CommunicationsPToolbox4-User_s-Guide

写mp3文件格式的matlab 源程序(Mp3 file format to write the matlab source code)

汉明码的仿真实现以及调试，结果演示。是基于MatLAB环境用simulink仿真的(Hamming code and debugging the simulation)

基于LMS算法的自适应滤波器仿真实现，一篇不错的论文(LMS adaptive filter algorithm based Simulation, a good paper)

matlab/GUI界面编程从入门到精通，适合初学者(Matlab/GUI interface programming, from entry to master, suitable for beginners)

一个很好用的matlab程序,基于遗传算法和bp神经网络的matlab程序(a good use of Matlab procedures, based on genetic algorithms and neural networks bp procedures Matlab)

WORKING CODE FOR IRIS SEGMENTATION

基于背景差法的动态目标检测的代码 希望对大家有用(Difference method based on the background of the dynamic target detection code for all of us hope that useful)

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.)

比较有用的matlab实例，给matlab初学者参考，有需要的还可以联系我(matlab)

采用线性卡尔曼滤波估计阻尼简谐振荡器的速度和位置(demonstration of a Kalman filter estimating the state (position and velocity) of a damped harmonic oscillator with constant forcing.)