TC

matlab code for a function of fsk

source code for Stereo matching algorithm

MATLAB的基础应用及粗糙集理论的基础知识（PDF）(The basis of MATLAB applications and rough set theory based on knowledge (PDF))

采用线性插值，双线性插值等方法进行图像匹配~~(Linear interpolation, bilinear interpolation method of image matching ~ ~)

matlab图像配准源程序，很实用的源码。(Image Registration matlab source code, source code is very useful.)

说明：  通过并行压缩进行二维信号压缩，提出了一种利用并行压缩传感技术对二维信号进行压缩的新方案。根据这个原理，译码器的重新构造可以并行进行。通过对二维信号进行某些排列，可以确保所有列具有大致相同的密度级别，并且可以使用相同的测量矩阵进行采样。这样可以避免二维信号的矢量化，从而大大减小测量矩阵的尺寸。我们证明，如果排列良好，重建均方误差的上限会更紧。为了说明这一方案，我们将其应用于视频压缩并使用不同帧的排列有两种：参考帧的基于Z字形扫描的排列和非参考帧的基于块测试的排列。仿真结果表明，在相同的压缩比下，峰值信噪比比比在无置换情况下可提高3-7db左右。(we propose a new scheme to compress 2D signals using parallel compressed sensing. According to this seheme, the reeonstruction at the decoder can be performed in parallel. By performing certain permutation on a 2D signal, all columns are insured to have approximately the same density level and can be sampled using the same measurement matrix. In this way, vectorization of a 2D signal can be avoided, and thus the size of the measurement matrix can be dramatically reduced. We prove that with a good permutation, we can have a tighter upper bound on reconstruction mean square error. To illustrate this scheme, we apply it to video compression and use two kinds of permutations for different frames: the zigzag-scanbased permutation for reference frames and the block-test-based permutation for non-reference frames. Simulation results show that under the same compression ratio, the peak signal-to-noise ratio can be improved by approximately 3-7 dB compared to the case without permutation.)

说明：  通过对比两电平，五电平，我们设计一种7电平切换，得到更平滑切换(By comparing two levels and five levels, we design a kind of 7-level switching to get smoother switching)

利用MATLAB的反馈网络-Elman神经网络处理时间序列问题的功能预测上证股市开盘价。(Elman Feedback network using MATLAB neural network for time series prediction problem function on card stock market opening.)

本文围绕压缩感知的三个基本问题, 从结构化测量方法、结构化稀疏表示和结构化信号重构三个方面对结构化压缩感知的基本模型和关键技术进行详细的阐述.(In this paper, the basic models and key techniques of structured compressive sensing are introduced in terms of the structured measurements, the structured dictionary representation and the structured signal reconstruction, which correspond to three basic aspects of compressive sensing.)

基于BP算法的 求解最优L1范数的程序和文章(BP algorithm based on L1 norm for solving optimal procedures and articles)