IEEE Signal Processing Magazine

Volume 35 Issue 4 • July 2018

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  • Front Cover

    Publication Year: 2018, Page(s): C1
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  • ICASSP 2019

    Publication Year: 2018, Page(s): C2
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  • Table of Contents

    Publication Year: 2018, Page(s):1 - 2
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  • Masthead

    Publication Year: 2018, Page(s): 2
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  • Highlights from the IEEE SPM's Editorial Board Meeting [From the Editor]

    Publication Year: 2018, Page(s):3 - 4
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  • Intelligent Machines and Planet of the Apes [President's Message]

    Publication Year: 2018, Page(s):5 - 7
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  • Top Downloads in IEEE Xplore [Reader's Choice]

    Publication Year: 2018, Page(s):8 - 10
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  • A Feature Article Cluster on Exploiting Structure in Data Analytics: Low-Rank and Sparse Structures [From the Guest Editor]

    Publication Year: 2018, Page(s):12 - 13
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  • Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation: Recent Theory and Fast Algorithms via Convex and Nonconvex Optimization

    Publication Year: 2018, Page(s):14 - 31
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2234 KB) | HTML iconHTML

    Low-rank modeling plays a pivotal role in signal processing and machine learning, with applications ranging from collaborative filtering, video surveillance, and medical imaging to dimensionality reduction and adaptive filtering. Many modern high-dimensional data and interactions thereof can be modeled as lying approximately in a low-dimensional subspace or manifold, possibly with additional struc... View full abstract»

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  • Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

    Publication Year: 2018, Page(s):32 - 55
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3066 KB) | HTML iconHTML

    Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA). Fo... View full abstract»

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  • Correlation Awareness in Low-Rank Models: Sampling, Algorithms, and Fundamental Limits

    Publication Year: 2018, Page(s):56 - 71
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2540 KB) | HTML iconHTML

    The role of correlation awareness in low-rank compressive inverse problems is studied in this article. In such inverse problems, the ultimate goal is to estimate certain physically meaningful parameters from measurements collected across space and time. The spatiotemporal correlation structure of the data can be judiciously exploited to design highly efficient samplers that allow reliable paramete... View full abstract»

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  • Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks

    Publication Year: 2018, Page(s):72 - 89
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3081 KB) | HTML iconHTML

    Modeling data is the way we-scientists-believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing and machine learning. Sparse representation theory (we shall refer to it as Sparseland) puts forward an emerging, highly effective, and universal model. Its core idea is the description of data as a linear combi... View full abstract»

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  • Transforming Energy Networks via Peer-to-Peer Energy Trading: The Potential of Game-Theoretic Approaches

    Publication Year: 2018, Page(s):90 - 111
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2726 KB) | HTML iconHTML

    Peer-to-peer (P2P) energy trading has emerged as a next-generation energy-management mechanism for the smart grid that enables each prosumer (i.e., an energy consumer who also produces electricity) of the network to participate in energy trading with other prosumers and the grid. This poses a significant challenge in terms of modeling the decisionmaking process of the participants' conflicting int... View full abstract»

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  • Cognitive Radars: On the Road to Reality: Progress Thus Far and Possibilities for the Future

    Publication Year: 2018, Page(s):112 - 125
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3636 KB) | HTML iconHTML

    This article describes some key ideas and applications of cognitive radars, highlighting the limits and the path forward. Cognitive radars are systems based on the perception-action cycle of cognition that senses the environment, learns relevant information from it about the target and the background, and then adapts the radar sensor to optimally satisfy the needs of the mission according to a des... View full abstract»

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  • Closed-Form Impulse Responses of Linear Time-Invariant Systems: A Unifying Approach [Lecture Notes]

    Publication Year: 2018, Page(s):126 - 132
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (619 KB) | HTML iconHTML

    In many signal processing applications, filtering is accomplished through linear time-invariant (LTI) systems described by linear constant-coefficient differential and difference equations since they are conveniently implemented using either analog or digital hardware [1]. An LTI system can be completely characterized in the time domain by its impulse response or in the frequency domain by its fre... View full abstract»

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  • On Hypothesis Testing for Comparing Image Quality Assessment Metrics [Tips & Tricks]

    Publication Year: 2018, Page(s):133 - 136
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (322 KB) | HTML iconHTML

    In developing novel image quality assessment (IQA) metrics, researchers should compare their proposed metrics with state-of-the-art metrics. A commonly adopted approach is by comparing two residuals between the nonlinearly mapped scores of two IQA metrics and the difference mean opinion score, which are assumed from Gaussian distributions with zero means. An F-test is then used to test the equalit... View full abstract»

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  • [Dates Ahead]

    Publication Year: 2018, Page(s): 137
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  • Extreme Whitening [Humor]

    Publication Year: 2018, Page(s): 139
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  • Automation Is Coming to Research [In the Spotlight]

    Publication Year: 2018, Page(s):140 - 138
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  • ICIP 2019 CFP

    Publication Year: 2018, Page(s): C3
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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. Its coverage ranges from fundamental principles to practical implementation, reflecting the multidimensional facets of interests and concerns of the community. Its mission is to bring up-to-date, emerging and active technical developments, issues, and events to the research, educational, and professional communities. It is also the main Society communication platform addressing important issues concerning all members.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Robert Heath
University of Texas at Austin
United States
http://www.ece.utexas.edu/people/faculty/robert-heath