matlab

uses the forward difference method for a European call.

matlab与C++的混合编程，实现计算rice图像中的米粒个数。(matlab and C++ mixed programming, to achieve calculate the number of rice grain image.)

升采样，用于分析ISAR/SAR成像算法成像质量，分析点目标距离向/方位向剖面切片，观察主瓣与旁瓣，也可以用于定性分析不同算法对同一目标点成像质量的好坏。也可用于分析峰值旁瓣比与积分旁瓣比等。(Liter sample for analysis ISAR/SAR imaging algorithm image quality, the analysis points to the target distance/azimuth cross-sectional slices, observe the main lobe and side lobes to be good or bad for the qualitative analysis of different algorithms on the same target image quality. It can also be used to analyze the ratio of peak sidelobe sidelobe integral ratio.)

粒子群优化算法（PSO）应用于神经网络优化程序。分为无隐含层、一隐含层、二隐含层。运行DemoTrainPSO.m即可(Particle Swarm Optimization (PSO) used neural network optimization program. Divided into no hidden layer, a hidden layer, two hidden layers. You can run DemoTrainPSO.m)

matlab设计程序用FFT对信号作频谱分析(matlab program designed to make the signal with the FFT spectrum analysis)

This function used for mapping gray data after equalization with many method into image.

英国谢菲尔德大学推出的GA算法工具箱，只能说，很好，很强大(GA toolbox by Sheffield Univ, excellent and perfect.)

计算了贝塞尔函数的一阶导数的零点。 更新BessDerivZerosBisect.m这些变化： 1。允许对m= 0 2。允许用户指定特定的m和k值理想。 3。允许公差输入参数 4。使用一个表的查找能为m和k的小值接近初始化 5。添加错误检查 6。计算衍生w.r.t.以X而不是w.r.t.米（虽然老方法的工作也。）(Calculates the zeros of the first derivatives of Bessel function. Updates BessDerivZerosBisect.m with these changes: 1. Allows for m = 0 2. Allow the user to specify the particular m and k values desired. 3. Allows for a tolerance input parameter 4. Uses a table look-up to get closer initializations for small values of m and k. 5. Adds error checking 6. Computes the derivative w.r.t. to x rather than w.r.t. m (although the old method works, too.))

The early detection of arrhythmia is very important for the cardiac patients. This is done by analyzing the electrocardiogram (ECG) signals and extracting some features from them. These features can be used in the classification of different types of arrhythmias. In this paper, we present three different algorithms of features extraction: Fourier transform (FFT), Autoregressive modeling (AR), and Principal Component Analysis (PCA). The used classifier will be Artificial Neural Networks (ANN). We observed that the system that depends on the PCA features give the highest accuracy. The proposed techniques deal with the whole 3 second intervals of the training and testing data. We reached the accuracy of 92.7083 compared to 84.4 for the reference that work on a similar data.

大作业上的一个迭代程序，可以供大家试用下(Great job on an iterative process, you can try for your next)