cds

Ole Sigmund编写的99行Matlab程序是拓扑优化方面的经典案例。该程序有5个可修改的输入参数：竖直方向网格划分数nely,水平方向网格划分数nelx,保留的材料的体积分数volfrac,惩罚因子penal和滤波器大小rmin。加载方式矩阵F和支承方式fixeddofs也可以分别选取不同的方式或组合。(Ole Sigmund prepared by the 99-line Matlab program is a classic example of topology optimization. The program has five can modify the input parameters: the number of vertical mesh nely, the number of horizontal mesh nelx, keep the volume fraction of material volfrac, penalty factor penal and filter size rmin. Loading mode and the supporting means fixeddofs matrix F can also be selected separately or combined in different ways.)

说明：  This MATLAB code calculates the array factor for N element linear array. N单元线性阵列的方向图函数MATLAB仿真代码(This MATLAB code calculates the array factor for N element linear array. N element linear array pattern function of the MATLAB simulation code)

resampling program in matlab

Fast search for Dirichlet process mixture models (This code implements the search algorithms (modulo a few minor changes) described in the Fast search for DPMMs paper at AI-Stats 2007. It should work out of the box with a reasonably recent version of matlab. Currently the code only contains the Dirichlet/Multinomial case, but the Gaussian case can be hacked in in about 5 minutes.)

Finally, probability analysis, based on the chosen database, is performed and judgment whether the person is recognized is pronounced. Two different modeling techniques can be suggested for purposes of person identification or verification. The approach based on Hidden Markov Speaker Models (HMMs) with the mixtures of Gaussian distribution is text-dependent one. For this technology a speaker is described by a set of HMMs, constructing the set of speech groups spoken by that person. The speech groups may be phonemes or words [7]. The text-independent technology is rested upon Gaussian Mixture Speaker Models. For this technology each speaker is defined by only one model describing all utterances of that person. Identification in the range of both approaches is executed by evaluation of maximum a posteriori probability [7].

说明：  用Maple和MATLAB解决科学计算问题（第三版）.pdf(Using Maple and MATLAB to solve problems in scientific computing (third edition). Pdf)

可以实现CIEDE2000,CIEDE94,CIEDE1976,CMC,LAB值计算(compute CIEDE2000,CIEDE94,CIEDE1976,CMC,LAB)

功率谱估计是利用有限长的数据估计信号的功率谱，广泛应用于各个领域。功率谱估计主要分为经典谱估计与现代谱估计。常用的经典谱估计方法有周期图法，相关法，周期图的改进法，常用的现代谱估计方法有最大熵谱估计，AR模型，MA模型，ARMA模型。经典谱估计适用于长序列的信号，其主要缺陷是描述功率谱波动的数字特征方差性能差，频率分辨率低，现代谱估计适用于短序列的信号，旨在改善谱估计的分辨率，并将其应用于实际地震资料的谱分析。 (Power spectrum estimation is the use of a finite length data to estimate the power spectrum of the signal, widely used in various fields. Power spectrum estimation is divided into classic and modern spectral estimation spectral estimation. Commonly used methods of classical periodogram spectrum estimation method, the relevant law, the cycle graph Improvement Act, commonly used in modern spectral estimation methods have maximum entropy spectral estimation, AR model, MA model, ARMA model. Classical spectral estimation applied to signal a long sequence, its main drawback is the fluctuation power spectrum describing digital signature variance performance is poor, the frequency resolution is low, modern spectral estimation applied to signal short sequences designed to improve spectral estimation resolution, and The analysis applied to real seismic data spectrum.)

电磁场经典算法模拟电荷法CSM求解步骤详解(Electromagnetic charge simulation method CSM classical algorithm for solving steps explain)

ARES Lab Machine learning