plot

介绍了MATLAB的图视化功能，工具箱函数汇总，MATLAB 程序设计与应用等(Introduction of the MATLAB visualization of map features, the toolbox function summary, MATLAB Program Design and applications)

基于Matlab/Simulink建立的变压器内部仿真模型，可以进行变压器内部整个绕组的单相接地、两相短路、两相接地短路、三相短路等故障的仿真。(The internal simulation model of transformer based on Matlab/Simulink can carry out the simulation of single phase grounding, two phase short circuit, two-phase short circuit and three-phase short circuit of the whole winding. )

Telecommunication Lecture - .

matlab 用户界面设计与应用，适用于初学者，简单易学(MATLAB GUI)

Matlab _ MIMO + QPSK + ML/MMSE/ZF 在Matlab的平台上,应用QPSK调制,以及3种不同的探测方法,实现并模拟了2x2的MIMO功能.(Matlab _ MIMO+ QPSK+ ML/MMSE/ZF on the Matlab platform, application QPSK modulation, as well as three different detection methods, and to achieve the simulated 2x2 MIMO function.)

matlab code for machine learn duda

OFDM with PEAK TO AVERAGE POWER RATIO

说明：  该MATLAB程序评估了viterbi算法在衰落信道下的卷积译码的性能，包含卷积编解码，衰落信道，维特比算法等.m程序(The MATLAB program to assess the viterbi algorithm in fading channels the performance of convolutional decoding, including the convolution coding and decoding, fading channels, Viterbi algorithm. M procedures)

matlab programming soft radio

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)