Aims and scope

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.

Special Issue on Science data with hidden periodic structure - new perspectives
截稿日期: 2024-12-31

Edited by:
Agnieszka Wyłomańska, Wroclaw University of Science and Technology, Poland
Antonio Napolitano, University of Napoli "Parthenope", Italy
Ran Tao, Beijing Institute of Technology, China

EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on 'Science data with hidden periodic structure - new perspectives.' This collection welcomes original research articles in the field of periodic (or quasi periodic) phenomena focusing at modeling, analysis, and exploitation of these hidden periodicities. This provides deep knowledge on the observed phenomenon and better performance in signal processing algorithms aimed at extracting information from the available data.

Special Issue on Advanced Signal Processing for Distributed and Autonomous Sensing Systems
截稿日期: 2025-03-20

Edited by:
Raj Thilak Rajan, PhD, Delft university of Technology (TUD), Netherlands
Usman Khan, PhD, Tufts University, USA

EURASIP Journal on Advances in Signal Processing is calling for submissions to our Collection on Advanced Signal Processing for Distributed and Autonomous Sensing Systems. This Special Issuer is linked to the 32nd European Signal Processing Conerence (EUSIPCO 2024). 

The past decade has seen a rise in the adoption of distributed autonomous sensing systems (DASS), in the field of drone swarms, automotives, satellite networks, industry automation, autonomous rovers and truck platooning to name a few. These networked cyber-physical systems are typically tasked with complex missions, which necessitate accurate PNT (Position, Navigation, and Timing), cooperative sensing, coordination and control, decision making, sensor fusion, distributed inference and learning, and timely decision making on the Edge. 

In many cases, these DASS are also deployed in inaccessible or intermittently accessible environments e.g., drone swarms in BVLOS scenarios, with limited access to cloud services and other critical infrastructure, which necessitates on-board or in-network inference, control, and decision. Signal processing and Machine learning play a vital role in providing efficient, optimal and robust solutions for these challenges. This special issue is a platform to address these challenges by presenting research on novel data models, signal processing and machine learning algorithms, resource constrained real-time Edge AI solutions, and fundamental insights into advanced optimization and statistical tools.