[first_author]_2013_Electric-Po787-Systems-Resear

动态系统建模方法介绍，学习怎样实现用MATLAB对非线性动态系统的建模(Simulink MATLAB)

一种bss盲源信号分离的工具包 可以用于盲信号的提取(a blind source separation of the tool kit can be used for Blind Signal Extraction)

无线传感定位的一个入门算法，介绍了信标节点密度与定位精度的关系(Wireless sensor positioning as an entry algorithm introduced beacon node density and the relationship between positioning accuracy)

matlab 制作歌曲 最炫民族风 满天都是小星星等(matlab produce songs coolest Ethnic twinkle little star)

BPSK, QPSK, 16-QAM Modulation Mapping BER in communication

说明：  matlab help，很不错的教程，对你一定会有帮助(GOOD)

solution of matlab project based on numercal methods

用GA训练BP网络的权值、阈值从而优化神经网络(GA training BP network with the right value, the threshold in order to optimize the neural network)

Advanced Digital Signal Processing project

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.