MATLAB_basic_syntax

MATLAB控制系统设计与仿真 赵文峰编著，适合于初学者，比较详细、贴切。(MATLAB Control Design and Simulation Edited by Wen-Feng Zhao, suitable for beginners, more detailed and appropriate.)

文章A New Efficient Low-Complexity Scheme for Multi-Source Multi-Relay Cooperative Networks的仿真MATLAB代码，值得拥有。(This paper proposes a new efficient scheme for the combined use of cooperative diversity and multiuser diversity.)

这是用SIMULINK的S-FUNCTION方式编写的系统仿真代码，系统是基于4G网络的核心技术空时分组编码的MIMO-OFDM通信系统的，涉及到空间分集、时间分集和频率分集的有机结合，包括QPSK调制解调、IFFT调制、控制编解码、基于训练符号的信道估计等通信模块。包括main_STBC_MIMO_OFDM和training_symbol两个M文件和一个DOC文件，对各部分的都有详细的代码和注释。(It is used SIMULINK the S-FUNCTION approach to the preparation of the system simulation code, the system is based on the 4G network s core technology space-time block coded MIMO-OFDM communication system, involving spatial diversity, time diversity and frequency diversity of the organic combination, including QPSK modulation and demodulation, IFFT modulation, control of encoding and decoding, symbol-based channel estimation training and other communication module. Including main_STBC_MIMO_OFDM and training_symbol two M file and a DOC file, have detailed the various parts of the code and comments. )

一个关于卡尔曼滤波仿真的Matlab小程序，希望对大家有帮助(A Matlab simulation on the Kalman filter small program, we want to help)

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.)

this is a source code for multiple input and multiple output receiver

讲解信号处理基于matlab实现方面的很好的电子书，大量的例子和讲解，不容错过(Explain the signal processing based on matlab realization of a good e-books, a large number of examples and explanations, not to be missed)

国外的一个惯性导航系统仿真工具箱，自己点滴注释过，能进行导航解算，组合导航。(An inertial navigation system simulation toolbox。 own drip comment,can solve navigate and integrated navigation.)

DCT- discrete cosine transform. compression ratio and psnr value are calculated

Matlab在复变函数中的应用，介绍留数的概念及Matlab的实现；介绍在复变函数中有重要应用的Taylor展开(Matlab in the application of complex variable functions, introduce the concept and the residue Matlab realization describes the complex function has important applications in the Taylor expansion)