nmf-example1

这个进行形象分解EMD,可以很理解EMD分解,便于学习入门EMD.(The image decomposition for EMD, EMD decomposition can be understood, easy to learn entry-EMD.)

用与合成孔径雷达的回波检测的编程的原代码(Using synthetic aperture radar detection of the echo of the original programming code)

用costas环实现载波同步。假设发送信号载波频率为3.563Mhz，本地初始频率为3.5628Mhz。程序中直接给出了环路滤波器的参数C1,C2。(this is synchoronization on time recovery basing on matlab)

Matlab命令大全，很全面地包涵了Matlab的命令，方面学习查询。(Matlab order Daquan, very comprehensive bear the Matlab command, in learning about the query.)

超宽带高斯噪声的计算1，与其他的几个共用(Ultra-Wideband Gaussian noise calculation 1, and some other common)

分别利用 Lagrange插值法、分段线性插值法与三次样条法进行插值逼近(Respectively, by Lagrange interpolation, piecewise linear interpolation and cubic spline interpolation approximation method)

一个简单的基于HOG的行人检测程序，MATLAB编写，其中read me文(-A simple HOG based pedestrian detection program, MATLAB write, read me the document which explains in some detail.)

前人提出的众多发酵动力学模型的基础上，结合工业乙醇连续发酵的实际情况，首先对经典的流加半经验半理论模型进行动态和稳态仿真，之后为了与实际生产过程联系更加紧密，逐步将温度，气体排放等因素考虑到模型中去，进行综合性仿真。最后再结合GUI技术，以动力学模型为核心，以人机界面友好为目标，制作乙醇连续发酵仿真软件，将脚本语言编程技术和模块化仿真技术结合起来，建立起工业乙醇发酵的仿真平台。(Made numerous predecessors fermentation kinetics model based on the combination of industrial ethanol continuous fermentation of the actual situation, the first of the classic semi-fed semi-empirical model for dynamic and steady-state simulation, after which the actual production process for closer ties, gradual temperature, gas emissions and other factors into the model to carry out a comprehensive simulation. Finally, combining GUI technology to kinetic model as the core, friendly interface for the target, producing continuous ethanol fermentation simulation software, the script language programming techniques and modular simulation technology combine to build industrial ethanol fermentation simulation platform.)

这个是matlab绘制分子结构的源程序，对于学习化学的人来说很有用。解压即可运行出结果。(This is a matlab drawing of the molecular structure of source code, for learning chemistry of people very useful. Decompression can run results.)

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.