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Special Issue "Graph Signal Processing-Based Classification with Applications to Sensors"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editor

Dr. Vladimir Stankovic
Website
Guest Editor
Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
Interests: signal and information processing with applications to sensing; microseismic signal analysis; environmental signal analysis and healthcare

Special Issue Information

Dear Colleagues,

This Special Issue will present both review and original research articles related to application of graph signal processing (GSP) tools to various sensor data analysis tasks, including sensor data classification, clustering, anomaly detection, and prediction. The Special Issue is open to both theoretical contributions and applied articles addressing time-series signals as well as image, video, and heterogeneous signals.

Graph signal processing (GSP) is an emerging field used to represent irregular data structures on graphs. GSP extends classical digital signal processing (DSP) to signals on graphs by combining algebraic and spectral graph theory with DSP and provides a potential solution to numerous real-world problems that involve signals defined on topologically complex domains, such as social networks, point clouds, biological networks, environmental and condition monitoring sensor networks, etc. GSP tools have emerged as a competitive unsupervised, semi-supervised, and supervised classifier learning method. The main idea is to represent classification labels as piecewise smooth signals on graphs and apply a graph signal smoothness prior. GSP-based classification methods are inherently effective when the training dataset is very small and/or when training (and testing) observations are corrupted. The goal of this Special Issue is to demonstrate the potential of GSP tools for various data processing tasks, including data classification, prediction, and anomaly detection.  

Dr. Vladimir Stankovic
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • graph signal processing
  • graph Laplacian
  • classification
  • sensing
  • clustering

Published Papers

This special issue is now open for submission.
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