image-processing-using-matlab

离散余弦变换(DCT)具有优于傅里叶变换、哈尔变换等正交变换的高度解相关性(apfft fir filter design)

这是完整的五子棋MATLAB源码，绝对能运行，能走五子棋。(this is a complete connective_five matlab code )

给出一个两个参数的matlab遗传算法的仿真程序,有一定的科研参考价值.值得一看.(wo have given a two algorithm matlab genetic algorithm,it can be useted by researchers.)

说明：  概率潮流使用matpower编写程序的基本步骤(The basic steps of probabilistic power flow by using matpower to write programs)

音频频谱的分析程序,在程序中实现了采样速率的提取(Audio spectrum analysis procedures, the procedures to achieve a sampling rate of extraction)

矩阵相乘，基于CUDA用并行算法实现矩阵的乘法(matrix muliplication with parallel algorithm implication matrix muliplication base on CUDA )

自己搭建的一个模糊控制与PID控制相切换的模块，波形非常不错(fuzzy PID)

computer manual for Matlab in pattern classification

独立成分分析（Independent Component Analysis, ICA）是近年来发展起来的一种有效的盲分离技术，最早是由法国学者Herault和Jutten于1986年提出。ICA方法的提出最初是用来解决“鸡尾酒会”问题，其过程可以归纳为，在源信号与传输通道参数均未知的情况下，仅根据源信号的统计特性，出现测信号恢复出源信号。ICA分析的关键在于根据一定的优化准则建立描述输出信号独立程度的优化判据，即目标函数，并设计相应的优化算法，寻求最优的分离矩阵，使得输出信号中各分量尽可能相互独立。采用ICA方法对信号进行盲分离时，可避免过多地受大能量噪声成分的影响，将部分小能量信号源识别出来。随着ICA技术的不断发展，目前ICA技术已经广泛应用于语音增强、音频识别、通信、医学信号处理、金融数据挖掘、图像消噪、数字水印、遥感等领域，并取得了一定的研究成果。(Independent Component Analysis (ICA) Component ini order, developed in recent years is a kind of effective separation technology, originally from French scholars Herault and Jutten in 1986. The proposed method is initially ICA to solve the "cocktail" problem, the process can be divided into, in the source signal transmission channels and in the case of unknown parameters according to the source of the signals, and the statistical characteristics of recovering the source signal. According to the analysis of the ICA key lies in the establishment of optimal criteria described the output signal of the independent, objective function optimization criterion, and the corresponding optimization algorithm, the optimum )

说明：  利用矩量法求解Hallen方程 画出场的电流分布(Hallen method of moment equations current distribution appearances painting)