IdiomsA

说明：  个人做的AM调制数字解调simulink仿真模块，大家多交流(The AM modulation personal digital demodulation simulink simulation module, we more exchanges)

卷积码是一种有记忆的编码，在任意给定的时间单元处，编码器的n个输出不仅与此时间单元的k个输入有关，而且也与前m个输入有关。卷积码通常表示为：（n,k,m） 本次仿真采用（2，1，3）卷积码。 (convolutional code is a kind of code memory, in any given unit of time, the encoder output n this time not only with the elements of the k input, but also with the input m. Convolutional codes are usually stated as : (n, k, m), the simulation used (2,1,3) convolutional code.)

求两幅存在一定位移或偏差的RGB图像的均方根误差MSE和峰值信噪比PSNR(Seeking two RGB images there is a certain displacement or deviation of the root mean square error (MSE) and peak signal to noise ratio PSNR)

以前写的数值分析里的追赶法的代码,我自己看着是可用的(Previous numerical analysis method to write the code in the catch)

利用Matlab及施工过程中采集的点云数据绘制剖面图(Point cloud data collected in the use the Matlab and construction process to draw a cross-sectional view)

利用类平均距离法，对遥感图像进行分类，操作时调用函数(Use the average distance category, the classification of remote sensing images, call the function when)

codigo de electronica digital

直线开关磁阻电机直接力矩控制仿真，其中的磁链开关表部分不是特别完整(Linear switched reluctance motor direct torque control simulation)

保证可用的，希望好评！！！HOG+SVM进行图片中行人检测，提供训练用的pos和neg样本，效果还可以（也是本网站下载代码，但是原始的作者故意改错一些地方，我已经一一修正，完全可以运行；没有SVM工具箱的，压缩包里已经提供了，安装一下即可） (HOG+SVM pedestrian detection)

说明：  隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)