LEACHmultihop

MATLAB程序设计基础教程的视频教程，对初学者比较有帮助(MATLAB Programming Essentials video tutorial for beginners and more help)

合成孔径雷达距离多普勒算法，模式为条带模式，条带模式为最基本的成像模式，有利于初学者掌握(SAR-RD；stripmap；for learning)

小生境遗传算法在函数优化中应用—自适应小生境递阶遗传算法(Niche Genetic Algorithm in function Optimization- adaptive niche hierarchy genetic algorithm)

ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)

Electromagnetic Waves and Antennas

Matlab实现高斯建模，算法简单，亲测可用，互相学习！(The realization of Gauss modeling, Matlab algorithm is simple, dear measurement available, learn each other! )

Fuzzy Clustering Initialization

说明：  电力系统潮流计算程序主程序，还有功能子程序(power float)

阵列处理第五章 源代码 matlab code(array processing matlab code)

利用matlab对随机过程课程的仿真 1、利用matlab绘制随机过程的谱密度G_x (ω)=2αβ/(β^2+ω^2 ) (α、β为正数)的图像。 2、绘制白噪声的功率谱密度函数G_x (ω)=N/2和自相关函数R_N (τ)=N/2 δ(τ)的图像。 3、分析低通白噪声和带通白噪声的功率谱密度函数和自相关函数的性质并举例同时绘制其图像。 (1, drawn using a random process matlab spectral density G_x (ω) = 2αβ/(β ^ 2+ ω ^ 2) (α, β positive) image. 2 for the white noise power spectral density function G_x (ω) = N/2 and the autocorrelation function R_N (τ) = N/2 δ (τ) of the image. 3, the analysis of low-pass white noise and band-pass white noise power spectral density function and autocorrelation function of the nature and the examples at the same time to draw its graphics.)