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Signal Processing Magazine, IEEE

Issue 3 • Date May 2010

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Displaying Results 1 - 25 of 38
  • [Front cover]

    Publication Year: 2010 , Page(s): C1
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  • Mini Circuits - [advertisement]

    Publication Year: 2010 , Page(s): C2
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  • Table of contents

    Publication Year: 2010 , Page(s): 1
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  • From Admiration, Celebration, and Guessing to Innovation [From the Editor]

    Publication Year: 2010 , Page(s): 2 - 18
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  • Staff listing

    Publication Year: 2010 , Page(s): 2
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  • Mini Circuits - [advertisement]

    Publication Year: 2010 , Page(s): 3
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  • Capturing the Breadth of Our Activities [President's Message]

    Publication Year: 2010 , Page(s): 4
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  • IEEE Marketing Department

    Publication Year: 2010 , Page(s): 5
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  • DSPs Evolving in Consumer Electronics Applications [Special Reports]

    Publication Year: 2010 , Page(s): 6 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1363 KB) |  | HTML iconHTML  

    You would be hard pressed to find a consumer electronic product that doesn't require digital signal processing. Consumer electronics is a big market-about US$165 billion this year (see "What About Consumer Electronics Market Growth and Innovation?"), and the requirement for digital signal processors (DSPs) just keeps growing with the introduction of new, innovative products. The requirement for DSPs just keeps growing with the introduction of new, innovative products. View full abstract»

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  • IEEE Member Digital Library [advertisement]

    Publication Year: 2010 , Page(s): 9
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  • Asilomar Conference

    Publication Year: 2010 , Page(s): 11
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  • Top Downloads in IEEE Xplore [Readers Choice]

    Publication Year: 2010 , Page(s): 12 - 18
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  • EDERC2010 [advertisement]

    Publication Year: 2010 , Page(s): 17
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  • Convex Optimization in Signal Processing [From the Guest Editors]

    Publication Year: 2010 , Page(s): 19 - 145
    Cited by:  Papers (1)
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  • Semidefinite Relaxation of Quadratic Optimization Problems

    Publication Year: 2010 , Page(s): 20 - 34
    Cited by:  Papers (224)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1826 KB) |  | HTML iconHTML  

    In this article, we have provided general, comprehensive coverage of the SDR technique, from its practical deployments and scope of applicability to key theoretical results. We have also showcased several representative applications, namely MIMO detection, B¿ shimming in MRI, and sensor network localization. Another important application, namely downlink transmit beamforming, is described in [1]. Due to space limitations, we are unable to cover many other beautiful applications of the SDR technique, although we have done our best to illustrate the key intuitive ideas that resulted in those applications. We hope that this introductory article will serve as a good starting point for readers who would like to apply the SDR technique to their applications, and to locate specific references either in applications or theory. View full abstract»

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  • Convex Optimization, Game Theory, and Variational Inequality Theory

    Publication Year: 2010 , Page(s): 35 - 49
    Cited by:  Papers (27)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1274 KB) |  | HTML iconHTML  

    In this article, we have provided a unified view of some basic theoretical foundations and main techniques in convex optimization, game theory, and VI theory. We put special emphasis on the generality of the VI framework, showing how it allows to tackle several interesting problems in nonlinear analysis, classical optimization, and equilibrium programming. In particular, we showed the relevance of the VI theory in studying Nash and GNE problems. The first part of the article was devoted to provide the (basic) theoretical tools and methods to analyze some fundamental issues of an equilibrium problem, such as the existence and uniqueness of a solution and the design of iterative distributed algorithms along with their convergence properties. The second part of the article made these theoretical results practical by showing how the VI framework can be successfully applied to solve several challenging equilibrium problems in ad hoc wireless (peer-to-peer wired) networks, in the emerging field of CR networks, and in multihop communication networks. We hope that this introductory article would serve as a good starting point for readers to apply VI theory and methods in their applications, as well as to locate specific references either in applications or theory. View full abstract»

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  • Real-Time Convex Optimization in Signal Processing

    Publication Year: 2010 , Page(s): 50 - 61
    Cited by:  Papers (28)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1513 KB) |  | HTML iconHTML  

    This article shows the potential for convex optimization methods to be much more widely used in signal processing. In particular, automatic code generation makes it easier to create convex optimization solvers that are made much faster by being designed for a specific problem family. The disciplined convex programming framework that has been shown useful in transforming problems to a standard form may be extended to create solvers themselves. Much work remains to be done in exploring the capabilities and limitations of automatic code generation. As computing power increases, and as automatic code generation improves, the authors expect convex optimization solvers to be found more and more often in real-time signal processing applications. View full abstract»

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  • Convex Optimization-Based Beamforming

    Publication Year: 2010 , Page(s): 62 - 75
    Cited by:  Papers (83)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (898 KB) |  | HTML iconHTML  

    In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems. View full abstract»

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  • L1-L2 Optimization in Signal and Image Processing

    Publication Year: 2010 , Page(s): 76 - 88
    Cited by:  Papers (62)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1330 KB) |  | HTML iconHTML  

    Sparse, redundant representations offer a powerful emerging model for signals. This model approximates a data source as a linear combination of few atoms from a prespecified and over-complete dictionary. Often such models are fit to data by solving mixed ¿1-¿2 convex optimization problems. Iterative-shrinkage algorithms constitute a new family of highly effective numerical methods for handling these problems, surpassing traditional optimization techniques. In this article, we give a broad view of this group of methods, derive some of them, show accelerations based on the sequential subspace optimization (SESOP), fast iterative soft-thresholding algorithm (FISTA) and the conjugate gradient (CG) method, present a comparative performance, and discuss their potential in various applications, such as compressed sensing, computed tomography, and deblurring. View full abstract»

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  • Enriching the Art of FIR Filter Design via Convex Optimization

    Publication Year: 2010 , Page(s): 89 - 101
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (970 KB) |  | HTML iconHTML  

    The effective design of FIR filters requires judicious compromises to be made between competing properties of the filter. As we have argued herein, when those properties lie in a rich class of design criteria that are convex in the design variables, the inherent tradeoffs between these properties, and filters that attain these tradeoffs, can be reliably obtained. Furthermore, some recently developed software provides a platform that enables these tradeoffs to be obtained with little programming effort. In our examples we only considered pair-wise tradeoffs, but the extension to tradeoff surfaces in higher dimensions is straightforward, even though they tend to be more difficult to visualize. Moreover, the general-purpose convex design platform provides the flexibility to combine convex design criteria in a variety of ways. This platform also offers the opportunity to develop customized algorithms that exploit the structure of particular classes of FIR filter design problems. View full abstract»

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  • Dynamic Resource Allocation in Cognitive Radio Networks

    Publication Year: 2010 , Page(s): 102 - 114
    Cited by:  Papers (47)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (880 KB) |  | HTML iconHTML  

    This article provides an overview of the state-of-art results on communication resource allocation over space, time, and frequency for emerging cognitive radio (CR) wireless networks. Focusing on the interference-power/interference-temperature (IT) constraint approach for CRs to protect primary radio transmissions, many new and challenging problems regarding the design of CR systems are formulated, and some of the corresponding solutions are shown to be obtainable by restructuring some classic results known for traditional (non-CR) wireless networks. It is demonstrated that convex optimization plays an essential role in solving these problems, in a both rigorous and efficient way. Promising research directions on interference management for CR and other related multiuser communication systems are discussed. View full abstract»

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  • Parameter Estimation of Statistical Models Using Convex Optimization

    Publication Year: 2010 , Page(s): 115 - 127
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (955 KB) |  | HTML iconHTML  

    Discriminative learning methods have achieved many successes in speech and language processing during the past decades. Discriminative learning of generative models is a typical optimization problem, where efficient optimization methods play a critical role. For many widely used statistical models, discriminative learning normally leads to nonconvex optimization problems. In this article we used three representative examples to showcase how to use a proper convex relaxation method to convert discriminative learning of HMMs and MMMs into standard convex optimization problem so that it can be solved effectively and efficiently even for large-scale statistical models. We believe convex optimization will continue to play important role in discriminative learning of other statistical models in other application domains, such as statistical machine translation, computer vision, biometrics, and informatics. View full abstract»

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  • Cognitive User Interfaces

    Publication Year: 2010 , Page(s): 128 - 140
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2326 KB) |  | HTML iconHTML  

    This article argues that future generations of computer-based systems will need cognitive user interfaces to achieve sufficiently robust and intelligent human interaction. These cognitive user interfaces will be characterized by the ability to support inference and reasoning, planning under uncertainty, short-term adaptation, and long-term learning from experience. An appropriate engineering framework for such interfaces is provided by partially observable Markov decision processes (POMDPs) that integrate Bayesian belief tracking and reward-based reinforcement learning. The benefits of this approach are demonstrated by the example of a simple gesture-driven interface to an iPhone application. Furthermore, evidence is provided that humans appear to use similar mechanisms for planning under uncertainty. View full abstract»

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  • Parametric Representation of Speech Signals [DSP History]

    Publication Year: 2010 , Page(s): 141 - 145
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (531 KB) |  | HTML iconHTML  

    This article presents a condensed summary of the Marconi presentation, devoted to parametric representation of speech signals. Regarding the future of speech coding. It is shown that "The future is certain to prove interesting!". View full abstract»

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  • Convex Optimizations for Distance Metric Learning and Pattern Classification [Applications Corner]

    Publication Year: 2010 , Page(s): 146 - 158
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (603 KB) |  | HTML iconHTML  

    The goal of machine learning is to build automated systems that can classify and recognize complex patterns in data. The representation of the data plays an important role in determining what types of patterns can be automatically discovered. Many algorithms for machine learning assume that the data are represented as elements in a metric space. The performance of these algorithms can depend sensitively on the manner in which distances are measured. When data are represented as points in a multidimensional vector space, simple Euclidean distances are often used to measure the dissimilarity between different examples. However, such distances often do not yield reliable judgments; in addition, they cannot highlight the distinctive features that play a role in certain types of classification, but not others. Naturally, for different types of clustering, different ways of measuring dissimilarity were needed. In particular, different metrics for computing distances between feature vectors. This paper describes two algorithms for learning such distance metrics based on recent developments in convex optimization. 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.

Full Aims & Scope

Meet Our Editors

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

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