qpsk-pca

颜色高斯模型在simulink 下的模型对应的sfunction表示方法(colored gmm s sfunction at simulink)

第一步是计算输人信号单边功率谱密度（ESD）。使用快 ％速傅里叶（FFr）算法将信号从时域转换到频域。因为FFr算法的输出是离散谱，而这 ％里我们需要的是连续谱，因此需要引人不同的比例因子来实现从离散谱到连续谱的转换。 ％在第二步中，我们利用迭代算法计算出相对于特定阂值的ESD的最高和最低频率，从而 ％估算出被检测信号所占用的带宽。这种算法同时适用于基带信号和已调制信号。最后， ％在第三步中，我们给出了输出图形的原代码。(The first step is calculating the input signal unilateral power spectral density (ESD). The use of fast Fourier speed (FFr) algorithm for the signal conversion from time domain to frequency domain. FFR algorithm because the output is a discrete spectrum, which we need is a continuous spectrum, so the need for food of different scale factor to achieve from the discrete spectrum to continuous spectrum conversion. In the second step, we use iterative algorithm to calculate a specific threshold in relation to the ESD of the highest and lowest frequency, thus estimated to be occupied by the signal detection bandwidth. This algorithm applies to both baseband signal and modulation signals. Finally, in the third step, we give the graphical output of the original code.)

支持向量机用于训练和分类，包括PCA降维等函数(Support vector machines for training and classification, including functions such as PCA dimension reduction)

LTE仿模型，十分好用，可以快速帮你理解 (ofdm simulation model, is very easy to use, can quickly help you understand)

Complex Power Flow, param,pfs_gsm, symetrical component with graph ,Symmetrical Fault Analysis ,Ybus and Zbus

FEM 作业用的一个小程序，用于计算梁的简单变形。(Finite Element Homework)

MATLAB7.x数字信号处理 光盘内容(Digital Signal Processing MATLAB7.x CD-ROM content)

lssvm多分类算法 (Multi-classification algorithm)

Stf滤波实现的目标跟踪程序，可实现针对非线性情况下的跟踪(Stf filter to achieve the target tracking process enables the tracking for nonlinear case)

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.