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Introduction-to-Simulink-
matlab的Simulink仿真实例，原版英文资料，比较基础。(Matlab Simulink simulation instance, the original information in English, the basis for comparison.)
- 2013-04-07 09:49:14下载
- 积分：1
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stepFun_fitting
说明： 多项式拟和阶跃函数的小程序！对于多项式反演有一定的启发(Polynomial to be a small step function and procedures! Polynomial inversion for some inspiration)
- 2008-11-15 02:02:31下载
- 积分：1
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awgn_ofdm
说明： 添加高斯白噪声的ofdm调制，matlab实现仿真。(OFDM modulation)
- 2011-04-10 22:14:38下载
- 积分：1
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as
模拟 AM FM DSB 信号傅里叶变换图 频域稀疏(Analog AM FM DSB frequency domain signal sparse Fourier transform Figure)
- 2014-01-13 17:18:37下载
- 积分：1
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粒子群算法详解-附matlab代码说明
粒子群算法初步讲解，粒子群算法分类，标准粒子群算法的实现(Preliminary analysis of particle swarm optimization)
- 2020-06-23 03:40:02下载
- 积分：1
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MIMOMUD
有关于mimo系统的多用户检测的仿真，分别对1发4收、2发4收、3发4收、4发4收的情况进行的仿真，并给出了误码率曲线图(Mimo systems on the detection of multi-user simulation, respectively, close to 1 made 4, 2 fat, 4 close, 3 fat, 4 close, 4 fat, 4 close to the situation of the simulation, and gives a bit error rate curves)
- 2009-05-29 11:56:21下载
- 积分：1
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fdtd.m
二维TM波金属圆柱的fdtd程序两边为MurABC，上下为PEMurPEC(Two-dimensional TM wave metal cylinder fdtd program)
- 2010-12-25 11:08:27下载
- 积分：1
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K-means
基于K-eans聚类算法的图像分割方法，对学习理解K-eans聚类能提供很大的帮助。(Image segmentation method based on K-eans clustering algorithm,which provides a great help in learning to understand the K-eans cluster.
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- 2013-05-26 20:54:25下载
- 积分：1
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lpc[1]
从人的语音提取特征值的一种方法，lcp可能有些旧，但是还是不错的一种方法。(Extracted from the voice of a method of eigenvalue, lcp may be old, but still quite a way.)
- 2009-01-21 15:28:30下载
- 积分：1
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Process
Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.
- 2013-01-01 20:25:49下载
- 积分：1
