高阶重采样滤波器的多项式近似实现

可变载频带限信号的重采样,一般归结为按转换比P/Q(P为内插比,Q为抽取比)对原采样序列做内插和抽取。当P值很大时,需要多路内插滤波器,由于抗镜像的需要,滤波器系数矩阵非常庞大,使得高阶重采样难以实现。该文提出一种多项式近似滤波器的方法,用一组低阶多项式近似内插滤波器系数矩阵,简化了滤波器的结构,运算效率高,且内插延迟可任意改变。计算机仿真的结果表明:该结构适用于可变延迟的高阶带限内插滤波器。在一定条件下,误差在容许的范围之内。     

光线投射算法中重采样的设计和实现

体绘制技术在医学成像和科学可视化领域有着极为广泛的应用，但由于其巨大的计算开销，限制了其实时动态体绘制的应用，因此许多研究人员致力于静态体绘制加速算法的研究，为了提高体绘制速度。分析了三维规则数据场重采样的原理。光线投射算法中对3D数据场重采样的实现方法；根据具体重建对象，提出了在3D数据场重采样中采用球形包围盒的方法，给出了人体头部和眼球的三维可视化结果，实验表明：这种算法能有效地减少重采样的计算量，并使求交计算更加简单。     

不平衡类数据挖掘研究综述

综述了近年来国内外对不平衡类数据挖掘的主要研究进展。首先分析了不平衡类数据挖掘的本质。其次,详细探讨了处理不平衡类数据挖掘的各种技术,并根据其本质区别,从数据层次和算法层次分别对目前存在的各种技术方法进行了深入剖析和全面比较。最后,指出当前不平衡类数据挖掘研究的热点以及将来需要重点关注的主要问题。     

聚类融合方法综述

在分类算法和回归模型中,正广泛而且成功地使用着融合方法,该方法能克服分类、回归中的不稳定性,并给出较好的结果。在非监督机器学习领域,由于缺乏数据集的先验知识,所以分类和回归中的融合方法就不能直接用于聚类算法,这导致了该领域中对融合方法研究的起步较晚;近几年的研究和实验表明,聚类融合方法能很好地提高聚类算法的鲁棒性和稳定性。对近年来聚类融合的方法进行了综述,阐述了近年来对聚类融合方法进行研究的主要内容与特点,并讨论了聚类融合方法的研究方向。     

基于重采样的混沌时间序列相空间重构研究

相空间重构是混沌时间序列分析中的重要一步,本文在总的观测时间确定的情况下,研究了重采样对混沌时间序列相空间重构的影响,提出了一种确定重采样间隔的双对数图法。模型实验及初步的理论证明表明,通过选择合适的重采样间隔进行相空间重构,重构相空间的基本结构和形状不会改变,且保持了原相空间的各种重要特性。此外带来的另一优点是计算复杂度的大大降低,这为应用较短的时间序列进行相空间重构以及计算各种不变量提供了一种可行有效的方法。     

CQ2RS: a new statistical approach to the response surface method for reliability analysis

Assessing the reliability of a complex structure requires a deal between reliability algorithms and numerical methods used to model the mechanical behavior. The Response Surface Method (RSM) represents a convenient way to achieve this purpose. The interest of such a method is that the user is allowed to choose and check the computed mechanical experiments. Nevertheless, this choice in an optimal way turns out to be not always an easy task. This paper proposes a response surface method named CQ2RS (Complete Quadratic Response Surface with ReSampling) allowing to take into account the knowledge of the engineer on one hand and to reduce the cost of the reliability analysis using a statistical formulation of the RSM problem on the other hand. Some academic and industrial examples illustrate the efficiency of the method.     

Automatic traffic incident detection based on nFOIL

Traffic incidents inevitably cause traffic delay and deteriorate road safety conditions. Incidents are increasing alongside the fast economic growth. Due to the rampant growth of traffic incidents, developing efficient and effective automated incident detection (AID) techniques has prompted a growing worldwide interest. In this paper, the great efforts on developing a new approach to this problem based on nFOIL, a novel inductive logic programming (ILP), are done. By way of illustration, a simulated traffic data generated from Ayer Rajah Expressway (AYE) highway in Singapore and a real traffic data collected in I-880 freeway in California are used to assess the detection performance of this approach, and performance metrics includes detection rate, false alarm rate, mean time to detection, classification rate and the area under Receiver Operating Characteristic (ROC) curve (AUC). For comparison, we conducted the experiments on neural networks and support vector machine. The experimental results showed that nFOIL is sensitive to the skewed distribution of positive and negative examples in the dataset, and we make use of two different techniques, resampling and ensemble learning, to cope with highly skewed data in the context of ILP classification problems and investigated the effect of them typicality on the performance of AID model. It is concluded that ILP based AID approach are feasible, and have a favorable performance compared to neural networks and support vector machines.     

基于小波变换的图象放大方法再探讨

图象放大 (又称图象变焦、图象重采样 )问题严格地说是一个病态问题 ,根据不同的模型 ,人们先后提出了多种图象放大方法 ,如线性插值、三次样条插值、分形插值以及基于小波变换的方法等 .图象放大问题的焦点是如何在图象放大过程中保持良好的视觉分辨率 ,表现在基于小波变换的图象放大方法中就是如何构造图象高频分量的问题 .针对目前常用的变换域内插方法 ,在一维信号上所作的模拟实验表明 ,该方法并不合理 .对常用的几种图象放大效果评价的方法进行了比较分析 ,结果认为 ,最理想方法还是多人主观评判法 .由于小波高频系数构建问题并未有理想方案 ,因此 ,对小波基函数的选择问题必须作进一步研究 .     

On resampling and uncertainty estimation in Linear System Identification

Linear System Identification yields a nominal model parameter, which minimizes a specific criterion based on the single input-output data set. Here we investigate the utility of various methods for estimating the probability distribution of this nominal parameter using only the data from this single experiment. The results are compared to the actual parameter distribution generated by many Monte Carlo runs of the data-collection experiment. The methods considered are collectively known as resampling schemes, which include Subsampling, the Jackknife, and the Bootstrap. The broad aim is to generate an empirical parameter distribution function via the construction of a large number of new data records from the original single set of data, based on an assumption that this data is representative of all possible data, and then to run the parameter estimator on each of these new records to develop the distribution function. The performance of these schemes is evaluated on a difficult, almost unidentifiable system, and compared to the standard results based on asymptotic normality. In addition to the exploration of this example as a means to evaluate the strengths and weaknesses of these resampling schemes, some new theoretical results are proven and demonstrated for Subsampling schemes.     

Dynamic resampling particle filter adaptive to changes in system model

A dynamic resampling particle filter (DRPF), integrated with scattering and migrating operations, is proposed for the flexibility in nonlinear systems subject to changes in the system model without a considerable increase in computational cost. Under the change detection with the tracking error, the scattering and migration are alternatively adopted where the scattering can boost the diversity in the particle population when no change, or slow change occurs, while the migrating updates particles with new observations if the change comes abruptly. Simulation results validate the proposed PF as a promising alternative to the existing PFs.