drawEigenmodes1Dify

实现了用burg法进行功率谱估计，并比较它的结果(Burg achieved by power spectrum estimation method, and compare its results)

说明：  stft的实验小程序，可以做个基础，然后据此展开做些复杂的东西(stft experiments applet)

可以根据不同需要，生成各级路面的空间路面谱(According to different needs, generate a spectrum at all levels of road space road)

Code to make edge detection extract this file and then read readme.txt file

说明：  Kalman filtering model built with matlab

采用小波变换稀疏方法和正交匹配跟踪算法处理图像(Sparse wavelet method and orthogonal matching tracking algorithm)

一个演化博弈的小程序。非常有用。用matlab写的，非常非常有用。(a evolution game thoery simulation,a matlab program,it s very useful.really usefull,really really usefuly. kao,20 zifu?)

turbo code simulation in matlab

深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

说明：  matlab经典程序集~~~~~~~~~~~~~~~~~~~~~~~(programs of matlab)