COmpression

两个正弦信号叠加白噪声 用fft进行频谱分析(failed to translate)

用matlab编写的，有限元三维梁单元的单元刚度矩阵计算程序，各节点6个自由度。(prepared by the three-dimensional finite element beam element of the element stiffness matrix calculation program, the 6-DOF nodes.)

基于双线性插值方法的图像缩小、放大程序，原创(Bilinear interpolation method based on image reduction, enlargement program, originality)

瑞丽衰落和莱斯衰落的对比 瑞利衰落（Rayleigh Fading）：在无线通信信道中，由于信号进行多径传播达到接收点处的场强来自不同传播的路径，各条路径延时时间是不同的，而各个方向分量波的叠加，又产生了驻波场强，从而形成信号快衰落称为瑞利衰落。瑞利衰落属于小尺度的衰落效应，它总是叠加于如阴影、衰减等大尺度衰落效应上。(rice fading and rayleigh fading)

估计点云的法线，选择点周围的面皮，面片法线相加，求均值(Estimated cloud normals, select points around the dough, adding the patch normal, averaging)

main_DSCDMA.m 这个仿真程序用于实现DS-CDMA通信系统仿真 ( Main_DSCDMA.m of this simulation program used to implement DS-CDMA communication system simulation)

UWB无线通信系统仿真，请大家下载 ，不知有没有用(This package simulates Q. Zhang s FSR-UWB (Frequency-Shifted Reference Ultra-Wideband) Wireless Communicatons System which is known as the TR-UWB without the delay element. )

用matlab仿真了LDPC码的编码译码程序，及其用BPSK调制传输。并测其误码率(Matlab simulation of the LDPC codes with coding and decoding process, and its transmission with BPSK modulation. And measure its error rate)

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：  BP_Adaboost模型即把BP神经网络作为弱分类器，反复训练BP神经网络预测样本输出，通过Adaboost算法得到多个BP神经网络弱分类器组成的强分类器，利用此方法设计出强分类器。(The BP_Adaboost model uses the BP neural network as a weak classifier, repeatedly trains the BP neural network to predict the sample output, and uses the Adaboost algorithm to obtain a strong classifier composed of multiple BP neural network weak classifiers. This method is used to design a strong classifier.)