Medical Image Retrieval Based on Multi-Layer Resampling Template

Medical image application in clinical diagnosis and treatment is becoming more and more widely, How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose. This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition, the image retrieval method consists of two retrieval process which is coarse and fine retrieval. Coarse retrieval process is the medical image retrieval process based on the image contour features. Fine retrieval process is the medical image retrieval process based on multi-layer resampling template, a multi-layer sampling operator is employed to extract image resampling images each layer, then these resampling images are retrieved step by step to finish the process from coarse to fine retrieval.     

采样技术在武器试验中的应用

根据武器试验的具体特点,提出采用重采样技术解决对试验测试系统的过高要求,并压缩数据量便于存储．某型多管火箭炮实测数据的处理结果与理论计算对比表明,该方法所得结果可信,可用于武器试验测试中．     

几何操作中重采样问题数据并行实现

几何操作是计算机图形和计算机视觉的基本操作之一。如何快速、实时地实现几何操作,是现代媒体应用中的主要问题。重采样操作是图像处理中非常关键的全局操作,一般是通过串行处理方法予以解决。在SIMD处理元阵列上进行数据并行处理几乎是空白,为了满足对图像进行实时处理的需要,提出几何操作中重采样操作的数据并行实现方法,并对结论进行了论证。通过SIMD PE阵列之间的数据并行传送,实现了重采样,从而使实现重采样的复杂度由存储器数据流访问实现方法的Ο（MN）降低到Ο（M＋N）,大大提高了处理速度,能更好地满足图像快速实时处理的需要。     

适于无源阵列跟踪机动目标的IMM-PF算法

针对无源阵列对机动目标跟踪效果较差的问题,在融合交互式多模型和粒子滤波方法的基础上,提出了一种基于粒子滤波的交互多模型(IMM-PF)算法.该算法采用多模型结构以跟踪目标的任意机动;各模型采用粒子滤波算法,以处理非线性、非高斯问题.各模型中相对固定数目的粒子群经过相互交互、粒子滤波后再进行重抽样以减少滤波退化现象.与典型的交互式多模型算法(IMM-KF)进行了比较,计算机仿真结果证实了新算法的正确性和有效性.     

基于空间重采样的宽带DOA估计新算法及其性能分析

首先介绍了宽带阵列信号模型,然后给出了一种基于空间重采样的新的宽带信号相干子空间处理算法。该算法将均匀线阵看成是连续线阵的离散采样,在各个频率点调整阵列空间采样率,将各频率分量的方向向量聚焦到参考频率上,聚焦后得到单一频率点的数据协方差矩阵,再应用窄带MUSIC算法进行DOA估计,无需预估计波达方向,因此可以避免角度预估计对测向性能的影响。仿真实验表明新算法的性能要优于其他典型算法(TCT,RSS和TLS算法)。文中还给出了分辨率与阵元个数、信噪比之间关系。     

一种新的基于序列蒙特卡罗方法的符号检测器

在衰落信道下检测传输符号是序列蒙特卡罗方法在通信领域中的一个重要应用.由于在序列蒙特卡罗方法中重要函数的选择直接会影响系统性能,针对这一问题,提出了一种新的混合重要函数,较大幅度提高了衰落信道下传输符号的检测水平,仿真结果也证实了这种新检测器的有效性.     

Removing the outliers in root-MUSIC via pseudo-noise resampling and conventional beamformer

A joint use of pseudo-noise resampling technique and a conventional beamformer is proposed to mitigate the effect of outliers in Root-MUSIC estimator. After resampling of Root-MUSIC via pseudo-randomly generated noise we combine it with a conventional beamformer (in the sense that identification of the signal roots is based on the power of the conventional beamformer response). The resulting estimator can be referred to as modified resampled Root-MUSIC. The estimator bank is formed from a number of modified resampled Root-MUSIC. Censored selection of the results of modified pseudo-randomly resampled Root-MUSIC estimators is exploited based on an appropriate local performance test.     

The efficiency of systematic sampling in stereology — reconsidered

In the present paper, we summarize and further develop recent research in the estimation of the variance of stereological estimators based on systematic sampling. In particular, it is emphasized that the relevant estimation procedure depends on the sampling density. The validity of the variance estimation is examined in a collection of data sets, obtained by systematic sampling. Practical recommendations are also provided in a separate section.     

Assessing Time‐Reversibility Under Minimal Assumptions

Abstract. This article considers a simple procedure for assessing whether a weakly dependent univariate stochastic process is time‐reversible. Our approach is based on a simple index of the deviation from zero of the median of the one‐dimensional marginal law of differenced data. An attractive feature of the method is that it requires no moment assumptions. Instead of relying on Gaussian asymptotic approximations, we consider using subsampling and resampling methods to construct confidence intervals for the time‐reversibility parameter, and show that such inference procedures are asymptotically valid under a mild mixing condition. The small‐sample properties of the proposed procedures are examined by means of Monte Carlo experiments and an application to real‐world data is also presented.