FrameworkdesignofCUWBsystemintegratingcognitiverad

matlab的常用数据库表，另外还有一些个人搜集的数据库资料(matlab commonly used database tables, plus some personal information collected by the database)

matlab一本学习非常好的书，大家可以读一下，快速入门(matlab a learning very good book, we can read the Quick Start)

16QAM编译码程序，在信号调制过程中是不可缺少的成分(16QAM encoding and decoding process, in the course of signal modulation is an indispensable ingredient)

基于牛顿法的配网潮流计算程序，采用改进牛顿法，对初学者有很大帮助(Calculation is based on Newton s method with network flow program, an improved Newton, a great help for beginners)

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

关于拉曼光纤激光器超连续谱的仿真程序，欢迎大家下载楼！(this is about supercontinue spectrum.)

matlab中实现的G.726编码的算法及测试程序，在matlab中已经通过测试，并能输出编码结果(matlab code to implement the G.726 algorithm and testing procedures in matlab has been tested and the results can be output coding)

说明：  主要介绍了准正交空时分组码，包括ABBA，jafarkhani(Introduced quasi-orthogonal space-time block codes, including ABBA, jafarkhani)

SVM multi classifier with diferent Kernels example

图像处理经典算法，基于PCA的人脸识别matlab代码(Matlab code of face recognition based on PCA)