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matlab绘图叙述了许多绘图的方法，对大家学习matlab有很大帮助。(matlab drawing method described in a number of drawings, all of us to learn matlab great help.)

匈牙利算法的matlab实现，自己处理的，克服了大部分的死循环问题(Hungary matlab solve death circle)

选定海域包括渤海和黄海北部（117.50°—126.67°E，34.00—41.00°N），空间分辨率是10′×10′，通过二维潮波控制方程，用fortran编程计算该海域的水位，并进行调和分析，利用matlab画出该海域的同潮时线和等振幅线图，并用T/P卫星高度计数据进行检验。(Bohai Sea and the Yellow Sea waters include selected northern (117.50 °-126.67 ° E, 34.00-41.00 ° N), the spatial resolution is 10 × 10 , controlled by the two-dimensional tidal wave equation to calculate the water level of the sea with fortran programming and harmonic analysis, using matlab draw lines with the tide when the waters, such as amplitude and line graphs, and tested using the T/P satellite altimeter data.)

实现FlightGear与外部通信，获得仿真的飞行参数及配置FlightGear。便于基于FlightGear的地面站设计(communication with FlightGear based on UDP in C++)

常微分方程数值解及其MATLAB实现，包含了数学原理，算法和程序。(Numerical Solution of Ordinary Differential Equations MATLAB implementation, including the mathematical principles, algorithms and procedures.)

16bit crc 编译码实例，matlab上运行。输入一行向量数字序列，获得其16bit的CRC校验码，并对其译码。(16bit crc encryption example, Matlab run. His input vector sequence number, access to its 16 bit CRC and its decoding.)

实验四 数字解调与眼图 1. 验证2DPSK 相干解调原理。 2. 验证2FSK 过零检测解调原理。 (Four digital demodulation experiment with eye diagrams 1. verify the 2DPSK coherent demodulation. 2. Verify 2FSK zero-crossing detection and demodulation principle.)

基于麦克风阵列的声源定位方法很多，相位变换加权的可控响应功率SRP-PHAT声源定位算法在混响环境中有较强的鲁棒性，可实现真实环境中的声源定位，因此该算法得到了广泛应用。SRP-PHAT对阵型没有特定要求，因此也适用于分布式阵列，事实上很多基于分布式阵列的定位系统采用了该算法。(This function creates an acoustic image from a set of microphone array signals using a steered response power (SRP algorithm).This particular version uses only integer shifts on the input signals for the delay and sum operation, and computes the coherent average power (rather than total average power), which does not include the power terms for each individual microphone signal. So the coherent power is effectively the cross-correlation between different microphone signals without the self power terms, and can therefore be negative.)

this file contains a ppt on feature selection for regression problem

neural network trained with extended kalman filter