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周期图法实现白噪声背景下的正弦函数功率谱估计,其中的傅里叶变换和谱估计的步骤都是直接写出,没有调用函数(Periodogram method the sine function in the white noise power spectrum estimation, one of the Fourier transform and spectral estimation steps are to write, not call the function)

A novel algorithm for sparse approximation in redundant dictionaries, called the M-Term Pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal components.

雷达模糊函数的构建以及各种信号的产生，包括正弦，余弦等等信号(the cross ambiguity function for radar system and many signal generators)

旅行商问题(traveling saleman problem,简称tsp) 的遗传算法matlab 程序，具体说明文件内有(matlab for traveling saleman problem )

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.

一个使用MATLAB实现基于HRTF理论下的SRS-3D立体虚拟环绕声技术的硕士论文(A use MATLAB HRTF theory based on the SRS-3 D virtual surround sound technical master thesis )

基于核的学习方法SVM(Support Vector Machine)有很好的分类识别性能，这是在SVM的基础上改进的一个更好的SKM(Support Kernel Machine)Package.(kernel-based learning methods SVM (Support Vector Machine) are good the classification performance, This is the SVM improved on the basis of a better SKM (Support Kernel Machi ne) Package.)

robust control using MATLAB

收集的一个不错的matlab PID实验程序，希望大家能喜欢(PID matlab)

Among the high-complexity methods, two methods dominate the literature Kalman filtering and Mixture of Gaussians (MoG). Both have their advantages, but Kalman filtering gets slammed in every paper for leaving object trails that can t be eliminated. As this seems like a possible deal breaker for many applications, I went with MoG. Also, MoG is more robust, as it can handle multi-modal distributions. For instance, a leaf waving against a blue sky has two modes—leaf and sky. MoG can filter out both. Kalman filters effectively track a single Gaussian, and are therefore unimodal: they can filter out only leaf or sky, but usually not both.