SQPM

应用MATLAB语言编写低通滤波器,对含噪图像作低通滤波处理( Using the MATLAB language compilation low pass filter, to contains chirp the image to make low-pass filtering processing )

实现BP神经网络的matlab运算，运算效率高，方便，快捷(BP neural network matlab realize operation, high operational efficiency, convenient and fast)

speed control of brushless dc moter

应用内点法中原-对偶路径跟踪法，求解线性规划问题，也可以用于求解最优潮流问题，不过KKT方程较复杂。(using the original- dual path following method is used to solve linear programming problems)

本代码设计的切比雪夫1型滤波器，与MATLAB中的切比雪夫滤波器不同的是输出为未归一化的零极点，输入只要是通带频率，通带波动，截止频率，阻带衰减即可，更便于抄做(The code design of Chebyshev Type 1 filter, and MATLAB in the Chebyshev filter output is not different is that the poles and zeros of the normalized input as long as the pass-band frequency, pass-band fluctuations, cutoff frequency, stopband attenuation can be more easily copied to do)

本程序中得到的收端OFDM信号的频谱波形，是与其发端信号的排步有关的。在发端的 ％ 载波安排上，128个载波有前后各32个载波是null载波(如果这前后各32各载波是带外频段， ％ 那么理论上它们都应该是零！)，中间的64个载波是数据载波。这样的排步很明显就是一个 ％ 两边低，中间高的频谱形式。所以，收端也应该是这个轮廓。 (this process the resumption end of the spectrum OFDM signal waveform, with the originator of the signal further on the row. The originator% carrier arrangements, 128 of the carrier after a 32 carrier is null carrier (if it was around the 32 is the carrier frequency band. % then theoretically they should be zero!) the middle 64 carrier data carrier. This arrangement is obviously further 1% lower on both sides, in the middle of the spectrum form high. Therefore, the admission should end this profile.)

使用MATLAB进行图像质心提取，用于求取质心坐标(MATLAB for image centroid extraction for obtaining centroid coordinates)

仿真相控阵雷达中一维线阵雷达的方向图指向和其他一些因素对雷达的影响(Effect of phased array radar simulation in one-dimensional linear array radar direction pointing and some other factors on the radar)

Electromagnetic Simulation using the FDTD method章节后实例2-1 flux_density2_1用matlab重新编写(Electromagnetic Simulation using the FDTD method example 2-1 flux density ，rewriting in matlab)

说明：  k-means 算法接受输入量 k ；然后将n个数据对象划分为 k个聚类以便使得所获得的聚类满足：同一聚类中的对象相似度较高；而不同聚类中的对象相似度较小。(The k-means algorithm accepts the input k; then divides n data objects into k clusters in order to make the obtained cluster satisfy: the similarity of objects in the same cluster is high; and the similarity of objects in different clusters Smaller.)