MATLAB

基于主成分分析的人脸识别的方法 在ORL人脸库中运行(Face recognition based on principal component analysis method in the ORL face database is running)

function histogram (simple) programmed

CC2530的定时器实验，使用IAR编写。(CC2530 key experiments, using IAR preparation procedures.)

说明：  线性规划的MATLAB书籍，英文版，清晰PDF格式，讲解透彻。(Linear Programing with MATLAB. The content was interpreted properly and understood easily.)

这是关于信号的调制与解调，是通信原理核心部分，也是通信中很常见的(This is about the signal modulation and demodulation, is the core part of communication theory, communication is very common )

使用与matlab中的小波熵程序，易于实现，注释清晰容易理解(With the the wavelet entropy program in matlab, easy to implement easy to understand, clear notes)

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.)

Axial Flux Machine_ Design Using Matlab

刚刚写的网上定餐系统 简单版本 可以参考 (Just write a simple version of the online system, scheduled meals can refer to)

说明：  利用matlab编程语言，对合成孔径雷达的range-doppler算法进行了仿真。(Using matlab programming language for synthetic aperture radar range-doppler algorithm simulation.)