MatlabSimulink

matlab中用于进行fourier变换的GUI程序(matlab in fourier transform used for GUI program)

假设一个接收到的信号为：x(t)=s(t)+n(t), 其中s(t)=A*cos(wt+a), 已知信号的频率w=1KHz， 而信号的幅度和相位未知，n(t)是一个服从N(0,1)分布的白噪声。为了利用计算机对信号进行处理， 将信号按10KHz的频率进行采样。 通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). 在未知信号频率的情况下，通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). (Assuming a received signal: x (t) = s (t)+ n (t), one of s (t) = A* cos (wt+ a), the known frequency signal w = 1KHz, And the signal amplitude and phase of the unknown, n (t) is a subject to N (0,1) the distribution of white noise. The use of computers in order to deal with the signal, The signal by sampling frequency of 10KHz. Of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t). Frequency signals in unknown circumstances, the adoption of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t).)

Digital Signal Processing Using MATLABr and Wavelets is a ebook contain basic matlab code

说明：  线列阵多倍频程宽频带恒定束宽的实现，吴国清的论文(Linear array multi-octave wideband constant beamwidth implementation, Wu Guoqing papers)

This software may be used for non-commercial purposes only, so long as this copyright notice is reproduced with each such copy made. The software in this folder focuses on the computations associated with general type-2 FLSs.

采用自编码器实现手写数字识别（matlab），是深度架构的一种。(Handwritten numeral recognition is realized by using the Auto-encoder (matlab), it is one of the deep structure.)

用有限差分法求双脊金属加载矩形波导的基模及第一个高阶模的色散特性曲线( Finite difference method seeking double-ridged metal loaded rectangular waveguide fundamental mode and first higher order mode dispersion characteristic curve)

声波模拟的去频散程序，能有效减少频散效应。。。。(Dispersion modeling to the sound process, can effectively reduce the frequency dispersion effect. . . .)

OFDM盲信道估计算法的编程，十分形象地模拟了OFDM盲信道估计算法。(ofdm blind channel estimation )

一本很经典的的关于概率统计的数学书，涵盖了各种经典的统计模型，值得好好学习。(One of the classic book on mathematical probability and statistics, covering a variety of classical statistical models, it is worth to learn.)