tcoug

关于GPS的MATLAB源程序，经运行验证，算法简单，结果有效。(MATLAB source code on the GPS, verified by running the algorithm is simple, and effective.)

为卷积神经网络的优化代码，广泛适用于图像处理。(Convolution neural network optimized code, widely used in image processing.)

很好用的Log-Gabor变换源代码，可以用于图像的特征提取(Good use of Log-Gabor transform source code, can be used for image feature extraction)

说明：   接收设备能对外界信息正确的接收、分析、处理的重要性是不言而喻的,它关系到社会的发展、人类的进步。然而在有用信号中往往伴随着噪声的存在。这种噪声分为电路内部固有的噪声与系统装置从外部接收而来的噪声。电路内部的噪声是系统固有的，与外界干扰无关，它可以通过对电路的合理设计、选择合理的元器件而得以控制。外部噪声主要由近几年的电子技术的高速发展，电子、电气设备的广泛应用而带来的电磁干扰引起的。这种噪声现象在工业发达的城市非常突出，它对各种系统接收装置的影响也越来越严重，使得这些系统装置不能正常工作，使得这些系统装置性能降低，甚至使得这些系统装置受到损坏。战胜噪声已是现代电子技术发展道路上必须逾越的巨大障碍。要解决噪声带来的问题需要我们从最基本的干扰源、信息传输通道（信道）、系统装置电路下手。(The receiving device and system noise)

这里具有一些MATLAB处理图象的源程序，希望对初学者有帮助(here with some MATLAB image of the source, and I hope to help beginners)

捷联式惯性导航的角速率匹配卡尔曼滤波，使用matlab语言编写(Strapdown inertial navigation of angular rate matching kalman filter)

This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar -xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. (This demo nstrates the use of the reversible jump MCMC algorithm for neural networks. It uses a hierarchical full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. The derivations and proof of geometric convergence are presented, in detail, in: Christophe Andrieu, Nando de Freitas and Arnaud Doucet. Robust Full Bayesian Learning for Neural Networks. Technical report CUED/F-INFENG/TR 343, Cambridge University Department of Engineering, May 1999. After downloading the file, type "tar-xf rjMCMC.tar" to uncompress it. This creates the directory rjMCMC containing the required m files. Go to this directory, load matlab5 and type "rjdemo1". In the header of the demo file, one can select to monitor the simulation progress (with par.doPlot=1) and modify the simulation parameters. )

The calculation of one segment kepstra Resco Consider a signal vm0.bin - raw data without the head? ky, fs = 16000 Hz, for on? reading to use the MATLAB loadbin.m

vc++6.0调用matlab的函数，画正弦曲线，比较详细的源码。(vc++6.0 to call matlab function, draw a sine curve, the more detailed source.)

关于调频信号的仿真的演示,利用matlab实现卫星调频信号的仿真和演示(Demo on the FM signal simulation using matlab satellite FM signal simulation and demonstration)