mmc1

matlab的一些最基本的应用(Matlab some of the most basic )

计算时间序列的Hurst系数有许多方法，但是其中最准确的就是运用小波法来计计算，本程序源码用Matlab实现了用小波来计算Hurst系数。 (There are many ways of calculating the Hurst coefficient of time series, but one of the most accurate calculation using wavelet method to count the program source code using Matlab achieve wavelet Hurst coefficient.)

电磁场分析。不错的。直流的导线、做的比较好的(Electromagnetic field analysis. Good. DC wire,,,)

对跳频通信同步状态的理解对跳频通信同步状态的理解(Synchronization of frequency hopping communication understood the state of synchronization status on the understanding of frequency-hopping communication)

ETF统计套利的书籍，目前正在用matlab进行回测，希望有喜欢的人，也可以了解下(ETF statistical arbitrage books, is currently in use of matlab to test, hope to have of the person you like, also can understand)

说明：  用遗传算法解决多车场多目标带时间窗车辆问题(Using genetic algorithm to solve the vehicle problem of multi depot and multi-objective with time windows)

说明：  一个用MATLAB实现的单机器人避障程序。效果很好。(Using MATLAB to achieve a single robot obstacle avoidance procedures. Good effect.)

本源码基于MATLAB R2010b基础上，通过GUI架构的支持下，设计了基于暗通道先验法对有雾图像的去雾系统。(The source code based on MATLAB R2010b basis, through support GUI architecture, the design method based on a priori dark passage foggy image defogging system.)

LTE and LTE-A uplink simulater

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.