冯久超
研究领域涉及数字信号处理、数字通信、非线性动力学及混沌理论与应用。
个性化签名
- 姓名:冯久超
- 目前身份:
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学术头衔:
博士生导师, 教育部“新世纪优秀人才支持计划”入选者
- 职称:-
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学科领域:
电子电路
- 研究兴趣:研究领域涉及数字信号处理、数字通信、非线性动力学及混沌理论与应用。
冯久超,教授,男,1986年、1997年和2002年分别获西南师范大学理学学士学位(物理专业)、华南理工大学工学硕士学位(通信与电子系统专业)和香港理工大学哲学博士学位(电子与资讯工程专业)。现任华南理工大学教授,博士生指导教师。2002年列入重庆市“优秀中青年骨干教师资助计划”,2003年获香港理工大学“杰出博士论文奖”(Distinguished Ph.D. Thesis Award), 2004年列入广东省高校“千百十工程”省级培养计划,2004年获教育部“新世纪优秀人才”基金资助,2004年获广东省自然科学奖(名称:复杂系统渐进性理论及其智能控制,一等奖,排名第四)。近年来,在国际、国内杂志公开发表学术刊物论文40余篇, 包括《IEEE 电路与系统汇刊-I》,《国际通信系统杂志》,《IEICE电子学、通信和计算机科学汇刊-A》,(美)《物理评论E》等,已被清华大学出版社审定出版混沌及基于混沌的信息处理方面的英文学术专著《Reconstruction of Chaotic Signals with Applications to Chaos-based Communications》。IEEE会员,中国电子学会高级会员,重庆市信息产业局通信技术专家组成员,《IEEE电路与系统汇刊-I》、《IEICE汇刊-A》等国际杂志的海外审稿人,清华大学电子、信息类教材编审委员会委员。研究领域涉及数字信号处理、数字通信、非线性动力学及混沌理论与应用。
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冯久超, 冯久超XX, 余英林XXX, 周曙XXXX
通信学报,1999,19(6):77~83,-0001,():
-1年11月30日
针对混沌信号具有的随机性、宽带性、似噪声以及对初始条件的极端敏感性的特点,本文用混沌序列取代扩频通信系统中的伪随机码,在提出并实现一种混沌网络同步化法的基础上,实现了语音、文字和图像的码分多址的扩频通信。用Lorenz混沌系统对同步和通信的仿真结果表明了本文所述方法的有效性和可靠性。进一步指出了这种通信方式具有简便性和自然的保密性的优点。
扩频通信 混沌 混沌网络同步 码分多址
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【期刊论文】A Neural-Network-Based Channel-Equalization Strategy for Chaos-Based Communication Systems
冯久超, Jiuchao Feng, Chi K. Tse, and Francis C. M. Lau
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS-I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 50, NO.7, JULY 2003,-0001,():
-1年11月30日
This brief addresses the channel-distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network incorporating a specific training (equalizing) algorithm.
Channel equalization,, chaos-based communications,, recurrent neural networks (, RNNs), .,
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冯久超, Tommy W. S. Chow, Jiu-Chao Feng, and K. T. Ng
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS—I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 47, NO.6, JUNE 2000,-0001,():
-1年11月30日
Chaotic modulation is an important spread spectrum (SS) technique amongst chaotic communications. The logistic chaotic signal acts as the modulation signal in this paper. An adaptive demodulator based on the radial basis function (RBF) neural network is proposed. The demodulator makes use of the good approximate capacity of RBF network for a nonlinear dynamical system. Using the proposed adaptive learning algorithm, the source message can be recovered from the received SS signal. The recovering procedure is on line and adaptive. The simulated examples are included to demonstrate the new method. For the purpose of comparison, the extended-Kalman-filter-based (EKF) demodulator was also performed. The results indicate that the mean square error (MSE) of the recovered source signal by the proposed demodulator is significantly reduced, especially for the SS signal with a higher signal-to-noise ratio (SNR).
Adaptive demodulator,, chaos,, RBF neural network,, spread spectrum.,
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【期刊论文】Channel Equalization for Chaos-Based Communication Systems **
冯久超, Jiu-chao FENG†a), Chi Kong TSE†, and Francis C. M. LAU†, Nonmembers
IEICE TRANS. FUNDAMENTALS, VOL. E85-A, NO.9 SEPTEMBER 2002,-0001,():
-1年11月30日
A number of schemes have been proposed for communication using chaos over the past years. Regardless of the exact modulation method used, the transmitted signal must go through a physical channel which undesirably introduces distortion to the signal and adds noise to it. The problem is particularly serious when coherent-based demodulation is used because the necessary process of chaos synchronization is difficult to implement in practice. This paper addresses the channel distortion problem and proposes a technique for channel equalization in chaos-based communication systems. The proposed equalization is realized by a modified recurrent neural network (RNN) incorporating a specific training (equalizing) algorithm. Computer simulations are used to demonstrate the performance of the proposed equalizer in chaos-based communication systems. The H
chaos-based communications,, recurrent neural net-works,, tracking of chaotic signals,, channel equalization
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【期刊论文】On-line adaptive chaotic demodulator based on radial-basis-function neural networks
冯久超, Jiu-chao Feng* and Chi K. Tse†
PHYSICAL REVIEW E, VOLUME 63, 026202,-0001,():
-1年11月30日
Chaotic modulation is a useful technique for spread spectrum communication. In this paper, an on-line adaptive chaotic demodulator based on a radial-basis-function (RBF) neural network is proposed and designed. The demodulator is implemented by an on-line adaptive learning algorithm, which takes advantage of the good approximation capability of the RBF network and the tracking ability of the extended Kalman filter. It is demonstrated that, provided the modulating parameter varies slowly, spread spectrum signals contaminated by additive white Gaussian noise in a channel can be tracked in a time window, and the modulating parameter, which carries useful messages, can be estimated using the least-square fit. The Henon map is chosen as the chaos generator. Four test message signals, namely, square-wave, sine-wave, speech and image signals, are used to evaluate the performance. The results verify the ability of the demodulator in tracking the dynamics of the chaotic carrier as well as retrieving the message signal from a noisy channel.
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