data-statistical--and-analyse

里面有大量的Matlab的应用实例，可以给大家的学习带来帮助，它不仅适合初学者而且也可以提高大家的Matlab编程能力。(Which a large number of Matlab application example, can help to bring everyone to learn, not only for beginners but also can improve our ability to Matlab programming.)

PDF aobut diferent book in electrical research for electrical student nd teacher

用MUSIC方法估计信号源的二维DOA,即对方位角和俯仰角进行估计。(MUSIC method estimates the signal source with a two-dimensional DOA, that is to estimate the azimuth and elevation.)

matlab7.0基础教程，清华出版社出版的，很好的学习材料(matlab7.0 based tutorials, Tsinghua University Publishing House, good learning materials)

cs的omp恢复算法,使用恢复矩阵，观测矩阵(The physics of compressive sensing (CS) and the gradient-based recovery algorithms are presented. First, the di® erent forms for CS are summarized. Second, the physical meanings of coherence and measurement are given. Third, the gradient-based recovery algorithms and their geometry explanations are provided. Finally, we conclude the report and give some suggestion for future work.)

这个是时域MUSIC算法。希望对大家有用。(This is the time domain MUSIC algorithm. Hope to be useful.)

Stochastic Dynamic Programming for Water Reservoir

matlab 下的patton copula 包括bivarnormcdf 、bivartcdfmc等(matlab Patton copula bivarnormcdf, bivartcdfmc)

光伏电源程序 基于M函数的matlab程序(Solar Power Program)

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one orthogonal frequency-division multiplexing (OFDM) block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. By inserting some null subcarriers in the proposed tile structure, we propose a computationally efficient subspace decomposition- based algorithm for CFO estimation. The issue of optimal tile length is studied both theoretically and through simulations. By judiciously designing the tile size of the pilot, the proposed algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the time-division multiplexing-based training method and the computationally demanding space-alternating generalized expectation-maximization