GPS码捕获，跟踪代码算法的仿真

说明：  基于MATLAB的语音信号处理之滤波器设计(MATLAB-based speech signal processing of the filter design)

matlab实现Floyd算法,一段简单的程序，用时修改相关的参数即可。(Floyd algorithm matlab realize, for a simple procedure, the parameters can be modified with.)

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.)

电磁数值等效在二维散射中的应用，是一个m文件(Magnetic field integral equation for 2-D cylinder scattering)

addmitance matrix for solving load flow analysis

说明：  MATLAB帮助,很好的初学者教程，很有用(MATLAb help，it is very usefull)

好用的matlab实现凸优化工具包，适用于压缩感知重构过程(Easy to use matlab to achieve a convex optimization tool kit for compressed sensing reconstruction process)

Matlab实现基于radon变换的图像重建 Matlab实现基于radon变换的图像重建(Matlab-based radon transform of the image reconstruction)

multi-object detection by cam shift

仿真效果非常好，LCMV优化设计阵列处理信号，利用贝叶斯原理估计混合logit模型的参数。( Simulation of the effect is very good, LCMV optimization design array signal processing, Bayesian parameter estimation principle mixed logit model.)