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预加重和去加重是语音处理必不可少的环节，得到纯净语音(Pre-emphasis and de-emphasis is essential for speech processing areas, are pure voice)

主要有以下文件 MATLAB程序设计 介绍MATLAB概述与运算基础 matlab讲义 文件操作 绘图功能(mainly in the following documents MATLAB procedures outlined MATLAB Design and Operational basis matl ab overhead graphics file manipulation functions)

一个基于matlab视频修复程序，可作为实验或是学习的参考。(A code for video inpaiting based on matlab ,and it can be used as the reference of the experiment or study. )

在matlab环境中将陀螺仪加速度计以及磁传感器数据转为四元数的脚本。(The script will change accelerometers and gyroscopes magnetic sensor data into a quaternion In the matlab environment.)

完成了对OFDM（正交频分复用）通信系统从信号输入，编码，发射，解码，求误码率的全过程，压缩包中word文件为系统仿真报告，其他为matlab代码。(Completes the OFDM (Orthogonal Frequency Division Multiplexing) communication system the signal input, encoding, transmitting, decoding, bit error rate of the entire process required)

多径理解4,理解多径对信号的影响 ,(four multi-path understanding, understanding of the multi-path signals and understanding of the multi-path signals,)

passive filter simulink file to reduce harmonics

主要介绍了基于（单、多）高斯统计模型的背景获取法(Mainly based on the (single, multi-) Gaussian statistical model to obtain the background of law)

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.)

Kalman滤波在跟踪轨迹时的使用，得到更好的图像(The use of Kalman filter in tracking trajectory, get a better image)