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Signal Processing Magazine, IEEE

Issue 3 • Date May 2004

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Displaying Results 1 - 23 of 23
  • Society News

    Publication Year: 2004 , Page(s): 5
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  • Leadership and life in the old bell labs

    Publication Year: 2004 , Page(s): 6 - 8
    Cited by:  Papers (1)
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  • Signal processing for mining information

    Publication Year: 2004 , Page(s): 12 - 13
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  • The embedded DSP trend

    Publication Year: 2004 , Page(s): 101
    Cited by:  Papers (1)
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  • New products

    Publication Year: 2004 , Page(s): 102
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  • Adverstisers Index

    Publication Year: 2004 , Page(s): 104
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  • Mining information from event-related recordings

    Publication Year: 2004 , Page(s): 66 - 77
    Cited by:  Papers (1)
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    In this article we describe a signal-processing framework for mining information from event-related recordings. Pattern-analytic tools are combined with graph-theoretic techniques and signal understanding methodologies in a user-friendly environment with the scope of learning, parameterization, and representation of the ST data manifold. Through the first part, we provide a general outline of our methodological approach while trying to demonstrate all the different stages, where DM tools can be applied. In the second part, we provide a more detailed demonstration, give a synopsis of the obtained results and take the opportunity to underline the merits of the adopted algorithmic procedures. To enable the full justification of our framework, instead of just including a technical demonstration of some of the incorporated DM and KDD tools, we address the problem of response variability: an issue of great neuroscientific importance and the subject of continuous debate. The major question in all the previous studies was the validity of "signal plus noise" model, i.e., whether a stereotyped evoked response is linearly superimposed on the ongoing brain activity after every stimulus presentation, a prerequisite for the validity of ensemble-averaging. Using data from a simple visual experiment targeting at the early neuromagnetic response known as N70m, we try to bridge the gap between the "conservative-party" that suggests heavy averaging as the only way to study the brain's response and the "neurodynamics-party" that claims the averaged-signal has very little to say about how the real-time processing of an input from a sensory pathway is actually performed in the cortex. View full abstract»

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  • Estimating the gradient in the Perona-Malik equation

    Publication Year: 2004 , Page(s): 39 - 65
    Cited by:  Papers (29)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (789 KB) |  | HTML iconHTML  

    An impressive and efficient improvement in the classical scale-space analysis was proposed by Perona and Malik (1990) where they describe the diffusion process known as the Perona-Malik (PM) equation. Despite the illposed nature of the PM equation, many of its applications could be carried with success in the signal processing field. On the other hand Weickert and Benamouda (1997) proved the regularization of the PM equation describing and analyzing a model on a semidiscrete system. In this article we present a regularized model of the PM diffusion equation for image segmentation. We start from the hypothesis of well-posedness in the discrete space and the stability conditions. We show two methods for automatic setting of the gradient threshold k, which is changed for each iteration of the partial differential equation (PDE) integration steps. Experimental segmentations are implemented for noise reduction of generic digital images and for segmentation of microcalcifications on X-ray biomedical images. View full abstract»

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  • Signal processing geography

    Publication Year: 2004 , Page(s): 4
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  • Visual data mining for modeling prior distributions in morphometry

    Publication Year: 2004 , Page(s): 20 - 27
    Cited by:  Papers (4)
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    This article presents a novel method for visual data mining based on exploratory factor analysis. Modern imaging modalities provide an overwhelming amount of information that cannot be effectively handled without computerized tools. Data mining techniques aim to discover new knowledge from the collected data and to statistically represent this knowledge in the form of prior distributions that may be used to validate new hypotheses. When applied to morphometric studies, factor analysis is able to minimize data redundancy and reveal subtle or hidden patterns. The characterization of structural shape is performed in a new lower-dimensional basis in which the variables account for the correlation among regions of interest and provide morphological meaning. Data analysis is based on a set of vector variables obtained from image registration. The method is applied to a magnetic resonance imaging (MRI) study of the human corpus callosum and is able to reveal differences in the callosal morphology between male and female samples, based on unsupervised analysis. View full abstract»

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  • Adjustment of nonuniform sampling locations in spatial data sets

    Publication Year: 2004 , Page(s): 47 - 56
    Cited by:  Papers (2)
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    This article describes a procedure for adjusting sampling locations in one spatially discretized data set to those in another when the value differences between these sets are mainly caused by the sampling intervals that locally lengthen and shorten. This adjustment is formulated into an optimization form that can be solved by dynamic programming. Unknown parameters involved in the form can be identified using the maximum likelihood procedure that employs nonlinear filtering for a generalized state-space model. This procedure is based on the fact that the optimal solution in dynamic programming is equivalent to the "maximum a posteriori estimate" in a Bayesian framework. View full abstract»

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  • Collaborations: an interview with Charles Rader

    Publication Year: 2004 , Page(s): 98 - 100
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    Charles Rader was born in 1939 in Brooklyn, New York. After receiving the B.E.E. (1960) and M.E.E. (1961) degrees, both in electrical engineering from the Polytechnic Institute of Brooklyn, New York, he started (and continues to) work for the Massachusetts Institute of Technology Lincoln Laboratory. In an interview, he gives a personal perspective on his career. View full abstract»

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  • Secure sensor information management and mining

    Publication Year: 2004 , Page(s): 14 - 19
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (612 KB) |  | HTML iconHTML  

    This article describes issues and challenges for secure sensor information management. In particular, we discuss data management for sensor information systems including stream data management, distributed data management for sensor data, sensor information management including mining sensor data, security for sensor databases, and dependable sensor information management such as tradeoffs between security, real-time processing, and fault tolerance. Finally we discuss object-based infrastructures for sensor systems as well as directions for sensor information management. View full abstract»

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  • A generic applied evolutionary hybrid technique

    Publication Year: 2004 , Page(s): 28 - 38
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (905 KB) |  | HTML iconHTML  

    In this contribution, a generic applied evolutionary hybrid technique that combines the effectiveness of adaptive multimodel partitioning filters and genetic algorithm (GAs) robustness has been designed, developed, and applied in real-world adaptive system modeling and information mining problems. The method can be applied to linear and nonlinear real-world data, is not restricted to the Gaussian case, is computationally efficient, and is applicable to online/adaptive operation. Furthermore, it can be realized in a parallel processing fashion, a fact that makes it amenable to very large scale integration (VLSI) implementation. View full abstract»

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  • Why not? The magazine way

    Publication Year: 2004 , Page(s): 2
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  • The swiss army knife of digital networks

    Publication Year: 2004 , Page(s): 90 - 100
    Cited by:  Papers (6)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (660 KB) |  | HTML iconHTML  

    This article describes a general discrete-signal network that appears, in various forms, inside many digital signal processing (DSP) applications. So the "DSP Tip" for this column is for every DSP engineer to become acquainted with this network. We show how the network's structure has the distinct look of a digital filter, a comb filter followed by a second-order recursive network. However, we do not call this unique general network a filter because its capabilities extend far beyond simple filtering. Through a series of examples, we illustrate the fundamental strength of the network: its ability to be reconfigured to perform a surprisingly large number of useful functions based on the values of its seven control parameters. View full abstract»

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  • Kernel methods and their potential use in signal processing

    Publication Year: 2004 , Page(s): 57 - 65
    Cited by:  Papers (20)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (786 KB) |  | HTML iconHTML  

    The notion of kernels, recently introduced, has drawn much interest as it allows one to obtain nonlinear algorithms from linear ones in a simple and elegant manner. This, in conjunction with the introduction of new linear classification methods such as the support vector machines (SVMs), has produced significant progress in machine learning and related research topics. The success of such algorithms is now spreading as they are applied to more and more domains. Signal processing procedures can benefit from a kernel perspective, making them more powerful and applicable to nonlinear processing in a simpler and nicer way. We present an overview of kernel methods and provide some guidelines for future development in kernel methods, as well as, some perspectives to the actual signal processing problems in which kernel methods are being applied. View full abstract»

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  • The Waterbed Effect in Spectral Estimation

    Publication Year: 2004 , Page(s): 88 - 100
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB) |  | HTML iconHTML  

    In this lecture note, we present a textbook-like derivation of the waterbed effect result in Ninness (2003). Compared with Ninness, our analysis is much simpler and yet slightly more general in that it is not limited to rational spectral densities as in Ninness (it is noted, however, that Ninness has also derived a closed-form expression for the finite-m CRB on the relative variance of Φ(ω, θˆ), which we do not). View full abstract»

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  • Characterization of protein secondary structure

    Publication Year: 2004 , Page(s): 78 - 87
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1324 KB) |  | HTML iconHTML  

    What do proteins look like? Proteins are composed of fundamental building blocks of chemical molecules called amino acids. When a protein is synthesized by the cells, initially it is just a string of amino acids. This string arranges itself in a process called protein folding into a complex three-dimensional structure capable of exerting the function of the specific protein. We briefly review the fundamental building blocks of proteins, their primary and secondary structure. View full abstract»

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  • IEEE Signal Processing Magazine

    Publication Year: 2004 , Page(s): 0_1
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2004 , Page(s): 1
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    Freely Available from IEEE
  • In Memoriam: Thomas G. Stockham, Jr. 1933-2004

    Publication Year: 2004 , Page(s): 10
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    Freely Available from IEEE
  • Announcement

    Publication Year: 2004 , Page(s): 105
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    Freely Available from IEEE

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.

Full Aims & Scope

Meet Our Editors

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

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