hundunchengxu

mimo code for using the ldpc

使用文本文件(.txt)进行数据存取的技巧总结(A technique summary uesd for a text file (. Txt))

宽带数字接收机,详细介绍了接收机的基本理论和实际应用。(wideband digital receiver)

Performance comparison of RLS and LMS channel estimation techniques with optimum training sequences for MIMO-OFDM systems Performance comparison of RLS and LMS channel estimation techniques with optimum training sequences for MIMO-OFDM systems

小波变换文档汇总：二维小波变换在图像去噪处理中的应用、matlab小波工具箱及其应用、morlet小波变换理论与应用研究及软件实现、二维小波变换去噪的MATLAB可视化设计等(Wavelet transform document summary: two-dimensional wavelet transform in image denoising processing, matlab wavelet toolbox and its application, the application morlet wavelet transform theory and application research and software implementation, the 2 d wavelet transform denoising matlab visual design, etc )

这是我编写的基于Matlab的QPSK调制解调的仿真程序，希望对大家有所帮助(This is what I prepared in Matlab based on the QPSK modulation and demodulation of the simulation program, and they hope to help everyone)

用c++编的matlab程序.是常微分方程解法.希望能给大家帮助,都是老师给布置的作业!谢谢!(using Matlab c addendum to the proceedings. Solution of ordinary differential equations. I hope that it provides help, teachers are to the layout of operations! Thank you!)

training for dvr with nn control

用vc调用matlab7.0写的小程序，可以实现简单的控制系统仿真演示(with vc call matlab7.0 write small programs, can achieve a simple control system simulation demo)

传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这 一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT) 系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表 示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的 语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法在抑制背景噪声和减少语音失真等方面优 于传统的语音增强方法。(The spectral components of speech are usually assumed to be independent in traditional short-time spectrum estimation, which is not the case in practice. Tosolve this problem, a new speech enhancement algorithm with multivariate Laplace speech model is proposed in this paper. Firstly, the speech Discrete Cosine Transform (DCT) coefficients are modeled by a multivariate Laplace distribution, so the correlations between speech spectral components can be exploited. And then a Minimum-Mean-Square-Error (MMSE) estimator based on the proposed model is derived using a Gaussian scale mixture representation of random vectors. Furthermore, the speech presence uncertainty with the new model is derived to modify the MMSE estimator. Experimental results show that the developed method has better noise suppression performance and lower speech distortion compared to the traditional speech enhancement method. )