QAM

将MATLM3应用于有限差分法的正演计算中．充分发挥了其强大而方便的功能。 通过对二维稳定电流场模型的试算表明，ATLAB在解决实际的工程和数学问题中，与其它计算机程序设计语言 FORTRAN相比，具有使用更为简便、语句功能更强(Will MATLM3 finite difference method applied to the forward calculation. Give full play to its powerful and convenient features. Through the two-dimensional current field model calculations show that, ATLAB in solving practical engineering and mathematical problems, and other computer programming language FORTRAN, compared with the use of more convenient, more powerful statement)

Code for Biginners Compressive Sensing

基于模糊控制的机器人避障，智能控制基础大四。(Obstacle avoidance of fuzzy control robot)

在用混沌理论和神经网络进行短期负荷预测时，神经网络的输入的选择至关重要，该程序用matlabl实现了基于混沌时间序列的嵌入维数的选择(using chaos theory and neural networks for short-term load forecasts, the neural network is essential to choose an input, The procedure used matlabl achieved a chaotic time series based on the embedding dimension of choice)

总结了数学建模中的常用算法，比较实用，可以借鉴(describ matlab)

耳蜗实质上相当于一个滤波器组，耳蜗的滤波作用是在对数频率尺度上进行的，在1000HZ下，人耳的感知能力与频率成线性关系；而在1000HZ以上，人耳的感知能力与频率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(on logarithm frequency scale, under the 1000 hz, the perception of the human ear and a linear relationship with frequency In more than 1000 hz, the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula )

实现Po计算电磁散射单站计算，对于一般的目标可进行简单的计算。(it can be used to calculate the monostatic RCS of the object in matlab)

packet error rate for direct wireless link from source wireless body area network node to the coordinator

clc close all clear all I=imread( desert.jpg ) [r c]=size(I) bs=16 Block Size (8x8) nob=(r/bs)*(c/bs) Total number of 8x8 Blocks Dividing the image into 8x8 Blocks kk=0 for i=1:(r/bs) for j=1:(c/bs) Block(:,:,kk+j)=I((bs*(i-1)+1:bs*(i-1)+bs),(bs*(j-1)+1:bs*(j-1)+bs)) end kk=kk+(r/bs) end Accessing individual Blocks figure imshow(Block(:,9,1)) This shows u the fist 8x8 Block in a figure window figure imshow(Block(:,:,2))

线性判别分析的分类器算法,用于脑电信号的判别分析(the classifier of linear discrimination analysis )