lenping_v82

TCT算法 用于宽带聚焦的一种算法 自己编的一段 也不一定对(TCT algorithm is an algorithm for broadband-focused section of its code is not necessarily)

这是小波分析的应用技术，介绍了程序的功能作用及一些小算例(This is the application of wavelet analysis techniques, introduced the functional role of the program and some small examples)

说明：  matlab图像处理的教学视频，详细介绍了视频的处理(matlab image processing, instructional videos, video processing described in detail)

求一个输入数组的最大值，并返回其值。配合C的dsp实现(Seeking a maximum input array and returns its value. With C dsp implementation)

一个关于matlab中gui界面设计的小程序，适合初学者对gui编程进行一定程度的了解。(A small program on matlab gui interface design, suitable for beginners gui programming to a certain degree of understanding.)

图像噪声消除,采用小波分析的方法，阈值法进行系数矫正(figure noise )

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.)

一个关于ofdm的MATLAB仿真源代码(OFDM MATLAB )

This is an A-Z guide to Matlab

数据分析 回归分析，使用matlab编程(Regression analysis)