Journal Information
Signal Processing
http://www.journals.elsevier.com/signal-processing/
Impact Factor:
4.086
Publisher:
Elsevier
ISSN:
0165-1684
Viewed:
7227
Tracked:
13

Call For Papers
Signal Processing incorporates all aspects of the theory and practice of signal processing (analogue and digital). It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing.

Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Speech Processing; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.

Type of Contributions:
The journal welcomes the following types of contributions.

Original research articles:
Research articles should not exceed 30 pages (single column, double spaced) in length and must contain novel research within the scope of the journal.

Review articles:
Review articles are typically 30-60 pages (single column, double spaced) in length, and provide a comprehensive review on a scientific topic. They may be relatively broad in scope, thereby serving a tutorial function, or be quite specialized, aimed at researchers in the chosen field.

Fast Communications:
A Fast Communication is a short, self-contained article highlighting ongoing research, or reporting interesting possibly tentative ideas, or comments on previously published research. Such articles should not exceed 10 pages (single column, double spaced) in length not including figures or tables which should accompany the submission as separate files. The editorial decision is typically binary to provide rapid dissemination of the results. The objective is to provide detailed, constructive feedback on submitted papers and publish high quality papers within a very short period of time. The target for a first reply is two months.

You may be requested by the Editor to submit a revision. Please assist us in achieving our ambitious goals for short publication times by submitting a revision at your earliest convenience. One set of page proofs in PDF format will be sent by e-mail to the corresponding author, to be checked for typesetting/editing. No changes in, or additions to, the accepted (and subsequently edited) manuscript will be allowed at this stage. Proofreading is solely your responsibility.
Last updated by Dou Sun in 2019-12-08
Special Issues
Special Issue on Processing and Learning over Graphs
Submission Date: 2020-11-30

An increasing amount of data is generated by networks such as data generated by social, economic, biological, communication, and sensor networks, to name a few. These data have a high-dimensional, irregular, and complex structure that can be naturally represented by a graph. The need for tools to process these data has been translated into a need for tools able to account for underlying structure in their inner-working mechanisms. This led to a series of interdisciplinary approaches spread mainly among the fields of graph signal processing and geometric deep learning. Graph signal processing focuses principally on modeling the structure using graphs, treating the data as signals on top of these graphs, and, then, extending signal processing concepts such as Fourier decomposition, filtering, and sampling to this new paradigm. Geometric deep learning aims to develop machine learning principles to learn meaningful representations from graph data. The two fields intersect in a number of aspects with the most popular one being the graph neural network. Despite the fact that both fields have seen an emerging success, a number of both theoretical and practical issues remain still unresolved, e.g., robust modeling and learning, higher-dimensional graph-data representation, and applications to financial and biological networks (brain, protein-to-protein interaction). This special issue aims at gathering the latest research advances about processing and learning over graphs with a particular focus on papers providing new results, methods, and applications. Topics of interest include (but are not limited to): Fundamentals of graph signal processing: transforms, sampling, filters Graph topology inference: scalable, online, and from non-linear relationships Higher-order irregular data modeling: hyper-graphs, simplicial complexes Statistical, non-linear, and robust processing Machine learning over graphs: kernel-based techniques, clustering methods, scalable algorithms Graph neural networks: convolutional, attention, recurrent Applications to biological data (brain networks, protein-to-protein interactions) Applications to communications, power, and transportation networks Applications to finance, economic, and social networks
Last updated by Dou Sun in 2020-09-09
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