sym

利用matlab来实现多线性主成分的算法，具有很强的实用性(Using matlab to implement multi-linear principal component algorithm, has a strong practical)

pid ziegler-nichols法实例(a example of ziegler-nichols in pid)

匹配滤波器的源代码，可以实现信号生成的匹配滤波(The source code matched filter, can achieve the matched filter the signal generated)

Eye Tracker is a very useful Algorithm which is very much useful in avoiding in immegration of unauthorized persons in Aeroplanes/trains/busses. Its a matlab based code

Active contours or snakes" can be used to segment objects automatically. The basic idea is the evolution of a curve, or curves subject to constraints from the input data. The curve should evolve until its boundary segments the object of interest. This framework has been used successfully by Kass

《MATLAB与科学计算》（第2版）PPT教程 王沫然 电子工业出版社("MATLAB and Scientific Computing" (2nd edition) PPT tutorial,Wang Mo, Electronics Industry Press)

可以在图上拖动的均衡器,但没有播放功能.可以自行添加。(a equalizer controlable by dragging on the map, without play function)

approximation with neural networks

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.)

二并行FIR算法具有速度快的优点，其算法对信号的处理有它独特的优势，其中的源程序对算法进行了仿真，与结论一致(FIR Parallel algorithm )