GA

实现图像的二维平稳小波变换，包含对源图像的分解与重构(2-D stationary wavelet transform, including the decomposition and reconstruction of the source image)

这是一个用matlab实现的语音信号预测编码中的得宾算法部分。还有实验报告。(This is a realization of using Matlab voice signals of predictive coding algorithm part in Pennsylvania. There experimental report.)

信号处理中的若干典型算法，很不错的，希望对大家游泳(signal processing algorithms typical of a number of very good, we hope to swim)

Systemview是重要的通信系统仿真软件，国外用的很多。这是我做硕士阶段仿真的一些源代码，对通信系统设计开发非常有用，由于没有Systemview这个板块，所以放在这里。这是一个信号平方仿真的Systemview源代码。(Systemview is an important communication system simulation software used in many foreign countries. This is what I do Simulation Master stage some source code, the communication system design and development is very useful. Because no Systemview the plate, so on here. This is a signal of Systemview square simulation source code.)

MATLAB实时仿真系统，xpc，cpu overload 问题，有助于解决系统过载问题(MATLAB, REAL TIME SIMULATION SYSTEM, XPC, CPU OVERLOAD, IT IS HELPFUL TO FIX THE PROBLEMS RAISED BY OVERLOAD.)

说明：  这是个基于matlab环境下，提取mfcc参数的小程序，希望能对大家有帮助。(This is a matlab-based environment, the applet parameter extraction mfcc, hoping to be helpful.)

levinsion-durbin估计参数发(levinsion-durbin estimate)

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.)

实现了parzen窗的方窗和高斯窗形式算法，输入只需输入样本，样本个数和窗宽能的出其概率密度函数(A window algorithm for Parzen windows)

rayleigh衰落信道下，卷积编码与M-QAM调制相结合的系统分组错误概率与信噪比的函数关系的matlab仿真程序(rayleigh channel, convolutional coding and M-QAM modulation system, the combination of packet error probability and SNR is a function of the matlab simulation program)