Kalman

matlab例题源代码，包含很多典型的例题，很值得借鉴学习。(Example matlab source code contains many typical Example, it is worth learning to learn.)

说明：  在matlab中实现主成分分析方法，并使用FERET库的灰度图像进行测试(This package implements basic Principal Component Analysis in Matlab and tests is with grayscale portion of the FERET database. )

面向单变量系统的隐式广义预测控制，时域预测域6，控制域3(Length of time domain nn=6 Length of prediction n=6 Length of control domain m=2 control weighting efficient=0.8 Diffusion coefficient a=0.3 format of (n+1) order square P:0- duijiao,1-square1 original value of (n+1) order square P:0 )

MATLAB code to access PDB database

Region filling and Growing-Logic

自己在毕业论文中所编写的源程序蒸发式冷水机组编程，希望大家共同学习(Evaporative chillers programming )

卷积码的编码和译码的matalb程序，译码算法采用viterbi算法(Convolutional encoding and decoding matalb procedures decoding viterbi algorithm using)

基准测试函数中的griewank函数代码(Griewank test function in the function code)

The objective is to measure the differences between two images, and measurement of image quality.

产生右图所示图像f1(m,n)，其中图像大小为256×256，中间亮条为128 ×32，暗处=0，亮处=100。对其进行FFT： ① 同屏显示原图f1(m,n)和FFT(f1)的幅度谱图； ② 若令f2(m,n)=（-1）m+n f1(m,n)，重复以上过程，比较二者幅度 谱的异同，简述理由； ③ 若将f2(m,n)顺时针旋转90 度得到f3(m,n)，试显示FFT(f3)的幅 度谱，并与FFT(f2)的幅度谱进行比较； ④ 若将f1(m,n) 顺时针旋转90 度得到f4(m,n)，令f5(m,n)=f1(m,n)+f4(m,n)，试显 示FFT(f5)的幅度谱，并指出其与FFT(f1)和FFT(f4)的关系； ⑤ 若令f6(m,n)=f2(m,n)+f3(m,n)，试显示FFT(f6)的幅度谱，并指出其与FFT(f2)和 FFT(f3)的关系，比较FFT(f6)和FFT(f5)的幅度谱。(Generating an image f1 (m, n) shown in the figure, wherein the image size is 256 256, the intermediate light bar 128 32 0 = dark, bright Department 100. Its FFT: ① screen display picture f1 (m, n) and the FFT (f1) of the amplitude spectrum ② If so f2 (m, n) = (-1) m+n f1 (m, n), repeat The above process, comparing the amplitude spectrum of the similarities and differences between the two, brief reasons ③ If f2 (m, n) 90 degrees clockwise to get f3 (m, n), try to display FFT (f3) the amplitude spectrum and with the FFT (f2) comparing the amplitude spectrum ④ If f1 (m, n) obtained by 90 degrees clockwise f4 (m, n), so f5 (m, n) = f1 (m, n)+f4 (m, n ), try to display FFT (f5) amplitude spectrum, and pointed out its relationship with the FFT (f1) and FFT (f4) of ⑤ If so f6 (m, n) = f2 (m, n)+f3 (m, n) and try to display the FFT (f6) amplitude spectrum, and pointed out its relationship with the FFT (f2) and FFT (f3), comparing FFT (f6) and FFT (f5) amplitude spectrum.)