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Titlebook: Electrical Engineering and Control; Selected Papers from Min Zhu Conference proceedings 2011 Springer-Verlag GmbH Berlin Heidelberg 2011 Co

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樓主: Goiter
41#
發(fā)表于 2025-3-28 18:07:38 | 只看該作者
42#
發(fā)表于 2025-3-28 22:42:49 | 只看該作者
43#
發(fā)表于 2025-3-29 00:45:15 | 只看該作者
Fast Implementation of Rainbow Signatures via Efficient Arithmetic over a Finite Field,on of multivariate signatures by using optimized arithmetics including multiplication, multiplicative inverse and Gaussian elimination over finite fields. Not only the speed but also the area are considered in the design. 27 parallel multipliers are adopted and the design has been fully implemented
44#
發(fā)表于 2025-3-29 03:05:05 | 只看該作者
Novel Resonant-Type Composite Right/Left Handed Transmission Line Based on Cascaded Complementary Sngle split ring resonator(CCSSRR). For deep insight, a circuit model is also presented and validated by the simulated full wave S-parameters. The nagative refractive index and backward wave propogation is demomonstrated by the constitutive effective electromagnetic parameters. This type of CRLH TL c
45#
發(fā)表于 2025-3-29 08:07:59 | 只看該作者
Design of Network Gateway Based on the ITS,r architecture. Its architectural style is based on the most basic of IPV6 network protocol has able to send short messages, radio-subscribe to the mechanism and the authentication mechanism and the protection of data security and other functions, is also a lightweight, scalable and good, open-ended
46#
發(fā)表于 2025-3-29 14:47:52 | 只看該作者
The Research of Improved Grey-Markov Algorithm,wn prerequisite condition before moving on to the next step. However, this kind of means is groundless, and the result we get is not necessarily the optimal solution. This paper introduces a different Grey-Markov model which is based on a kind of improved GM (1,1) algorithm and applied it to the sal
47#
發(fā)表于 2025-3-29 17:52:11 | 只看該作者
48#
發(fā)表于 2025-3-29 22:29:50 | 只看該作者
A Method of State Diagnosis for Rolling Bearing Using Support Vector Machine and BP Neural Network,r the normal and fault state, then two kinds of samples caused by the distinct states are trained to judge whether the rolling bearing is normal or false. If the rolling bearing is in the fault state, all the fault samples were trained by the classifier composed of BP neural network to recognize whi
49#
發(fā)表于 2025-3-30 01:32:32 | 只看該作者
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發(fā)表于 2025-3-30 07:20:46 | 只看該作者
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