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多进多出的空时编码

一般详细讲述多进多出的空时编码的教材与研究入门资料。

2018-07-17

非系统辨识

非线性系统辨识,专著包括维纳系统,volterra系统的辨识方法与算法

2018-01-22

低功耗UWB雷达传感器

低功耗UWB雷达传感器可以作为UWB的应用参考

2018-01-22

time interleaved adc

应用自适应滤波算法fxlms对多通道交织失配进行自适应校正,消除线性失配误差以及提高采样性能。

2018-01-22

usb 系统架构详细讲解和手册

The MindShare Architecture book series includes: ISA System Architecture, EISA System Architecture, 80486 System Architecture, PCI System Architecture, Pentium Processor System Architecture, PCMCIA System Architecture, PowerPC System Architecture, Plug and Play System Architecture, CardBus System Architecture, Pro- tected Mode Software Architecture, Pentium Pro and Pentium II System Architecture, USB System Architecture, FireWire System Architecture, PCI-X System Architecture, and AGP System Architecture. The book series is published by Addison-Wesley. Rather than duplicating common information in each book, the series uses the building-block approach. ISA System Architecture is the core book upon which the others build. Table 1 on page 1 illustrates the relationship of the books to each other.

2009-05-18

神经网络信号处理 hanbook

一般神经网络用于信号处理的经典教科书,The field of artificial neural networks has made tremendous progress in the past 20 years in terms of theory, algorithms, and applications. Notably, the majority of real world neural network appli- cations have involved the solution of difficult statistical signal processing problems. Compared to conventional signal processing algorithms that are mainly based on linear models, artificial neural networks offer an attractive alternative by providing nonlinear parametric models with universal approximation power, as well as adaptive training algorithms. The availability of such powerful modeling tools motivated numerous research efforts to explore new signal processing applications of artificial neural networks. During the course of the research,many neural network paradigmswere proposed. Some of them are merely reincarnations of existing algorithms formulated in a neural network-like setting, while the others provide new perspectives toward solving nonlinear adaptive signal processing. More importantly, there are a number of emergent neural network paradigms that have found successful real world applications.

2009-05-18

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