mtl_reg_sim_complinearized

使用Matlab编程，对教材74页例3.3.1进行编程仿真。其中，正电压A的值、噪声方差值、每个码元周期内的采样点数N自行设定（可设置为可调的变量）。噪声值可使用Matlab中产生高斯随机数的函数来进行仿真。贝叶斯检测判决式中，先验概率P(H1)=P(H0)，判断错误的代价因子设为1，判断正确的代价因子设为0. 按照上述参数的设定进行仿真，实现对仿真数据的贝叶斯检测判决；循环生成仿真数据，并进行判决结果的统计，即记录判决正确的次数，估计正确判决的概率。(The use of Matlab programming, teaching materials 74 pages of 3.3.1 programming simulation. Among them, the value of positive voltage A, the variance of noise, the number of sampling points in each symbol cycle (N) can be set by itself (adjustable variable). Noise values can be simulated using the function of generating Gauss random numbers in Matlab. In the Bias detection decision, the prior probability P (H1) =P (H0), the factor of error judgment is set to 1, and the correct cost factor is set to 0. According to the simulation parameters and realize the judgment of Bayesian detection simulation data; generating cycle simulation data, statistics and decision, which records the correct number of judgment, the estimated probability of correct decision.)

基二、基四、基八 FFT变化源代码，有详细注解(Second base, the base four or eight FFT-based changes in the source code, detailed comments)

求函数极小值，适用于不带约束的、函数可以为非线性的无约束规划问题的求解(Demand function is minimum applicable to non-binding, the function can be non-binding for the non-linear programming problem solving)

CHEMKIN 2 源代码，使用Fortran语言编写，需要的可以下载(CHEMKIN 2 source code, written in Fortran, need can be downloaded)

复合材料强度计算的程序，采用与ansys软件集成后计算(Composite material strength calculation procedures, the use of ANSYS software and the integration of computing)

Douglas-Peucker algorithm for a 2-dimensional data with example in matlab.

基于最小二乘的多项式拟合法的GPS周跳探测程序(Based on the least square polynomial fitting method of GPS cycle slip detection program )

用简单易懂的图文方式阐释算法的奥秘，快速了解每个算法的使用场景、原理(Explain the mysteries of the algorithm with simple and understandable graphics and text, and quickly understand the scene and principle of each algorithm)

based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature.

Edge element based 2D FEM code to calculate Electromagnetic Simulations