Browsing by Subject "Goal-oriented signal processing"

Now showing 1 - 3 of 3

Open Access
Innovation based transmission in AI-enabled sensor networks for 6G IoT scenario
(2022-09) Atalık, Ahmet Arda
Goal-oriented signal processing and communications are assured to play a key role in developing the next generation of sensor devices and networks, e.g., 6G IoT networks. A critical task of semantic signal processing is the detection of innovation and transmission scheduling based on the innovation in AI-enabled sensor networks and IoT for 6G. This thesis proposes efficient and optimal sampling and transmission strategies for goal-oriented sensor networks for various data models, investigates their performances both analytically and numerically; and introduces the use of dimensionality reduction algorithms in semantic signal processing and highlights its effectiveness in real life case studies. That is, the proposed methods are explained rigorously and demonstrated through simulations and case studies based on real-world computer vision examples with recorded video signals. Numerical results indicate that the next generation sensor devices and networks can benefit significantly from the proposed methods in terms of energy efficiency and semantic innovation detection performances.
Open Access
Reliable extraction of semantic information and rate of innovation estimation for graph signals
(Institute of Electrical and Electronics Engineers , 2022-12-19) Kalfa, Mert ; Yetim, Sadık Yağız ; Atalik, Arda ; Gök, Mehmetcan; Ge, Y.; Li, R.; Tong, W.; Duman, Tolga Mete; Arıkan, Orhan
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.
Open 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.