tezheng

基于MATLAB的信号处理，经过脉冲压缩时加窗与不加窗对比以及分析。(MATLAB-based signal processing, after pulse compression and non-add window add contrast as well as the analysis window.)

心率数据库的导入,包含头文件数据文件和注释文件(MIT/BIHdata insert,including .hea .dat .atr)

运用传输矩阵法计算仿真一维光子晶体双缺陷(use TTM calculating the double defects of 1D PC)

说明：  matlab求解TSP问题源代码，程序可动态显示路径寻优过程(matlab source code for solving TSP problems, the program can show the path of dynamic process optimization)

its says about finger print technology

Robust control for matlab

说明：  BESS通常用于负载均衡，调峰，负载转移应用等。 此BESS块从工作空间获取每小时负载曲线（kW）输入，并计算电网和电池使用量输出到工作空间。 必须以两列格式准备负载配置文件，其中第一列是从一天的0小时开始的时间。第二列是以kW为单位的功耗。（例如，将SampleLoadProfile.mat加载到工作空间中）。 模拟运行时间是从一天的0小时开始的每小时单位。例如，为了模拟24小时负载配置文件，模拟运行时间设置为23，一周运行时间设置为167，一个月30天运行时间设置为719和31天运行时间设置为743。 在“求解器”选项下的模型配置参数中，设置为固定步长类型并将固定步长（基本采样时间）设置为1并运行模拟。(BESS are commonly used for load leveling, peak shaving, load shifting applications and etc. This BESS Block takes hourly Load Profile (kW) input from workspace and compute the Grid and Battery usage output to workspace. The load profile has to be prepared in two column format, where the first column is time starting from 0 hour of the day. The second column is power consumption in kW unit. (For example, load the SampleLoadProfile.mat into workspace). The simulation run time is in hourly unit starting from 0 hour of the day. For example to simulate a 24 hours load profile, the simulation run time is set to 23, one week run time is set to 167, one month 30 days run time is set to 719 and 31 days run time set to 743. In model configuration parameters under Solver options, set to fixed-step type and set the fixed-step size (fundamental sample time) to 1 and run the simulation.)

说明：  经典MUSIC算法仿真，并有信号相干情况下，采用前向以及前后向平滑技术估计波达方向。(Classical MUSIC algorithm simulation, and a signal coherent case, the forward and after smoothing technique to estimate DOA.)

this file simulate Direct Sequence Spread Spectrum Transmitter/Receiver systems With Time-Synchronization

Generalization ability of extreme learning machine with uniformly ergodic Markov chains