MATLAB

特征提取是模式识别中最重要的一个部分，这里提供的matlab代码计算一些基本的话音特征，而且，提供了一个简单的基于直方图的特征分类方法的实现。基本的特征包括：特征统计、Energy Entropy Standard Deviation (std)、均值信息熵、过零率检测、频谱衰减等等六个特征(Pattern recognition feature extraction is the most important part, here' s matlab code to compute some basic voice features, but also provides a simple classification method based on histogram characteristics of the implementation. Basic features include: Features and Statistics, Energy Entropy Standard Deviation (std), mean entropy, the zero-rate detection, attenuation, and so the six characteristics of the spectrum)

用平均值法对图片序列经行背景建模，并保存。(Method of image sequences with the average background modeling by the line and save. （Attached picture) )

基于汽车控制系统的系统辨识建模，基于汽车控制系统的系统辨识建模(Based on the auto control system identification system modeling)

利用差分法检测QRS波，代码完整，可以直接运行(QRSdifference and Tompkinsdifference)

histogram function to generate and calculate histogram

这是一个Noiselet变换的程序，应该会比较有用，有兴趣的可以到MGA网站查看其具体应用。(This is a Noiselet transformation process, should be more useful, interested can go to the MGA Web site to view their specific applications.)

近场声源定位TDOA的MATLAB仿真，采用传统的相关算法CC和广义相关算法中的相位转换算法GCC_PHAT(Sound localization of MATLAB simulation, using the traditional algorithms of CC and phase transformation algorithm of generalized correlation algorithm GCC PHAT)

Photos with people (e.g., family, friends, celebrities, etc.) are the major interest of users. Thus, with the exponentially growing photos, large-scale content-based face image retrieval is an enabling technology for many emerging applications. In this work, we aim to utilize automatically detected human attributes that contain semantic cues of the face photos to improve contentbased face retrieval by constructing semantic codewords for efficient large-scale face retrieval. By leveraging human attributes in a scalable and systematic framework, we propose two orthogonal methods named attribute-enhanced sparse coding and attributeembedded inverted indexing to improve the face retrieval in the offline and online stages. We investigate the effectiveness of different attributes and vital factors essential for face retrieval. Experimenting on two public datasets, the results show that theproposed methods can achieve up to 43.5 relative improvement in MAP compared to the existing methods.

Array factor of an antenna using a simple code. Specifically for 1D antenna array

This example shows partial shading of a 250-W PV module consisting of 60 cells connected in series.