GPS接收机P(Y)直捕方法研究

GPS接收机P(Y)直捕算法是基于存储的滑动相关搜索,并利用FFT将时频二维联合搜索变成时域一维搜索。同时,考虑位同步点已知的条件,接收资源池只需存储一段接收序列,缩短了捕获时间且利于硬件实现。仿真结果表明,多普勒频移为±5kHz时,所带来的峰值衰减为2dB左右,本地信号与接收信号对齐时出现明显的相关峰,很好地实现了捕获功能;提出了用重叠分段补零FFT操作实现并行相关,成倍降低了捕获时间;提出了利用位同步信息,避免了码极性翻转可能引起的信噪比损耗;针对码相位漂移,提出了优化捕获策略,可在短时间内完成重捕。     

Deconvolution with supersmooth distributions

Nonparametric deconvolution problems require one to recover an unknown density when the data are contaminated with errors. Optimal global rates of convergence are found under the weighted L_p-loss (1 ≤ p ≤ ∞). It appears that the optimal rates of convergence are extremely low for supersmooth error distributions. To resolve this difficulty, we examine how high the noise level can be for deconvolution to be feasible, and for the deconvolution estimate to be as good as the ordinary density estimate. It is shown that if the noise level is not too high, nonparametric Gaussian deconvolution can still be practical. Several simulation studies are also presented.     

基于分数本征特征的手写数字识别

特征提取是手写体数字识别研究中的重要问题,有效、稳定的特征是提高识别率和识别精度的关键。该文提出了一种基于分数本征特征和核非线性分类器的手写数字识别方法,首先找到时频平面的一个轴进行分数傅里叶变换,使不同类别样本在这个轴上最大限度地分开,然后用主元分析进行降维,得到比较稳健的低维特征,再将常用分类器用于特征分类,实现对手写数字的识别。对实际数据进行实验,结果表明上述本征特征与核非线性分类器相结合有较高的识别率和训练、分类效率。     

L~1空间上的Fourier乘子判定定理

本文利用Torchinsky分解和一种全新的二进制分解得到一个极为有用的L1空间上的Fourier乘子的判定定理.该定理较之经典的判定定理适用于更为广泛的一类乘子.     

复调和函数的混合边值问题

利用Ｆｏｕｒｉｅｒ方法得到复调和函数的混合边值问题解的表示式及解的存在性定理并讨论了解的唯一性．     

A second order model for cylindrical data

Modeling cylindrical data, comprised of a linear component and a directional component, can be done using Fourier series expansions if we consider the conditional distribution of the linear component given the angular component. This paper presents the second order model which is a natural extension of the Mardia and Sutton (1978) first order model. This model can be parameterized either in polar or Cartesian coordinates, and allows for parameter estimation using standard multiple linear regression. Characteristic of the new model, how to compare the adequacy of the fit for first and second order models, and an example involving wind direction and temperature are presented.     

快速傅立叶变换在数字信号处理器上的实现研究

结合数字信号处理器的性能特点,对基2、基4、分裂基和Bruun FFT等快速傅立叶变换算法及其在TMS320C30上的实现进行了研究,开发出高效的FFT算法和程序。     

相位舍位对DDS谱分布的影响

采用严格的信号分析方法，运用离散傅里叶变换（ＤＦＴ）和傅里叶变换（ＦＴ）详细推导了理想状态和相位舍位条件下直接数字频率合成器（ＤＤＳ）的频谱分布规律。所得到的理论推算结果与目前公认的结果一致，这对实际的ＤＤＳ系统设计有着极大的参考价值     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.     

Reliability of global sensitivity indices

Uncertainty and sensitivity analysis is an essential ingredient of model development and applications. For many uncertainty and sensitivity analysis techniques, sensitivity indices are calculated based on a relatively large sample to measure the importance of parameters in their contributions to uncertainties in model outputs. To statistically compare their importance, it is necessary that uncertainty and sensitivity analysis techniques provide standard errors of estimated sensitivity indices. In this paper, a delta method is used to analytically approximate standard errors of estimated sensitivity indices for a popular sensitivity analysis method, the Fourier amplitude sensitivity test (FAST). Standard errors estimated based on the delta method were compared with those estimated based on 20 sample replicates. We found that the delta method can provide a good approximation for the standard errors of both first-order and higher-order sensitivity indices. Finally, based on the standard error approximation, we also proposed a method to determine a minimum sample size to achieve the desired estimation precision for a specified sensitivity index. The standard error estimation method presented in this paper can make the FAST analysis computationally much more efficient for complex models.