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Signal Processing Magazine, IEEE

Issue 2 • Date March 2008

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Displaying Results 1 - 25 of 30
  • Front cover - IEEE Signal Processing Magazine

    Publication Year: 2008 , Page(s): c1
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    Freely Available from IEEE
  • Table of contents

    Publication Year: 2008 , Page(s): 1
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    Freely Available from IEEE
  • Periodically Reconsidering the Impossible [From the Editor]

    Publication Year: 2008 , Page(s): 2 - 4
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    Freely Available from IEEE
  • Bring Signal Processing to the Public [President's Message]

    Publication Year: 2008 , Page(s): 6
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    Freely Available from IEEE
  • New SPS Fellows and a Call for Nominations [Society News]

    Publication Year: 2008 , Page(s): 8 - 10
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    Freely Available from IEEE
  • Call for papers

    Publication Year: 2008 , Page(s): 9
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    Freely Available from IEEE
  • Compressive Sampling [From the Guest Editors]

    Publication Year: 2008 , Page(s): 12 - 13
    Cited by:  Papers (18)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (516 KB) |  | HTML iconHTML  

    The ten articles in this special section provide the reader with specific insights into the basic theory, capabilities, and limitations of compressed sensing (CS). The papers are summarized here. View full abstract»

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  • Imaging via Compressive Sampling

    Publication Year: 2008 , Page(s): 14 - 20
    Cited by:  Papers (158)  |  Patents (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1026 KB) |  | HTML iconHTML  

    Image compression algorithms convert high-resolution images into a relatively small bit streams in effect turning a large digital data set into a substantially smaller one. This article introduces compressive sampling and recovery using convex programming. View full abstract»

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  • An Introduction To Compressive Sampling

    Publication Year: 2008 , Page(s): 21 - 30
    Cited by:  Papers (1090)  |  Patents (30)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1437 KB) |  | HTML iconHTML  

    Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. View full abstract»

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  • Sparse Sampling of Signal Innovations

    Publication Year: 2008 , Page(s): 31 - 40
    Cited by:  Papers (81)  |  Patents (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1289 KB) |  | HTML iconHTML  

    Sparse sampling of continuous-time sparse signals is addressed. In particular, it is shown that sampling at the rate of innovation is possible, in some sense applying Occam's razor to the sampling of sparse signals. The noisy case is analyzed and solved, proposing methods reaching the optimal performance given by the Cramer-Rao bounds. Finally, a number of applications have been discussed where sparsity can be taken advantage of. The comprehensive coverage given in this article should lead to further research in sparse sampling, as well as new applications. One main application to use the theory presented in this article is ultra-wide band (UWB) communications. View full abstract»

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  • Sampling Signals from a Union of Subspaces

    Publication Year: 2008 , Page(s): 41 - 47
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (954 KB) |  | HTML iconHTML  

    The single linear vector space assumption is widely used in modeling the signal classes, mainly due to its simplicity and mathematical tractability. In certain signals, a union of subspaces can be a more appropriate model. This paper provides a new perspective for signal sampling by considering signals from a union of subspaces instead of a single space. View full abstract»

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  • Compressive Sampling and Lossy Compression

    Publication Year: 2008 , Page(s): 48 - 56
    Cited by:  Papers (61)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1100 KB) |  | HTML iconHTML  

    Recent results in compressive sampling have shown that sparse signals can be recovered from a small number of random measurements. This property raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense. Through both theoretical and experimental results, we show that encoding a sparse signal through simple scalar quantization of random measurements incurs a significant penalty relative to direct or adaptive encoding of the sparse signal. Information theory provides alternative quantization strategies, but they come at the cost of much greater estimation complexity. View full abstract»

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  • A Tutorial on Fast Fourier Sampling

    Publication Year: 2008 , Page(s): 57 - 66
    Cited by:  Papers (23)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1291 KB) |  | HTML iconHTML  

    This article describes a computational method, called the Fourier sampling algorithm. The algorithm takes a small number of (correlated) random samples from a signal and processes them efficiently to produce an approximation of the DFT of the signal. The algorithm offers provable guarantees on the number of samples, the running time, and the amount of storage. As we will see, these requirements are exponentially better than the FFT for some cases of interest. View full abstract»

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  • Compression at the Physical Interface

    Publication Year: 2008 , Page(s): 67 - 71
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1284 KB) |  | HTML iconHTML  

    This article focuses on recent progress in physical compressive sampling under the Defense Advanced Research Agency's Analog-to-Information (A-to-I) and Multiple Optical Non-Redundant Aperture Generalized Sensors (MONTAGE) programs. A-to-I and MONTAGE focus on aggressive forms of generalized sampling under which measurements consist of transformations, projections, or encodings of the signal onto discrete digital data. The central system design challenge is to select features, sampling strategies and hardware that enable high fidelity signal estimation from these features. The A-to-I and MONTAGE projects differ in application and implementation A-to-I seeks to revolutionize very high temporal bandwidth analog to digital signal conversion whereas MONTAGE seeks to revolutionize very high spatial bandwidth analog to digital signal conversion. View full abstract»

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  • Compressed Sensing MRI

    Publication Year: 2008 , Page(s): 72 - 82
    Cited by:  Papers (117)  |  Patents (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3893 KB) |  | HTML iconHTML  

    This article reviews the requirements for successful compressed sensing (CS), describes their natural fit to MRI, and gives examples of four interesting applications of CS in MRI. The authors emphasize on an intuitive understanding of CS by describing the CS reconstruction as a process of interference cancellation. There is also an emphasis on the understanding of the driving factors in applications, including limitations imposed by MRI hardware, by the characteristics of different types of images, and by clinical concerns. View full abstract»

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  • Single-Pixel Imaging via Compressive Sampling

    Publication Year: 2008 , Page(s): 83 - 91
    Cited by:  Papers (271)  |  Patents (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1496 KB) |  | HTML iconHTML  

    In this article, the authors present a new approach to building simpler, smaller, and cheaper digital cameras that can operate efficiently across a broader spectral range than conventional silicon-based cameras. The approach fuses a new camera architecture based on a digital micromirror device with the new mathematical theory and algorithms of compressive sampling. View full abstract»

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  • Compressed Sensing for Networked Data

    Publication Year: 2008 , Page(s): 92 - 101
    Cited by:  Papers (104)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1130 KB) |  | HTML iconHTML  

    This article describes a very different approach to the decentralized compression of networked data. Considering a particularly salient aspect of this struggle that revolves around large-scale distributed sources of data and their storage, transmission, and retrieval. The task of transmitting information from one point to another is a common and well-understood exercise. But the problem of efficiently transmitting or sharing information from and among a vast number of distributed nodes remains a great challenge, primarily because we do not yet have well developed theories and tools for distributed signal processing, communications, and information theory in large-scale networked systems. View full abstract»

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  • Why Gaussianity?

    Publication Year: 2008 , Page(s): 102 - 113
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1022 KB) |  | HTML iconHTML  

    In this article, we try to answer the question: "Why the ubiquitous use and success of the Gaussian distribution law?". The history of the Gaussian or normal distribution is rather long, having existed for nearly 300 years since it was discovered by de Moivre in 1733, and the related literature is immense. An extended and thorough treatment of the topic and a survey of the works in the related area are given in the posthumously edited book of E.T. Jaynes (2003), and we partially follow this source, in particular while considering the history of the posed question. The important aspects of the general history of noise, especially of Brownian motion, are given by Cohen (2005). Our main contribution to the topic is concerned with highlighting the role of Gaussian models in signal processing based on the optimal property of the Gaussian distribution minimizing Fisher information over the class of distributions with a bounded variance. We deal only with the univariate Gaussian distribution, omitting the properties of multivariate Gaussian distribution. First of all, we present the ideas of classical derivations of the Gaussian law. Then we consider its properties and characterizations including the central limit theorem (CLT) and minimization of the distribution entropy and Fisher information. Finally, we dwell on the connections between Gaussianity and robustness in signal processing. View full abstract»

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  • Signal and Image Processing with Belief Propagation [DSP Applications]

    Publication Year: 2008 , Page(s): 114 - 141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (567 KB) |  | HTML iconHTML  

    In this column, we review a particularly effective inference algorithm known as belief propagation (BP). After describing its message-passing structure, we demonstrate the interplay of statistical modeling and inference in two challenging applications: denoising discrete signals transmitted over noisy channels, and dense 3-D reconstruction from stereo images. View full abstract»

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  • The Physiome Projects and Multiscale Modeling [Life sciences]

    Publication Year: 2008 , Page(s): 121 - 144
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (248 KB) |  | HTML iconHTML  

    The Physiome Projects are a diverse set of scientifically independent projects addressing integrative systems physiology and biology, conducted by individual investigators and teams from different countries. The emphasis in these projects is on medically related physiology and pharmacology. They gather modeling work, information processing methods and tools, databases, and other resources and make them available to a large research community. Multiscale methods have been exploited in signal and image processing and in a wide range of other fields including biology, nanotechnology, materials, and aerodynamics. In this article, we present an overview of the status, accomplishments, and challenges of the Physiome Projects and outline the role played by multiscale modeling. View full abstract»

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  • Effective Communication: Excellence in a Technical Presentation [DSP Education]

    Publication Year: 2008 , Page(s): 124 - 127
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (119 KB) |  | HTML iconHTML  

    "DSP Education" introduces a cycle on "effective communication." This cycle includes articles on technical presentations, technical writing, technical management, and preparation for entrepreneurship. View full abstract»

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  • Locality-Sensitive Hashing for Finding Nearest Neighbors [Lecture Notes]

    Publication Year: 2008 , Page(s): 128 - 131
    Cited by:  Papers (21)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (384 KB) |  | HTML iconHTML  

    This lecture note describes a technique known as locality-sensitive hashing (LSH) that allows one to quickly find similar entries in large databases. This approach belongs to a novel and interesting class of algorithms that are known as randomized algorithms. A randomized algorithm does not guarantee an exact answer but instead provides a high probability guarantee that it will return the correct answer or one close to it. By investing additional computational effort, the probability can be pushed as high as desired. View full abstract»

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  • DC Blocker Algorithms [DSP Tips & Tricks]

    Publication Year: 2008 , Page(s): 132 - 134
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (200 KB) |  | HTML iconHTML  

    This paper presents a nonlinear-phase, but computationally efficient, dc blocking filter that achieves ideal operation when output data quantization is used. In addition, we described an alternate dc blocking filter that, at the expense of larger data memory, exhibits a linear-phase frequency response. View full abstract»

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  • The Scalable Video Coding Extension of the H.264/AVC Standard [Standards in a Nutshell]

    Publication Year: 2008 , Page(s): 135 - 141
    Cited by:  Papers (21)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (279 KB) |  | HTML iconHTML  

    The current article focuses on the scalable video coding (SVC) extension in terms of technology, performance, and targeted application scenarios. View full abstract»

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  • EvalWare: Granular Computing for Web Applications [Best of the Web]

    Publication Year: 2008 , Page(s): 142 - 144
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (253 KB) |  | HTML iconHTML  

    This month's resource list focuses on granular computing (GC) and applications. 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/