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Image-Descriptions-for-Browsing-and-Retrieval
Image-Descriptions-for-Browsing-and-Retrieval.rar 纹理分析的好文章(Image-Description-for-Browsing-and- Retrieval.rar texture analysis in good)
- 2007-04-20 16:37:44下载
- 积分：1
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pskdigital
MATLAB程序，用以实现2PSK数字信号的产生(MATLAB program to achieve generation of 2PSK digital signal)
- 2013-03-24 17:53:39下载
- 积分：1
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uu_la_audio_wm2001
audio watermarking using dc level shifting
- 2012-04-14 21:58:56下载
- 积分：1
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untitled5
model reference with simulink
- 2011-10-24 03:57:14下载
- 积分：1
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The-Booth-Tolls-for-Thee
元胞自动机源程序代码，用来模拟组织相变以及动态再结晶过程(Cellular automata source code, to solve organizational phase transition and the dynamic recrystallization)
- 2015-10-29 14:25:49下载
- 积分：1
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蚁群算法
完整的蚁群算法，可以参考使用。。。。。。。。。。。。。。。(Ant colony algorithm)
- 2021-04-15 08:08:54下载
- 积分：1
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MFP_based_on_High_order_Statistics-master
说明： 浅层海洋环境由信源组成声源，海洋形成信道，和水听器阵列组成接收器。在这个传播模型中，信源，信道和接收信号这三者，通常能知二求一，具体应用诸如利用海洋环境参数和接收到的信号来定位声源，或者通过计算发射信号和接收信号之间的差异，反演海洋环境参数。
而在接收器方面，我们通过设置各向同性的水听器阵列。通过算法和处理器，我们便能量化模型，传统是处理器主要基于接收信号是高斯信号，而海洋中存在着大量的有色噪声。本课题的研究目的便是在前人的基础上，在海洋声层析成像的背景下，在信源与接收器阵列之间，引入信号的高阶统计量，对非高斯过程的水下信号源进行定位，并提高算法的性能和准确性。
利用非高斯过程的高阶累积量不恒为零的特点，滤去高斯有色噪声对信号的影响，其又包含了信号的相位信息，便可以极大的优化匹配场处理过程的性能和准确性。(After receiving signals based on high order cumulant matched field processor after matched field localization, the positioning effect will be more accurate, sidelobe suppression more effectively, and compared with other traditional matched field processor in low SNR environment, it can position more accurately.)
- 2020-10-28 12:29:58下载
- 积分：1
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MATLAB
常用matlab数据运算的程序和集，是学习matlab
与数学的不错源码(Commonly used Matlab data operation of the program and set a good source to learn matlab with math)
- 2012-05-21 17:14:27下载
- 积分：1
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tulun
图的着色问题的计算程序，根据输入的矩阵计算(Calculation program graph of coloring problem，base on the input matrix to calculat)
- 2013-10-07 10:42:01下载
- 积分：1
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SOM_NN_CODE
An important aspect of an ANN model is whether it needs
guidance in learning or not. Based on the way they learn, all
artificial neural networks can be divided into two learning
categories - supervised and unsupervised.
• In supervised learning, a desired output result for each input
vector is required when the network is trained. An ANN of the
supervised learning type, such as the multi-layer perceptron, uses
the target result to guide the formation of the neural parameters. It
is thus possible to make the neural network learn the behavior of
the process under study.
• In unsupervised learning, the training of the network is entirely
data-driven and no target results for the input data vectors are
provided. An ANN of the unsupervised learning type, such as the
self-organizing map, can be used for clustering the input data and
find features inherent to the problem.
- 2015-04-15 00:03:32下载
- 积分：1
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