Optimal bandwidth matrices in functional principal component analysis of density functions

In order to explore and compare a finite number T of data sets by applying functional principal component analysis (FPCA) to the T associated probability density functions, we estimate these density functions by using the multivariate kernel method. The data set sizes being fixed, we study the behaviour of this FPCA under the assumption that all the bandwidth matrices used in the estimation of densities are proportional to a common parameter h and proportional to either the variance matrices or the identity matrix. In this context, we propose a selection criterion of the parameter h which depends only on the data and the FPCA method. Then, on simulated examples, we compare the quality of approximation of the FPCA when the bandwidth matrices are selected using either the previous criterion or two other classical bandwidth selection methods, that is, a plug-in or a cross-validation method.     

论正规矩阵的充要条件

由矩阵对角化、矩阵分解、谱分解、矩阵实部和虚部及特征向量等方面论证了正规矩阵的充要条件 .     

关于矩阵乘积特征值估计的一点注记

由矩阵奇异值的表示定理,给出了一个关于矩阵乘积的特征值的估计,进而说明[1]、[2]的结果都是没有意义的。     

改进的基于听觉掩蔽特性的语音增强

该文利用人耳感知的掩蔽特性,研究了一种改进的基于人耳听觉掩蔽特性的语音增强方法。该方法改进了对含噪语音中噪声的估计,即在估计噪声时,先在帧上判断语音的有无,然后在频率点上判断语音的有无,最后再从感知的角度,利用估计出的语音信号计算掩蔽阈值,以此在时域和频域上合理地调整增强系数。仿真结果表明,该方法不仅使信噪比有较大的提高,而且减少了语音失真,同时也很好地抑制了背景噪声和残余音乐噪声。     

Local Whittle likelihood approach for generalized divergence

There are many approaches in the estimation of spectral density. With regard to parametric approaches, different divergences are proposed in fitting a certain parametric family of spectral densities. Moreover, nonparametric approaches are also quite common considering the situation when we cannot specify the model of process. In this paper, we develop a local Whittle likelihood approach based on a general score function, with some special cases of which, the approach applies to more applications. This paper highlights the effective asymptotics of our general local Whittle estimator, and presents a comparison with other estimators. Additionally, for a special case, we construct the one-step ahead predictor based on the form of the score function. Subsequently, we show that it has a smaller prediction error than the classical exponentially weighted linear predictor. The provided numerical studies show some interesting features of our local Whittle estimator.     

FAAS法对矿石中的铜的测定

用浓盐酸、浓硝酸溶解试样后,在WFX2B2型原子吸收光谱仪上采用铜的灵敏线249.2nm和次灵敏线327.4nm作为分析线,对铜矿石试样进行了测定,实验证明:在0.2mol/L的盐酸溶液中,以FAAS积分法测定铜元素,其它金属离子不干扰铜元素的测定,与碘量法相对比,方法具有快速、简便、准确的优点。变异系数小于百分之一。     

有界凸体中一类临界方程的离散纵标逼近

在C空间研究有界凸体迁移系统中一类单能、各向同性、非均匀介质的临界方程，使用Banach空间上的拟总体列紧算子理论，证明了近似计算临界尺寸及其相应的非负本征函数的离散纵标方法的收敛性．     

On consistent testing for serial correlation in seasonal time series models

The author considers serial correlation testing in seasonal time series models. He proposes a test statistic based on a spectral approach. Many tests of this type rely on kernel-based spectral density estimators that assign larger weights to low order lags than to high ones. Under seasonality, however, large autocorrelations may occur at seasonal lags that classical kernel estimators cannot take into account. The author thus proposes a test statistic that relies on the spectral density estimator of Shin (2004), whose weighting scheme is more adapted to this context. The distribution of his test statistic is derived under the null hypothesis and he studies its behaviour under fixed and local alternatives. He establishes the consistency of the test under a general fixed alternative. He also makes recommendations for the choice of the smoothing parameters. His simulation results suggest that his test is more powerful against seasonality than alternative procedures based on classical weighting schemes. He illustrates his procedure with monthly statistics on employment among young Americans.     

一种自适应跳频中的干扰检测算法

为提高跳频通信系统的可靠性和稳定性,根据线性数字调制信号的特点,对信号进行非线性变换,应用变换后信号的功率谱密度中所包含的信号特征信息,提出了一种适合于自适应跳频的干扰检测算法。该算法基于贝叶斯决策理论,属于盲信干(信噪)比检测。仿真结果表明,该算法满足跳频通信系统的要求。     

求解线性互补问题的AOR算法

建立了求解线性互补问题的加速松驰迭代算法，并在一定条件下，证明了新算法的收敛性。