SignalModulations

利用FFT变换，对sine2进行频谱分析，画出频谱图 利用iir源程序对输入波形sine2进行低通滤波，并将结果输出至result.dat。 (Using FFT transform, spectral analysis of sine2, draw the spectrum graph use the input waveform iir source sine2 to low-pass filtering, and the results output to result.dat.)

some simulation of dtc coommand my name is belmhel ahmed my tel 0797611456

this source code is transormers 2 code

经典的瑞利衰落信道仿真模型-jakes模型。程序可以修改不同的入射路径个数。仿真结果是不同入射路径个数的jakes模型和同分布的瑞利衰落的包络分布对比。直接运行“tu.m”(The classic Rayleigh fading channel simulation model-jakes model. Program can modify the number of incident path. The simulation results compare jakes model of different incident paths with distribution of Rayleigh fading.)

说明：  RBF神经网络例子，有助于学习径向基神经网络。matlab实现学习例程。(An example of RBF neural network is helpful to learn radial basis function neural network)

最全的基于MATLAB的30个神经网络案例分析pdf文件，适用于模式识别以及数学建模(Most complete 30 based on MATLAB neural network case analysis of PDF file, suitable for pattern recognition and mathematical modeling)

二分法的Matlab程序实现。求解非线性方程f(x)=0的问题。而方程f(x)是多项式或超越函数又分为代数方程或超越方程。(Dichotomy Matlab program. Solving nonlinear equations f (x) = 0 problems. The equation f (x) is a polynomial or a transcendental function is divided into algebraic equations or transcendental equation.)

说明：  1， 自动去除导频，空子载波的simulink模块 2， 接下来是转二进制的模块（集成在解交织解卷积模型里，用一个Interger2Bit实现） 3， 然后是解交织解卷积模型—deconv. 负责解交织和解卷积 4， 再就是解RS解随机化模型—decode_new. 负责解RS解随机化 5， decode.m文件： 负责主程序的运行，实现参数的输入和修改，支持各种调制编码方式 (1, automatically remove the pilot, take advantage of the simulink Carrier Module 2, followed by a binary switch module (integrated in deinterleave deconvolution model, with a Interger2Bit achieve) 3, followed by deconvolution model deinterleave-deconv . responsible for reconciliation deinterleave convolution 4, Then solution RS solution model random-decode_new. responsible solution RS solution randomized 5, decode.m documents: the main program is responsible for the operation, to achieve input and modify parameters to support a variety of modulation encoding)

[model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an SVM classifier and returns trained model. X is the matrix of training examples. Each row is a training example, and the jth column holds the jth feature. Y is a column matrix containing 1 for positive examples and 0 for negative examples. C is the standard SVM regularization parameter. tol is a tolerance value used for determining equality of floating point numbers. max_passes controls the number of iterations over the dataset (without changes to alpha) before the algorithm quits.

低通IIR滤波器的源码，简单有效。低通IIR滤波器的源码，简单有效(Low-pass IIR filter source, simple and effective. Low-pass IIR filter source, simple and effective)