circpendulum

运动补偿编码和检测，matlab程序和相应的文档说明(Motion compensation coding and testing, matlab procedures and the corresponding documentation)

说明：  人和座椅的汽车3自由度振动系统，simulink工程文件(And seat of the car-degree-of-freedom vibration system, simulink project file)

FFt例程，具有Pdf文档的具体说明过程。不错的啊！(FFt routines, Pdf file with the specified process. Good ah!)

说明：  处理数据中有很好的应用，里面有验证函数，可以进行验证。(Processing data has a good application, there are validation functions, can be validated.)

sep protocol where no direct transmission is allowedto base station even if it is near to base station

Root-MUSIC算法MATLAB程序,用于阵列信号的分析定位、定向，近场声全息中。(good,very good.)

说明：  基于主分类分析的SAR图像变化检测MATlt实验程序(Based on the analysis of the main SAR image classification change detection experiment)

Table Of Integrals, Series And Products (7Ed , Elsevier, 2007 Gradshteyn I , Ryzhik 1220S),非常实用的积分手册！(Table Of Integrals, Series And Products (7Ed , Elsevier, 2007 Gradshteyn I , Ryzhik 1220S)

Guangxi M achinery No．2，2002 工程设计资料线图数据的MATLAB程序化(Guangxi M achinery No. 162-163 engineering design information and map data MATLAB program)

说明：  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.)