卷积深度信念网络进行图像建模、分类等应用代码(Convolution depth belief network for image modeling, classification and other application code.)

tensorflow框架下的卷积层，池化层说明与样例，代码(Convolution layer, pooling layer illustration and sample code)

所附代码提供了上述三种反卷积策略的实现。要开始，用户应该检查文件demo.m。反褶积子程序在以下文件中实现:deconvL2频率.m:在频域反褶积，假设高斯导数在前面。2.deconvL2.m:利用共轭梯度算法，假设在空间域上存在高斯导数先验的反褶积。3.deconvSps.m:利用迭代重加权最小二乘算法，在空间域上假设一个稀疏导数先验的反褶积。(The enclosed code provides the implementation of the above three deconvolut

OFDM code with 16-QAM modulation. Can be easily Understood and modified.(This code basically computes the BER of an OFDM system. The ifft size is 64 .16-QAM is the modulation Technique and convolution encoding rate 1/2 is used as the coding scheme.)