SAXS-Integrated-Intensity

这是一个关于图像识别的程序，可以根据图像中的某些点，识别出图像的形状(This is a program on the image recognition, according to some point in the image, identify the shape of the image)

说明：  1-D信号压缩传感的实现(正交匹配追踪法Orthogonal Matching Pursuit) 沙威大人的 多多关照(1-D signal compression sensor to achieve (orthogonal matching pursuit method Orthogonal Matching Pursuit) Javert more concern adults)

geolocation sample 微软提供的，具有一定的参考价值， 好东西大家一起分享(Geolocation sample Microsoft provides, has the certain reference value, good stuff to share with everyone )

说明：  这些说明包括了该课程的数值部分上半年AMAT3122数学 微分方程的方法。他们不是一个完整的笔记。(MATLAB (MATrix LABoratory) is a very powerful package designed for numerical analysis, matrix calculations and visualisation (as well as many other applications). One of MATLABs strengths is that it can be used as a simple ‘calculator’ and also is easy to program more complicated tasks. Many mathematical algorithms are already built in to MATLAB and many more are easily added)

基于年龄累计直方积累的人脸识别与鉴别算法比较(Cumulative Histogram Based on the accumulation of age, face recognition compared with the discrimination algorithm)

小波图像融合，采用用量一样的图像（但每张图像不同的位置发生模糊），融合产生尽量清晰的图(Wavelet image fusion, the amount of the same image (of the location of each image blur), and fusion to produce as clear as possible in Figure)

Learning Vector Quantization (LVQ) is a supervised version of vector quantization

加性高斯噪声信道下BPSK信道估计，matlab程序，做得还比较好！希望能帮助你们！(AWGN channel under BPSK channel estimation, matlab procedure done better! To help you!)

说明：  matlab混合编程（书中程序） 第四章 (matlab mixed programming (program book) Chapter IV)

说明：  神经网络实例集。包括以下几个程序单层线性神经网络实例、感知器神经元解决较复杂输入向量的分类问题、基于感知器神经网络处理复杂的分类问题、数值分析程序matlab-GUI、用BP网络完成函数的逼近源程序、自组织特征映射应用实例(Examples of neural network sets. Procedures include the following examples of single-layer linear neural network, perceptron neuron input vector to solve more complex classification problems, based on the perceptron neural network to deal with complex classification problems, numerical analysis matlab-GUI, using BP network function source approximation, self-organizing feature map application)