Mahalanobis

放大器及数据转换器选择指南,放大器电路设计的好助手。(Amplifier and Data Converter Selection Guide, a good amplifier circuit design assistant.)

此程序适合平行二元阵天线，三维的，方向图乘积定理。(This procedure is suitable parallel binary array antenna, three-dimensional, the direction of graph product theorem.)

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.)

用MATLAB仿真谱估计中的伯格（Burg）算法(Spectrum estimation using MATLAB simulation of Berg (Burg) algorithm)

用于改进的wilson叶片设计，亲测可行。(wilson design for wind blade)

关于经过多径信道后，对于接收到的信息进行符号速率的估计，代码简洁易懂，有完整注释(After about multipath channel, the estimate for the received information symbol rate, the code is simple and easy to understand, complete comment)

CANNY EDGE DETECT SOURSE CODE

有源电力滤波器模块，可以进行谐波抑制以及无功补偿，可以用于逆变器输出滤波器(Active power filter modules, the reactive power compensation and harmonic control, can be used for inverter output filter )

光谱提取,丰度调整 效果好，高光谱目标检测和端元提取(spectral abundance adjustment )

Article of Algorithm to Power Flow: Load Flow Solution of Radial Distribution.