editing

mmse接收 不错的程序,希望能够给大家参考(mmse receiving good procedures, we hope to be able to make reference to)

多任务cs，压缩感知中的一种重构方法，主要用于重构(Multitasking cs)

一种很好用的光流法，对于图像处理学习的初学者是很不错的参考程序。(A good use of optical flow for image processing is very good for beginners to learn the reference procedure.)

说明：  改进的PRI变换应用于3个重频滑变脉冲序列(The PRI transform to improve the sliding variable frequency pulse sequence re-)

这是一本书的一个章节的源代码关于图像优化的我觉得真的挺好的(This is a chapter of a book about image optimization of the source code, I think it is quite good)

Matlab code and Simulink model for mass damper system with and without force.

说明：  几种传统的图像插值放大方法：最近邻插值法，双线性插值法，双三次插值法(Several traditional interpolation to enlarge the image : nearest neighbor interpolation, bilinear interpolation, bicubic interpolation.)

matlab 的基本操作书籍，对初学matlab很有用，很值得推荐(The basic operation matlab books, matlab very useful for beginners, it is worth recommending)

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.)

50－channel的polyphase filter，基于FIR滤波器(50-channel of the polyphase filter, based on the FIR filter)