matlab

用matlab实现灰色序列残差预测模型GM（1，1）(Using matlab realize residual sequence gray prediction model GM (1,1))

正弦光栅和一个其他范例代码，大家可以根据这些进行改写，修改自己的参数(Sinusoidal grating and other examples of code, you can rewrite these, modify their own parameters)

说明：  关于心电信号的各种滤波方法，IIR，FIR，空域相关，软硬阈值，基于IIR的自适应滤波等(the method for ECG denoising,such as IIR,FIR,spatial correlation,soft and hard threshold,based on IIR filter s adaptive filter)

雷达matlab仿真，波束形成，角度测量，跟踪等等(radar simulation, beamforming, angle measurement, trace and so on)

说明：  此程序为均匀线阵情况下的相干信号入射后，阵列所采集的数据，各种参数说明见文件内部(This procedure for uniform linear array of coherent signals incident cases, the array of data collected, within the parameters described in the file)

针对模型中人为设定参数值的不合理性，对机动时间常数的倒数进行建模，提出新的模型，并给出相应的α-Jerk滤波算法。该模型在估计目标状态的同时，能够实时估计参数α的值。仿真结果表明α-Jerk模型比Jerk模型收敛速度更快，状态估计更精确。并与jerk算法进行了比较。(Artificially set for the model parameter values ​ ​ of the irrational nature of the reciprocal of the motor time constant modeling, and propose new models and the corresponding alpha-on Jerk filtering algorithm. The model to estimate the target state at the same time, real-time estimate of the value of the parameter α. The simulation results show that alpha-on Jerk model faster convergence than the Jerk model, state estimation more accurate. And jerk algorithm.)

用Matlab实现的一个星路径查找的源码 很完整很详细(Find the source code using Matlab achieve a star path is very complete and very detailed)

serarch even code in matlab

第一，二种边界条件的三次样条插值法matlab代码(First, the two kinds of boundary conditions of the cubic spline interpolation matlab code)

In this session, I will focus on the gridding method for fast image reconstruction from non- Cartesian data [1-5]. While gridding is not the only game in town, it is the reconstruction of choice when reconstruction time is an issue. Other reconstruction techniques include rBURS [6] and non-Cartesian iterative methods [7,8]. Non-Cartesian iterative methods generally use gridding and inverse gridding as their core operation, so even when gridding is not the right solution for an application, an understanding of gridding can be useful. I will cover the main steps of the gridding algorithm and then discuss some implementation tips that can signicantly reduce the computation time. Finally, I will present some illustrations and viewpoints that I have found to aid in an intuitive understanding of the gridding method, which can be helpful when designing the density compensation function and judging the appropriateness of gridding as a solution for a particular application.