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cemcmc_knapsack
to determine cost using MATLAB
- 2010-10-18 02:43:47下载
- 积分：1
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HierachicalStateMachines
Article on state machines
- 2010-11-11 04:44:12下载
- 积分：1
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mdfsfd
matlab源码(Matlab source Matlab source Matlab FO)
- 2007-03-11 02:37:22下载
- 积分：1
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Trace-seismic
a code source to calculate a seismic trace with Ricker function
- 2014-11-03 22:21:29下载
- 积分：1
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exsm
This package is used for exponential smoothing models
- 2013-09-25 03:12:37下载
- 积分：1
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channel_estimation
MIMO-OFDM中基于DFT的信道估计(In MIMO-OFDM channel estimation based on DFT)
- 2013-12-04 17:11:23下载
- 积分：1
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xihaochuli
这是一个有关于对信号处理的matlab的程序代码，里面详细描述了关于信号的处理(This is a signal processing matlab on the program code, which describes in detail the signal processing on)
- 2011-05-10 08:15:45下载
- 积分：1
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costas--m
本程序是用matlab程序语言编写的costas环仿真程序，并对于模拟的costas环的性能进行了分析，costas环主要用于载波同步，本设计实现了对于载波频率跟踪的实现，并且给出图形对比(The program is written in matlab program costas loop simulation program, and the simulated performance of costas loop analysis, costas loop is mainly used for carrier synchronization, the design achieved the realization of the carrier frequency tracking, and gives graphic correlation)
- 2011-05-12 16:21:36下载
- 积分：1
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beylkin-sar-spie
Many synthetic aperture radar (SAR) image formation algorithms require the computation of a multidimensional
Fourier transform of irregularly-sampled or unequally-spaced data samples. We apply a recently developed
algorithm, the unequally-spaced FFT (USFFT),2 to SAR image formation and compare its accuracy and complexity
to a conventional algorithm. We find that the USFFT algorithm allows comparable accuracy to traditional
approaches at a slightly reduced computational cost. We briefly discuss extensions of the USFFT algorithm to
multiresolution SAR imaging
- 2012-05-27 18:09:28下载
- 积分：1
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classical_music_1
MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)
- 2013-09-15 20:25:40下载
- 积分：1
