Iometer

神经网络解决旅行商问题，讨论了矩阵大小对算法稳定性的影响。土木工程领域中，常用于进行结构参数识别等。(Neural network to solve the traveling salesman problem, discussed the impact of the size of the matrix algorithm stability. The field of civil engineering, structural parameters commonly used in identification.)

云模型的matlab实现~具体实例~~~~~~~~~~~~~~~~~~~~~~~~~~(Cloud model implementation of the matlab ~~~~~~~~~~~~~~~~~~~~~~~~~~ ~ concrete examples)

hilbert spectrum and DFT

拉格朗日多项式拟合卫星精密星历。。。。。(lagrange)

脑波FFT变换 调用格式见m文件头部说明（下边也复制了一份） 有配套.DLL.h.lib.ctf文件，可以在C或C++环境中调用 ------------------------------------ 保存为FFT.m文件 输入参数： N：进行FFT变换的点数（要求是2的N次幂）； fs：信号的采样频率（frequency of simpling） sig：进行FFT变换的数组（一维）。 输出参数： f_sig:变换后的信号（一维数组） E_delta、E_theta 、E_alpha、E_beta分别表示δθαβ相应频段的能量加和。 other为其余频段的能量加和（供参考用） -------------------------------------------------------------------------- function[ f_sig ,E_delta , E_theta , E_alpha , E_beta , other ] = FFT( N , fs , sig )(EEG FFT transform is called m file format, see head illustrate the matching DLL files, you can be C or C++ to call------------------------ simpling) sig: to FFT transform array (one dimension). Output Parameters: f_sig: transformation of the signal (one-dimensional array) E_delta, E_theta, E_alpha, E_beta δθαβ, respectively, the energy increases and the corresponding frequency band. Other for the rest of the energy band increases, and (for reference) -------------------------------------------------------------------------- function [f_sig, E_delta, E_theta, E_alpha, E_beta , other] = FFT (N, fs, sig))

利用MATLAB实现图像拼接，所谓图像拼接就是将有共同点的两张或者多张图像进行融合。(Use MATLAB to achieve image mosaic, image mosaic is the so-called common ground will be two or more fused images.)

偏微分方程的MATLAB解法,包括偏微分方程工具箱，图形用户界面，典型方程及实用例子，工具箱命令，有限元和有限差分法常微分方程解法(matlab solutions for partial differential equation )

(cvpr12)Automatic Mitral Leaflet Tracking源代码((cvpr12)Automatic Mitral Leaflet Tracking)

High voltage electricity use, short-circuit fault indicator! Sensor plus display instrument procedures

说明：  GA算法，分步实现，更加清晰简单易学，快速掌握GA算法的使用(GA algorithm, step-by-step implementation, more clear and easy to learn, quick to master the use of GA)