edgedetect

BP网络实现的基本MATLAB程序，非常容易看懂(BP identification)

这是一个用于消除用户间干扰的MATLAB代码，利用了最小化干扰泄露的迭代优化算法，将用户接收到的干扰功率逼近于零来实现干扰消除。(This is a MATLAB code for eliminating interference between users, the use of iterative optimization algorithms minimize interference leaked, the user receives interference power approximation to zero to achieve interference cancellation.)

降压dc变换器输出功能仿真，根据可控晶闸管调节电压的压降，实现降压电路，采用pwm波控制。(buck DC Converter)

利用remez设计rrc滤波器,通过迭代算法实现(use the remez to design rrc filter)

voltage source inverter

说明：  香农第一定理指出了平均码长与信源之间的关系，同时也指出了可以通过编码使平均码长达到极限值，这是一个很重要的极限定理。(Shannon first theorem that the average yards long with the letter of the relationship between the source, it has also pointed out the code can make the average yards long reach limits, this is a very important Limit Theorem.)

分形算法与程序设计Visual C++实现完整版。介绍了许多分形算法，并给出了算法实现的关键部分。(To realize the full version of fractal algorithm and program design of Visual C++. Introduced many fractal algorithm, and gives the key part of the algorithm to achieve.)

在MATLAB中将seg-y格式的读入，并可以进行SEG-Y与asc2的转换以及加地震道头等操作(Seg-y in the MATLAB format will be read, and can SEG-Y and asc2 the conversion and operation of additional seismic top)

this code of Particle Swarm Optimization algorithm

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.