SAR-rd

改程序是由于降低papr的包括信道的产生，接收机的设计以及图形的打印(PAPR)

tutorial matlab berbahasa indonesia

电力系统潮流计算，matlab编程，适合电力系统大三大四学生课程设计(Power flow calculation system )

基于脉冲耦合神经网络的图像分割 ,初学者使用 (Image segmentation based on pulse coupled neural network, for beginners to use)

说明：  这是求解连通图的最小生成树的一种经典算法：Kruskal 算法！值得推广，还带例题哦!(This is a connected graph of the minimum spanning tree for solving a classical algorithm: Kruskal algorithm! Worth promoting, but also with examples Oh!)

说明：  simulink的PMSM svpwm模型。(simulink model of the PMSM svpwm.)

TDOA matlab实现对信号的时延估计，代码量少，经过验证(TDOA matlab implementation of the signal delay estimation, less code, proven)

EA名称：套利精灵 EA类型：对冲套利 一、功能介绍 本EA遵行顺大势，逆小势策略入场交易； 二、核心优势 多货币套利，对冲风险获取盈利，每月有一次机会单局最大利润达到30% 三、注意事项 （1）如适用周期：该EA只须在欧美一分钟图表上，就能运行多对货币。 （2）资金要求：建议3000美金，最少入金不少于1000美金 （3）时间段：全时段，正常情况无需人工干预，在盈利的情况下，可视情况自行平仓;(EA Name: Arbitrage Elf EA Type: Hedging Arbitrage I. Introduction of Functions This EA follows the general trend and counter-minor strategy to enter the market. II. Core Advantages Multi-currency arbitrage, hedging risk for profit, once a month, the maximum profit of a single board reached 30%. Notes (1) If applicable: the EA can run multiple pairs of currencies only on a one-minute chart in Europe and the United States. (2) Capital requirements: Recommend $3000, at least $1000 (3) Time period: Full-time period, normal conditions without manual intervention, in the case of profit, according to the circumstances of self-liquidation;)

从均匀PCM方法入手，产生一振幅为1和频率ω=1的正弦信号，分别用8电平和16电平进行量化，在同一坐标轴上绘出原信号和以量化信号，并比较这两种情况下的信号/量化噪声比(Starting from the uniform PCM method to produce a 1 amplitude and frequency ω = 1 of the sinusoidal signal, respectively, with 8 levels and 16 levels were quantified in the same plot of the original signal and the axes to quantify the signals, and to compare these two cases, the signal/quantization noise ratio)

In order to reproduce Figure 3 in the Replica Paper, do the following in order: 1. Run "Linear_test.m" 2. Run "Lasso_Optimal Regularization.m" 3. Run part 0,1 and 2 (not part 3) from "Lasso_test.m" 4. Run "Zero Norm Regularization_Optimal Regularization.m"