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Run
说明: 包括OFDM符号的加循环前缀,去循环前缀,加时延3个程序。(Including OFDM symbol cyclic prefix added to the cyclic prefix, plus 3 delay procedures.)
- 2011-03-18 11:21:15下载
- 积分:1
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viterbi_decoding
Convolution Encoder and Decoder 1/2 , Viterbi algorithm
- 2013-09-21 19:41:02下载
- 积分:1
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disflowtry
潮流计算:算例为IEEE33 计算网络节点电压 (Computing network node voltage power flow calculation examples for the IEEE33)
- 2011-11-23 14:43:36下载
- 积分:1
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kmeans
用matlab写的一个k-means聚类程序,简单实用(Using matlab to write a k-means clustering procedure, simple and practical)
- 2012-01-10 23:54:39下载
- 积分:1
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predict
说明: Arima模型选择差分项,并验证序列正确性(Arima model validates sequence correctness)
- 2020-06-22 14:40:01下载
- 积分:1
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实现小波数字水印,比较简单
采用量化数组嵌入
实现小波数字水印,比较简单
采用量化数组嵌入-The realization of wavelet digital watermarking, is relatively simple to quantify the use of embedded array
- 2022-05-28 16:22:26下载
- 积分:1
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RBF神经网络的MATLAB实例代码
该文件包含了径向基函数神经网络的例子,该例子是一类非线性的,具有特征的;
- 2022-08-19 06:23:19下载
- 积分:1
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FFT-time-frequency
可以实现FFT变化的时域图分析,可以清楚的知晓信号随时间的规律变化(Changes can achieve FFT time domain analysis, the signal can be clearly aware of the law change with time)
- 2013-08-19 17:55:12下载
- 积分:1
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Sim-Phone
对获得的声音进行处理。得到相关参数的同时进行滤波。以达到降噪的目的。(To deal with and filter a kind of input sound modify the parameters of this voice.)
- 2014-10-04 08:26:12下载
- 积分:1
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active-learning-code
Learning_random.m : 随机选择样例,从(90)pool里随机选择样本,删除版本空间树的数量
activeLearning.m:根据最大熵原则,从pool里选择样本,删除版本空间树的数量
getlabel.m:根据树和测试样例,得到根据该树得到的类标
getTrees.m:从提供的大量树结构(随机生成的)中,随机挑选一定数量的树(如果该树的叶子节点无类标随机添加)
RandomCreateTree_d_unbalance:根据样本点割点中的非平衡割点建造树,
RandomCreateTree_d_all.m:根据所有样本点的割点建造树
randomdata.m:给定属性取值,造数据
randomselect.m:从数据中随机选择一部分作为
showTree.m:显示树的结构
test.m:给出树,测试样例,得到正确率(Learning_random.m: randomly selected sample, randomly selected sample from (90) pool the The deleted version space tree quantity activeLearning.m: selecting a sample from the pool based on the principle of maximum entropy, delete the number of version space tree getlabel.m: According to the tree and the test sample obtained according to the class standard getTrees.m the tree: from the tree structure (randomly generated), randomly selected a certain number of trees (the leaves of the tree node class marked randomly adding ) RandomCreateTree_d_unbalance: According to the sample point cut point unbalanced cut point construction tree, RandomCreateTree_d_all.m: construction of the tree randomdata.m all sample points cut point: given the value of the property, manufacturing data randomselect.m: random data Select as part showTree.m: tree structure test.m: tree, the test sample is given to get the correct rate)
- 2012-10-10 22:33:44下载
- 积分:1