inversion

Our project is to describe the Brownian Motion and Geometric Brownian Motion. This project is to simulate the price shock. Then we use Geometric Brownian motion to describe the movement of shocks’ price. The program is produced by Matlab.(Application of Ito’s Lemma to a Stock Price Process)

Read and separate headers in a CSV file. This is important for separating the different headers into separate arrays

its a Face Identification Project y using the matlab

此程序利用小波包分形理论，计算一维信号的盒维数，判定此复杂度(This procedure using the wavelet packet theory, the fractal calculation of one dimensional signal box dimension, determine the complexity)

说明：  MATLAB数学实验之数值微分与数值积分，里面有课件以及作业，含程序以及分析过程(MATLAB Mathematical experiments and numerical differential numerical integration, and they courseware and operations, including procedures and analysis process)

The program RANDOM_WALK_2D_PLOT plots the trajectories of one or more random walks. The program RANDOM_WALK_2D_SIMULATION plots averaged data for any number of random walks that each use the same number of steps. The data plotted is the average and maximum of the distance squared at each time step. The average distance squared should behave like the number of time steps.

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)

tabu search for vehicle routing problem

本例子清晰表达了傅里叶变换的基础及FFT变换的 要领，能清楚的了解FFT变换，观察频谱特征。(This example clearly expresses the foundation and essentials Fourier Transform FFT transform, can clearly understand the FFT transform, observed spectral characteristics)

设计一个完整的室内无线通信系统物理层传输方案，并用matlab进行仿真根据设计要求分析计算收发机结构及主要参数，提出了一个完整的MIMO-OFDM系统设计方案，得到误比特率仿真曲线。(Design of a complete indoor wireless communication system physical layer transmission scheme, and simulation using matlab design analysis and calculation according to the structure and main parameters of the transceiver, made a complete MIMO-OFDM system design, simulation curve obtained bit error rate.)