Gauss_Process

自己编的，改进的自适应语音增强算法，且参考噪声与输入噪声不相关。(Own, and improved adaptive speech enhancement algorithm, and the reference noise and input noise are not relevant.)

SCI communication between ST7 and PC (SCI communication between ST7 and PC)

音频水印水印算法，利用vc调用matlab引擎检测水印算法 ()

本代码是用来检测图像的局部不变特征的，主要参考DAVID G. LOWE的sift的论文。变成工具是matlab。(This code is used to detect the image of local invariant features, the main reference DAVID G. Lowe s sift paper. Become a tool is Matlab.)

本书为matlab电力系统学习书籍，详细介绍了simpower的使用方法和功能应用，适合初学者尽快掌握和学习(This book is a study of power system matlab books, detailing the use and functionality of the application simpower, suitable for beginners to master and learn as quickly as possible)

基于无刷直流电机的simulink仿真图，可以通过示波器查看电机的反电动势波形和电流波形以及可以使电机转速达到期望值(Based on the simulink simulation diagram of the brushless DC motor, you can view the motor s back electromotive force waveform and current waveform through the oscilloscope and can make the motor speed reach the desired value)

包括调制，解调，信噪比计算，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块，能量熵的计算。( Includes the modulation, demodulation, signal to noise ratio calculation, PV modules contain, MPPT module, BOOST module, inverter module, Energy entropy calculation.)

说明：  噪声中的非平稳信号的频谱分析的matlab代码(Matlab code for spectrum analysis of non-stationary signals in noise)

最小均方误差信道估计LS_MSE_calc.m仿真程序。用于ofdm信道估计。(Simulation of main channel LS_MSE. You wish to be useful.I hope that they will bring convenience to. If there is a need to contact.I wish good!)

计算机视觉大牛Andrew Blake著的书。 《视觉和图像处理中的马尔科夫随机场》 Markov random fields for vision and image processing(This book sets out to demonstrate the power of the Markov random field (MRF) in vision.It treats the MRF both as a tool for modeling image data and, coupled with a set of recently developed algorithms, as a means of making inferences about images. The inferences concern underlying image and scene structure to solve problems such as image reconstruction, image segmentation, 3D vision, and object labeling. This chapter is designed to present some of the main concepts used in MRFs, both as a taster and as a gateway to the more detailed chapters that follow, as well as a stand-alone introduction to MRFs.)