ICA

说明：  程序为了设置自定义的天线阵列模式，仿真了各种阵列形式的性能(Procedures in order to set up a custom antenna array model, simulation of various forms of performance arrays)

说明：  使用matlab进行时域采样序列分析，得到分析图(Time domain using matlab sampling sequence analysis, analysis of Figure)

说明：  时间反转镜算法进行定位估计，利用声场波导特性，实现信号的增强和减弱(time reversal mirror APTRM)

基于FDTD算法，用MATLAB实现了波导色散特性的仿真研究(Based on the FDTD algorithm, implemented using MATLAB simulation of waveguide dispersion characteristics)

different filter designing bandpaff lowpass highpass ...

panel for reducing gap in subfigures in matlab

说明：  使用matlab实现结构光在大气湍流中传输，并且实现了通信实验以及误码率的检索。(Matlab is used to realize the transmission of structured light in the atmospheric turbulence, and the communication experiment and error rate retrieval are realized.)

this source code zy2abcd in matlab

利用lqg进行飞机的闭环控制，包括反馈增益求取等。(by the use of lqg, we can find the close loop control of the aircraft.)

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.)