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这是一个MATLAB编写的机载空投精确制导弹药的全弹道仿真，包括空投段，巡航段，末制导段的仿真，且有详细的注释，值得研究。(This is a MATLAB prepared by dropping precision guided munitions on-board the full trajectory simulation, including the drop section, cruise segment, the simulation of terminal guidance, and detailed notes, worth studying.)

L曲线具有很多的优点，这是用MATLAB编写的病态方程的L曲线解决方法，可以通过L曲线确定所需的参数。(L curve has many advantages, it is written in MATLAB morbid equation L curve solution that will be needed through the L curve to determine the parameters.)

说明：  matlab 基于形状的图像检索算法 关于七个不变矩的主要算法 中调用步骤！ ！！！！！！(matlab image retrieval algorithm based on the shape of seven invariant moments on the main algorithm, called the steps! ! ! ! ! ! !)

基于ESPRIT算法的相干分布式多信号源的DOA估计(Distributed coherent signals DOA estimation based on the ESPRIT algorithm)

Power electronics plays an important role in controlling the grid-connected renewable energy sources. This paper presents a novel adaptive neuro-fuzzy control approach for the renewable interfacing inverter. The main objective is to achieve smooth bidirectional power flow and nonlinear unbalanced load compensation simultaneously, where the conventional proportional-integral controller may fail due to the rapid change in the dynamics of the highly nonlinear system. The combined capability of neuro-fuzzy controller in handling the uncertainties and learning from the processes is proved to be advantageous while controlling the inverter under fluctuating operating conditions. The inverter is actively controlled to compensate the harmonics, reactive power, and the current imbalance of a three-phase four-wire (3P4W) nonlinear load with generated renewable power injection into the grid simultaneously.

用matlab进行谱分析的源码，适合初学者(Spectral analysis carried out by the matlab source code for beginners)

DSP2 using matlab v4

结构分析的有限元法与matlab程序设计6-1(Structural analysis of the finite element method and matlab programming 6-1)

matlad code for corgnitive radio

Description All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size)) input inp_img=2d data(row * col) w=cover size w=[3 3] = 3x3 cover pixel generation output o_img=result 3d o_img(w(1),w(2),r*col) if inp_img=[1 2 3 4 5 6 ] w=[3 3] Soloution it will construct new image new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0] result o_img(:,:,1)=[0 0 0 0 1 2 0 4 5] o_img(:,:,2)=[0 0 0 1 2 3 4 5 6] finally o_img(1:3,1:3,6) example a=imread( pears.png ) b=rgb2gray(a) c=cover_window( b,[3 3]) 3x3 window construction