Content Gazette - September 2017 - 20

IEEE Journal on Selected Topics in Signal Processing
Call for Papers
Special Issue on
"Information-Theoretic Methods in Data Acquisition, Analysis, and Processing"
The field of information theory addresses fundamental questions in various areas including statistical decision theory,
data communications, data compression, security, and networking. In particular, information-theoretic methods can
be used to illuminate fundamental limits and gauge the effectiveness of algorithms for various problems associated
with these fields.
Recent years have witnessed a renaissance in the use of information-theoretic methods to address various problems
in the general field of information processing beyond communications and networking, including signal acquisition,
signal analysis and processing, compressive sensing, dictionary learning, supervised and unsupervised learning,
reinforcement learning, graph mining, and more.
With a world-wide drive in both academia and industry for new approaches to data science, it is generally believed
that information-theoretic methods have the potential to illuminate theory and algorithms that will underpin this
emerging field.
This special issue covers emerging topics at the interface of information theory and data acquisition, analysis, and
processing, with applications to the general area of data science. Its overarching aim is to map out this emerging
research landscape as well as current and future research directions.
Topics of interest include (but are not limited to):
New information measures to capture limits in modern data acquisition, analysis, and processing problems
Information-theoretic limits on and algorithms for data acquisition and processing
Limits on and algorithms for feature extraction, data sketching, and information embedding
Limits on and algorithms for community detection, graph selection, and ranking
Limits in active learning, supervised and unsupervised learning, reinforcement learning, and deep learning
Limits on and algorithms for data acquisition, analysis, and processing problems in the presence of communication
and / or computation constraints
* New approaches from the fields of approximation theory and harmonic analysis to unveil limits on and algorithms
for data acquisition, analysis, and processing
* Application of new techniques to problems in signal processing, imaging, decision theory, machine learning, data
analysis, security, and privacy.
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Prospective authors should follow the instructions given on the IEEE JSTSP webpage:
https://signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing,
and
submit their manuscript through the web submission system at: https://mc.manuscriptcentral.com/jstsp-ieee.
Important Dates:
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Manuscript submission: December 1, 2017
1st review completed: February 1, 2018
Revised manuscript due: April 1, 2018
2nd review completed: June 1, 2018
Final manuscript due: July 1, 2018
Publication: October 2018

Guest Editors:
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Helmut Bölcskei, ETH Zurich
Stark Draper, University of Toronto
Yonina Eldar, Technion - Israel Institute of Technology
Miguel Rodrigues, University College London
Vincent Tan, National University of Singapore

www.signalprocessingsociety.org

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SEPTEMBER 2017
https://www.signalprocessingsociety.org/publications-resources/ieee-journal-selected-topics-signal-processing https://mc.manuscriptcentral.com/jstsp-ieee http://www.signalprocessingsociety.org
Table of Contents for the Digital Edition of Content Gazette - September 2017
