jake1

说明：  此MATLAB程序用于读取MIT-ECG数据、将二进制数据转换为十进制数据(The MATLAB program used to read the MIT-ECG data, binary data is converted to decimal data)

用matlab实现在midi音频中的隐写过程(Matlab achieve midi audio steganography)

对高维数据进行小波去噪，并得到降噪效果图(High-dimensional data on wavelet denoising and get noise reduction effect diagram)

Matlab有关于天线结构设计的源代码分析，适用于RF的天线设计工程师(Matlab about the antenna structure design source code analysis for RF antenna design engineer)

it is the file which consists of ofdm using matlab

人工神经网络基于matlab开发的单层传感器的源程序例子(Artificial neural network matlab-based development model exact type rbf)

神经模糊预测控制及其matlab实现，包含书籍中各类案例代码(Neuro-Fuzzy Predictive Control and Its Matlab Implementation)

课程设计，实现简单的计算器功能，希望多多交流(Curriculum design, simple calculator function, I hope more exchanges)

用Matlab程序， 播放世界名曲卡农 播放世界名曲卡农播放世界名曲卡农 (Matlab play world music canon)

Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重： 如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules: If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)