basicboostconverter

Teste de reconhecimento facial

说明：   此函数用于研究Mallet算法及滤波器设计 此函数用于消噪处理 分解的高频系数采用db9较好,即它的消失矩较大 分解的有用信号小波高频系数基本趋于零 对于噪声信号高频分解系数很大，便于阈值消噪处理(this function for research Mallet filter algorithm design and function for Denoising decomposition of high-frequency coefficients Terminal better that it's vanishing moments larger decomposition of useful signal, high-frequency coefficients tends to zero for high-frequency signal noise decomposition coefficient great ease threshold Denoising)

Ridgelet transform and curvelet transform

八皇后问题，用matlab解决八皇后问题，以及多皇后问题，采用追溯法(Eight queens problem)

用于实现节点定位的三边定位算法的matlab代码，包含了对定位误差的仿真(Trilateral achieve node localization algorithm is used to locate the matlab code, including the simulation of positioning error)

还可以了电动饿了丰富额份额分积分二分法而非(it can den edfjief ef efef efe ff f )

cycloconverter 1 phase is simulated

这是一个很好的控制台应用程序，可以方便的在其中添加模块。(This is a good console application, you can easily add modules where.)

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.

说明：  动态矩阵控制MATLAB源程序，是基于阶跃响应的模型预测控制(Dynamic Matrix Control MATLAB source code is based on the step response of model predictive control)