microstrip

说明：  给定同样的M条样本轨道，计算出欧式看涨期权的价格调用 underlying函数(M be given the same sample path, calculate the price of European call option)

信号处理书中所附光盘，包括各个章节，很全，大部分能直接拿来用，很实用(The book accompanying CD-ROM signal processing, including various chapters, it is the whole, most can be brought directly to use, very practical)

lms alogorithm used for nois cancellation in sinosidal siganl further we can also input the speech signal for output

定向能武器相关的文章 学习新的军事技术的好文章 了解知识(directed-energy weapons-related articles to learn new military technology in good understanding of knowledge)

Simulink 仿真环境基础学习的教程文档。对于初学者来说是很好的入门。(Simulink simulation environment based learning tutorial document. For beginners it is a good entry.)

axis break used to break the x axis to a limit

按转子磁链定向的矢量控制系统matlab仿真；(Press the rotor flux oriented vector control system matlab simulation )

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. )

隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)

matable 生成V图,代码　　在要要要要(english,matlab voronil diagram)