untitled

SSVEP脑电分析评价系统，用Matlab的GUI完成，完成判断和评分(SSVEP EEG analysis and evaluation system, complete with Matlab' s GUI to complete the judging and scoring)

antenna designing matlab

matlab guide ,用户手册，，，很好很全的资料。。。。(matlab guide ,用户手册，)

程序为相关干涉仪测向方法的matlab仿真。以5元圆天线阵列为接收阵列，计算两元间的相位差，形成标准库，并通过相关的方式测得信号来波方向。运行untitled1，输入相关参数即可实现功能(Matlab simulation of the method of correlation interferometer direction finding. With 5 element circular array antenna for receiving array to calculate phase difference between two dollars, forming the standard library, and by means of the measured signal direction of arrival. Run untitled1, input the relevant parameters to achieve the function)

source code for image processing in matlab

基于李雅普诺夫稳定性分析的模型参考自适应系统仿真(Based on the lyapunov stability analysis model reference adaptive system simulation based on lyapunov stability analysis model reference adaptive system simulation )

Performance analysis of BICM based cooperative network over AWGN channels.

双极性归零码的功率谱密度(Bipolar power spectral density of NRZ)

理想、巴特沃斯高低通--哈弗曼编解码-图像处理(ideal、butterworth、huffman for image processing)

AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。 具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后， 计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法....... 直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。 可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。 AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。 对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目， 过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。 同时可以通过删除离决策边界比较远的样例。 (AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative. Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way ....... 0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value). As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing. AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method. For )