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Texture Synthesis using a gaussian mixture model

Matlab source code for AR-MAC research paper.

This Code SimulTION PSO BASIC CODE IN MATLAB IS CODE IS VWRY USEFUL FOR UNDERESTAND PSO HOW WORK

包括了对不同通信系统的simulink仿真，如AM, DSB-SC, FM, PLL, Data Acquistion, Digital Data Transmission, PCM and Delta Modulation。通过这些可以帮助用户对通信仿真有更深的理解。(This project allows you to learn the communication systems in greater depth by giving you the reins to play with it ! It contains the simulink files (*.mdl) which are block design files of various communication systems such as AM, DSB-SC, FM, PLL, Data Acquisition, Digital Data Transmission, PCM and Delta Modulation. )

基于Matlab分析矩量法在线天线中的应用(Analysis of the line antennas method of moments based on Matlab)

matlab vehicle dynamics programe软件编写汽车动力性相关程序(matlab vehicle dynamics programe)

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.)

潮流计算 牛顿拉夫逊方法(chaoliujisuan niu dun la fu xun chao liu ji suan )

完成一个利用矩形信号星座图的M=16的QAM通信系统的MONTECARLO仿真，()

关于三相SVPWM仿真的模型，我做电力电子仿真作业的一道题，花了很多时间下载和阅读文献，最后总算成功了，只是仿的结果有点慢，一直找不到为什么。 注意：此模型仿真时需要svpwm.m这个文件，并且matlab软件版本为Matlab7.1。(About three-phase SVPWM simulation model, I do the job of power electronic simulation of a question, spend a lot of time to download and read the literature, but finally succeeded, but the result of imitation of a little slow, could not find why. Note: This model simulation requires svpwm.m this file and matlab software version Matlab7.1.)