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.     

开区间连续函数的最大(小)值

本文将微积分中闭区间连续函数最大(小)定理推广到开区间内连续函数,在一定条件下证明了在开区间内连续函数最大(小)值的存在性,并在此基础上给出了一个求开区间内连续函数最大(小)值的一般方法.     

四面山植物园的植物引种配置研究

根据前人调查数据，结合野外实地调查，初步确定了754种四面山植物园的引种植物，并在调查植物园引种地立地条件和引种植物配置的观赏性、系统性和生态性之间关系的基础上，确定了引种植物的配置原则，并据此原则绘制出了四面山植物园引种植物配置图．     

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。     

关于测量值和测量误差的讨论

从实验数据处理的角度，利用概率、数理统计理论，深入地讨论了物理实验中的测量值、测量误差的基本概念以及表达式的科学。     

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。     

周期函数的导函数的周期问题

本文将证明：在整个实轴上可微分的周期函数与其导函数的周期相同这一推测．