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ieee
IEEE4,5,16,118节点参数数据及数据表明。文档说明了118节点对应个数据的意义和存储格式。
在实际C和MATLAB中可以直接打开文件读入数据。(IEEE 118 node parameter data and data show. The document describes the 118 nodes corresponding to the significance of the data storage format.)
- 2013-05-13 17:57:11下载
- 积分：1
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LICENSE-PLATE_1_final
This program will detect the license plate region with segmentation and extraction and recognition
- 2014-02-20 19:11:05下载
- 积分：1
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自适应滤波LMS算法语音降噪
自适应滤波LMS算法语音降噪，采集一段wma文件，加入高斯白噪声，然后实现降噪。(LMS adaptive filtering algorithm for speech noise reduction)
- 2020-07-04 19:00:01下载
- 积分：1
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MIMO-OFDM系统原理、应用及仿真李莉（实例代码）
有关MIMO-OFDM系统原理、应用及matlab仿真的实例代码(About the MIMO-OFDM system principle, application and MATLAB simulation example code)
- 2021-04-12 21:48:56下载
- 积分：1
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RESEARCH_ON_TURBO_EQULIZATION
传统均衡器无法有效地同时对抗这两种信道衰落，而Turbo均衡技术作为一种联合信道均衡和信道译码的方法，能有效地对抗符号间干扰（ISI），提供迭代增益，提高接收机的整体性能。因此，本文根据下一代无线通信系统的设计要求，分别对单载波和OFDM系统的Turbo均衡算法进行了研究。
(Conventional equalizer can not effectively fight both at the same time fading, while the Turbo equalization as a joint channel equalization and channel decoding method is robust against inter-symbol interference (ISI), provides iteration gain, improved receiver overall performance. Therefore, this next-generation wireless communication system according to the design requirements, namely, the single carrier and OFDM systems Turbo equalization algorithm is studied.)
- 2010-11-26 12:36:55下载
- 积分：1
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RSana
说明： 通过在Matlab上实现对指定时间序列的赫斯特指数的计算
(Matlab to achieve by the specified time series Hurst index of)
- 2010-04-28 19:11:53下载
- 积分：1
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QPSK_TX
Quadrature phase shift keying transmitter matlab m file.
- 2009-10-01 01:49:51下载
- 积分：1
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MatLab_tutorial
matlab tutorial digital signal processing material
- 2012-10-11 13:53:26下载
- 积分：1
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CHAZHI
插值；线性插值；样条插值；普通插值 ；编程(Interpolation one)
- 2015-01-18 10:40:45下载
- 积分：1
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半监督分类算法
说明： 半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)
- 2021-04-12 11:28:57下载
- 积分：1
