Lecture7-MATLABexamples

里面包含主元分析PCA的Matlab代码，包括提取主元，求方差贡献率，绘制贡献率直方图等(Which contains the principal component analysis PCA of the Matlab code, including the extraction of the main element, seeking variance contribution rate, contribution rate of histogram mapping, etc.)

Codes for Contrast Stretching Algorithm(Image Processing-Gonzalez).

packet error rate computation when source wireless body area sensor node communicates to the destination coordinator through wireless link in presence of two relays

IEEE14节点系统参数的matlab程序，可以用于计算14节点系统潮流。(IEEE14 node system parameters matlab procedures can be used to calculate the 14-node system trends.)

lingjian.m-----蒙特卡罗方法 lingjian.m使用零件初始值，用蒙特卡罗方法算出总费用。其中使用了自己编制的正态分布随机数发生器产生正态分布随机数。lingjian.m是对蒙特卡罗方法的一次练习。 accyouhua为标定值的函数，而lingjian不是一个函数，在其中已给出了一组标定值的值。 退火确定标定值/unitanneal()----模拟退火 连续型多个变量组合优化问题 这是对模拟退火方法的一次练习，结果证明模拟退火确实是一个行之有效的方法。 当参数选择较好时（一般也伴随着运行时间的加长），模拟退火的结果较好，然而用MATLAB的FMIMCON()一般可达到更高的精度。(lingjian.m---- Monte Carlo method. 氀椀渀最樀椀愀渀.m Part initial value, using the Monte Carlo method to calculate the total cost. The preparation of their own normal distribution random number generator to generate normally distributed random numbers. lingjian.m is the first practice of the Monte Carlo method. accyouhua calibration value the function lingjian not a function, which gives the value of a set of calibration values. Annealing to determine the calibrated value/unitanneal ()---- Simulated Annealing 吀栀攀 continuous multiple variables combinatorial optimization problems 吀栀椀猀 is an exercise of the simulated annealing method, the results show that the simulated annealing is an effective method. 圀栀攀渀 the parameter selection is better (generally accompanied by a longer running time), simulated annealing results, however using MATLAB FMIMCON () generally achieve higher accuracy.)

matlab 频谱分析matlab 频谱分析matlab 频谱分析matlab 频谱分析matlab 频谱分析(matlab matlab spectrum analysis spectrum analysis spectrum analysis matlab matlab matlab spectrum analysis spectrum analysis)

P. Perona&J. Malik算法的改进算法，以及包括了源图片(P. Perona & J. Malik algorithm algorithm, and includes the source image)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

harrlike算法实现人脸检测和定位方法，MATLAB环境，经测试，效果非常好(harrlike algorithm face detection and localization methods, MATLAB environment, tested, very good results)

通信系统中基于MATLAB的四相移相键控调制的仿真(MATLAB-based communication system of the four-phase phase shift keying modulation simulation)