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本程序是建立了三维椭球随机分布模型，并绘制出分布图像。(This procedure is to establish a three-dimensional ellipsoid model of random distribution, and to map out the distribution of the image.)

说明：  该程序为用MATLAB编写的一个小波变换工具箱，跟之前的toolbox1程序包实现的功能有所不同(The program prepared for the use of a MATLAB toolbox wavelet transform with the package before toolbox1 function to achieve different)

Image Filters for Noise Reduction in Images

正则化工具包Hansen2008_Regularization Tools, A Matlab Package for Analysis and Solution of Discrete ill-posed Problems(Hansen2008_Regularization Tools, A Matlab Package for Analysis and Solution of Discrete ill-posed Problems)

用于流型学习的算法：局部邻域保持 可以分析任何符合流型分布的数据集，常用语人脸识别(For the flow pattern learning algorithm: the local neighborhood to maintain consistent flow pattern can be analyzed the distribution of any data set, common language recognition)

综合粒子群和蚁群算法，再利用遗传算法中交叉编译算子；再引入模糊技术，形成模糊自适应粒子群和蚁群混合算法，求解TSP问题(Integrated particle swarm and ant colony algorithm, and then genetic algorithm is used to cross compile operator then introduced the fuzzy technology, forming a fuzzy adaptive particle swarm and Ant Colony Hybrid Algorithm for solving TSP problem )

说明：  一段波束形成的小程序，用matlab实现(Beamforming for some small procedures, using matlab to achieve)

matlab编程的求解h(n)的自相关函数(zixiangguanhanshu)

这个一个很全面的数字信号处理的实验工具包，里面包含了大部分的数字信号处理实验的各个方面。希望对大家有帮助。(This a very comprehensive test of digital signal processing tool kit, which contains most of the digital signal processing aspects of the experiment. Hope to help.)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)