Browsing by Subject "Graph-based languages"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    ItemOpen Access
    Semantic and goal-oriented signal processing: semantic extraction
    (2022-08) Gök, Mehmetcan
    Advances in machine learning technology have enabled real-time extraction of semantic information in signals, which has the potential to revolutionize signal processing techniques and drastically improve their performance for next-generation applications. A graph-based semantic language and a goal-oriented semantic signal processing framework are adopted for structured and universal representation and efficient processing of semantic information. In the adopted framework, preprocessing of input signals is followed by a semantic extractor which identifies components from a set of application-specific predefined classes where the states, actions, and relations among the identified components are described by another application-specific predefined set called predicates. For additional information, the resulting semantic graph is also embedded with a hierarchical set of attributes. In this thesis, we focus on the crucial semantic extractor block, and to illustrate the proposed framework’s applicability, we present a real-time computer vision application on video-stream data where we adopt a tracking by detection paradigm for the identification of semantic components. Next, we show that with the adopted semantic representation and goal-filtering, the semantic signal processing framework can achieve an extremely high reduction in data rates compared to traditional approaches. Finally, we demonstrate a way to identify points of significant innovation over extended periods of time by tracking the evolution of multi-level attributes and discussing future research directions.
  • Thumbnail Image
    ItemOpen Access
    Towards goal-oriented semantic signal processing: Applications and future challenges
    (Elsevier, 2021-06-15) Kalfa, Mert; Gök, Mehmetcan; Atalık, Arda; Tegin, Büşra; Arıkan, Orhan; Duman, Tolga Mete
    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.