anneal

包括进行标准Hough变换，检测Hough变换中的n个峰值，并提取峰值直线像素点，绘制直线等(Including the standard Hough transform, Hough transform detection of the n-peak and peak extraction of straight-line pixels and draw a straight line, etc.)

这是一个利用维纳滤波方法进行消噪的MATLAB 程序，器效果比谱减法好在没有尖锐的噪声(it is a delete noise program.it is based on matlab)

An Evolutionary Routing Protocol for Dynamic Clustering of Wireless Sensor Networks using HCR

计算机MATLAB程序通过动画来实现辐射问题的可视化 (Computer Program Animation-Visualization Of Radiation Problems )

IIR滤波器的设计及其使用，可产生幅度相同的单频信号，其中进行了傅里叶变换等。(IIR filter design and its use, may have the same single-frequency signal amplitude, which carried out the Fourier transform.)

说明：  PFC电压模型,供大家参考使用,共同学习.(PFC voltage model for reference use, learning together.)

Curvelet变换是最近图像处理较新的一种多尺度几何变换算法，有详细的快速变换fdct_wrapping的matlab代码(Curvelet transform is the latest image processing a multiscale geometric transform algorithm is relatively new, there is a rapid transformation of fdct_wrapping with the matlab code)

Seismic Analysis Code，简写为SAC，是天然地震学领域使用最广泛的数据分析软件包之一(Natural seismology field s most widely used data analysis software One package)

说明：  2机5节点系统，可实现简单牛拉法，快速解耦法迭代潮流计算(2-machine 5-node system can realize simple Niula method and fast decoupling method to iterate power flow calculation)

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.)