load_parameters_dependent

自己编写的实现时间序列中的关建点搜索，在相似搜索和分类特征提取中常用,程序中有详细说明(achieve to find out the key point of time series ,it often to be used in similarity search and classification)

采用遗传算法优化传统PID控制，选取了合适的优化函数，采用实数编码(Genetic algorithm to optimize the traditional PID control, select the appropriate optimization function using real-coded)

used to calculate energy efficiency in wireless sensor nodes

A new method of itertions for dictionary traning via KSVD

利用相关分析法辨识系统的脉冲响应并比较离散、递推、矩阵三种算法结果，包括M序列的生成(Impulse Response Identification system using correlation analysis and comparison of discrete, recursion, matrix three algorithms results, including the M sequence generation)

Two-dimensional Discrete Wavelet Transforms (Two-dimensional Discrete Wavelet Transf orms)

勒让德多项式的拟合，包括对新的数值的求解(Legendre polynomial fitting, including the new value of solving)

：压缩感知是近年来应用数学界提出的一套关于稀疏信号采集和重构的新理论，它突破了传统奈奎斯特采样定理 的限制，以远少于传统奈奎斯特采样定理所需的测量数据就能够精确恢复原信号或估计信号的相关参数。将压缩感知理论应 用到DOA估计，可以解决传统DOA估计中高采样率、以及较多辐射源信号条件下难以定位的限制。研究了基于压缩感知理 论的DOA估计方法，并利用MATLAB进行仿真，通过与传统MUSIC算法比较可知，基于压缩感知的DOA估计方法具有显著 的优势(The theory of Compressive Sensing(CS)is the application of mathematics in the field of a sparse signal acquisition and reconstruction of the new theory. It breaks through the traditional limitation of Nyquist sampling theorem. It can recover certain signals or estimate their parameters far fewer samples or measurements than traditional methods. The application of compressed sensing theory to DOA estimation can solve the traditional DOA estimation in high sampling rate, as well as more of the radiation source signal conditions is difficult to position limits. This paper studies the theory of DOA estimation method based on compressed sensing theory, and simulation uses MATLAB, compared with the traditional MUSIC algorithms, DOA estimation based on compressed sensing theory method has the obvious advantage)

合成孔径雷达（SAR），正侧视情况下的距离多普勒算法（RDA），多点目标仿真，Matlab语言。(Range Doppler Algorithm of Synthetic Aperture Radar in side_looking using Matlab)

Hidden_Markov_model_for_automatic_speech_recognition This code implements in C++ a basic left-right hidden Markov model and corresponding Baum-Welch (ML) training algorithm. It is meant as an example of the HMM algorithms described by L.Rabiner (1) and others. Serious students are directed to the sources listed below for a theoretical description of the algorithm. KF Lee (2) offers an especially good tutorial of how to build a speech recognition system using hidden Markov models.