该文描述带有矩量序列{v_m}_0~∞■C^(q×q)的完全不确定Hamburger矩阵矩量问题:v_m=integral from n=-∞to∞x^m dρ(x),m=0,1,…的有限阶解,即该问题的那些解ρ,使得C^(q×q)-值多项式的线性空间P在对应的空间L^2(R,dρ/E(x)...该文描述带有矩量序列{v_m}_0~∞■C^(q×q)的完全不确定Hamburger矩阵矩量问题:v_m=integral from n=-∞to∞x^m dρ(x),m=0,1,…的有限阶解,即该问题的那些解ρ,使得C^(q×q)-值多项式的线性空间P在对应的空间L^2(R,dρ/E(x))内稠密,这里E(x)为在实轴R上取正值的某个数值多项式.作为预备知识,作者考虑所谓广义Akhiezer插值的矩阵变种与它的相关矩阵矩量问题之间的一种关系.展开更多
语音识别技术中说话人快速自适应技术受到普遍关注。最大似然模型插值 (maxim um likelihood model inter-polation,ML MI)算法是一种有效的快速自适应算法 ,它的主要缺点是需要存储大量的特定人模型。为克服这一缺点 ,该文提出一种改...语音识别技术中说话人快速自适应技术受到普遍关注。最大似然模型插值 (maxim um likelihood model inter-polation,ML MI)算法是一种有效的快速自适应算法 ,它的主要缺点是需要存储大量的特定人模型。为克服这一缺点 ,该文提出一种改进方法——矩阵线性插值自适应算法。该算法用表示说话人特性的矩阵代替 ML MI中的特定人模型进行线性插值。而插值系数由测试者提供的语音数据按照最大似然准则确定。插值后的线性矩阵与非特定人模型相作用得到最终的说话人自适应模型。该算法大大减少了计算存储量 ,且自适应性能基本与 ML展开更多
In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures...In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.展开更多
文摘该文描述带有矩量序列{v_m}_0~∞■C^(q×q)的完全不确定Hamburger矩阵矩量问题:v_m=integral from n=-∞to∞x^m dρ(x),m=0,1,…的有限阶解,即该问题的那些解ρ,使得C^(q×q)-值多项式的线性空间P在对应的空间L^2(R,dρ/E(x))内稠密,这里E(x)为在实轴R上取正值的某个数值多项式.作为预备知识,作者考虑所谓广义Akhiezer插值的矩阵变种与它的相关矩阵矩量问题之间的一种关系.
文摘语音识别技术中说话人快速自适应技术受到普遍关注。最大似然模型插值 (maxim um likelihood model inter-polation,ML MI)算法是一种有效的快速自适应算法 ,它的主要缺点是需要存储大量的特定人模型。为克服这一缺点 ,该文提出一种改进方法——矩阵线性插值自适应算法。该算法用表示说话人特性的矩阵代替 ML MI中的特定人模型进行线性插值。而插值系数由测试者提供的语音数据按照最大似然准则确定。插值后的线性矩阵与非特定人模型相作用得到最终的说话人自适应模型。该算法大大减少了计算存储量 ,且自适应性能基本与 ML
基金Supported by the Youth Fund for Science and Technology Research of Institution of Higher Education in Hebei Province in 2016(QN2016243)~~
文摘In order to investigate the restoration of low resolution images, the linear and nonlinear interpolation methods were applied for the interpolation of the com- mon information matrix obtained from a series of pictures, getting the restructuring matrix. The characteristic block with the best restoration effect was determined by analyzing the pixel difference of the common information of each image at the same position. Then the characteristic blocks and their original blocks were used to build and train neural network. Finally, images were restored by the neural network and the differences between pictures were reduced. Experimental results showed that this method could significantly improve the efficiency and precision of algorithm.
