MMOS

说明：  本程序基于MATLAB设计了数字滤波器实现的对含有噪声的文件的过滤(The program is based on MATLAB implementation of a digital filter paper with noise filtering)

说明：  卡尔曼滤波原理及应用代码程序，中国工信出版社(Kalman Filter Principle and Application Code Program,China Industrial and Information Press)

MATALB 编写的最小二乘支持向量机，可实现多分类问题，包含双参数优化。(LSSVM compiled by MATLAB language,It can realize multi-classification,and work very well.)

Newton下山法的计算程序，通过它可以求解复杂函数的根(Newton downhill method the computer program, through which can solve the root of complex functions)

This document describes the CDMA AT command, its syntax, its responses, and result codes. It serves as the reference for wireless application development based on the CDMA modem and for its integration and testing. The intended audience is expected to be familiar with CDMA data services protocol and AT modem commands. This document is also intended for use in the North American market. SIM/R-UIM and its related AT commands are not documented here.

WINNERⅡ信道代码的主程序，主要是参考历程进行系统的配置，给出WINNERⅡ信道调用的参考例程(WINNER ii main channel code, mainly with reference to the history of the system configuration, the channel is given WINNERⅡ reference routine calling)

用music方法估计功率谱，并估计信号频率和峰值(Music method estimates the power spectrum, and estimate the signal frequency and peak)

相邻系数引入到小波变换，用局域均值分解特征提取(Adjacent coefficient is introduced into the wavelet transform, using the local mean decomposition feature extraction)

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 )

比较不同约束长度下和不同回溯深度下的卷积码性能(Convolutional codes Performance comparison of different constraint lengths and different depths of backtracking)