matlab

精通matlab金融计算 Financial_Time_series_analysis in Matlab(Financial_Time_series_analysis in Matlab)

1. 安装MATLAB软件。安装时，将MATLAB安装在C:MatlabR12目录下 Matlab的详细安装方法见有关安装手册 2. 拷贝文件:即将光盘中的/ctl/program和/ctl/functions下的 文件复制到用户目录下（例如，D:User) 3. 设置路径:先运行MATLAB,在桌面操作环境下，点击File下拉式菜单 的Set path选项，添加用户目录（例如，D:User)(含子目录ctl等） 到MATLAB的工作路径 4. 运行程序:先运行MATLAB,如第3步已运行，可直接键入示例命令。 例如，键入ex1_1等 5. 阅读教材，并对示例进行操作。">

卷积码的详细维特比译码,适合初学者 (For more than Witte convolution code decoding, suitable for beginners)

matlab 数字水印的处理，可实现文件的加密处理(matlab digital watermark processing, encrypted files can be realized)

伪随机信号发生器，可生成PN63,PN511序列 ,调试通过，可直接使用，也可以稍微修改用在其他伪随机序列生成(Pseudo-random signal generator, can generate PN63, PN511 sequence, through debugging, can be used directly, or you can use a little change in other pseudo-random sequence generation)

this is a presentation that explains matlab in detail.useful for engineers

Matlab User s guide pdf

用于计算内弹道各参数，采用经典内弹道模型。(used to calculate The interior ballistic Parameters)

室内环境的人体感应是物联网应用在安保、智能家居、节能环保方面的关键问题，近年来多普勒雷达体制的微波人体感应器作为一种高性能的室内人体传感器成为了一种热门的解决方案。微波人体感应器以雷达体制主动发射微波信号，探测回波中由目标人体产生的多普勒，输出触发信号。 国内外现有微波开关面临的主要问题是静止人体目标检测。本项目系统检测人体呼吸心跳微信息，拟通过长时间累积和滤波器提取微多普勒信息检测动目标。( Of the indoor environment of the human body induction is the Internet of things application in security, smart home, energy-saving environmental protection, the key problem of doppler radar system in recent years as a kind of high performance microwave sensor body interior human body sensor has become a popular solution. Body of microwave sensor to active microwave signal launch radar system, the detection by the target echo in the bodies of doppler, trigger signal output. Of the main problems in the domestic and foreign existing microwave switch target detection is still the human body. The project system to detect human breathing heartbeat micro information, through long time accumulation and filter extract micro-doppler information detection target. )

说明：  输入特征向量训练随机森林分类模型，并计算分类结果的Kappa系数，混淆矩阵，准确性，特异性和敏感性。(Input eigenvector to train random forest classification model, and calculate kappa coefficient, confusion matrix, accuracy, specificity and sensitivity of classification results.)