期刊
DIGITAL SIGNAL PROCESSING
卷 119, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2021.103134
关键词
Semantic signal processing; Graph-based languages; Semantic communications; Goal-oriented communications
The advances in machine learning technology enable real-time extraction of semantic information in signals. A formal graph-based semantic language and a goal filtering method are proposed for goal-oriented signal processing. The framework can be tailored for specific applications and goals in diverse signal processing applications.
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. (C) 2021 Elsevier Inc. All rights reserved.
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