FINAL_RUN_WAVELET

本编码是关于算数编码的，使用了matlab来时实现有关算数编码的内容(The code is on arithmetic coding, using matlab realize when related to arithmetic coding content)

无刷直流电机的能量回馈控制，调制方式为可切换的半桥调制和全桥调制，控制方法为DTC(Energy brushless DC motor feedback control, the modulation scheme for the half-bridge and full-bridge modulation modulation can be switched, the control method for the DTC)

使用BEM方法迭代轴向切向诱导因子a和b(compute a and b of wind blade with BEM method)

支持向量机，用于分类，含训练集与测试集，用SVM进行多分类(Support Vector Machine (SVM), used for classification, including training set and test set, and multi-support vector machine (SVM))

基于Scilab的粒子群算法代码，Scilab是一个功能和matlab差不多的开源软件(Based on the Particle Swarm Optimization Scilab code, Scilab is a functional and open source software similar to matlab)

this is classifier using Euclidean distance measure for palm print recognition

对数谱 语谱图和倒谱代码 适用于语音识别 说话人识别 自己编写 亲测可以运行(Logarithmic spectrum and spectrogram cepstrum code for voice recognition speech recognition to write your own pro-test can be run)

此算法描写了怎样根据图片的灰度值来找边界，以及在什么条件下可以作为图像轮廓。(This algorithm describes how to find the gray value according to the picture border, and under what conditions can be used as image contours.)

this program treat image processing and segmentation methods and can help for functions which concern segmentation, hidden markov segmentation

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.)