t3eq

本例中通过使用MATLAB把声音信号进行分析，把有声音的部分进行过滤，分别过滤后 再把3个信号连在一起，使3个单独的声音信号转换为一个单独的连续声音信号。(In this case through the use of MATLAB to analyze acoustic signals, the voice has some filtering, respectively, after filtering the three signals together, so that three separate voice signal is converted to a single continuous sound signal.)

使用EKF、UKF、PF、EKPF算法实现简单的单个目标跟踪滤波(Using EKF, UKF, PF, EKPF simple tracking algorithm filter)

数字通信中2FSK调制与解调原理及详细源代码(2FSK Digital Communication Principles of modulation and demodulation and detailed source code)

Modeling for ISAR using MATLAB

用于求解多个信号源波达方向的求根MUSIC算法,不需要进行二维谱峰搜索(Root MUSIC algorithm for solving multiple sources DOA and calculation is greatly reduced than the MUSIC algorithm )

通过MATLAB对一幅多个人物的图像进行，人脸轮廓的提取，识别。效果不错，譬如毕业照这样的照片能有很好的结果。(By MATLAB to more than one person in the image , extract facial contour recognition . Good results, such as this photo graduation photo can have a good result .)

Matlab图形背景代码 包括导入数据、进度条、当前时间、主界面窗口设置等等。(Matlab codes of graphic background)

CASS软件的实习教程 对于初学CASS的人很有用(CASS software training tutorials for the beginner who is useful CASS)

这是computer modeling 的英文版教材 第三版 权威教材(this is the 3rd edition of computer modeling textbook )

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)