Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of electroencephalographic changes. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of electroencephalogram (EEG) signals were classified by five ANFIS classifiers. To improve diagnostic accuracy, the sixth ANFIS classifier (combining ANFIS) was trained using the outputs of the five ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the EEG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the EEG signals.     

基于TMS320F2812的高速数据采集处理系统

介绍一种基于TMS320F2812的多路数据采集系统的设计方案。采用DSP芯片TMS320F2812实现对多路数据的实时采集处理。通过USB2．0将数据传到上位机,由上位机对数据进行具体分析并将结果反馈给DSP进行控制。该系统具有结构简单、可靠性高、使用灵活和性价比高等优点。     

Conditions for local almost sure asymptotic stability

In this paper, we investigate local asymptotic stability ensured by the addition of Gaussian white noise into dynamical systems. There are different stability notions for stochastic systems, such as asymptotic stability in probability (ASiP) and uniform almost sure asymptotic stability (UASAS). The local ASiP property is incapable of ensuring that sample paths converge to the origin with probability one, whereas the local UASAS property is capable of it. However, in general, the local UASAS property requires tight conditions. Here, we provide our notion of local almost sure asymptotic stability (local ASAS) to relax the conditions with both almost sure convergence of sample paths to the origin and the existence of bounded (weak) invariant sets. We find that the addition of Gaussian white noise always prevents the origin from being locally UASAS as long as we consider smooth Lyapunov functions; however, it is possible to make the origin locally ASAS. The result is confirmed by a simple example of elimination of unstable equilibria by deliberately adding Gaussian white noise.     

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.     

基于互信息的颅脑MR影像序列的三维配准

医学图像配准具有重要的临床应用价值,并一直是医学图像处理领域的热点研究问题。基于互信息的配准方法由于自动化程度高和配准精度高的优点而被广泛应用于三维医学图像配准;但是,也存在着数据量大、计算速度慢的问题。采用归一化互信息测度,并将一种新的正交优化技术应用于颅脑MR影像序列的三维配准,旨在缩短处理时间。将该方法与使用传统优化算法的配准方法作了比较,实验结果表明,提出的方法能够显著提高配准速度,且不降低配准精度。     

Robust stability and H-infinity control for uncertain discrete-time Markovian jump singular systems

The robust stability and stabilization, and H-infinity control problems for discrete-time Markovian jump singular systems with parameter uncertainties are discussed. Based on the restricted system equivalent （r.s.e.） transformation and by introducing new state vectors, the singular system is transformed into a discrete-time Markovian jump standard linear system, and the linear matrix inequality （LMI） conditions for the discrete-time Markovian jump singular systems to be regular, causal, stochastically stable, and stochastically stable with 7- disturbance attenuation are obtained, respectively. With these conditions, the robust state feedback stochastic stabilization problem and H-infinity control problem are solved, and the LMI conditions are obtained. A numerical example illustrates the effectiveness of the method given in the oaoer.     

电力系统结线分析的有色Petri网方法

从离散事件动态系统角度考虑电力系统结线分析问题,提出了基本分析单元的有色Petri网模型以及基于该Petri网的结线分析算法,Petri网中的令牌代表结线分析中的结点,令牌的颜色表示连接在同一个结点上的元件,变迁将包含相同元件的结点连接在一起,论文对基于Petri网的结线分析算法及传统的算法进行了比较,并计算了一个模型系统各变电站的结点及网络的结线,表明了该方法的可行性及有效性。     

基于FPGA的AVS视频解码芯片验证平台设计

针对AVS视频解码芯片仿真和验证的要求,提出了基于FPGA的验证平台框架。该验证平台主要用于对AVS解码芯片进行硬件模块的验证,从而为整个视频解码芯片的开发提供可靠的依据。该平台基于Nios II软核处理器,可使软件模块和硬件模块在一个平台下真正实现软硬件协同工作。基于该平台实现了多个硬件模块和AVS视频解码芯片的验证,其结果证明了该验证平台的正确性和可靠性。     

南四湖水体富营养化时空比较分析与评价

南四湖是南水北调东线工程重要的输水通道和调蓄湖泊之一,随着通水时间的临近,其水质安全和水体富营养化状况备受关注．根据2006、2007、2010和2011年4次32个采样点的南四湖水质空间分布监测数据,采用综合营养状态指数法进行评价．结果表明,4个监测年枯水期南四湖水体总体平均处于富营养轻度水平,在南四湖水质得到大幅度改善的情况下,各年营养水平变化不大;南阳湖和独山湖水体的综合营养状态指数出现下降趋势,而昭阳湖（下）和微山湖岛北水体的综合营养状态指数出现上升趋势,这与全湖总磷出现均一化趋势有关．2010-2011年除微山湖岛南水体处于中营养水平外,其他湖区水体均处于富营养轻度水平．对南四湖水质空间分布监测数据的分析表明,南四湖藻类水华的繁殖生长主要受水体中TP的制约．