xierbotebianhuan

利用CVX进行优化，得到期望加权矢量，从而的得到需要的波束形成(CVX using optimized to give the desired beamforming)

时频分析，同步挤压小波变换，一种更精细的时频表示！(Signal time-frequency analysis, synchronous extrusion wavelet transform, a finer time-frequency said!!!!)

用matlab编写找到向量维度的波峰波谷的值.(Prepared using matlab to find vector value of the dimension of the peaks and valleys.)

本人自己编写的小波变换脊提取程序，可用与信号分析．欢迎指导．(I have written wavelet transform ridge extraction procedures, can be used with the signal analysis. Welcomed the guidance.)

mimo_BP用于十字型阵列雷达，利用匹配滤波理论进行算法仿真(mimo_BP Used for cross-array radar, using matched filter theory for algorithm simulation)

提取图像显著图，小波融合 非常详细，有用(Extract image saliency map, wavelet fusion is very detailed, useful for)

一维信号的小波去噪,这是一个很好的基于小波变换的小波去噪，是针对一维的，去噪效果非常好！(1DWT wavelet denoide)

Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously with a smooth reconstructed image. Both contourlets as well as complex-valued dual-tree wavelets are considered for their highly directional representation, while bivariate shrinkage is adapted to their multiscale decomposition structure to provide the requisite sparsity constraint. Smoothing is achieved via a Wiener filter incorporated into iterative projected Landweber compressed-sensing recovery, yielding fast reconstruction. The proposed approach yields images with quality that matches or exceeds that produced by a popular, yet computationally expensive, technique which minimizes total variation. Additionally, reconstruction quality is substantially superior to that from several prominent pursuits-based algorithms that do not include any smoothing

说明：  傅立叶功率谱法求纹理特征 可解压缩后直接执行(Fourier power spectrum method for texture features can be extracted directly after implementation)

主要用于图像经小波变换压缩图像，并计算压缩后的图像压缩性能即压缩比和信噪比(Mainly used for image compression by wavelet transform image, and calculate the compressed image compression performance that the compression ratio and signal to noise ratio)