Content Gazette - July 2012 - 17

Call for Papers

IEEE Signal Processing Society

IEEE Journal of Selected Topics in Signal Processing

Special Issue on Anomalous Pattern Discovery for Spatial, Temporal,
Networked, and High-Dimensional Signals
Anomaly detection broadly deals with the problem of identifying patterns in data that deviate from expected
behavior. Anomaly detection arises in many application domains including financial fraud detection, airport
security, video surveillance, communication and computer networks, spectrum violation in cognitive radios,
rare event detection of material defects, change point detection for quality control in industrial processes,
hyperspectral imagery for land cover change detection, anomalies in genetic network patterns and
anomalous activity patterns in social networks. Approaches to anomaly detection include model-based
parametric approaches and have been employed for change point detection in the context of time-series
analysis for fraud detection and fault detection. In general when nominal parametric models are unknown
approaches based on robust detection theory have attempted to incorporate model uncertainty. Nevertheless,
structured model-based approaches may not be meaningful in other contexts such as applications that arise
in finance, computer and social networks, imaging, video surveillance and biology. In these cases nonparametric approaches may be more appropriate. Many of the applications in this setting are highdimensional. Consequently, new methods that exploit the underlying structure such as sparsity, manifold
structures, or incorporate dependencies such as in graphical models are warranted. The goal of our
proposed guest issue is to solicit submissions from researchers from different disciplines (Statistics, Signal
Processing, Decision and Control, Networks, Information Theory, and Machine Learning) and in various
application domains. Topics of interest include, but are not limited to:
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Anomaly/Outlier/Novelty Detection in high-dimensional data.
Sparsity, Manifold, low-rank structured anomaly detection.
Anomaly detection in spatial, temporal and sequential data.
Bayesian change detection and fault detection in multi-dimensional stochastic processes.
Information theoretic analysis and bounds for anomaly detection
Bayesian non-parametric approaches
Applications: imaging, video, biology, computer networks, social networks, and homeland security

Prospective authors should visit http://www.signalprocessingsociety.org/publications/periodicals/jstsp/ for
information on paper submission. Manuscripts should be submitted using the ScholarOne Manuscripts
system at http://mc.manuscriptcentral.com/jstsp-ieee. Manuscripts will be peer reviewed according to the
standard IEEE process.
Manuscript submission due:
First review completed:
Revised manuscript due:
Second review completed:
Final manuscript due:

August 1, 2012
Sept 7, 2012
Oct. 19, 2012
Nov. 16, 2012
Nov 30, 2012

Lead guest editor:
Venkatesh Saligrama, Boston University, Boston, MA, USA, srv@bu.edu
Guest editors:
Prof. Rama Chellappa, University of Maryland, College Park, Rama@umiacs.umd.edu
Prof Alfred Hero, University of Michigan, Ann-Arbor, hero@eecs.umich.edu
Prof. Robert Nowak, University of Wisconsin, Madison, nowak@ece.wisc.edu
Prof. Venu Veeravalli, University of Illinois, Urbana-Champaign, vvv@illinois.edu
Prof. Ery Arias-Castro, University of California, San Diego, eariasca@math.ucsd.edu

www.signalprocessingsociety.org

[17]

JULY 2012


http://www.signalprocessingsociety.org/publications/periodicals/jstsp/ http://mc.manuscriptcentral.com/jstsp-ieee http://www.signalprocessingsociety.org

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