Concatenation of space‐time block codes and turbo trellis coded modulation (ST‐TTCM) over Rician fading channels with imperfect phase

In this paper, the performance of space time‐turbo trellis coded modulation (ST‐TTCM) is evaluated over Rician and Rayleigh fading channels with imperfect phase. We modify Baum–Welch (BW) algorithm to estimate the fading and phase jitter parameters for multi‐antenna configurations. Thus, we assume that the channel parameters change slower than carrier frequency. We know that, at high data rate transmissions over wireless fading channels, space–time block codes (STBC) provide the maximal possible diversity advantage. Here, the combined effects of the amplitude and the phase of the received signal are considered, each one modelled by Rician and Tikhonov distributions, respectively. We investigate space time‐turbo trellis coded modulation (ST‐TTCM) for 8‐PSK for several Rician factor K and phase distortion factor η. Thus, our results reflect the degradations both due to the effects of the fading on the amplitude and phase noise of the received signal while the channel parameters are estimated by BW algorithm. Copyright © 2004 John Wiley & Sons, Ltd.     

周期图法功率谱估计中的窗函数分析

在经典谱估计中，由Welch提出的修正周期图法，由于其简单，易于理解，便于计算等优点，被广泛应用。本文运用Matlab工具，对修正周期图法功率谱估计进行了仿真与性能分析，并重点分析了窗函数对谱估计的影响，指出了他的优缺点。     

改进周期图法功率谱估计中的窗函数仿真分析

周期图法是功率谱估计的一种基本方法,但该方法不满足一致性估计条件,谱估计的分辨力和方差都很难满足实际应用需要.因此采用其改进方法(即Welch法)估计信号功率谱,并结合该方法对四种典型窗函数的谱估计性能进行了具体的仿真,就其中的频率分辨率、采样信号的数据长度与窗函数的特性等因素之间的关系,进行了详细的讨论,重点分析了窗函数对谱估计的影响,指出了它们的优缺点,得出了不同数据类型与估计方式下的功率谱标准方差的均值.最后,根据实验结果并结合窗函数的评价指标,指出了选取合适的窗函数应注意的一般原则,具有一定的实际指导意义.     

对经典隐马尔可夫模型的经验性改进

文章分析了经典隐马尔可夫模型（Hidden Markov Model,HMM）齐次假设的理论缺陷,以及两种非齐次HMM。语音识别对比实验表明,经验性的惩罚概率法是稳健的、且更有效的补偿方法。实验结果还指出在最优惩罚概率下,经典HMM达到了与非齐次的基于段长分布的HMM（Duration Distribution Based HMM,DDBHMM）几乎相同的识别率,证明了齐次假设并不影响经典HMM在实用中的重要性。文章提出了一种改进Baum－Welch重估算法的初值的经验方法,用于HMM参数的估计,在汉语连续语音识别实验中一致性地降低了音节误识率。     

Robust and adaptive tests for the two-sample location problem

In the two-sample location problem the application of thet-test depends on very restrictive assumptions such as normality and equal variances of the two random variablesX andY. If the assumptions of thet-test are not satisfied it is more appropriate to apply a robust version of thet-test, like the Welch test or the trimmedt-test, or a nonparametric test, like the Wilcoxon. But usually we have no information about the underlying distribution of the data. Therefore, an adaptive test should be applied. Some of these tests are discussed and compared with each other and with the classicalt-test under different models of nonnormality and for unequal variances. It is shown that an adaptive test behaves well over a broad class of distribution functions.     

A parametric bootstrap approach for ANOVA with unequal variances: Fixed and random models

This article is about testing the equality of several normal means when the variances are unknown and arbitrary, i.e., the set up of the one-way ANOVA. Even though several tests are available in the literature, none of them perform well in terms of Type I error probability under various sample size and parameter combinations. In fact, Type I errors can be highly inflated for some of the commonly used tests; a serious issue that appears to have been overlooked. We propose a parametric bootstrap (PB) approach and compare it with three existing location-scale invariant tests—the Welch test, the James test and the generalized F (GF) test. The Type I error rates and powers of the tests are evaluated using Monte Carlo simulation. Our studies show that the PB test is the best among the four tests with respect to Type I error rates. The PB test performs very satisfactorily even for small samples while the Welch test and the GF test exhibit poor Type I error properties when the sample sizes are small and/or the number of means to be compared is moderate to large. The James test performs better than the Welch test and the GF test. It is also noted that the same tests can be used to test the significance of the random effect variance component in a one-way random model under unequal error variances. Such models are widely used to analyze data from inter-laboratory studies. The methods are illustrated using some examples.     

A comparison between different windows in spectral and cross spectral analysis techniques with Kalman filtering for estimating power quality indices

Fourier based methods are still popular for calculating power quality indices (PQIs) in the technical community such as IEC and IEEE. They are robust towards noise and can be calculated efficiently using the fast Fourier transform (FFT) method. IEEE Std 1459-2010 provides formulations for calculating important PQI for single and three phase systems. The aim of this paper is to present the theory for estimating PQIs using the Welch spectral and cross spectral analysis techniques, and show experimentally the accuracy of the proposed methods when calculating the PQIs. Further it provides a comparison on the windowing techniques (WTs) that accompany the PQI estimations when using FFT based methods. This information has not been documented extensively in the technical literature when trying to calculate the PQI as specified by IEEE Std 1459-2010. Further we present a virtual instrument (VI) with a Kalman filter (KF) to estimate the PQIs. The KF is used to reduce the variance in measurements for the PQI estimation. We also compare experimentally the accuracy of the VI without the KF with the VI using the KF. Specifically we propose good and bad WTs to estimate such PQIs.     

Evaluation of windowed ESPRIT virtual instrument for estimating Power Quality Indices

The use of subspace and Matrix Algebra (MA) methods for estimating Power Quality Indices (PQIs) as defined by IEEE Std 1459-2010 is relatively new. For this reason there is room for further research about these methods and their efficiency applied to estimating PQIs. This document presents the design of a Virtual Instrument (VI) based on modern subspace, MA and Least Square (LS) techniques with a Kalman Filter (KF) for estimating PQIs. Further we claim that the efficiency of the subspace method is comparable to the Fourier based methods in typical power system analysis. Simulation and experiments are conducted to further compare the performance of this VI with other VI's which do not use the KF. We also compare this VI with VI's that use the classical Fast Fourier Transform (FFT) based method of estimation.     

基于混合二阶隐马尔可夫模型的基因结构预测

在基因预测时,待研究的生物序列中的残基(氨基酸或核甘酸)具有高度的相关性,因此,观测噪声和马尔可夫链不相互独立的条件下的混合隐马尔可夫模型比普通隐马尔可夫模型更适用于基因预测功能.介绍了一种基于混合二阶隐马尔可夫模型(HMM2)的基因识别系统,该系统的预测精度在在核苷酸和外显子水平上均与国外同类研究水平相当.