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ofdm_simulation
OFDM 的整个流程的 仿真程序 适于初学者学习ofdm的整个过程()
- 2007-09-21 10:21:01下载
- 积分：1
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sxxxxxx
FOR HELP AND DOCUMENT FOR POWER cvd ffg
- 2015-03-06 20:36:26下载
- 积分：1
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CAR-4
说明： MATLAB程序（附图像），在彩色图像中对汽车车牌的分割。(MATLAB (AP like), the color images of the vehicle license plate division.)
- 2006-04-02 22:08:33下载
- 积分：1
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lisaru
李萨茹图形动画的matlab程序,适用于大学物理的课件演示(matlab source code for lisaru flash)
- 2012-04-16 21:54:28下载
- 积分：1
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matpower4.0
matpower4.0的matlab程序，及说明文件。每个程序都有详细的解释说明(matpower4.0 matlab procedures, and documentation. Each program has a detailed explanation)
- 2013-09-23 21:50:58下载
- 积分：1
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data
对实验采集数据进行线性处理，使得最后得到的实验结果近似于一条直线。(Linear processing of experimental data collection, so that the final experimental results obtained approximates a straight line.)
- 2013-05-05 01:33:07下载
- 积分：1
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STLFMCWtest
stlfmcw雷达对目标的测速和测距，通过上下差频得到差拍信号，在进行快速fft得到目标的一维距离和速度(st fmcw radar target speed and distance measurement, frequency beat signal obtained by the difference between up and down, making quick fft obtained one-dimensional target distance and speed)
- 2020-12-05 19:49:23下载
- 积分：1
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pisare
用Pisarenko谐波分解法估计一组实验数据的正弦频率及幅度。得出真实功率谱密度。(Pisarenko harmonic decomposition method with a set of experimental data estimate the sinusoidal frequency and amplitude. The true power spectral density obtained.)
- 2010-10-22 11:54:42下载
- 积分：1
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chap8_3
说明： 离散系统灰色PID控制,对不确定部分建立灰色估计模型,根据参数的不确定部分进行一定的补偿(PID control of gray discrete-time systems, part of the establishment of the uncertainty estimation model gray, according to the parameters of uncertainty to a certain part of the compensation)
- 2008-09-17 16:39:22下载
- 积分：1
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OMB_DHC
该matlab代码实现了“综合多分支分裂式层次化聚类”
(Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC)
“A competitive omnibus performance criterion for divisive multi-branching
hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package
is implemented in Matlab code and therefore demands the Matlab core program, as well as the
Statistics toolbox of Matlab.
Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the
identification of sub-structures in multivariate data. Of particular interest is multi-branching
DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly
explored issue in multi-branching DHC concerns the actual importance of the two
performance criteria (and their associated algorithms) to automatically create clusters and
select number of clusters, respectively. Another interesting but hitherto unexplored issue is
the possibility to employ a single omnibus performance criterion that guide)
- 2013-01-29 11:05:02下载
- 积分：1
