guidedfilter_color

基于小波变换的图像增强，包含整个处理过程(Image enhancement based on wavelet transform, including the whole process)

简化洛伦兹系统的同步方法编程，具有可通用性，简单易学(Simplify the programming of synchronization of Lorenz system has can be versatile, easy to learn)

 Subcarrier Allocation in a Multiuser MIMO Channel Using Linear Programming (err)

egion growing approach is the opposite of the split and merge approach: An initial set of small areas are iteratively merged according to similarity constraints. Start by choosing an arbitrary seed pixel and compare it with neighbouring pixels. Region is grown from the seed pixel by adding in neighbouring pixels that are similar, increasing the size of the region. When the growth of one region stops we simply choose another seed pixel which does not yet belong to any region and start again.

将一个512×512的lena.bmp图像进行DCT变换，对得到的DCT系数按8×8块进行分层（分三层），重组填充得到新的系数矩阵，结果发现原图像的能量集中在了新图像的左上角低频部分。(The lena.bmp an image of 512 × 512 is subjected to DCT transform, the DCT coefficients obtained by layering 8 × 8 block (of three), to obtain a new recombinant filling coefficient matrix, the energy of the original image is found concentrated in upper left corner of the low-frequency part of the new image.)

This code will display a gear in 3D. this code is done in MATLAB.

惯性测量单元的C源代码 用于测量物体的姿态变化 可应用与惯性导航 小飞机等设计(IMU(inertial measurement unit) source code)

李雅普诺夫指数时需用到的两个参数，即时间延迟与嵌入维数的求其方法(When Lyapunov exponent required to two parameters, namely the time delay and embedding dimension seeking its methods)

信号稀疏表示是一种新兴的信号分析和综合方法，其目的就是在过完备字典中用尽可能少的原子来表示信号。采用时频原子字典的信号稀疏表示能够有效地揭示非平稳信号的时变特征。信号稀疏表示吸引了研究者的大量关注，这种方法已经被应用到信号处理的许多方面，例如非平稳信号分析，信号编码、识别与信号去噪等。(Signal sparse representation is a new method of signal analysis and synthesis, and its purpose is in over-complete dictionary of atoms using as little as possible to represent the signal. When using frequency atom dictionary signal sparse representation can effectively reveal the time-varying characteristics of non-stationary signals. Signal sparse representation attracted a lot of attention of researchers, this method has been applied to many aspects of signal processing, such as non-stationary signal analysis, signal encoding, recognition and signal denoising.)

SVD滤波方法，主要是通过对信号进行奇异值分解，将噪声部分的值归零，再进行信号的重构，顺利地进行滤波。(SVD filtering method, mainly through singular value decomposition of the signal, the noise part of the value of zero, and then signal reconstruction, smoothly filtering.)