IEEE Signal Processing Magazine

Issue 6 • Nov. 2018

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  • Front Cover

    Publication Year: 2018, Page(s): C1
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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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  • Making Papers, Code, and Data Accessible [From the Editor]

    Publication Year: 2018, Page(s):3 - 4
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  • Twinkle, Twinkle, Little Star [President's Message]

    Publication Year: 2018, Page(s):5 - 7
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  • Something to Talk About: Signal Processing in Speech and Audiology Research: Promising Investigations Explore New Opportunities in Human Communication [Special Reports]

    Publication Year: 2018, Page(s):8 - 12
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1136 KB) | HTML iconHTML

    Speech, the expression of thoughts and feelings by articulating sounds, is an ability so taken for granted that few people bother to think about how complex and nuanced the process actually is. Yet, as more devices gain the ability to listen to and interpret what speakers are saying, speech and audiology technologies are attracting the interest of a growing number of academic researchers. Signal p... View full abstract»

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  • Signal Processing Leads to New Clinical Medicine Approaches: Innovative Methods Promise Improved Patient Diagnoses and Treatments [Special Reports]

    Publication Year: 2018, Page(s):12 - 15
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (773 KB) | HTML iconHTML

    Popular consumer and business technologies, such as smartphones, tablets, wearable devices, and sophisticated photoimaging-all driven or supported by signal processing-are leading to a generation of powerful new diagnostic tools designed to help physicians working in clinical medicine. In Rochester, New York, for instance, a team of engineers and clinicians at the Rochester Institute of Technology... View full abstract»

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  • Model Selection Techniques: An Overview

    Publication Year: 2018, Page(s):16 - 34
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1675 KB) | HTML iconHTML

    In the era of big data, analysts usually explore various statistical models or machine-learning methods for observed data to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and rep... View full abstract»

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  • Sub-Nyquist Radar Systems: Temporal, Spectral, and Spatial Compression

    Publication Year: 2018, Page(s):35 - 58
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4147 KB) | HTML iconHTML

    Radar is an acronym for "radio detection and ranging." However, the functions of today's radar systems, both in civilian and military applications, go beyond simple target detection and localization; they extend to tracking, imaging, classification, and more and involve different types of radar systems, such as through-the-wall [1], ground-penetration [2], automotive [3], and weather [4]. Although... View full abstract»

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  • Privacy-Aware Smart Metering: Progress and Challenges

    Publication Year: 2018, Page(s):59 - 78
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2068 KB) | HTML iconHTML

    The next-generation energy network, the so-called smart grid (SG), promises tremendous increases in efficiency, safety, and flexibility in managing the electricity grid as compared to the legacy energy network. This is needed today more than ever, as global energy consumption is growing at an unprecedented rate and renewable energy sources (RESs) must be seamlessly integrated into the grid to assu... View full abstract»

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  • Deep Convolutional Neural Networks [Lecture Notes]

    Publication Year: 2018, Page(s):79 - 87
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1428 KB) | HTML iconHTML

    Neural networks are a subset of the field of artificial intelligence (AI). The predominant types of neural networks used for multidimensional signal processing are deep convolutional neural networks (CNNs). The term deep refers generically to networks having from a "few" to several dozen or more convolution layers, and deep learning refers to methodologies for training these systems to automatical... View full abstract»

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  • Sliding Discrete Fourier Transform with Kernel Windowing [Lecture Notes]

    Publication Year: 2018, Page(s):88 - 92
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (714 KB) | HTML iconHTML

    The sliding discrete Fourier transform (SDFT) is an efficient method for computing the N-point DFT of a given signal starting at a given sample from the N-point DFT of the same signal starting at the previous sample [1]. However, the SDFT does not allow the use of a window function, generally incorporated in the computation of the DFT to reduce spectral leakage, as it would break its sliding prope... View full abstract»

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  • Join the IEEE Signal Processing Cup 2019

    Publication Year: 2018, Page(s): 92
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  • Utility Metrics for Assessment and Subset Selection of Input Variables for Linear Estimation [Tips & Tricks]

    Publication Year: 2018, Page(s):93 - 99
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (411 KB) | HTML iconHTML

    This tutorial article introduces the utility metric and its generalizations, which allow for a quick-and-dirty quantitative assessment of the relative importance of the different input variables in a linear estimation model. In particular, we show how these metrics can be cheaply calculated, thereby making them very attractive for model interpretation, online signal quality assessment, or greedy v... View full abstract»

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  • Observer-Based Recursive Sliding Discrete Fourier Transform [Tips & Tricks]

    Publication Year: 2018, Page(s):100 - 106
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (934 KB) | HTML iconHTML

    In the field of digital signal analysis and processing, the ubiquitous domain transformation is the discrete Fourier transform (DFT), which converts the signal of interest within a limited time window from discrete time to the discrete frequency domain. The active use in real-time or quasi-real-time applications has been made possible by a family of fast implementations of the DFT, called fast Fou... View full abstract»

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  • Dates Ahead

    Publication Year: 2018, Page(s): 107
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  • 2018 Index IEEE Signal Processing Magazine Vol. 35

    Publication Year: 2018, Page(s):108 - 122
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  • An Overview of the IEEE SPS Speech and Language Technical Committee [In the Spotlight]

    Publication Year: 2018, Page(s):125 - 126
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  • Conference Planning for Professors [Humor]

    Publication Year: 2018, Page(s): 127
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  • Spotlight on Bioimaging and Signal Processing [In the Spotlight]

    Publication Year: 2018, Page(s):128 - 125
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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