REDA-Matlab

this is a lens designing book

说明：  单神经元PID控制的例子,用于伺服电机的调速(single neuron example PID control for the servo motor speed control)

说明：  带限信号通过带通滤波器的信噪比改善，通过2000-5000KHZ和3000-4000KHZ的带通滤波器(Band-limited signal to noise ratio by improving the band-pass filter, through the 2000-5000KHZ and 3000-4000KHZ band-pass filter)

kinect的simulink接口，通过simulink控制kinect(kinect based on simulink)

CELP (CELl Processing) is a functional programming package for Matlab.

Test if two matrices have the same column space

MATLAB语言的随机森林算法的例程，可以用于学习随机森林算法(Random Forest algorithm MATLAB language routines can be used to learn Random Forest algorithm)

L3_1.m: 純量量化器的設計(程式) L3_2.m: 量化造成的假輪廓(程式) L3_3.m: 向量量化器之碼簿的產生(程式) L3_4.m: 利用LBG訓練三個不同大小與維度的碼簿並分別進行VQ(程式) gau.m: ML量化器設計中分母的計算式(函式) gau1.m: ML量化器設計中分子的計算式(函式) LBG.m: LBG訓練法(函式) quantize.m:高斯機率密度函數的非均勻量化(函式) VQ.m: 向量量化(函式) L3_2.bmp: 影像檔 lena.mat: Matlab的矩陣變數檔 (L3_1.m: scalar quantizer design (the program) L3_2.m: quantitative result of the false contour (the program) L3_3.m: Vector quantizer code book of the generation (the program) L3_4.m: training in the use of LBG three different size and dimension of the code book and separate VQ (program) gau.m: ML quantizer design in the denominator of the calculation formula (function) gau1.m: ML quantizer design in the calculation of molecule-type ( function) LBG.m: LBG training method (function) quantize.m: Gaussian probability density function of the non-uniform quantization (function) VQ.m: vector quantization (function) L3_2.bmp: image file lena.mat: Matlab matrix variable file)

FCM Data set clustering using fuzzy c-means clustering. [CENTER, U, OBJ_FCN] = FCM(DATA, N_CLUSTER) finds N_CLUSTER number of clusters in the data set DATA. DATA is size M-by-N, where M is the number of data points and N is the number of coordinates for each data point. The coordinates for each cluster center are returned in the rows of the matrix CENTER. The membership function matrix U contains the grade of membership of each DATA point in each cluster. The values 0 and 1 indicate no membership and full membership respectively. Grades between 0 and 1 indicate that the data point has partial membership in a cluster. At each iteration, an objective function is minimized to find the best location for the clusters and its values are returned in OBJ_FCN.

OSG中怎样创建多视图，利用OSG+Win32 console application实现。(make multiply views in osg)