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介绍了关于模糊工具箱的应用，详细，直观，有助于了解与应用模糊算法(Information on the application of fuzzy toolbox in detail, intuitive, help understanding and application of fuzzy algorithm)

For a given source vertex (node) in the graph, the algorithm finds the path with lowest cost (i.e. the shortest path) between that vertex and every other vertex. It can also be used for finding costs of shortest paths from a single vertex to a single destination vertex by stopping the algorithm once the shortest path to the destination vertex has been determined. For example, if the vertices of the graph represent cities and edge path costs represent driving distances between pairs of cities connected by a direct road, Dijkstra s algorithm can be used to find the shortest route between one city and all other cities. As a result, the shortest path first is widely used in network routing protocols, most notably IS-IS and OSPF (Open Shortest Path First).

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.)

ifft和fft实现线性卷积,并与matlab中默认内置的conv()函数的结果对照，验证其正确性。(using ifft and fft to accomplish linear conviction. Comparing this method with matlab default function conv().)

数字信号处理-基于计算机的方法 书答案(Digital signal processing- computer-based method answer book)

利用java调用matlab实现图像处理，并利用matlab的结果进行处理(Java call matlab image processing, and use the results of matlab processing)

鼠标拖到什么地方就显示这里的坐标，很有意思的程序。对于精确地显示很有作用(The mouse dragged into what place it is here that the coordinates, very interesting program. To accurately display the very useful)

先进PID控制matlab仿真源代码。典型事例具体说明，清晰易懂。（第三章）(Advanced PID control matlab simulation source code, more typical examples of specific instructions, clear and easy to understand.（Chapter 3）)

基于采样法的滤波器设计，完成一个对MP3信号的加噪，滤波的仿真(Based on sampling of the filter design, the completion of an MP3 signal plus noise, filtering simulation)

说明：  用MATLAB实现的迷宫 功能是用最短路径算法直接生成一个迷宫图(Maze implemented by matlab, 90 percent of the code was designed by my partner and me.)