Tag Archives: distributed signal processing

CFP: T-SIPN Special Issue on Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems

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IEEE Signal Processing Society
IEEE Transactions on Signal and Information Processing over Networks
Special Issue on Distributed Signal Processing for Security and Privacy in Networked Cyber-Physical Systems

GUEST EDITORS:

SCOPE
The focus of this special issue is on distributed information acquisition, estimation, and adaptive learning for security and privacy in the context of networked cyber-physical systems (CPSs) which are engineering systems with integrated computational and communication capabilities that interact with humans through cyber space. The CPSs have recently emerged in several practical applications of engineering importance including aerospace, industrial/manufacturing process control, multimedia networks, transportation systems, power grids, and medical systems. The CPSs typically consist of both wireless and wired sensor/agent networks with different capacity/reliability levels where the emphasis is on real-time operations, and performing distributed, secure, and optimal sensing/processing is the key concern. To satisfy these requirements of the CPSs, it is of paramount importance to design innovative “Signal Processing” tools to provide unprecedented performance and resource utilization efficiency.

A significant challenge for implementation of signal processing solutions in CPSs is the difficulty of acquiring data from geographically distributed observation nodes and storing/processing the aggregated data at the fusion center (FC). As such, there has been a recent surge of interest in development of distributed and collaborative signal processing technologies where adaptation, estimation, and/or control are performed locally and communication is limited to local neighborhoods. Distributed signal processing over networked CPSs, however, raise significant privacy and security concerns as local observations are being shared by neighboring nodes in a collaborative and iterative fashion. On one hand, applications of CPSs are severely safety critical where potential cyber and physical attacks by adversaries on signal processing modules could lead to a variety of severe consequences including customer information leakage, destruction of infrastructures, and endangering human lives. On the other hand, the need for cooperation be- tween neighboring nodes makes it imperative to prevent the disclosure of sensitive local information during distributed information fusion step. At the same time, efficient usage of available resources (communication, computation, bandwidth, and energy) is a pre-requisite for productive operation of the CPSs. To accommodate these critical aspects of CPSs, it is of great practical importance and theoretical significance to develop advanced “Secure and Privacy Preserving Distributed Signal Processing” solutions.

The spirit and wide scope of distributed signal processing in revolutionized CPSs calls for novel and innovative techniques beyond conventional approaches to provide precise guarantees on security and privacy of CPSs. The objective of this special issue is to further advance recent developments of distributed signal processing to practical aspects of CPSs for real-time processing and monitoring of the underlying system in a secure and privacy preserving manner while avoiding degradation of the processing performance and preserving the valuable resources. To provide a systematic base for future advancements of CPSs, this special issue aims to provide a research venue to investigate distributed signal processing techniques with adaptation, cooperation, and learning capabilities which are secure against cyber-attacks and protected against privacy leaks. The emphasis of this special issue is on distributed/network aspects of security and privacy in CPSs. Papers with primary emphasis on forensics and security will be redirected to IEEE Transactions on Information Forensics and Security (TIFS). Topics of interest include, but are not limited to:

  • Security and Privacy of distributed signal processing in networked CPSs.
  • Distributed and secure detection, estimation, and information fusion.
  • Security and privacy of consensus and diffusive strategies in networked systems.
  • Secure and privacy preserving distributed adaptation and learning.
  • Security and privacy of distributed sensor resource management in networked systems.
  • Distributed event-based estimation/control in networked CPSs.
  • Detection and identification of potential attacks on distributed signal processing mechanisms.
  • Application domains including but not limited to, smart grids, camera networks, multimedia network, and vehicular networks.

SUBMISSION GUIDELINES
Authors are invited to submit original research contributions by following the detailed instructions given in the “Information for Authors” page or TSIPN page. Manuscripts should be submitted via Scholar One(Manuscript Central) system. Questions about the special issue should be directed to the Guest Editors.

IMPORTANT DATES:

    • Paper submission deadline: December 15, 2016
    • Notification of the first review: March 1, 2017
    • Revised paper submission: April 15, 2017
    • Notification of the re-review: June 15, 2017
    • Minor revision deadline: August 1, 2017
    • Final notification: September 1, 2017
    • Final manuscript due: October 15, 2017

Publication: Advance posting in IEEExplore as soon as authors approve galley proofs

Expected inclusion in an issue: March 2018

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CFP: IEEE JSTSP and T-SIPN Special Issues on Graph Signal Processing

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IEEE Journal of Selected Topics in Signal Processing
IEEE Transactions on Signal and Information Processing over Networks
Special Issues on Graph Signal Processing

Numerous applications rely on the processing of high-dimensional data that resides on irregular or otherwise unordered structures which are naturally modeled as networks (such as social, economic, energy, transportation, telecommunication, sensor, and neural, to name a few). The need for new tools to process such data has led to the emergence of the field of graph signal processing, which merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process signals on structures such as graphs. This important new paradigm in signal processing research, coupled with its numerous applications in very different domains, has fueled the rapid development of an inter-disciplinary research community that has been working on theoretical aspects of graph signal processing and applications to diverse problems such as big data analysis, coding and compression of 3D point clouds, biological data processing, and brain network analysis.

The purpose of these special issues is to gather the latest advances in graph signal processing and disseminate new ideas and experiences in this emerging field to a broad audience. We encourage the submission of papers with new results, methods or applications in graph signal processing. In particular, the topics of interest include (but are not limited to):

  • Sampling and recovery of graph signals
  • Graph filter and filter bank design
  • Uncertainty principles and other fundamental limits
  • Graph signal transforms
  • Graph topology inference
  • Prediction and learning in graphs
  • Statistical graph signal processing
  • Non-linear graph signal processing
  • Applications to visual information processing
  • Applications to neuroscience and other medical fields
  • Applications to economics and social networks
  • Applications to various infrastructure networks

Submission Procedure:
Prospective authors should follow the instructions given on the IEEE JSTSP webpages and submit their manuscript with the web submission system at https://mc.manuscriptcentral.com/jstsp-ieee. The decisions on whether the accepted papers will be published in IEEE JSTSP or IEEE TSIPN will depend on the respective themes of the papers and will be made by the Guest Editors.

Schedule (all deadlines are firm):

Manuscript due: Nov 1, 2016
First Review Completed: Jan 1, 2017
Revised manuscript due: Mar 1, 2017
Second Review Completed: May 1, 2017
Final manuscript due: June 1, 2017
Publication date: September 2017

Guest Editors:

  • Pier-Luigi Dragotti, Imperial College, London (p.dragotti@imperial.ac.uk)
  • Pascal Frossard, EPFL, Lausanne (pascal.frossard@epfl.ch)
  • Antonio Ortega, USC, Los Angeles (ortega@sipi.usc.edu)
  • Michael Rabbat, McGill University, Montreal (michael.rabbat@mcgill.ca)
  • Alejandro Ribeiro, UPenn, Philadelphia (aribeiro@seas.upenn.edu)

Postdoctoral Position at Rutgers with… me!

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I keep posting ads for postdocs with other people but this is actually to work with little old me!

Postdoctoral Position
Department of Electrical and Computer Engineering
Rutgers, The State University of New Jersey

The Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a dynamic and motivated Postdoctoral Fellow to work on developing distributed machine learning algorithms that work on complex neuroimaging data. This work is in collaboration with the Mind Research Network in Albuquerque, New Mexico under NIH Grant 1R01DA040487-01A1.

Candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas with experience in one of

  • distributed signal processing or optimization
  • image processing with applications in biomedical imaging
  • machine learning theory (but with a desire to interface with practice)
  • privacy-preserving algorithms (preferably differential privacy)

are welcome to apply. Strong and self-motivated candidates should also have

  • a strong mathematical background: this project is about translating theory to practice, so a solid understanding of mathematical formalizations is crucial;
  • good communication skills: this is an interdisciplinary project with many collaborators

The Fellow will receive valuable experience in translational research as well as career mentoring, opportunities to collaborate with others outside the project within the ECE Department, DIMACS, and other institutions.

The initial appointment is for 1 year but can be renewed subject to approval. Salary and compensation is at the standard NIH scale for postdocs.

To apply, please email the following to Prof. Anand D. Sarwate (anand.sarwate@rutgers.edu):

  • Curriculum Vitae
  • Contact information for 3 references
  • A brief statement (less than a page!) addressing the qualifications above and why the position is appealing.
  • Standard forms: Equal Employment Opportunity Data Form [PDF] Voluntary Self-Identification of Disability Form [PDF] Invitation to Covered Veterans to Self-Identify [PDF].

    Applications are open until the position is filled. Start date is flexible but sometime in Fall 2016 is preferable.

    Rutgers, The State University of New Jersey, is an Equal Opportunity / Affirmative Action Employer. Qualified applicants will be considered for employment without regard to race, creed, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability status, genetic information, protected veteran status, military service or any other category protected by law. As an institution, we value diversity of background and opinion, and prohibit discrimination or harassment on the basis of any legally protected class in the areas of hiring, recruitment, promotion, transfer, demotion, training, compensation, pay, fringe benefits, layoff, termination or any other terms and conditions of employment.

2015 Bellairs Workshop on Large-Scale Inference and Optimization

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A few weeks ago I got to go to Bellairs in Holetown, Barbados for a workshop organized by Mike Rabbat and Mark Coates of McGill University. It’s a small workshop, mostly for Mike and Mark’s students, and it’s a chance to interact closely and perhaps start some new research collaborations. Here’s a brief summary of the workshop as I remember it from my notes.

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