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LMSRLS
主要介绍LMS-RLS滤波器的原理及实现方法，可以参考的(Mainly introduces LMS- RLS filter of principle and method, reference may be made)
- 2011-01-14 21:20:47下载
- 积分：1
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Frequency-Hopping-Spread-Spectrum-in-Matlab
This file containing a source code for Frequency Hopping Spread Spectrum in Matlab. Also this file has some guide files.
- 2013-02-04 16:03:00下载
- 积分：1
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Textembedkey
Embedding text into random pixels of an image
- 2013-11-19 15:27:54下载
- 积分：1
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particle-filter
说明粒子滤波器的功能，给了5个例题和他的源代码。(Explain the function of particle filter)
- 2012-10-31 20:19:11下载
- 积分：1
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LinearKalmanFilter
source code for linear kalman filter analysis
- 2011-10-15 16:09:55下载
- 积分：1
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blackmano
利用blackman窗设计Ⅱ型数字带通滤波器MATLAB实现代码(Blackman window design II digital bandpass filter MATLAB implementation code)
- 2012-06-02 18:24:33下载
- 积分：1
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nadi
a m-file to use the psat program and find the switching sequence for restoration the fualted distribution system and shed the load in the system. to reach this goal we must detect the loop in the system and open a circuit breaker. so in the system graph the loops must be detected.
- 2011-08-14 13:39:07下载
- 积分：1
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menterkalo
本程序是自己编写关于蒙特卡洛仿真的基本例子，对于做蒙特卡洛反演的同学是很好的入门例子(This program is to write your own basic example of Monte Carlo simulation for students to do Monte Carlo inversion is a good example of the entry)
- 2013-03-27 14:39:12下载
- 积分：1
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angle.lidar
【无人驾驶传感器之64线激光雷达】
velodyne hdl64es2 64线激光雷达点云数据处理之雷达扫描范围图([64 line laser radar sensor of the unmanned] velodyne hdl64es2 64 lines LIDAR point cloud data processing of the scan range radar chart)
- 2013-12-02 14:10:34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
