MEMS

说明：  Kalman滤波器仿真研究，包括自己写的小论文及其详细的MATLAB程序仿真。(Kalman filter simulation studies, including a small paper to write my own detailed procedures for the MATLAB simulation.)

正弦信号加载2 的白噪声后的功率频谱图以及加载后的失真度计算(Sinusoidal signal loading of 2 white noise power spectrum and the calculated distortion after loading)

贝叶斯分类器MATLAB经典程序 朴素贝叶斯分类matlab实现 贝叶斯分类算法(bayes matlab program)

基于Matlab的CDMA通信系统下行信道的仿真_张兵(CDMA communication system simulation )

采用matlab实现图像融合算法研究，内容不错。(Using matlab to achieve image fusion algorithm, the content is true.)

This is face recognition system developed using matlab. this is a paper best describes methods of face recognition using matlab.

提升法97经典程序此程序是矩阵实现，所以图像水平分量和垂直分量估计被交换位置 为了保证正交，所有边界处理，全部采用循环处理(upgrade classic procedures of this process is to achieve matrix, So images of horizontal component and the vertical component is estimated to be exchanged orthogonal position to ensure that all borders, the use of cycles)

计算已知激励频率以及平板厚度情形下的Lamb波传播组群速度及相速度(calculate Lamb waves phase velocity and group velocity)

信号在信道进行传输时，经常会出现串扰，采用基于最大似然序列估计（MLSE）的电子色散均衡器的方法，克服光纤通信中的由各种色散引起的码间干扰。 研究基于最大似然序列估计（MLSE）的均衡器，采用维特比算法实现，用matlab仿真出采用MLSE后的性能指标（眼图和误码率）的提升。 (Signal transmission channel, often occur crosstalk, based on the maximum likelihood sequence estimation (MLSE) method for electronic dispersion equalizer, overcome the optical fiber communication in a variety of dispersion caused by intersymbol interference. Research Based on the maximum likelihood sequence estimation (MLSE) equalizer, using the Vitby algorithm, the performance index using MLSE after matlab simulation (eye diagram and bit error rate) promotion.)

奇异谱分析是近年来兴起的一种研究非线性时间序列数据的强大的方法。它根据所观测到的时间序列构造出轨迹矩阵，并对轨迹矩阵进行分解、重构，从而提取出代表原时间序列不同成分的信号，如长期趋势信号、周期信号、噪声信号等，从而对时间序列的结构进行分析，并可进一步预测。(Singular spectrum analysis is a powerful method to study nonlinear time series data in recent years. According to the time sequence structure of the observed path matrix, and the trajectory matrix is decomposed and reconstructed, thus extracting the signal representative of the original time series of different components, such as the long term trend signal and periodic signal and noise signal, through analyzing the structure of the time series, and further predict.)