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样本熵,MATLAB 模糊熵,MATLAB 近似熵,MATLAB(Entropy is a measure of complexity. It quantifies the unpredictability of fluctuations in a time series.)

DQM法matlab代码，用于求解输液管道的失稳问题，在边界弹簧刚度变化的情况下，给出了临界失稳流速。(DQM method matlab code for solving the instability problem pipes conveying fluid, the spring stiffness in the border cases, given the critical buckling velocity.)

Mobile robot localization sample code with Extended Kalman Filter (EKF). This code is useful for mobile robot navigation is performed using a Kalman filter.

说明：  病态矩阵HILBERT，正则化高斯消元法，cholesky分解法(Ill-conditioned matrix HILBERT, regularization of Gaussian elimination method, cholesky decomposition)

用matlab集成工具包方便地实现神经网络，神经网络源代码 - Matlab工具箱的神经网络理论与应用程序(neutral net by matlab)

stbc for 2 tx and 2 rx

用SwiftLanguage编写的A Star Routing.(A Star Routing that is created with Swift Language.)

提出了一种新的宽带DOA估计方法:频域子空间正交性测试方法(TOFS:Test of orthogonality of frequency sub- space)。该方法通过同时测试频域信号共同带宽内各频段噪声子空间与阵列流形之间的正交性来进行DOA估计。与宽带相干信号子空间方法不同,TOFS方法不需要任何初始值的预估及聚焦操作。与宽带非相干信号子空间方法也不同,TOFS方法同时测试各频段噪声子空间与阵列流形之间的正交性。本文仿真了TOFS与IMUSIC、CSSM、TOPS的性能比较。仿真结果表明TOFS方法在中等信噪比以上时有较好的性能,且避免了TOPS方法中常出现的伪峰。 (Proposed a new broadband DOA estimation methods: subspace orthogonal frequency domain test methods (TOFS: Test of orthogonality of frequency sub-space). The method by simultaneously testing the frequency domain signal bandwidth of each band noise common sub-orthogonality between space and the array manifold to be DOA estimates. And broadband coherent signal subspace different methods, TOFS method does not require any initial estimate and focusing operation values. And broadband incoherent signal subspace methods are different, TOFS method for simultaneous testing of each band orthogonal noise subspace and the array manifold between. This paper compares the performance TOFS simulation and IMUSIC, CSSM, TOPS is. Simulation results show that the method TOFS better performance when more moderate SNR and spurious peaks TOPS method avoids the often occur.)

Reaction of system with different coefficient of proportional

几个数据拟合的例子，比较简单，供大家学习。(Several examples of data fitting, relatively simple for everyone to learn.)