases computational speed.The modified algorithm also facilitates the development of nonparametric quantile regressionmethods. The pseudo design matrices employed in nonparametric quantile regression smoothing areinherently sparse in both the fidelity and roughness penalty components. Exploiting the sparsestructure of these problems opens up a whole range of new possibilities for multivariate smoothingon large data sets via ANOVA-type decomposition and partial linear models.
文摘启发于过完备字典中稀疏线性组合的高分辨率图像的块与其对应的低分辨率局部块能很好地匹配,提出一种回归函数结合局部自相似的单帧图像超分辨率算法;该算法结合了实例图像块的学习和局部自相似图像块的学习,实例图像块的局部回归避免了从低分辨率到高分辨率图像块映射的病态性问题;通过局部自相似实例图像块学习获得非线性映射函数的一阶近似,从而获得低分辨率图像块相对应的高分辨率图像块,克服了实例图像块算法不足的问题;实验采用峰值信噪比(Peak Signal to Noise Ratio,PSNR)和均方误差(Root-mean-square error,RMSE)比较各算法效果;从实验结果数据可以看出,大多数情况下,提出的算法具有最高的峰值信噪比和最低的均方根误差,从实验结果图可以看出,提出的算法的纹理保留的最好,图像自然性最好,且运行时间也少于其他几种较新的算法,表明提出的算法更适合用于解决实际问题。
