HuSquare

基于matlab的ofdm系统仿真，通过仿真实现了ls 和LMMSE准则的信道估计，并仿真比较了两种方法的性能(Matlab ofdm systems based on the simulation, the simulation criteria to achieve the ls and LMMSE channel estimation, and simulation performance of the two methods compared)

说明：  harris 角点检测 MATLAB 代码(Harris corner detection MATLAB code)

说明：  模拟退火算法各个文件，帮助你求解tsp问题(The simulated annealing algorithm)

城市轨道交通的列车间隔追踪仿真，主要在不同最大速度下的追踪时间间隔(Urban rail transit train interval tracking simulation, mainly in the different maximum speed tracking time interval)

H无穷在汽车减震系统应用的仿真,有系统的具体数值及仿真图像 (H∞simulation used in the automobile suspension system，Concreting numerical and simulation system images )

OFDM系统性能分析，参数设定，原理验证，通过该程序更清楚的知道系统性能。(Principle of OFDM system performance analysis, parameter setting and verification, by the program more clearly know the performance of the system.)

用MATLAB对单载波频域均衡进行仿真，并绘制出误码率仿真的结果。(Using MATLAB on a single carrier frequency domain equalization simulation, and to map out the bit error rate simulation results.)

数据挖掘的作业，基于DBSCAN的分类，数据集为iris，可以学习下(Data mining, classification based on DBSCAN, the data set is iris, you can learn under )

大学大作业，量化的压缩感知1-bit的各种方法，FPC，AOP，CoSaMP等等。(University of large operations, a variety of methods to quantify the compressed sensing of 1-bit, FPC, AOP, CoSaMP so.)

MUSIC算法[1] 是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(algorithm MUSIC is a method based on matrix feature space decomposition. From a geometric point of view, the signal processing of the observation space can be decomposed into signal subspace and noise subspace, it is clear that these two spaces are orthogonal. Signal subspace by the array received data covariance matrix and signal corresponding to the feature vector and noise subspace is by the covariance matrix of all minimum feature values (noise variance) corresponding to the feature vectors are composed. MUSIC algorithm is to use the orthogonal characteristics of these two complementary space to estimate the azimuth of spatial signals. All vectors of the noise subspace are used to construct spectral, and the peak position in all spatial azimuth spectra correspond to the direction of arrival of the signal. MUSIC algorithm greatly improves the direction finding resolution, and at the same time it is suitable for the antenna array of any shape, but the prototype MUSIC algorithm requir)