Seg Source Code

BPSK,QPSK等多个调制方式的详细说明及matlab代码(BPSK, QPSK modulation and other detailed instructions and matlab code)

RobustICA和FastICA算法功能一样，但比FastICA更稳定更简单，包含演示程序，简单易用(RobustICA has the same function as FastICA,but is more stable and easier,including demo programme,it s easy to use.)

说明：  数字时钟的代码非常有创意，大家可以下载试试看看，不错喔(Digital clock code very creative，You can download to try to see, oh well )

WIMAX系统下行仿真，采用matlab语言实现，包括编码解码，子载波映射等(WIMAX system downlink simulation using matlab language, including encoding and decoding, sub-carrier mapping)

pdf of envelope for Rayleigh Channel

DCDC变换器模型,采用双闭环控制，电压PI外环控制输出电压，电流PI内环加快相应速度(Model of a DC-DC converter，the completion of the current loop, double closed loop control scheme of voltage outer loop.)

《高阶谱分析》源代码，包括高阶谱的计算，谱分析，模拟，等等(Higher-Order Spectral Analysis (HOSA) Toolbox)

Numerical Heat Transfer and Fluid Flow数值传热方面经典教材，作者Patankar，其SIMPLE算法最为经典。(Numerical Heat Transfer and Fluid Flow numerical heat transfer aspects of the classic textbook, the author is Patankar, its SIMPLE algorithm is the most classic.)

Matlab codes for Leach

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 )