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Signal Processing Magazine, IEEE

Issue 6 • Date Nov. 1997

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Displaying Results 1 - 12 of 12
  • Neural networks for dynamics modeling

    Publication Year: 1997 , Page(s): 33 - 35
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  • Model-based Neural Networks for Image Processing

    Publication Year: 1997 , Page(s): 35 - 36
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  • A Unified Perspective of Statistical Learning Networks

    Publication Year: 1997 , Page(s): 36 - 38
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  • Neural Networks for Eigen-Structure Based Signal Processing

    Publication Year: 1997 , Page(s): 38 - 39
    Cited by:  Papers (1)
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  • From Pattern Classification to Active Learning

    Publication Year: 1997 , Page(s): 39 - 43
    Cited by:  Papers (1)
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  • Generalization: The Hidden Agenda of Learning

    Publication Year: 1997 , Page(s): 43 - 45
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  • On-Line Step-Size Selection for Training of Adaptive Systems

    Publication Year: 1997 , Page(s): 45 - 46
    Cited by:  Papers (3)
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  • Applications of neural networks to speech recognition

    Publication Year: 1997 , Page(s): 46 - 48
    Cited by:  Papers (1)
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  • Examples in Medical Applications

    Publication Year: 1997 , Page(s): 48
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  • The past, present, and future of neural networks for signal processing

    Publication Year: 1997 , Page(s): 28 - 48
    Cited by:  Papers (8)
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    The article provides a review of the fundamental of neural networks and reports recent progress. Topics covered include dynamic modeling, model-based neural networks, statistical learning, eigenstructure-based processing, active learning, and generalization capability. Current and potential applications of neural networks are also described in detail. Those applications include optical character recognition, speech recognition and synthesis, automobile and aircraft control, image analysis and neural vision, and several medical applications. Essentially, neural networks have become a very effective tool in signal processing, particularly in various recognition tasks View full abstract»

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  • An introduction to discrete finite frames

    Publication Year: 1997 , Page(s): 84 - 96
    Cited by:  Papers (8)
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    The frame concept was first introduced by Duffin and Schaeffer (1952), and it is widely used today to describe the behavior of vectors for signal representation. The Gabor (1946) expansion and wavelet transform are two special well-known cases. The goal of this article is to describe the frame theory and introduce a simple tutorial method to find discrete finite frame operators and their frame bounds. An easily implementable method for finding the discrete finite frame and subframe operators has been presented by Kaiser (1994). We introduce the method of Kaiser to compute the discrete finite frame operator. Using subframe operators, the biorthogonal basis and projection vectors in a subspace can be easily calculated. Gabor and wavelet analysis are two popular tools for signal processing, and they can reveal time-frequency distribution for a nonstationary signal. Both schemes can be regarded as signal decompositions onto a set of basis functions, and their basis functions are derived from a single prototype function through simple operations. Therefore, the basis functions used in Gabor and wavelet analysis can be regarded as special frames. For completeness we also make some simple introductions on the results of special frames such as discrete Gabor and wavelet analysis View full abstract»

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  • Space-time processing for wireless communications

    Publication Year: 1997 , Page(s): 49 - 83
    Cited by:  Papers (451)  |  Patents (91)
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    Space-time processing can improve network capacity, coverage, and quality by reducing co-channel interference (CCI) while enhancing diversity and array gain. This article focuses largely on the receive (mobile-to-base station) time-division multiple access (TDMA) (nonspread modulation) application for high-mobility networks. We describe a large (macro) cell propagation channel and discuss different physical effects such as path loss, fading delay spread, angle spread, and Doppler spread. We also develop a signal model incorporating channel effects. Both forward-link (transmit) and reverse-link (receive) channels are considered and the relationship between the two is discussed. Single- and multiuser models are treated for four important space-time processing problems, and the underlying spatial and temporal structure are discussed as are different algorithmic approaches to reverse link space-time professing with blind and nonblind methods for single- and multiple-user cases. We cover forward-link space-time algorithms and we outline methods for estimation of multipath parameters. We also discuss applications of space-time processing to CDMA, applications of space-time techniques to current cellular systems, and industry trends View full abstract»

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Aims & Scope

IEEE Signal Processing Magazine publishes tutorial-style articles on signal processing research and applications, as well as columns and forums on issues of interest.

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Meet Our Editors

Editor-in-Chief
Min Wu
University of Maryland, College Park
United States 

http://www/ece.umd.edu/~minwu/