msk_demodulate

these are the gold codes used in the cdma systems.

matlab 与ENVI中数据格式转换，可以很好地实现两者之间的数据格式转换，包括enviwrite,enviread等(matlab with ENVI data format conversion, you can achieve a good conversion between data formats, including enviwrite, enviread etc.)

hardware implementation of digital filters for signal processor

差分脉冲编码调制（）的matlab模块仿真，实现抽样，量化，预测，编码并解码(DPCM)

这是关于层次分析法、聚类分析、主成分分析的源代码(This is about the level of analysis, cluster analysis, principal component analysis of the source code)

Extraire des maximas et minimas des signaux avec l axe temporel

蒙特卡洛模拟单离子一维鞘层代码,可作为入门学习使用(Monte Carlo simulation of a single one-dimensional ion sheath code)

SVM工具箱，可用于混合编程，需要按说明先对matlab进行相应的配置[farutoUltimateVersion1.0](SVM toolbox)

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)

采用K近邻算法预测时间序列的源代码，希望对大家能有所帮助。(Predict time series using kNN.)