power_HalfBridgeMMC

Set based stochastic optimization for evolving optimum instinct learning balance

Digital Image Processing教材配套 matlab源程序，可以同时参看这本教材，相信可以得到更好的学习效果(supporting Matlab source materials, can see this material, I believe it can be a better learning results)

DNMF在matlab中的实现，基于R语言的示例改写(Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner.)

RF Circuit design mfiles

matlab 数字图像处理基础实验教程，能够帮助你快速入门matlab在数字图像处理中的应用。(basic experiment digital image processing matlab tutorial to help you quick start matlab in Digital Image Processing.)

说明：  关于飞机设计的程序。包括全机总体参数、气动曲线、机翼载荷、螺旋桨特性等完整的设计流程。(On the aircraft design process. Including the general parameters of the whole machine, pneumatic curves, wing loading, propeller characteristics of such a complete design flow.)

本段代码主要是为了实现讲多光谱图像进行光谱分离然后再合并成一种可以在matlab中显示的格式.tif的图像，同时保证了原始的图像信息不丢失(This code is mainly to achieve multispectral images speak for spectral separation and then combined into a format that can be displayed in matlab. Tif image, while maintaining the original image information is not lost)

BAMIC是一种multi-instance聚类的算法。(BAMIC is a package for multi-instance clustering. This package includes the MATLAB implementation of BAMIC, which is designed to deal with unsupervised multi-label learning problems. It is particularly useful when real-world objects are represented under multi-instance setting while the labels of these objects are unknown. )

基于静脉细化的基础上，判断是否为分叉点或者节点，去除静脉中毛刺。(Refinement based on the basis of vein, to determine whether the bifurcation point, or node, remove the veins in the burr.)

高分辨率雷达 距离向压缩算法滤波器设计 三种方法 全面设计(clc clear all close all 2013年9月28日 ---------------参数---------- Tr=42e-6 fs=9.5e6 K=0.7e11 Nfft=2048 -------------匹配滤波 时域生成-------------- t=(-Tr/2+1/fs:1/fs:Tr/2-1/fs) w = kaiser(length(t),2.5) w=w w = ones(1,length(t)) ht1=w.*exp(-1i*pi*K*t.^2) ht2=w.*exp(1i*pi*K*t.^2) hf1=fft(ht1,Nfft) hf2=fft(ht2,Nfft) figure subplot(211) plot(abs(hf1)) title( 匹配滤波器1 幅频响应 ) subplot(212) plot(phase(hf1)) title( 匹配滤波器1 相位 ) figure subplot(211) plot(abs(hf2)) title( 匹配滤波器2 幅频响应 ) )