tusvd

说明：  求出最小逼近的算法，帮同学写的直接就可以运行 (Find the lowest approximation algorithm, to help students write directly on the run)

simulink model for boost converter

相关的 BSS 盲源分离问题和 CS压缩感知的结合4篇论文打包，以及一个声音信号混合分离的简单实现(the realization of BSS(blind source separation) and CS(compressed sensing), and a sample of volume signal s matlab illustration)

This file contains Least Square method program which is one of en bloc approaches to identifying a system. The target is calculating parameter vector of a system

算法实现:Jieping Ye. Generalized low rank approximations of matrices. Machine Learning, Vol. 61, pp. 167-191, 2005. (Algorithm : Jieping Ye. Generalized low rank approximatio ns of matrices. Machine Learning, Vol. 61. pp. 167-191, 2005.)

基于红外图像的弱小目标检测与跟踪，本程序是基于局域概率分布的小目标检测(Based on infrared image small target detection and tracking, this program is based on a small target detection probability distribution of local)

Introduvtion to signal processing with matlab

one dimensional PCF band simulation(one dimensional simulation PCF band)

研究表明超高斯分布更加贴近语音信号的实际分布，然而语音信号很难用单一的概率密度 函数准确描述，针对这一情况，提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法，并推导了 基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明：与传统的短时谱估计算法相比，该算法不 仅能够进一步提高增强语音的信噪比，而且可以有效减小增强语音的失真度，提高增强语音的主观感知 质量。 (Recent research indicates that the speech spectral amplitude distributions could be fairly described with super-Gaussian probability density function. However, the complexity of speech signal determines that the distribution statistics ofspeech signal could not be well described by single simple function. Thus a super-Gaussian mixture model for speech spectral amplitude is proposed, and with this model, a minimum mean-square error (MMSE) estimator for speech signals spectral amplitude is derived. The simulation results show that this algorithm based on Gaussian and super-Gaussian speech model could achieve better noise suppression and lower speech distortion as compared with the conventional short-time spectral amplitude estimation algorithm. )

边缘检测算子研究及其在医学图像中的应用.rar(Edge detection operator and its application in medical image. Rar)