IEEE Signal Processing Magazine

Issue 1 • Jan. 2018

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Displaying Results 1 - 25 of 25
  • Front Cover

    Publication Year: 2018, Page(s): C1
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  • ICIP CFP

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

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

    Publication Year: 2018, Page(s): 2
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  • Taking the Next Step for IEEE Signal Processing Magazine [From the Editor]

    Publication Year: 2018, Page(s):4 - 171
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  • Collaboration Empowers Innovation [President's Message]

    Publication Year: 2018, Page(s):5 - 6
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  • SPS Announces 2018 Class of DLs and Creates New Distinguished Industry Speaker Program [Society News]

    Publication Year: 2018, Page(s):7 - 12
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  • 2017 Members-at-Large and Regional Directors-at-Large Election Results [Society News]

    Publication Year: 2018, Page(s): 12
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  • Signal Processing Powers Next-Generation Prosthetics: Researchers Investigate Techniques That Enable Artificial Limbs to Behave More Like Their Natural Counterparts [Special Reports]

    Publication Year: 2018, Page(s):13 - 16
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  • Errata

    Publication Year: 2018, Page(s): 16
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (46 KB)

    Presents corrections to the paper, “Perfecting protection for interactive multimedia: A survey of forward errror correction for low-delay interactive applications,” (Badr, A. et al), IEEE Signal Process. Mag., vol. 34, no. 2, pp. 95–113, Mar. 2017. View full abstract»

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  • Deep Learning for Visual Understanding: Part 2 [From the Guest Editors]

    Publication Year: 2018, Page(s):17 - 19
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  • Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods

    Publication Year: 2018, Page(s):20 - 36
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2902 KB)

    Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches. Unlike analytical methods for which the problem is explicitly defined a... View full abstract»

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  • Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction

    Publication Year: 2018, Page(s):37 - 52
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (3722 KB) | HTML iconHTML

    Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model ... View full abstract»

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  • Generative Adversarial Networks: An Overview

    Publication Year: 2018, Page(s):53 - 65
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2998 KB)

    Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, i... View full abstract»

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  • Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans

    Publication Year: 2018, Page(s):66 - 83
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (4581 KB)

    Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping between images and class labels, as well as the affordability of powerful graphics processing units (GPUs). T... View full abstract»

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  • Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey

    Publication Year: 2018, Page(s):84 - 100
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2670 KB)

    Object detection, including objectness detection (OD), salient object detection (SOD), and category-specific object detection (COD), is one of the most fundamental yet challenging problems in the computer vision community. Over the last several decades, great efforts have been made by researchers to tackle this problem, due to its broad range of applications for other computer vision tasks such as... View full abstract»

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  • The Deep Regression Bayesian Network and Its Applications: Probabilistic Deep Learning for Computer Vision

    Publication Year: 2018, Page(s):101 - 111
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2710 KB)

    Deep directed generative models have attracted much attention recently due to their generative modeling nature and powerful data representation ability. In this article, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures. We focus on a specific structure that consists of layers of Bayesian networks (BNs) due to... View full abstract»

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  • Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content

    Publication Year: 2018, Page(s):112 - 125
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2006 KB)

    With the recent renaissance of deep convolutional neural networks (CNNs), encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient and fully annotated training data. However, to scale the recognition to a large number of classes with few or no training samples for each class remains an unsolved problem. One approach is to develop models capa... View full abstract»

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  • Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges

    Publication Year: 2018, Page(s):126 - 136
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1753 KB)

    In recent years, deep neural networks (DNNs) have received increased attention, have been applied to different applications, and achieved dramatic accuracy improvements in many tasks. These works rely on deep networks with millions or even billions of parameters, and the availability of graphics processing units (GPUs) with very high computation capability plays a key role in their success. For ex... View full abstract»

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  • Analog-to-Digital Cognitive Radio: Sampling, Detection, and Hardware

    Publication Year: 2018, Page(s):137 - 166
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (17070 KB)

    The radio spectrum is the radio-frequency (RF) portion of the electromagnetic spectrum. These spectral resources are traditionally allocated to licensed or primary users (PUs) by governmental organizations. As discussed in "Radio-Frequency Spectral Resources," most of the frequency bands are already allocated to one or more PUs. Consequently, new users cannot easily find free frequency bands. Spur... View full abstract»

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  • Introducing Information Measures via Inference [Lecture Notes]

    Publication Year: 2018, Page(s):167 - 171
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1395 KB)

    Information measures, such as the entropy and the Kullback-Leibler (KL) divergence, are typically introduced using an abstract viewpoint based on a notion of "surprise." Accordingly, the entropy of a given random variable (rv) is larger if its realization, when revealed, is on average more "surprising". The goal of this lecture note is to describe a principled and intuitive introduction to informa... View full abstract»

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  • Calendar [Dates Ahead]

    Publication Year: 2018, Page(s): 172
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  • Errata

    Publication Year: 2018, Page(s): 178
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (52 KB)

    Presents corrections to the paper, “Deep learning for image-to-text generation" (He, X. and Deng, L.), IEEE Signal Process. Mag., vol. 34, no. 6, pp. 109–116, Nov. 2017. View full abstract»

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  • Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives]

    Publication Year: 2018, Page(s):180 - 177
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (124 KB)

    Artificial intelligence (AI) is a branch of computer science and a technology aimed at developing the theories, methods, algorithms, and applications for simulating and extending human intelligence. Modern AI enables going from an old world-where people give computers rules to solve problems-to a new world-where people give computers problems directly and the machines learn how to solve them on th... View full abstract»

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  • IEEE GlobalSIP

    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