hs

基于前馈补偿的PID控制算法例程。可以参考下，matlab下的(Feedforward compensation based on the PID control algorithm routines. Can refer to, matlab under)

雷达课程的作业，在了解雷达原理后，对实际过程的仿真，有Chirp的形成，匹配滤波器，MTI，MTD的具体实现(Concrete realization of radar courses operating in the understanding of the radar principle, the simulation of the actual process, the formation of the Chirp matched filter, MTI, MTD)

说明：  Leslie模型实例：基于Leslie矩阵的算法，同时将人口按10年一组分成m个年龄段，并根据一些影响人口数量和结构的重要因素（建立模型。再根据我国近十年的二孩出生率的实际情况并结合少数民族不受影响的因素，通过Matlab画出拟合曲线对2017-2030年我国每年人口总数做出预测。此后，根据近十年0-14岁、15-64 岁、65岁及以上三个年龄段的人数，计算出人口抚养比，用Matlab预测至2030年的人口结构。为了使预测更加精确，我们还将夫妇的生育意愿考虑到模型当中。(Based on the Leslie matrix algorithm, the population is divided into m age groups according to a group of 10 years, and according to some important factors that affect population size and structure Modeling. Then according to the actual situation of the birth rate of second-born children in China in the past ten years and combining the factors that are not affected by ethnic minorities, drawing a fitting curve through Matlab will make a prediction for the total population of China in 2017-2030. After that, based on the number of people aged 0-14, 15-64, and 65 years of age in the last ten years, calculate the dependency ratio of the population and use Matlab to predict the population structure by 2030. In order to make the prediction more accurate, we also consider the couple's fertility intention in the model.)

进行LTE的预编码时，所需要的码本，在lTE系统的链路级仿真中，发端的设计编程。(codebook，LTE)

龙格库塔法源码 解决问题的matlab例程(Runge-Kutt ode45)

私服系统低速摩擦的条件下PID控制Matlab仿真程序源码(私服system under conditions of low-speed friction PID control simulation program Matlab source code)

采用的是脉冲对消法，计算多重分形非趋势波动分析，利用matlab GUI实现的串口编程例子。( It uses a pulse of consumer law, Calculate the multifractal trend fluctuation analysis, Use serial programming examples matlab GUI implementation.)

说明：  simulating a 3 phase voltage with various frequencies with matrix converter

该函数实现标准的卡尔曼滤波算法，用于状态估计，目标跟踪(kalman filter)

The problem of image registration subsumes a number of problems and techniques in multiframe image analysis, including the computation of optic flow (general pixel-based motion), stereo correspondence, structure from motion, and feature tracking. We present a new registration algorithm based on spline representations of the displacement field which can be specialized to solve all of the above mentioned problems. In particular, we show how to compute local flow, global (parametric) flow, rigid flow resulting from camera egomotion, and multiframe versions of the above problems. Using a spline-based description of the flow removes the need for overlapping correlation windows, and produces an explicit measure of the correlation between adjacent flow estimates. We demonstrate our algorithm on multiframe image registration and the recovery of 3D projective scene geometry. We also provide results on a number of standard motion sequences.