w1

用Matlab实现中值滤波与Otsu算法 包括彩色图转256灰度，灰度图中值滤波处理，再用Otsu算法处理。(Median filter implementation using Matlab and Otsu algorithms including 256-color map turn gray, grayscale median filter, and then Otsu algorithm processing.)

粒子滤波 PF( Particle Filtering) 是在 贝叶斯滤波框架下基于 Monte Carlo 采样的统计滤波方法，可解 决非线性、非高斯的滤波问题，声源跟踪就属于这类问题。近年 来，基于粒子滤波的声源跟踪算法已成为研究的热点。 (he real acoustic data recorded in a typi- cal meeting room using a small-scale microphone array is used for tracking experiments． Results show that the proposed algorithm can realise effectively the orientation tracking of the randomly walking speaker，in which，the root mean square error of tracking in direction of the hori- zontal angle and the elevation are all less than 5°． )

基于MATLAB语音信号采集与分析加上高频噪声并去噪声源程序(the source program of voice signal acquisition and analysis with high-frequency noise based on matlab.)

A tutorial on Principal Components Analysis

递归法求解整数划分。 整数划分，是指把一个正整数n写成如下形式： n=m1+m2+…+mi （其中mi为正整数，并且1 <= mi <= n），则{m1,m2,...,mi}为n的一个划分。 如果{m1,m2,...,mi}中的最大值不超过m，即max(m1,m2,...,mi)<=m，则称它属于n的一个m划分。这里我们记n的m划分的个数为f(n,m) (Recursive method integer division. Integer division, refers to a positive integer n written as follows: n = m1+m2+ ...+mi (where mi is a positive integer, and 1 < = mi < = n), then {m1, m2, ..., mi} is a division of n. If {m1, m2, ..., mi} does not exceed the maximum value of m, i.e., max (m1, m2, ..., mi) < = m, m n is said that it belongs to a division. Where m is the number of division n our mind is f (n, m) )

说明：  简单的CI融合MATLAB程序，生成实际误差的协方差椭圆用于验证融合精度。(A simple CI fusion matlab program generates a covariance ellipse of the actual error to verify the fusion accuracy.)

matlab编程，实现对视频中运动人体的检测，代码简单易懂(matlab programming, video detection of the human body in motion, the code is easy to understand)

使用matlab软件编程，进行字母识别处理(using Matlab software programming, in alphabetical identification processing)

说明：  使用matalab语言应用PLS算法对近红外光谱进行回归。(PLS algorithm is used to regress near infrared spectrum with MATALAB language)

TRAINPSO is a network training function that updates weight and bias values according to particle swarm optimization.