bellman_1

该代码主要实现了OVTDM(时域重叠复用系统)的仿真，包含脉冲成型、匹配滤波，白化滤波，改进的viterbi译码等。(This code is mainly to simulate the OVTDM system.)

ofdm最简单平台，加入加兴高斯白噪声，其他模块需要自己添加(ofdm The easiest platform)

Poincare截面的matlab程序实现(Poincare section of the matlab program to achieve)

频点滤波代码 设计滤波器对干扰信号衰减，增强期望信号 从而取得滤波结果(Frequency filtering code design filter attenuation of interfering signals, and enhance the desired signal to obtain the filtering results)

The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (e.g. road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented.(The goals of this tool is to manipulate the fast marching algorithm in 2D and 3D. Application to shortest path extraction (eg road tracking and tubular structure extraction in medical images), shape statistics and geodesic remeshing are presented.)

integrated photonics ebook

function [a,b] = pade(x,p,q) PADE Model a signal using the Pade approximation method ---- Usage: [a,b] = pade(x,p,q) The input sequence x is modeled as the unit sample response of a filter having a system function of the form H(z) = B(z)/A(z) The polynomials B(z) and A(z) are formed from the vectors b=[b(0), b(1), ... b(q)] a=[1 , a(1), ... a(p)] The input q defines the number of zeros in the model and p defines the number of poles.

matlab程序设计的符号设计，有利于初学者了解MATLAB的编程思想与方法，使初学者快速入门(Matlab programming symbol design, be helpful for beginners to learn matlab programming thought and method, make the beginner quick start)

计算视频图像中每一帧的能量,matlab 代码，可以用于分析图像原能量与编码解码后，误差与原图像能量的倍数关系(computing the energy of each frame in image processing)

SVM方法的基本思想是：定义最优线性超平面，并把寻找最优线性超平面的算法归结为求解一个凸规划问题。进而基于Mercer核展开定理，通过非线性映射φ，把样本空间映射到一个高维乃至于无穷维的特征空间（Hilbert空间），使在特征空间中可以应用线性学习机的方法解决样本空间中的高度非线性分类和回归等问题。svm 程序，即支持向量机的代码。(The basic idea of SVM method are: the definition of the optimal linear hyperplane, and the search algorithm for optimal linear hyperplane by solving a convex programming problem. Then based on Mercer nuclear expansion theorem, through a nonlinear mapping φ, the sample space is mapped to a high-dimensional and even infinite dimensional feature space (Hilbert space), so that in the feature space can be applied to solve the linear learning machine method, the sample space The highly nonlinear classification and regression problems. svm procedures that support vector machine code.)