FCC_UWB

《Matlab遗传算法工具箱及应用》Sheffield大学gatbs工具箱所有算例下载及调试说明(" Matlab toolbox and application of genetic algorithms" Sheffield University, all gatbs toolbox download and debug an example that)

基于天线选择的准正交空时分组码的自适应分析。(Analysis of adaptive antenna selection quasi-orthogonal space-time block codes.)

对萨德系统AN/TPY-2雷达的威力范围进行仿真计算。原创！！(The power range of SAD AN/TPY-2 radar is simulated. Original!!)

BP神经外那个罗可以逼近任意函数，此为拟合函数的源代码(BP neural network can approximate any function, the source code for the fitting function)

Digital Bandpass Transmission(Digital Transmission)

speech compression using fft

基于matlab的光学仿真实验--关于菲涅尔透镜衍射的仿真(Matlab-based optical simulation- simulation on the Fresnel lens diffraction)

pid controleur with optimisation

正弦信号通过幅度调制（AM,DSB,SSB）所得到的时域波形及频域波形(AM,DSB,SSB waveforms in frequency- domain and Time domain)

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...)