Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals

• Published in: IEEE journal on selected areas in communications Vol. 41; no. 1; pp. 119 - 140
• Main Authors: Kalfa, Mert, Yetim, Sadik Yagiz, Atalik, Arda, Gok, Mehmetcan, Ge, Yiqun, Li, Rong, Tong, Wen, Duman, Tolga M., Arikan, Orhan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Computational modeling; Computer vision; Data mining; Feature extraction; goal-oriented communications; goal-oriented signal processing; Graphical representations; Hidden Markov models; Information retrieval; Innovations; Machine learning; Markov chains; semantic communications; semantic graph signals; Semantic signal processing; Semantics; Signal processing; Task scheduling; Technological innovation; Time integration
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2022.3221950

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.