Analysis of sub-anatomic diffusion tensor imaging indices in white matter regions of Alzheimer with MMSE score

In this study, an attempt has been made to find the correlation between diffusion tensor imaging (DTI) indices of white matter (WM) regions and mini mental state examination (MMSE) score of Alzheimer patients. Diffusion weighted images are obtained from the ADNI database. These are preprocessed for eddy current correction and removal of non-brain tissue. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (DA) indices are computed over significant regions (Fornix left, Splenium of corpus callosum left, Splenium of corpus callosum right, bilateral genu of the corpus callosum) affected by Alzheimer disease (AD) pathology. The correlation is computed between diffusion indices of the significant regions and MMSE score using linear fit technique so as to find the relation between clinical parameters and the image features. Binary classification has been employed using support vector machine, decision stumps and simple logistic classifiers on the extracted DTI indices along with MMSE score to classify Alzheimer patients from healthy controls. It is observed that distinct values of DTI indices exist for the range of MMSE score. However, there is no strong correlation (Pearson's correlation coefficient ‘r’ varies from 0.0383 to −0.1924) between the MMSE score and the diffusion indices over the significant regions. Further, the performance evaluation of classifiers shows 94% accuracy using SVM in differentiating AD and control. In isolation clinical and image features can be used for prescreening and diagnosis of AD but no sub anatomic region correlation exist between these features set. The discussion on the correlation of diffusion indices of WM with MMSE score is presented in this study.     

TD-SCDMA系统下行波束赋形技术研究

TD-SCDMA系统最重要的技术特征是智能天线技术,智能天线技术的核心是下行波束赋形。综合考虑了干扰和噪声对移动台的影响,模拟了在不同信道环境下的基于最小均方误差准则的理想波束赋形算法及其实现方法,并对算法进行了性能仿真,分析结论并说明系统性能的改善。     

MIMO-OFDM系统的低阶信道估计的改进

在慢衰落信道条件下,MMSE估计有较好的估计性能但复杂度较高。为了降低信道估计的运算复杂度,分析并总结了原有的MMSE信道估计算法以及其改进算法,提出一个基于MIMO-OFDM系统的OLR-MMSE信道估计算法。OLR-MMSE估计是结合了MMSE算法的低秩近似以及SVD分解,大大降低了大矩阵的阶数,有效地降低运算复杂度并可以保持良好的估计性能。通过计算机模拟仿真验证了提出的信道估计可以达到系统性能和复杂度之间较好的权衡。     

Modelling and joint monitoring of input and output of systems with arbitrary order autoregressive disturbance

Considerable studies have been done on modelling and joint monitoring of input and output of systems with autoregressive moving average (ARMA) disturbance. Most of these studies focus on systems with ARMA (1, 1) disturbance. However, many kinds of systems are not conform to ARMA(1, 1) disturbance. Motivated by the fact that an autoregressive (AR) model with high order can be implemented to approximate the stationary ARMA model at any precision, a new generic model and a joint monitoring scheme of systems with arbitrary order AR(p) disturbance are developed. A minimum mean squared error (MMSE) controller with arbitrary order AR disturbance is designed to reduce the system variability. The mathematical expectation and average run length of MMSE-controlled outputs are derived. A new joint chart for monitoring the input and output simultaneously is explored. Two out-of-control rules for the joint monitoring chart are developed. The monitoring performances of the input chart, the output chart and the joint monitoring chart are also discussed. The results of simulation experiments and case studies validate the effectiveness of the developed model and joint monitoring chart.     

多中继通信系统中量化误差补偿的MMSE中继预编码方案

为了补偿量化误差造成的系统性能的降级,文中提出将Grassmannian码本量化误差的统计特性引入多中继系统有限反馈中继预编码的设计,首先在QoS要求下的完全反馈中继预编码与接收均衡MMSE两步优化算法的基础上,给出了有限反馈下量化误差补偿的中继预编码与接收均衡MMSE两步优化方案,进而文中在中继和功率约束下,提出了一种有限反馈下量化误差补偿的中继预编码与接收均衡MMSE联合优化方案,并给出了中继预编码矩阵设计的闭合表达式.计算机仿真表明,文中提出的中继和功率约束下量化误差补偿的MMSE联合优化方案的系统性能优于QoS要求下量化误差补偿的MMSE两步优化方案,而且相比没有采用量化误差补偿的MMSE联合优化方案可以获得更低的误码率性能.     

一种基于最小均方误差原理的位同步捕捉算法

提出一种数字通信系统中基于最小均方误差原理的位同步捕捉算法,对算法的提出、原理以及性能进行了理论分析,同时在加性高斯白噪声信道下进行了大量的计算机仿真,给出了在某无线通信系统中的应用结果.此算法通用性强,实现位同步捕捉速度快,易于高速单片机及DSP实现,具有良好的抗噪性能.     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

PFED:一种基于预测的公平的主动队列管理算法

对多个著名的主动队列管理算法进行了深入的理论分析和实验比较，对它们的优点和不足进行了总结，并在此基础上提出了一种新的主动队列管理算法PFED（prediction-based fair early drop）．PFED的主要目标是：①通过对流量较为精确的预测，结合对分组丢弃概率更为合理的计算，将队列长度的变化稳定在一个理想的水平；②对非响应流实施有效的惩罚，提高算法的公平性；③通过合理的分组丢弃将队列（分组）的到达速率控制在链路的服务速率之下．仿真实验表明，PFED很好地实现了上述3个目标．