BNT_SLP.tar

LTI系统的频域分析：理论分析和代码实现(LTI system frequency domain analysis: theoretical analysis and code implementation)

给出了不同分割数量V条件下的系统的PAPR性能，分割方法为随机分割，辅助相位集合的相位数P=4(即 可选择的范围为{0,π/2,π,3π/4})，N=128，子载波采用QPSK调制，仿真的OFDM符号数为1000个。(V gives the number of different partitions of the system under PAPR performance of random segmentation segmentation, the number of secondary phase of the phase set P = 4 (to select the range {0, π/2, π, 3π/4} ), N = 128, sub-carrier using QPSK modulation, simulation of the OFDM symbol number 1000.)

实现混合高斯背景建模，对给定图像序列进行背景建模.内置的参数可以自行修改，以适用不同的应用需求。(mixture of gaussian，it will help you to construct the background model of series frames)

用改进SVM实现人脸特征提取和识别，用matlab编写，和结果分析(A matlab code of enhanced SVM for face recognition)

[Numerical Methods in Engineering with MATLAB]2005

三次样条曲线拟和，能够很好的处理离散点的拟和问题，值得收藏(Cubic spline curve fitting and)

可逆跳跃马尔科夫蒙特卡洛贝叶斯模型选择，主要用于神经网络(Reversible Jump MCMC Bayesian Model Selection This demo demonstrates 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.)

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, lossy compression of audio (e.g. MP3) and images (e.g. JPEG) (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions.

该文档主要介绍了什么是PLC，及OFDM在LPC中的应用。(The document describes what is a PLC, and OFDM in the LPC application.)

this is a example about matlab and VC