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多种小波去噪的方法，包括阈值，一维和二维(Multiple wavelet denoising methods, including threshold, one and two dimensional)

用vc把相片的信息读入电脑,然后可以利用矩阵将数据进行编辑，以达到某种效果！(read the information of photo into the computer.)

MATLAB 信处理基本函数，对信号处理仿真初学者有一定的帮助(matlab_signal_processing_function)

采用symmetrized split-step method数值求解非线性薛定谔方程(Numerical solution of nonlinear Schrodinger equation by symmetrized split-step method)

The program about sliding mode control: chap1_1ctrl.m chap1_1plant.m chap1_1plot.m chap1_1sim.m ....

gravitational search algorithm 1

多种降维方法的一个程序包，包括PCA,ISOMAP, LLED等,GUI，界面直观简单(A variety of dimensionality reduction method package, including PCA, ISOMAP, LLED etc., GUI, simple and intuitive interface)

1、验证窗函数 N 变化时，验证其频谱主瓣副瓣比、主瓣宽度的变化。 a)矩形窗函数的 N 变化时，验证其其频谱主瓣副瓣幅度比基本不会发生变化， 而主瓣宽度将会变窄。这说明，当用矩形窗函数设计滤波器时，增大 N 不能使 得阻带衰减减小，但能够减小过渡带。 b)再选取其他的窗如 hamming/hanning 窗，验证当 N 变化时，其频谱主瓣宽 度变化、主瓣副瓣比值变化情况。 2、用窗函数法设计线性相位 FIR 低通，通带截止频率 wp=0.5PI, 阻带截止频率 ws=0.6PI, 阻带衰减不小于 40dB,通带衰减不大于 3dB. a)选取 Hanning,Hamming 窗查看设计出来的 FIR 的过渡带宽和阻带衰减是否 满足要求，二者有什么不同。 b)使用 hamming 窗，将窗长增大 1 倍，设计 FIR。验证同样的窗函数类型 (hamming)，不同窗长度时，设计出来的 FIR 的过渡带宽和阻带衰减都有什么变 化。 3、不使用 Matlab 系统函数得到上一项指标的低通，并与上一项结果比较。 4 用窗函数法设计线性相位 FIR 高通，通带截止频率 wp=0.8PI, 阻带截止频率 ws=0.7PI, 阻带衰减不小于 30dB,通带衰减不大于 3dB.(An actual filter is implemented using the truncated unit impulse response sequence of the ideal filter. For the ideal low pass filter, the unit impulse response h, N, D () is intercepted by a length of N, and the length is The N sequence H (n), when intercepted, guarantees causality and linear phase requirements for the filter. In order to reduce the Gibbs effect, the H (n) window is added, and the appropriate window function is selected to ensure the stopband attenuation and Transition zone requirement. Note that the sidelobe of the window function affects the stopband attenuation of the filter, and the main lobe width affects the filter Transition bandwidth.)

MATLAB CODE FOR SIMULATION OF A SUCCESSIVE APPROXIMATION CONVERTER(MATLAB CODE FOR SIMULATION OF ASUCCESSIVE APPROXIMATION CONVERTER)

Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model.