MMS_light

matlab 入门教程，包含讲义和相关实例，非常实用，对于初学者(matlab tutorial, including handouts and relevant examples, very useful for beginners)

一个简单的相控阵超声波探伤及成像系统模型。现在可以模拟三种声波束的成像结果，适合理解超声波探伤原理。( 1 ：for Plane B-scan 2 : for Focused B-scan 3 : for Sector B-scan)

二阶锁频环Matlab代码，使用叉积鉴频器，内有仿真图(TwoOrderPLL Matlab)

Fuzzy tuning parameter PID controler, Simulation Matlab

《脑电信号分析方法及其应用》书中示例程序，书的附录中有该程序列表(EEG analysis method and its application（the book program）)

ILC算法中PID学习率中PID参数的选择优化程序(Select the PID algorithm optimization program ILC learning rate in PID parameters)

CentralDifference code for multi dimention strucures

线性调频脉冲压缩雷达仿真，包括回波，脉冲压缩处理，雷达分辨力(Linear frequency modulated pulse compression radar simulation, including the echo, pulse compression processing, the radar resolution)

angular code to new task

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.