In this paper, Lia Ahrens, Julian Ahrens and Hans D. Schotten proposes a novel approach to detecting anomalies in time series exhibiting periodic characteristics, where we applied deep convolutional neural networks for phase classification and automated phase similarity tagging. They evaluated their approach on three example datasets corresponding to the domains of cardiology, industry, and signal processing, confirming that their method is feasible in a number of contexts.
The Erratum to this article has been published in EURASIP Journal on Advances in Signal Processing 2016 2016:85
Integrated Space-Ground-Sea Wireless Networks for B5G
Collection published: 5 August 2021
Intelligent Sensors trends in Real-time Signal Processing
Collection published: 5 August 2021
Edge Intelligent Sensors for Infectious Diseases
Collection published: 27 July 2021
The European Association for Signal Processing (EURASIP) was founded on 1 September 1978 to improve communication between groups and individuals that work within the multidisciplinary, fast growing field of signal processing in Europe and elsewhere, and to exchange and disseminate information in this field all over the world. The association exists to further the efforts of researchers by providing a learned and professional platform for dissemination and discussion of all aspects of signal processing including continuous- and discrete-time signal theory, applications of signal processing, systems and technology, speech communication, and image processing and communication.
EURASIP members are entitled to a 10% discount on the article-processing charge. To claim this discount, the corresponding author must enter the membership code when prompted. This can be requested from their EURASIP representative.
The aim of the EURASIP Journal on Advances in Signal Processing is to highlight the theoretical and practical aspects of signal processing in new and emerging technologies. The journal is directed as much at the practicing engineer as at the academic researcher. Authors of articles with novel contributions to the theory and/or practice of signal processing are welcome to submit their articles for consideration. All manuscripts undergo a rigorous review process. EURASIP Journal on Advances in Signal Processing employs a paperless, electronic review process to enable a fast and speedy turnaround in the review process.
The journal is an Open Access journal since 2007.
Sparse/Low-rank Tensor Signal Processing for Communication and Radar Systems
Deadline for submission: 30 December 2021
Signal Processing over Higher Order Networks
Deadline for submission: 31 January 2022
Artificial Intelligence and Cognitive Computing for Smart Internet of Vehicle
Deadline for submission: 15 January 2022
The EURASIP Journal on Advances in Signal Processing (JASP) welcomes Special Issues on timely topics related to the field of signal processing at large. The objective of Special Issues is to bring together recent and high quality works in a research domain, to promote key advances in signal processing, and to provide overviews of the state-of-the-art in emerging domains, to the general signal processing audience.
Your browser needs to have JavaScript enabled to view this video
We are pleased to announce that the following paper published in EURASIP Journal on Advances in Signal Processing has been awarded a EURASIP best paper award!
Research
Cooperative local repair in distributed storage
Ankit Singh Rawat, Arya Mazumdar, and Sriram Vishwanath
The award ceremony will take place at EUSIPCO 2020 in the Netherlands. For more information see the EURASIP newsletter.
Learn more about the impact the EURASIP Journal on Advances in Signal Processing has worldwide
Speed
133 days to first decision for reviewed manuscripts only
110 days to first decision for all manuscripts
215 days from submission to acceptance
32 days from acceptance to publication
Citation Impact
1.63 - 2-year Impact Factor
1.788 - 5-year Impact Factor
0.85 - Source Normalized Impact per Paper (SNIP)
0.317 - SCImago Journal Rank (SJR)
Usage
752,306 downloads
31 Altmetric mentions
