Optimization, Learning, and Adaptation over Networks
EURASIP Journal on Advances in Signal Processing welcomes submissions to the thematic series on Optimization, Learning, and Adaptation over Networks.
Networked systems are ubiquitous in modern science. Consequently, performing optimization and learning tasks over networks is of utmost importance in several research fields including signal processing, machine learning, optimization, control, biology, economics, computer and social sciences. The combined interplay among distributed processing of information, self-organization, and adaptation is the essential feature of a network designed to perform real-time optimization and learning tasks. A network typically constrains the interaction among individual components: Agents are linked together through a sparse (possibly time-varying) communication topology, and cooperate with each other by relying solely on in-network processing and local sharing of information, in order to accomplish an assigned inferential objective. On the other side, a network may also define structural relationships of data. For instance, high-dimensional data collected over networks (such as social, economic, energy, transportation, telecommunication, biological, to name a few) are naturally modeled as signals defined over graphs, where the graph typically accounts for the topology of the irregular structure of the data. The need for novel analysis tools for graph signals has led to the emergence of the field of graph signal processing, which has been catalyzed by the numerous potential applications such as big data mining, biological data processing, topological data analysis, etc. The goal of this special issue is to gather the latest research efforts toward developing the methodologies necessary to: a) endow networks with distributed optimization, learning, and adaptation capabilities; b) process and analyze signals defined over graphs; b) exploit, disclose, or learn complex relationships and/or patterns hidden in the data collected over networked systems. Therefore, this call for papers encourages submissions related (but not limited to) the following topics of interest.
Potential topics include but are not limited to:
- Learning and adaptation over networks
- Distributed estimation, detection, and filtering
- Distributed optimization
- Signal processing over graphs
- Distributed machine learning
- Graph-based machine learning
- Applications to technological, biological, economic, and social networks
Before submitting your manuscript, please ensure you have carefully read the submission guidelines for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate special issue in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on Biomedical Informatics with Optimization and Machine Learning . All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.
Deadline for submissions: December 31st 2017
Lead Guest Editor
Paolo Di Lorenzo,University of Perugia, Italy
Antonio G. Marques, King Juan Carlos University, Spain
Gonzalo Mateos, University of Rochester, USA
Submissions will also benefit from the usual advantages of open access publication:
- Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
- High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
- No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
- Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed
For editorial enquiries please contact email@example.com.
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