基于小波包变换与前向线性预测滤波的光纤陀螺去噪算法

为了减小环境噪声对光纤陀螺输出的影响,提出了一种新的基于小波包变换(WPT)和前向线性预测滤波算法(FLP)的去噪方法--WPT-FLP算法.首先介绍了小波包分解与FLP算法的原理,并对2种算法进行了融合,利用小波包变换进行信号的分解与重构,在此过程中对小波包分解后的高频系数进行强制去噪,对小波包分解后的低频系数进行F...     

小波包变换叠前地震资料去噪方法研究

给出了一种不同常规的消除面波和高频随机干扰波的小波包变换方法。小波包变换是一种新的时频分析方法，优于小波变换和传统的Fourier变换，它能够对地震资料进行时频精细划分，准确描述具有相同频率的有效波与干扰波在时间—空间域的分布。利用小波包变换可以从叠前资料分离出面波、随机干扰等，再通过小波包重构，可有效地剔除干扰信息，保留有效信号。实际资料实验表明，小波包变换在去噪方面十分有效。     

ReliefF算法在雷达辐射源信号识别中的应用

采用小波包变换提取雷达辐射源信号特征能够有效对信号进行识别,然而,由小波包变换提取的信号特征维数高,部分信号特征受噪声污染严重.基于此,采用ReliefF算法对信号特征的分类能力进行评价,选择出小波包中分类能力强的信号特征,再通过特征相关度算法去除分类能力相近的冗余特征,利用剩余的分类能力强的信号特征组成特征向量进行分类.仿真实验结果显示,该方法用较少的信号特征能够获得较高的正确识别率.     

Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Mental fatigue is an extremely sophisticated phenomenon, which is influenced by the environment, the state of health, vitality and the capability of recovery. A single parameter cannot fully describe it. In this paper, the effects of long time sustained low-workload visual display terminal （VDT） task on psychology are investigated by subjective self-reporting measures. Then power spectral indices of HRV, the P300 components based on visual oddball and wavelet packet parameters of EEG are combined to analyze the impacts of prolonged visual display terminal （VDT） activity on autonomic nervous system and central nervous system. Finally, wavelet packet parameters of EEG are extracted as the features of brain activity in different mental fatigue states. Kernel principal component analysis （KPCA） and support vector machine （SVM） are jointly applied to differentiate two states. The statistic results show that the level of both subjective sleepiness and fatigue increase significantly from pre-task to post-task, which indicate that the long time VDT task induces the mental fatigue to the subjects. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. The P300 components and wavelet packet parameters of EEG are strongly related with mental fatigue. Moreover, the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve a high recognition accuracy （87%） of mental fatigue state. Multipsychophysiological measures and KPCA-SVM method could be a promising tool for the evaluation of mental fatigue.     

基于验证包标记策略实现DDoS攻击源追踪

包标记方案作为追踪DoS(DDoS)攻击源的最有前景的技术,有了很多的实现方法,但都有着一些大大小小的缺陷。文中提出了一种新的技术方案,在概率包标记方案自身的安全性能方面有了很大的改进。     

QoS Support Polling Scheme for Multimedia Traffic in Wireless LAN MAC Protocol

Quality of service （QoS） support is a key attribute for multimedia traffic including video, voice, and data in wireless local area networks （LANs） but is limited in 802.11-based wireless LANs. A polling-based scheme called the point coordination function （PCF） was developed for 802.11 LANs to support the transmission of multimedia traffic. However, the PCF is not able to meet the desired practical traffic differentiation requirements for real-time data. This paper describes a QoS support polling scheme based on the IEEE 802.11 medium access control （MAC） protocol. The scheme uses a two-level polling mechanism with the QoS classes differentiated by two different access policies. Stations with higher priority traffic such as key or real-time data form the first level and can access the common channel through an exhaustive access policy. Other stations with lower priority traffic form the second level and can access the channel through a gated access policy. A system model based on imbedded Markov chain theory and a generation function were setup to explicitly analyze the mean information packet waiting time of the two-level polling scheme. Theoretical and simulation results show that the new scheme efficiently differentiates services to guarantee better QoS and system stability.     

NS2扩展突发性业务流:设计与实现

结合Markov调制泊松过程(MMPP)和混合泊松流,为突发性分组业务设计了Gamma调制泊松过程(GMPP)业务流模型.面向开源仿真器NS2的功能扩展,给出了纯泊松流、MMPP流和GMPP流的NS2扩展设计和实现方案,并经仿真实验进行了验证.分析表明GMPP更适用于实际业务流.     

基于WPD-PSO算法的整流电路故障诊断

针对电力电子电路故障类型多、诊断正确率低的问题，提出基于小波包分解和粒子群算法优化概率神经网络的方法。建立三相桥式全控整流电路仿真模型，利用小波包分解技术对故障电压信号进行三层小波包分解与重构，提取特征值，并对数据进行归一化处理；用粒子群算法优化概率神经网络寻找合适的平滑因子，对数据进行训练和诊断；将该方法与未优化的概率神经网络作对比。仿真结果表明，该方法在训练效果和诊断正确率上都要优于未优化的概率神经网络。