OFDM

clustring(Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jö rg Sander and Xiaowei Xu in 1996.[1] It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature.

很经典的fisher分类算法，可以用于对信号进行分类，非常好用！(Very classic fisher classification algorithm, and can be used to classify the signal, is very nice!)

精通MATLAB数字图像处理与识别第二章练习代码(Proficient in MATLAB digital image processing and recognition chapter exercises code)

求单变量反馈控制回路的输出时间延迟 X为闭环回路输出时间序列 寻找时间延迟tau：即Sdeltmean第一个极小值点对应的t(Find a single variable feedback control loop time delay the output of X is closed-loop output time series to find the time delay tau: the Sdeltmean the first minimum point corresponding to t)

数字图像 的小波算法及 快速小波变换(Digital image wavelet algorithm and fast wavelet transform)

瑞利信道与加性高斯白噪声Matlab自编源代码(Rayleigh channel with additive white Gaussian noise, self-compiled Matlab source code)

数字方阵，实现基本的方阵功能，保证功能的完整 (Digital matrix)

线性杆元以及二次杆元有限元分析时可直接利用的编码(Linear bar element and the second pole element finite element analysis can be directly used in the encoding)

cosine transform and wavelet transform

A new blind adaptive multiuser detection scheme based on a hybrid of Kalman filter and subspace estimation is proposed. It is shown that the detector can be expressed as an anchored signal in the signal subspace and the coefficients can be estimated by the Kalman filter using only the signature waveform and the timing of the desired user.(A new blind adaptive multiuser detection s cheme based on a hybrid of the Kalman filter and subs pace estimation is proposed. It is shown that th e detector can be expressed as an anchored signa l in the signal subspace and the coefficients ca n be estimated by the Kalman filter using only th e signature waveform and the timing of the desir ed user.)