一种基于段的基音检测算法

作为语音信号处理领域一项基本、关键的技术,基音检测在语音信号处理中扮演着重要的角色,一直是语音信号处理的一个研究热点.首先对传统的基于短时自相关函数法的基音检测进行了研究;在此基础上提出了一种能同时检测一段语音信号基音周期的方法,有效地克服了传统基音检测算法只能检测一帧语音信号基音的缺点.进行了实验仿真,结果表明通过去野点,中值平滑等后处理的基于段的基音检测算法比传统的基于帧的基音检测算法具有更好的抗噪性.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.     

亚纯函数与其导数的公共Julia方向

本文研究不指定级的亚纯函数与其导函数的公共Julia方向的存在性问题。     

关于不确定性本—量—利分析的理论探讨

根据市场经济条件下，成本、产销量及利润不确定的特点，讨论了具有现实意义的不确定性本－量－利决策模型的分类和一般分析，并建立了多品种，多不确定因素本－量－利决策模型。     

SrFCl:Eu~(2+)晶体的电子自旋共振谱研究

用程序降温法生长了SrFCl:Eu~(2+)晶体,并测量了它的电子自旋共振谱。应用最小二乘法拟合技术对g因子及自旋哈密顿参数进行了精确拟合。报道了各向异性的g因子及有关自旋哈密顿参量。发现并归属了Eu~(2+)的高阶禁阻跃迁。     

多参数一致椭圆扩散的一个比较定理及其应用

本文主要讨论多参数一致椭圆扩散的若干样本轨道性质。我们首先建立了多参数一致椭圓扩散与多参数布朗运动之间的一个比较定理,在此基础上进一步得到了多参数一致椭圆扩散象集的Hausdorff维数与Packing维数的上界,以及样本轨道连续模的上界估计。为了记号上的简单,这里只叙述两参数的情形,所有结果均可推广到N参数(N>2)的情形。     

特征结构提取的高阶Hopfield神经网络实现

本文首先建立了特征结构提取问题的罚函数表示，通过对罚函数求极小可以求得原始协方差矩阵的主特征向量及其对应的特征值。为了求得其他特征结构，特构造了一个协方差矩阵序列。如果将罚函数展开并进行整理，高阶Ｈｏｐｆｉｅｌｄ神经网络可被引入到特征结构提取中。这种方法比较直观，它将网络稳定时的输出与所求协方差矩阵的主特征向量的各个分量相对应，而网络稳定时的能量则对应于协方差矩阵的迹与所求特征值之差，计算机仿真结果验证了这种方法的正确性。