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duoyonghujianceqi

于 2010-05-13 发布 文件大小:3KB
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  一个多用户检测器的matlab代码程序 采用m序列作为扩频码 非常不错(very good)

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  • 0307
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    2008-03-07 12:26:19下载
    积分:1
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    2013-12-23 14:58:25下载
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    S curve of PLL System
    2009-04-09 05:27:49下载
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    Content aware image downsampling appears in Seam Carving for Content-Aware Image Resizing
    2014-08-18 22:45:00下载
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    用凯瑟窗设计一个数字带通滤波器,设计指标为 低阻带:ws1=0.2pi,as=70dB, 低通带:wp1=0.35pi,ap=1dB, 高通带:wp2=0.65pi,ap=1dB, 高阻带:ws2=0.8pi,as=60dB(Kaiser window design with a digital band-pass filter design specifications for the Low resistivity zone: ws1 = 0.2pi, as = 70dB, Low-pass band: wp1 = 0.35pi, ap = 1dB, High-pass band: wp2 = 0.65pi, ap = 1dB, Impedance with: ws2 = 0.8pi, as = 60dB )
    2011-12-03 00:22:27下载
    积分:1
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    IT IS MATLAB FILE FOR LOAD FLOW ANALYSIS USING FAST DECOUPLED METHOD IN POWER SYSTEM
    2015-02-18 17:38:54下载
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    2015-06-30 12:58:11下载
    积分:1
  • 数字信号处理MATLAB
    说明:  120多个用MATLAB编写的信号处理程序,它们是本书各个章节的大部分例题。用MATLAB编写的程序的名称由“exa”开头,接下来是所在的章、节及例题的序号,如exa010101,指的是第1章第1节(即1.1节)的第1个例题,即例1.1.1。如果该程序是为了说明某一个m文件的应用,则在上述名称的后面跟一个下划线,再在后面加上所说明的MATLAB文件的名称,如exa011001_rand,即是例1.10.1,该例用来说明rand.m文件的应用。(More than 120 signal processing programs written in MATLAB are most of the examples in each chapter of this book. The name of the program written in MATLAB begins with "exa", followed by the ordinal number of the chapters, sections and examples, such as exa0101, which refers to the first example of chapter 1, section 1 (i.e. section 1.1), namely example 1.1.1. If the program is to illustrate the application of an M file, then follow the above name with an underscore, and then add the name of the MATLAB file, such as exa011001_rand, which is Example 1.10.1. This example is used to illustrate the application of rand.m file.)
    2020-11-18 14:59:38下载
    积分:1
  • PCAFaceRecog
    基于线性判别的人脸识别,本设计对主成分分析和线性判别两种子空间方法进行了深入研究,包括LDA及其在人脸识别应用的若干问题(Face recognition based on linear discriminant, the design of the principal component analysis and linear discriminant subspace methods are two in-depth study, including the LDA and its Application in Face Recognition Issues)
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