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说明：  关于MATLAB应用于调制解调器的设计。其中有详细的关于信号的调制与解调的MATLAB的程序(MATLAB used on modem design. Including detailed information about the signal modulation and demodulation of the MATLAB program)

利用不动点迭代和牛顿迭代求解非线性方程解的理论(Theory of Solving Nonlinear Equations by Fixed Point Iteration and Newton Iteration)

CM Li在Ieee Image会上的最新文章代码(C.M Li)

示例用法的以下的统计工具箱函数 LinearModel NonLinearModel GeneralizedLinearModel (Example code illustrating the use of the following Statistics Toolbox functions LinearModel NonLinearModel GeneralizedLinearModel)

本文件提供了在线的雷达数据导入IPIX matlab算法，主要是用于验证目的,包括导入程序和IPIX雷达数据下载地址，做SAR和图像遥感的必备资料。(This document provides the online radar data import the IPIX matlab algorithm for authentication purposes, including import procedures and IPIX radar data download address, must do the SAR and images of remote sensing.)

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.)

异步电机空间矢量控制，使用了磁链观测器，虽然是开环观测 但是性能依然良好(Space vector control induction motor, using a flux observer, though, is the open-loop observer but the performance is still good)

5 bus power flow for power systems and analysis

实现图像分块压缩感知，实现了多种稀疏基下的压缩感知，包含CT、DCT、DDWT、DWT等(Realize the image compression sense, and realize a variety of sparse base compressed sensing, including CT, DCT, DDWT, DWT and so on)