Compet7

本程序中得到的收端OFDM信号的频谱波形，是与其发端信号的排步有关的。在发端的 ％ 载波安排上，128个载波有前后各32个载波是null载波(如果这前后各32各载波是带外频段， ％ 那么理论上它们都应该是零！)，中间的64个载波是数据载波。这样的排步很明显就是一个 ％ 两边低，中间高的频谱形式。所以，收端也应该是这个轮廓。 (this process the resumption end of the spectrum OFDM signal waveform, with the originator of the signal further on the row. The originator% carrier arrangements, 128 of the carrier after a 32 carrier is null carrier (if it was around the 32 is the carrier frequency band. % then theoretically they should be zero!) the middle 64 carrier data carrier. This arrangement is obviously further 1% lower on both sides, in the middle of the spectrum form high. Therefore, the admission should end this profile.)

matlab源码，实现利用霍夫变换的直线检测(matlab source code, line detection using Hough Transform)

非常有用的蚁群算法，比较有用,使用与图像分割等领域里(Ant colony algorithm is very useful, useful, use with fields such as image segmentation)

Introduction to Power Flow Analysis Toolbox

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.)

说明：  Household变换，利用仿真数据进行ftt，最小二乘法的多项式拟合(House transform, polynomial fitting of FTT and least square method with simulation data)

Un ejemplo de aplicacion de inteligencia de particulas con red multicuadratica si se utiliza por favor comentarme para crecer nuestros conocimientos

用于填冲图片中的空白域，设定一个种子点，然后从种子点开始填冲(Used to fill in the blank fields punch picture, set a seed point, and then rushed from the seeds begin to fill)

目的：实现多gui间的通信。 1. 由gui1打开gui2，并从gui2读取数据画图； 2. 数据包的传递：在gui1定义一个数据包，包括字符串，数据，把数据包传递给gui2并由其更新，gui2更新好的数据在传回gui1并显示出来。（注：程序中加黑划线部分为实现预期功能的要点） (Purpose: communication gui. Method: 1. Opened by gui1 gui2, and to read data from gui2 drawing (2) the transfer of the packet: a packet defined in gui1, including strings, data packets passed to gui2 updated by gui2 the updated data in the returned gui1 and displayed. (Note: the program plus the the black underlined part to achieve the desired functionality of the points))

掌握舌象裂纹分析技术，能够根据舌象的纹理粗糙与细腻区别老嫩舌。(Grasp the tongue as crack analysis technology, according to the texture of the tongue as rough and delicate difference between the old tender tongue.)