QPSO

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征：发烧、疼痛、咳嗽和血细胞异常，当确定了患肺炎与否时，四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据（500行，每行前4个数对应4个特征变量，第5个数对应患肺炎是否为真，以0表示False，1表示Ture），编写Matlab 程序 maininference.m，从example.txt中读取病人的症状信息（0表示False，1表示True，-1表示not given），并计算对应的患肺炎的可能性。所给的信息按发烧、疼痛、咳嗽、血细胞异常的顺序排列。将计算结果保存在answer.txt中。(Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. According to the data pneumonia.tex file (500 lines, each line of the first four numbers corresponding to the four characteristic variables, number 5 corresponds to the risk of pneumonia is true, 0 represents False, said Ture), to write Matlab program maininference. m from example.txt read the information of the patient' s symptoms (0 = False, said True, the-1 indicates not given), and to calculate corresponding to the likelihood of suffering from pneumonia. The information given by the order of fever, pain, cough, blood cell abnormalities. The calculated results is be saved in answer.txt.)

利用MATLAB产生三角波的模型， 功放模型仿真中的三角载波，经过测试，数据还是比较准确的(Using MATLAB to produce triangle wave model)

Okumura hata model power against frequencies

一篇非常经典且全面的多视点方面的综述性文章，研究3ＤＴＶ领域的同仁会用得到(a very classic paper on multiview)

这篇文章主要介绍pocs算法，详细的介绍了pocs的步骤。(pocs ,image super-resolution)

图像空域锐化增强，用matlab分别实现Roberts、Sobel、Priwitt算子的锐化处理，以及实现拉普拉斯图像和原始图像叠加的增强处理(Image airspace sharpening, respectively, to achieve with matlab Roberts, Sobel, Priwitt operator sharpening, and achieve enhancement Laplace image and the original image overlay)

人工合成地震记录人工合成地震记录人工合成地震记录人工合成地震记录(seismic data seismic data seismic data)

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.

随机森林 MATLAB版算法，可以运行，能较好的实现分类算法，只是速度有点慢(Random forest MATLAB version of the algorithm can be run, better classification algorithms, but the speed is a little slow)

Wavelets and Filter Banks Wavelets and Filter Banks Mallat Mallat pyramid algorithm pyramid algorithm