线性调频信号，脉冲信号ambiguity-function

双通道的频分多址技术的SIMULINK仿真模型(Dual-channel frequency division multiple access technology, SIMULINK simulation model)

说明：  Matlab从入门到精通中所有的源程序。(Matlab from entry to master all of the source.)

IIR Decomposition Parallelization Methods

基于DTW算法的语音识别原理与实现，相关论文及Matlab实现流程(The principle and implementation of speech recognition based on DTW algorithm, the relevant papers and Matlab implementation process)

说明：  压缩感知（Compressed sensing），也被称为压缩采样(Compressive sampling)，稀疏采样(Sparse sampling)，压缩传感 [1] 。它作为一个新的采样理论，它通过开发信号的稀疏特性，在远小于Nyquist 采样率的条件下，用随机采样获取信号的离散样本，然后通过非线性重建算法完美的重建信号 [1] 。压缩感知理论一经提出，就引起学术界和工业界的广泛关注。(Compressed sensing (Compressed sensing), also known as compression sampling (Compressive from), Sparse sampling (Sparse from), Compressed sensing [1].As a new sampling theory, it develops the sparse characteristics of signals, obtains the discrete samples of signals with random sampling under the condition that the sampling rate is much lower than Nyquist sampling, and then perfectly reconstructs the signals through the nonlinear reconstruction algorithm [1].Once the theory of compressed sensing was put forward, it attracted extensive attention from academia and industry.)

Matlab编写的路标识别程序。能够对路标进行分类和识别，即使雾化，干扰严重，仍能较准确的判断。(Signs written in Matlab identification procedures. Able to classify and identify road signs, even atomization, serious interference, still a more accurate judgments.)

这是一篇关于博士论文，论文上详细的讲了非线性时间序列分析：辨识、预测与控制，很有用哦！(This is an article on the doctoral dissertation, the paper stresses on the detailed analysis of nonlinear time series: identification, prediction and control, useful Oh!)

matlab gui开发的实用教材 还有一位网友写的初步认识 对想迅速掌握matlab GUI开发的朋友的确有很大的帮助(matlab gui development of practical materials Another netizen wrote an initial understanding of the development to quickly grasp the matlab GUI friend indeed a big help)

This is also related to shift keying mod technique.....it is also very helpfull for the biggner who want to do digital mod techniques.........

Face Recognition Based on Statistical Moments