基于分块思想的最小化总变差去噪新算法

现有的图像分块方法在处理前将源噪声图像分割成不同的小块,分别独立进行去噪,最后将所有小块拼接。该方法所得图像的去噪效果有一定程度的提高,但在相邻分块结合处存在不连续。对去噪方法的现状进行了分析,并结合最小化总变差及分块思想,提出新的去噪方法:在每小块处理过程中,分块值参与判断终止,整幅图像值计算去噪图像;对各块所得去噪图像取均值,得到最终的结果;对新算法进行优化,提高了算法运行效率。分析实验结果表明,该方法在计算效率和信噪比上均有显著提高。     

基于信息势能的鲁棒估计器及其在同时定位与地图构建问题中的应用

提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建（SLAM）问题．结构化的室内环境可以用线段近似表示．然而动态环境中,测距传感器测得的数据通常湮没在大量的噪声信号中．本文采用“分割与合并”（split—and．merge）方法进行线段的分类,根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量．按照信息优化理论设计估计器,选择信息量贡献大的样本点作为信息内点提取线段参数,构建局部地图．采用粒子滤波器进行地图及机器人路径的更新．采用递推的方法估计信息势能,降低了对样本点的信息量贡献做估计时的复杂度．仿真和实验结果证明,本文所提出的方法具有较强的鲁棒性,提高了SLAM策略的准确性和实时性．     

基于最大特征值和能量之差的频谱感知方法

针对认知无线电频谱感知问题,利用随机矩阵理论的相关研究成果,提出一种新型频谱感知方法.该方法以最大特征值和平均能量之差构建判决统计量,以采样协方差矩阵最大特征值的极限值近似代替最大特征值,结合噪声平均能量的分布特性推导判决门限.仿真结果表明,该方法在低信噪比环境下能够克服噪声不确定度的影响,获得相较于ED更好的检测性能.     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

Fe-Ga磁致伸缩位移传感器驱动电流位置调整与回波速度校正

通过对传统Fe￣Ga磁致伸缩位移传感器驱动脉冲电流输入端位置的改进,降低了驱动脉冲电流噪声对检测线圈输出电压的影响,并使检测线圈输出电压信噪比由15.5 dB提高至23.7 dB。基于应力波无阻尼反射原理提出一种新的回波速度校正法,确立了回波速度与波导丝长度、应力波传播时间、反射波传播时间的数学关系,并给出此表达式适用的驱动脉冲电流频率范围。制作了样机,通过实验验证了此方法最大位移测量误差减小到原来的1/5,为Fe￣Ga磁致伸缩位移传感器输出性能研究提供了理论依据。     

基于Wollaston棱镜阵列的目标识别系统

针对传统目标识别方法识别率低、受背景环境影响大等问题,设计了一种基于Wollaston棱镜阵列的偏振光谱成像系统.为了有效地探测野外目标的微弱信号,采用了高光通量的光学天线、偏振模块及干涉模块的设计.为了保证实时采集及高稳定性的设计要求,采用了全静态光学器件完成偏振分光及空间光程调制.系统由偏振成像模块与光谱成像模块构成,可实现同一视场内4个偏振方向图像及光谱图像的同时采集,并经图像融合算法重建被测区域的偏振光谱图像.实验在不同测试距离上分别以钢板和某型火炮作为被测目标,分别采用本系统偏振模块与传统非偏振系统进行测试.实验结果显示,偏振目标识别信噪比高、受距离影响小.基于此实验,再将偏振识别图像与光谱识别图像相融合,从而构成偏振光谱图像.与传统目标识别图像相比较,本系统合成图像中目标清晰可见,受背景环境影响小、可探测距离远,具有很高的野外适应性及稳定性.     

阈值阵列模型下的超阈值随机共振信噪比增益

研究了阈值阵列模型和超阈值随机共振现象.对该模型进行剖析,认为阈值阵列系统可以分解为单个阈值系统与集总平均器的级联.为了研究周期输入下的超阈值随机共振现象,理论分析了周期输入下的阈值阵列模型输出随机过程的统计特性,以输出信噪比增益作为随机共振的测度,固定输入信噪比,观测输出信噪比增益相对于阈值噪声方差的变化规律.证实当输入噪声为高斯噪声时,在阈值阵列系统中加入统计独立的高斯白噪声可使输出信噪比增益大于1,当输入噪声为非高斯噪声时,可获得更高的输出信噪比增益.     

An extension of an over-dispersion test for count data

While over-dispersion in capture-recapture studies is well known to lead to poor estimation of population size, current diagnostic tools to detect the presence of heterogeneity have not been specifically developed for capture-recapture studies. To address this, a simple and efficient method of testing for over-dispersion in zero-truncated count data is developed and evaluated. The proposed method generalizes an over-dispersion test previously suggested for un-truncated count data and may also be used for testing residual over-dispersion in zero-inflation data. Simulations suggest that the asymptotic distribution of the test statistic is standard normal and that this approximation is also reasonable for small sample sizes. The method is also shown to be more efficient than an existing test for over-dispersion adapted for the capture-recapture setting. Studies with zero-truncated and zero-inflated count data are used to illustrate the test procedures.     

基于稀疏Parzen窗密度估计的快速自适应相似度聚类方法

相似度聚类方法(Similarity-based clustering method,SCM)因其简单易实现和具有鲁棒性而广受关注.但由于内含相似度聚类算法(Similarity clustering algorithm,SCA)的高时间复杂度和凝聚型层次聚类(Agglomerative hierarchicalclu...     

Patterns for multigrid equidistributed functions: Application to general parabolas and length estimation

We show that for some special functions (called k-multigrid equidistributed functions), we can compute the limit of the frequency of patterns in the discretization of their graph, when the resolution tends to zero. This result is applied to parabolas. We deduce also that local length estimators almost never converge to the length for the parabolas.