Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges
Abstract
Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic language and a goal filtering method that enables goal-oriented signal processing. The proposed semantic signal processing framework can easily be tailored for specific applications and goals in a diverse range of signal processing applications. To illustrate its wide range of applicability, we investigate several use cases and provide details on how the proposed goal-oriented semantic signal processing framework can be customized. We also investigate and propose techniques for communications where sensor data is semantically processed and semantic information is exchanged across a sensor network.
Cite
@article{arxiv.2109.11885,
title = {Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges},
author = {Mert Kalfa and Mehmetcan Gok and Arda Atalik and Busra Tegin and Tolga M. Duman and Orhan Arikan},
journal= {arXiv preprint arXiv:2109.11885},
year = {2021}
}
Comments
44 pages, 29 figures, 3 tables. This preprint will be published in the Digital Signal Processing Journal's 30th anniversary issue on Future Signal Processing