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equalization
cma mma avma mcma 多种盲均衡算法 完全重构 归一化盲均衡 (cma mma avma mcma NPR )
- 2020-12-19 16:59:11下载
- 积分：1
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单相接地故障的simulink仿真 single-phaseto-ground-fault-
中性点不解地系统单相接地故障的simulink仿真(Simulation of single- phase- to- ground fault in neutral point)
- 2020-08-15 23:08:27下载
- 积分：1
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fusechuli
能够编译运行，改正了好多网上程序的错误，完整的肤色分割程序，下载者只要在D目录中加一张图片，就可以运行了(To compile and run, full color segmentation process)
- 2011-04-22 12:44:33下载
- 积分：1
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Video-Object-Tracking
video object tracking using camera
- 2012-02-12 21:42:53下载
- 积分：1
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MMBEBHE
minimum mean brightness error bi-histogram equalization method MMBEBHE for image enhancement
- 2010-07-08 16:36:02下载
- 积分：1
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ASRCODE
DTW+VQ实现的语音识别
UIUC提供，包含样本
只需将.txt改成.m(DTW+ VQ implementation of speech recognition . provide by UIUC, including samples simply. Txt into. M)
- 2009-03-16 15:38:39下载
- 积分：1
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askd
MATLAB 通信原理ask调制源代码文件 (Communication Theory ask modulation MATLAB source code files)
- 2010-01-14 12:40:51下载
- 积分：1
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sbs
在时间上使用隐式有限差分法，空间上使用后向差分法，对耦合波方程组离散化，进而对受激布里渊散射的能量反射率和产生SBS的阈值能量进行数值模拟。(Begin with the coupled wave equations which describe the interactions of the pump, the sound and the Stokes wave in the process of SBS. The equations are simplified according practical conditions, and one-dimension theoretical models are obtained aiming at unfocused pump as well as focused pump. The coupled equations are turned discrete using the method of implicit finite difference in terms of time and backward difference in terms of space.)
- 2021-03-21 20:49:17下载
- 积分：1
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Elman-
用matlab实现Elman神经网络(Elman neural network using matlab)
- 2013-12-13 22:11:53下载
- 积分：1
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sons
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.
- 2020-12-03 13:19:24下载
- 积分：1
