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51.
All-optical wavelength conversion based on multi-section semiconductor optical amplifiers (SOAs) in a symmetrical Mach-Zehnder interferometer (SMZI) is modeled for use in optical networks. It incorporates an enhanced SOA model that is implemented using the time domain transfer matrix approach and hence the overall numerical model determines simultaneously the wavelength and gain parameters for the wavelength converter. The overall model accurately predicts the optimal conditions for the SMZI arrangement in order to achieve the best results for the chirp, the phase inversion and the converted probe signal power. It is also demonstrated that large chirp and mismatch of the phase inversion reduces the eye opening ratio (EOR) which can seriously affect the performance of the wavelength converter to be used as a sub-system component in all-optical networks. 相似文献
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Yasmine A. Fahmy Hebat-Allah M. Mourad Emad K. Al-Hussaini 《Wireless Personal Communications》2006,38(3):325-341
In this paper, a generalized multiple-input multiple-output (MIMO) antenna system that can be fitted to the uplink of a wireless communication system is considered for the general case of multi-user. At the transmitter, the information bits are Turbo coded, then interleaved and passed through a serial-to-parallel converter. The channel is assumed bad urban suffering from multipath Rayleigh fading resulting in inter-symbol and multiple access interferences (ISI and MAI). At the front-end of the receiver, a number of receiving antennas are used followed by a joint multi-user estimator based on the Minimum Mean Square Error Block Linear Equalizer (MMSE-BLE).Computer simulations demonstrate a significant performance improvement in both single user and multi-user cases.This paper depends in parts on that presented at the 11th European Wireless Conference, Cyprus, Nicosia, pp. 187–192, April 2005.
Yasmine A. Fahmy was born in Guiza, Egypt, on June 4, 1976. She received the B.Sc., M.Sc. and Ph.D. degrees in Communication and Electronics engineering from Cairo University, Egypt on 1999, 2001 and 2005 respectively. She is presently an assistant professor at Cairo University, Egypt. Her current field of interest is wireless communication and channel estimation.
Hebat-Allah M. Mourad received her B.Sc., M. Sc. and Ph.D. degrees in electrical communication engineering from Cairo University, Egypt, in 1983, 1987 and 1994 respectively. Since 1983, she has been with the Department of Electronics and Communications, Faculty of Engineering, Cairo University, and is currently associate professor there. Her research interests include optical fiber communications, mobile and satellite communications.
Emad K. Al-Hussaini received his B.Sc degree in Electrical Communication Engineering from Ain-Shams University, Cairo, Egypt, in 1964 and his M.Sc and Ph.D. degrees from Cairo University, Giza, Egypt, in 1974 and 1977, respectively. From 1964 to 1970, he was with the General Egyptian Aeroorganization. Since 1970, he has been with the Department of Electronics and Communications, Faculty of Engineering, Cairo University, and is currently professor there. He was a research fellow at Imperial College, London, UK, and at the Moore School of Electrical Engineering, University of Pennsylvania, Philadelphia, PA, USA, in the academic years 1976/1977 and 1981/1982, respectively. In 1990, he received the Egyptian national encouragement award for outstanding engineering research. He has written several papers for technical international journals and conferences. His research interests include signal processing, fading channel communication, modulation, and cellular mobile radio systems. Dr Al-Hussaini is a senior member of IEEE. He is listed in Marquis Whos Who in the World and in the IBC (International Biographical Center, Cambridge) for outstanding people of the 20th century. 相似文献
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基于遗传BP神经网络算法的主被动遥感协同反演土壤水分 总被引:4,自引:0,他引:4
提出了一种基于遗传神经网络算法的主被动遥感协同反演地表土壤水分的方法.首先,建立一个BP神经网络,并采用遗传算法对BP网络的节点权值进行了优化.然后分别将TM数据(TM3,TM4,TM6)、不同极化和极化比的(VV,VH,VH/VV)ASAR数据作为神经网络的输入,土壤水分含量作为网络的输出,用部分实测数据对网络进行训练并反演得到研究区土壤水分布图.最后,利用地面实测数据分别对遗传神经网络优化算法的有效性和主被动遥感协同反演的效果进行了验证,结果表明,新优化算法是有效可行的,且TM和ASAR协同反演的结果比两者单独反演的结果明显要好,体现了主被动遥感协同反演土壤水分的优势与潜力. 相似文献
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