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用matlab生成服从泊松分布的随机数,为信号分析提供一个函数模块(Using matlab to generate the random number of poisson distribution, provide a function module for signal analysis)
Date : Size : 112kb User : uuu就是

生成Rayleigh 分布的随机数,为信号在瑞利信道中传输,生成的瑞利衰落提供函数(The random number of Rayleigh distribution is generated to transmit the signal in Rayleigh channel and the generated Rayleigh fading provides functions)
Date : Size : 426kb User : uuu就是

生成高斯分布的随机数,在加性高斯信道中使用,增加高斯噪声(Generate the random number of gaussian distribution and use it in additive gaussian channel to increase gaussian noise)
Date : Size : 120kb User : uuu就是

DL : 0
deep learning:random forest
Date : Size : 3kb User : 星玥

用于lsb图像隐藏,生成随机秘密信息嵌入图片中,基于matlab语言实现(LSB image hiding, generating random secret information embedded in pictures, based on MATLAB language implementation)
Date : Size : 1kb User : healeri

Metropolis Hastings code
Date : Size : 31kb User : Dan.act

一种模拟加速度信号的计算功率谱密度的方法,加速度信号由1个特定频率和抖振组成(a example of APSD, the acceleration signal is simulated by sin and random)
Date : Size : 1kb User : 明天831019

一个MATLAB例程,模拟高斯白噪声通过带通滤波器,生成一个窄带随机过程。(In a MATLAB routine, the Gauss white noise is simulated by a bandpass filter to generate a narrow band random process.)
Date : Size : 8kb User : paodaouin

圆形在平面内不重叠随机分布的模型,其中圆形模型的半径和数量可自行设置(Nonoverlapping random distribution in a circular plane)
Date : Size : 1kb User : njzwzw123

在matlab中,运用随机森林算法来解决图像特征处理的问题。(In Matlab,the random forest method is used to solve the classification of image features.)
Date : Size : 304kb User : *哈哈*

DL : 0
快速扩展随机树(rrt)基本算法的实现,222222222222(The Implementation of the basic algorithm of fast spread random tree (RRT))
Date : Size : 1kb User : zswx

DL : 0
可重启的随机游走算法 读入已知矩阵后列正则化作为迭代方程参数(random walk with restart)
Date : Size : 1kb User : tukkk

随机神经网络 Matlab实例+源代码,随机神经网络(Random neural network Matlab example + source code)
Date : Size : 3kb User : 李响2666

MATLAB implementation of compressive sensing example as described in R. Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118], July 2007. The code acquires 250 averaged random measurements of a 2500 pixel image. We assume that the image has a sparse representation in the DCT domain (not very sparse in practice). Hence the image can be recovered from its compressed form using basis pursuit.
Date : Size : 1kb User : sabry

MATLAB implementation of compressive sensing example as described in R. Baraniuk, Compressive Sensing, IEEE Signal Processing Magazine, [118], July 2007. The code acquires 250 averaged random measurements of a 2500 pixel image. We assume that the image has a sparse representation in the DCT domain (not very sparse in practice). Hence the image can be recovered from its compressed form using basis pursuit.
Date : Size : 106kb User : sabry

使用随机搜索方法,进行局部最优求解,matlab语言版本。(The random search method is used to perform local optimal search.)
Date : Size : 53kb User : tdcqren

DL : 0
对于一个具体的数据,用交叉验证进行分类,随机森林进行训练,用AUC,AUPR,Precision评价分类器的性能(For a specific data, use cross validation to classify, train random forests, evaluate the performance of the classifier with AUC, AUPR, and Precision.)
Date : Size : 19kb User : Katherine_

scale.m 音频加入水印 scale_tiqu.m 加入水印的音频提取水印 jiazao_tiqu.m 加入水印的音频加入随机噪声后,提取水印 jianqie_tiqu.m 剪切加入水印的音频后提取水印(Scale.m: Audio adding watermark Scale_tiqu.m: audio watermark extraction with watermark Jiazao_tiqu.m: after adding the watermark to the random noise, the watermark is extracted Jianqie_tiqu.m: the watermark is extracted after the watermark is added to the audio)
Date : Size : 5.7mb User : malvina

DL : 0
压缩感知用高斯随机矩阵作为测量矩阵,用BP算法重构(The Gauss random matrix is used as the measurement matrix for the compression perception, and the BP algorithm is reconstructed)
Date : Size : 1kb User : 一人土土

深度神经网络在测试时面对如此大的网络是很难克服过拟合问题的。 Dropout能够很好地解决这个问题。通过阻止特征检测器的共同作用来提高神经网络的性能。这种方法的关键步骤在于训练时随机丢失网络的节点单元包括与之连接的网络权值。在训练的时候,Dropout方法可以使得网络变得更为简单紧凑。在测试阶段,通过Dropout训练得到的网络能够更准确地预测网络的输出。这种方式有效的减少了网络的过拟合问题,并且比其他正则化的方法有了更明显的提升。 本文通过一个简单的实验来比较使用Dropout方法前后网络的性能优劣情况。(It is difficult to overcome the problem of fitting a deep neural network in the face of such a large network in testing. Dropout can solve this problem well. The performance of the neural network is improved by preventing the common action of the feature detector. The key step of this method is that the node unit of the random loss network consists of the network weights connected to it during the training. In training, the Dropout method can make the network more compact. In the test phase, the network trained by Dropout can predict the output of the network more accurately. This method effectively reduces the over fitting problem of the network, and has a more obvious improvement than the other regularization methods. In this paper, a simple experiment is used to compare the performance and performance of the Dropout method.)
Date : Size : 304kb User : 转角的狐狸
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