By Topic

Signal Processing Magazine, IEEE

Issue 3 • Date May 2013

Filter Results

Displaying Results 1 - 25 of 25
  • Front Cover

    Publication Year: 2013 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (2713 KB)  
    Freely Available from IEEE
  • Table of Contents

    Publication Year: 2013 , Page(s): 1
    Save to Project icon | Request Permissions | PDF file iconPDF (355 KB)  
    Freely Available from IEEE
  • Dissemination of Research Findings: What Role Can We Play? [From the Editor]

    Publication Year: 2013 , Page(s): 2 - 8
    Save to Project icon | Request Permissions | PDF file iconPDF (266 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Editorial Listing

    Publication Year: 2013 , Page(s): 2
    Save to Project icon | Request Permissions | PDF file iconPDF (240 KB)  
    Freely Available from IEEE
  • Membership [President's Message]

    Publication Year: 2013 , Page(s): 4
    Save to Project icon | Request Permissions | PDF file iconPDF (331 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • SPS Members Recognized with Awards [Society News]

    Publication Year: 2013 , Page(s): 6 - 8
    Save to Project icon | Request Permissions | PDF file iconPDF (194 KB)  
    Freely Available from IEEE
  • Top Downloads in IEEE Xplore [Reader's Choice]

    Publication Year: 2013 , Page(s): 10 - 12
    Save to Project icon | Request Permissions | PDF file iconPDF (1254 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Adaptation and learning over complex networks [From the Guest Editors]

    Publication Year: 2013 , Page(s): 14 - 15
    Save to Project icon | Request Permissions | PDF file iconPDF (274 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Models for the Diffusion of Beliefs in Social Networks: An Overview

    Publication Year: 2013 , Page(s): 16 - 29
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1675 KB) |  | HTML iconHTML  

    Our article compared models for social learning and opinion diffusion in the context of economics and social science with those used in signal processing over networks of sensors, explaining how learning emerges or fails to emerge in both scenarios. It also critically discusses how the advent of the Internet and of smartphones is generating a wealth of data and enhancing the decision-making capabilities of social agents in ways that were never conceivable before. The article also argues that more engineering research is needed to advance modeling and inference algorithms and enhance even further the cyberinteractions of social agents. This area has tremendous potential as well as carries tremendous risks. Consumers lose their privacy and can be influenced in undesired ways. Hence, a critical consideration to make in expanding our understanding on this subject is to what extent it is safe to increase the ability of hardware and software to capture contextual data about the customers. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning in network games with incomplete information: asymptotic analysis and tractable implementation of rational behavior

    Publication Year: 2013 , Page(s): 30 - 42
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1737 KB) |  | HTML iconHTML  

    The role of social networks in learning and opinion formation has been demonstrated in a variety of scenarios such as the dynamics of technology adoption [1], consumption behavior [2], organizational behavior [3], and financial markets [4]. The emergence of network-wide social phenomena from local interactions between connected agents has been studied using field data [5]?[7] as well as lab experiments [8], [9]. Interest in opinion dynamics over networks is further amplified by the continuous growth in the amount of time that individuals spend on social media Web sites and the consequent increase in the importance of networked phenomena in social and economic outcomes. As quantitative data become more readily available, a research problem is to identify metrics that could characterize emergent phenomena such as conformism or diversity in individuals? preferences for consumer products or political ideologies [10]. With these metrics available, a natural follow-up research goal is the study of mechanisms that lead to diversity or conformism and the role of network properties like neighborhood structures on these outcomes. All of these questions motivate the development of theoretical models of opinion formation through local interactions in different scenarios. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Social learning and bayesian games in multiagent signal processing: how do local and global decision makers interact?

    Publication Year: 2013 , Page(s): 43 - 57
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1642 KB) |  | HTML iconHTML  

    How do local agents and global decision makers interact in statistical signal processing problems where autonomous decisions need to be made? When individual agents possess limited sensing, computation, and communication capabilities, can a network of agents achieve sophisticated global behavior? Social learning and Bayesian games are natural settings for addressing these questions. This article presents an overview, novel insights, and a discussion of social learning and Bayesian games in adaptive sensing problems when agents communicate over a network. Two highly stylized examples that demonstrate to the reader the ubiquitous nature of the models, algorithms, and analysis in statistical signal processing are discussed in tutorial fashion. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience

    Publication Year: 2013 , Page(s): 58 - 70
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1933 KB) |  | HTML iconHTML  

    The observation and description of the living brain has attracted a lot of research over the past centuries. Many noninvasive imaging modalities have been developed, such as topographical techniques based on the electromagnetic field potential [i.e., electroencephalography (EEG) and magnetoencephalography (MEG)], and tomography approaches including positron emission tomography and magnetic resonance imaging (MRI). Here we will focus on functional MRI (fMRI) since it is widely deployed for clinical and cognitive neurosciences today, and it can reveal brain function due to neurovascular coupling (see ?From Brain Images to fMRI Time Series?). It has led to a much better understanding of brain function, including the description of brain areas with very specialized functions such as face recognition. These neuroscientific insights have been made possible by important methodological advances in MR physics, signal processing, and mathematical modeling. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Seeing the Bigger Picture: How Nodes Can Learn Their Place Within a Complex Ad Hoc Network Topology

    Publication Year: 2013 , Page(s): 71 - 82
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3185 KB) |  | HTML iconHTML  

    This article explained how nodes in a network graph can infer information about the network topology or its topology related properties, based on in-network distributed learning, i.e., without relying on an external observer who has a complete overview over the network. Some key concepts from the field of SGT were reviewed, with a focus on those that allow for a simple distributed implementation, i.e., eigenvector or Katz centrality, algebraic connectivity, and the Fiedler vector. This paper also explained how the nodes themselves can quantify their individual network-wide influence, as well as identify densely connected node clusters and the sparse bridge links between them. The addressed concepts, as well as more advanced concepts from the field of SGT, are believed to be crucial catalysts in the design of topology-aware distributed algorithms. Examples were provided on how these techniques can be exploited in several nontrivial distributed signal processing tasks. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains

    Publication Year: 2013 , Page(s): 83 - 98
    Cited by:  Papers (39)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4173 KB) |  | HTML iconHTML  

    In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Consensus + innovations distributed inference over networks: cooperation and sensing in networked systems

    Publication Year: 2013 , Page(s): 99 - 109
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1095 KB) |  | HTML iconHTML  

    This article presents consensus + innovations inference algorithms that intertwine consensus (local averaging among agents) and innovations (sensing and assimilation of new observations). These algorithms are of importance in many scenarios that involve cooperation and interaction among a large number of agents with no centralized coordination. The agents only communicate locally over sparse topologies and sense new observations at the same rate as they communicate. This stands in sharp contrast with other distributed inference approaches, in which interagent communications are assumed to occur at a much faster rate than agents can sense (sample) the environment so that, in between measurements, agents may iterate enough times to reach a decision-consensus before a new measurement is made and assimilated. While optimal design of distributed inference algorithms in stochastic time-varying scenarios is a hard (often intractable) problem, this article emphasizes the design of asymptotically (in time) optimal distributed inference approaches, i.e., distributed algorithms that achieve the asymptotic performance of the corresponding optimal centralized inference approach (with instantaneous access to the entire network sensed information at all times). Consensus + innovations algorithms extend consensus in nontrivial ways to mixed-scale stochastic approximation algorithms, in which the time scales (or weighting) of the consensus potential (the potential for distributed agent collaboration) and of the innovation potential (the potential for local innovations) are suitably traded for optimal performance. This article shows why this is needed and what the implications are, giving the reader pointers to new methodologies that are useful in their own right and in many other contexts. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonparametric Bayesian modeling of complex networks: an introduction

    Publication Year: 2013 , Page(s): 110 - 128
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3744 KB) |  | HTML iconHTML  

    Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models for complex networks can be derived and point out relevant literature. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dynamic Network Cartography: Advances in Network Health Monitoring

    Publication Year: 2013 , Page(s): 129 - 143
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4841 KB) |  | HTML iconHTML  

    Communication networks have evolved from specialized research and tactical transmission systems to large-scale and highly complex interconnections of intelligent devices, increasingly becoming more commercial, consumer oriented, and heterogeneous. Propelled by emergent social networking services and high-definition streaming platforms, network traffic has grown explosively thanks to the advances in processing speed and storage capacity of state-of-the-art communication technologies. As "netizens" demand a seamless networking experience that entails not only higher speeds but also resilience and robustness to failures and malicious cyberattacks, ample opportunities for signal processing (SP) research arise. The vision is for ubiquitous smart network devices to enable data-driven statistical learning algorithms for distributed, robust, and online network operation and management, adaptable to the dynamically evolving network landscape with minimal need for human intervention. This article aims to delineate the analytical background and the relevance of SP tools to dynamic network monitoring, introducing the SP readership to the concept of dynamic network cartography? a framework to construct maps of the dynamic network state in an efficient and scalable manner tailored to large-scale heterogeneous networks. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Swarming Algorithms for Distributed Radio Resource Allocation: A Further Step in the Direction of an Ever-Deeper Synergism Between Biological Mathematical Modeling and Signal Processing

    Publication Year: 2013 , Page(s): 144 - 154
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1474 KB) |  | HTML iconHTML  

    In this article, we have showed some examples illustrating how natural swarming mechanisms can be a source of inspiration for devising innovative resource allocation algorithms in ad hoc cognitive networks having self-organization capabilities. Even though the illustrated mechanisms are rather simple, they are able to tackle some basic issues like decentralized resource allocation with spatial reuse capability. We have illustrated how natural swarms can suggest different levels of adaptation and learning, including cooperative sensing. At the same time, we have shown how the swarming models can benefit from signal processing tools to become more robust and suitable for the application at hand. As an example, we have shown how to make the swarming mechanism robust against random packet drop, quantization, and estimation errors. The simplicity of the swarming model has been instrumental to allow for mathematically tractability and to grasp the fundamental properties of the proposed techniques. This work is only an initial step, together with many parallel approaches in the increasing literature on bioinspired methods, in the direction of an ever-deeper synergism between biological mathematical modeling and signal processing. This is expected to be particularly useful for applications requiring some sort of self-organization. Further developments can be expected from a deeper interaction between the learning phase and the swarming mechanism in a dynamic environment. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Diffusion strategies for adaptation and learning over networks: an examination of distributed strategies and network behavior

    Publication Year: 2013 , Page(s): 155 - 171
    Cited by:  Papers (47)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2306 KB) |  | HTML iconHTML  

    Nature provides splendid examples of real-time learning and adaptation behavior that emerges from highly localized interactions among agents of limited capabilities. For example, schools of fish are remarkably apt at configuring their topologies almost instantly in the face of danger [1]: when a predator arrives, the entire school opens up to let the predator through and then coalesces again into a moving body to continue its schooling behavior. Likewise, in bee swarms, only a small fraction of the agents (about 5%) are informed, and these informed agents are able to guide the entire swarm of bees to their new hive [2]. It is an extraordinary property of biological networks that sophisticated behavior is able to emerge from simple interactions among lower-level agents [3]. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • History, applications, and subsequent development of the FXLMS Algorithm [DSP History]

    Publication Year: 2013 , Page(s): 172 - 176
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3700 KB) |  | HTML iconHTML  

    Our guest in this column is Dr. Dennis R. Morgan. Destined to have a stellar engineering career, Dr. Morgan started experimenting in the family basement workshop with all sorts of electrical experiments only a few years after he had learned to read. In sixth grade, he read books about radio and electronics and used that knowledge to repair a radio, the first of many. He pursued his electronic calling, converting the family workshop into an electronics laboratory, where he continued all sorts of investigations, repairs, and built many kits, which included a 5-in oscilloscope (which he built while he was a high school sophomore). Later that year, he obtained his first part-time job in a radio repair shop, where he continued to work part time throughout high school and college, learning many practical tricks of diagnosis and troubleshooting involving measurements, logic, and often tapping around for intermittent problems. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Subpixel rendering: from font rendering to image subsampling [Applications Corner]

    Publication Year: 2013 , Page(s): 177 - 189
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5127 KB) |  | HTML iconHTML  

    Subpixel rendering technologies take advantage of the subpixel structure of a display to increase the apparent resolution and to improve the display quality of text, graphics, or images. These techniques can potentially improve the apparent resolution because a single pixel on color liquid crystal display (LCD) or organic light-emitting diode (OLED) displays consists of several independently controllable colored subpixels. Applications of subpixel rendering are font rendering and image/video subsampling. By controlling individual subpixel values of neighboring pixels, it is possible to microshift the apparent position of a line to give greater details of text. Similarly, since the individual selectable components are increased threefold by controlling subpixels rather than pixels, subpixel-based subsampling can potentially improve the apparent resolution of a down-scaled image. However, the increased apparent luminance resolution often comes at the price of color fringing artifacts. A major challenge is to suppress chrominance distortion while maintaining apparent luminance sharpness. This column introduces subpixel arrangement in color displays, how subpixel rendering works, and several practical subpixel rendering applications in font rendering and image subsampling. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Gaussian Assumption: The Least Favorable but the Most Useful [Lecture Notes]

    Publication Year: 2013 , Page(s): 183 - 186
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (466 KB) |  | HTML iconHTML  

    Gaussian assumption is the most well-known and widely used distribution in many fields such as engineering, statistics, and physics. One of the major reasons why the Gaussian distribution has become so prominent is because of the central limit theorem (CLT) and the fact that the distribution of noise in numerous engineering systems is well captured by the Gaussian distribution. Moreover, features such as analytical tractability and easy generation of other distributions from the Gaussian distribution contributed further to the popularity of Gaussian distribution. Especially, when there is no information about the distribution of observations, Gaussian assumption appears as the most conservative choice. This follows from the fact that the Gaussian distribution minimizes the Fisher information, which is the inverse of the Cramer-Rao lower bound (CRLB) (or equivalently stated, the Gaussian distribution maximizes the CRLB). Therefore, any optimization based on the CRLB under the Gaussian assumption can be considered to be min-max optimal in the sense of minimizing the largest CRLB (see [1] and the references cited therein). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • TechWare: Spoken Language Understanding Resources [Best of the Web]

    Publication Year: 2013 , Page(s): 187 - 189
    Save to Project icon | Request Permissions | PDF file iconPDF (286 KB) |  | HTML iconHTML  
    Freely Available from IEEE
  • Video surveillance: past, present, and now the future [DSP Forum]

    Publication Year: 2013 , Page(s): 190 - 198
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2036 KB) |  | HTML iconHTML  

    Video surveillance is a part of our daily life, even though we may not necessarily realize it. We might be monitored on the street, on highways, at ATMs, in public transportation vehicles, inside private and public buildings, in the elevators, in front of our television screens, next to our baby?s cribs, and any spot one can set a camera. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • [Dates Ahead]

    Publication Year: 2013 , Page(s): 199
    Save to Project icon | Request Permissions | PDF file iconPDF (155 KB)  
    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/